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+https://mega.nz/file/IAAXGRIT#JWdgpFh3ZBrku78IEqf_u-EFmVC3uju9jSDXqYkmxek,60,,porta,55193241,5b5e7edbf1602ce3d2427cb41ffce353,RVC v2,https://drive.google.com/file/d/1qZnVX_28gSIdMSatCZMf9DwlydiNIaFC/view?usp=drive_link,,,,,,,,,,,,,,,,,,
+https://pixeldrain.com/u/LZ5URqLM,60,,KiryuCoco,55229246,1ed7ee16868fc6e1daac8f97d0c9d377,RVC v2,https://drive.google.com/file/d/1RsjGKPaGZtYty4dAOmno0HZU0EUEP39M/view?usp=sharing,https://drive.google.com/file/d/1gX8MfS6hbB7A_n_GLKQBRtsAsZcqX0zw/view?usp=sharing,https://drive.google.com/file/d/1yDjim1tzV7rYUIA1l7etxFbRkWHjg1TA/view?usp=sharing,https://drive.google.com/file/d/12L9W4EWz8XdVFFNUCicPjQYkkCEql9iX/view?usp=sharing,https://drive.google.com/file/d/15gEMPtOku5CF-kOBpjXJn-zXuX9oEo18/view?usp=sharing,https://mega.nz/file/tphl1IiT#VNqDguHXqenLIiueUWSvP4JeSjq1qwkod2IDqMEq9d4,https://drive.google.com/file/d/1oNkdRqGS7YOD5eBOfSDlncKCnybgYrqh/view?usp=sharing,https://huggingface.co/juuxn/RVCModels/resolve/main/KiryuCoco.zip,,,,,,,,,,,
+https://drive.google.com/file/d/13I1i2dzDBA9y0mw1z5drCEZ61DM9pYy-/view?usp=sharing,60,,litkillah,55229705,7b409a06866bb74a6146c3af81c2b09e,RVC v2,https://drive.google.com/file/d/13I1i2dzDBA9y0mw1z5drCEZ61DM9pYy-/view,https://drive.google.com/file/d/1UENVyjQWGLmuNjhvwwgPuB90Yx7vA10i/view?usp=sharing,https://drive.google.com/file/d/1pma0hQdCUMvBOoR0rMhqD2QlTw3-XMt0/view?usp=sharing, https://drive.google.com/file/d/13I1i2dzDBA9y0mw1z5drCEZ61DM9pYy-/view?usp=sharing,https://drive.google.com/file/d/13I1i2dzDBA9y0mw1z5drCEZ61DM9pYy-/view?usp=sharing ,https://drive.google.com/file/d/13I1i2dzDBA9y0mw1z5drCEZ61DM9pYy-/view?pli=1,,,,,,,,,,,,,
+https://drive.google.com/file/d/1yC4Vc3ADufkCKMf6iaqc_FEbxSQ5u2YR/view?usp=drive_link,60,,taemin,55216420,b981e6b75d4769340966ec694ff2b359,RVC v2,https://drive.google.com/file/d/1yC4Vc3ADufkCKMf6iaqc_FEbxSQ5u2YR/view?usp=drivesdk,https://drive.google.com/file/d/1yC4Vc3ADufkCKMf6iaqc_FEbxSQ5u2YR/view?usp=drive_link,https://drive.google.com/file/d/1yC4Vc3ADufkCKMf6iaqc_FEbxSQ5u2YR/view,https://drive.google.com/file/d/1yC4Vc3ADufkCKMf6iaqc_FEbxSQ5u2YR/view?usp=sharing,https://drive.google.com/file/d/1yC4Vc3ADufkCKMf6iaqc_FEbxSQ5u2YR/view?usp=drive_link ,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1GfF8x-qUEDtKM58KN2GZx6ZnN08O7NS2/view?usp=sharing,60,,jiwoo2333333,54996633,814bce905a05620e301605e3800c108d,RVC v1,https://drive.google.com/file/d/1GfF8x-qUEDtKM58KN2GZx6ZnN08O7NS2/view,https://drive.google.com/file/d/15hxtfPdZOvKGTuJndhuiI9xFjmaXOAGB/view?usp=drivesdk,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1ho-ST5Celf44KIZyvDNZtLEwxe8P8tIS/view?usp=sharing,60,,MakotoNaegi,55225115,ad2977dbaa1722125d851e4fa0c12f4c,RVC v2,https://drive.google.com/file/d/1ho-ST5Celf44KIZyvDNZtLEwxe8P8tIS/view,https://drive.google.com/file/d/1ho-ST5Celf44KIZyvDNZtLEwxe8P8tIS/view?usp=drive_link,https://drive.google.com/file/d/1PHK042M7MY-wYnL70_j3gpmiPVlpqUqW/view?usp=sharing, https://drive.google.com/file/d/1ho-ST5Celf44KIZyvDNZtLEwxe8P8tIS/view?usp=sharing,,,,,,,,,,,,,,,
+https://huggingface.co/xZAFUEx/ZafueRVCModels/resolve/main/Rihanna_GGGB.zip,60,,Rihanna_GGGB,57590442,8940b7521ca6762c128a06c5472f173f,RVC v2,https://drive.google.com/file/d/1M5W107Iie34sBlHbeubYsDVi2aFdmaTM/view?usp=sharing,https://drive.google.com/file/d/1UHm8tPAJWB2eM3aw2sVXXbLYYLAdm4QR/view?usp=sharing,https://drive.google.com/file/d/1DG6JyNUy7PpbZa-iaM9blWh8H-uXJvHn/view?usp=drive_link,https://mega.nz/file/PZoUFCjb#zOtDaAmaMnsII5_m2QMdoh8iz9CyNGgqJovL5c9_H5Q,https://drive.google.com/file/d/11RyIpzDBOydY06SDe5AfX_as5L09Ko2j/view?usp=sharing,,,,,,,,,,,,,,
+https://drive.google.com/uc?id=1v8f-Jss43B-Ox3NZrZXuTK_vfm31Q3SS,59,,KenjiroTsuda,55225574,b3d84135804fce5cb83035fee5f98075,RVC v2,https://drive.google.com/file/d/1v8f-Jss43B-Ox3NZrZXuTK_vfm31Q3SS/view,https://drive.google.com/file/d/1v8f-Jss43B-Ox3NZrZXuTK_vfm31Q3SS/view?usp=sharing,https://mega.nz/file/jNoX3Tgb#kZWQmOHMHczgYcovQnQBl-RnwrH-RH2l3OFmVYrhbH8,https://drive.google.com/file/d/1v8f-Jss43B-Ox3NZrZXuTK_vfm31Q3SS/view?usp=drive_link,https://drive.google.com/file/d/1ulbGYcdot0rWXTL1-CdHjX1gs279_x_-/view?usp=drivesdk,https://mega.nz/file/1SFXVQAI#HzDbHIap6EMNEF0kEVHDPOFXVhZCQidFJimsZXYIQrU,https://drive.google.com/file/d/1ulbGYcdot0rWXTL1-CdHjX1gs279_x_-/view?usp=drive_link,,,,,,,,,,,,
+https://drive.google.com/file/d/1TPLLwcGdhMTPL0lrnhRUMc3LP0D905gK/view?usp=share_link,59,,Herta2333333,54996633,107814a38b1cfe22419f6f0dc4185357,RVC v1,https://drive.google.com/file/d/1TPLLwcGdhMTPL0lrnhRUMc3LP0D905gK/view?usp=share_link,https://mega.nz/file/AbQhWLpS#1oSm5hiZG-7ms-d0ABE5xxzs44Ha99f8YimRgr6hss0,https://drive.google.com/file/d/1TPLLwcGdhMTPL0lrnhRUMc3LP0D905gK/view,https://drive.google.com/file/d/1TPLLwcGdhMTPL0lrnhRUMc3LP0D905gK/view?usp=drive_link,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1SFgnR-wLaEdjPUv5-q9XOY-83skBHoVl/view?usp=sharing,59,,lightningv1,55225115,9d1ecf9501fd1750217e17d2f7db6b42,RVC v2,https://drive.google.com/file/d/1SFgnR-wLaEdjPUv5-q9XOY-83skBHoVl/view,https://huggingface.co/cass/CassidyBFDIModelsRVC/resolve/main/lightningv1.zip,https://drive.google.com/file/d/1SFgnR-wLaEdjPUv5-q9XOY-83skBHoVl/view?usp=sharinghttps://drive.google.com/file/d/1SFgnR-wLaEdjPUv5-q9XOY-83skBHoVl/view?usp=sharing,,,,,,,,,,,,,,,,
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+https://drive.google.com/file/d/10lOqNc8YjQ6C7LZI-NwOYsf5tbmt7NIG/view?usp=drive_link,59,,olivia,55216420,d82d6f2808925de923e4acd201fa32ad,RVC v2,https://drive.google.com/file/d/10lOqNc8YjQ6C7LZI-NwOYsf5tbmt7NIG/view?usp=sharing,https://drive.google.com/file/d/10lOqNc8YjQ6C7LZI-NwOYsf5tbmt7NIG/view?usp=drivesdk,https://drive.google.com/file/d/10lOqNc8YjQ6C7LZI-NwOYsf5tbmt7NIG/view,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/15W-G6AXj6s_JdndlLe2ywAxKAvj7LyXf/view?usp=drive_link,59,,spongebob-spanish-v1,55233441,9febefb797a55dcbc10e8c6177c30d50,RVC v2,https://drive.google.com/file/d/15W-G6AXj6s_JdndlLe2ywAxKAvj7LyXf/view,https://drive.google.com/file/d/15W-G6AXj6s_JdndlLe2ywAxKAvj7LyXf/edit,https://drive.google.com/uc?id=15W-G6AXj6s_JdndlLe2ywAxKAvj7LyXf,https://drive.google.com/file/d/15W-G6AXj6s_JdndlLe2ywAxKAvj7LyXf/view?usp=sharing,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1MR6h43cbNLK5GnW17NmbL0hFBm1Mr98f/view?usp=sharing,59,,heisenberg,55224656,976faa34de1c94388d3d465bd61e4447,RVC v2,https://mega.nz/file/UfNUnSBa#UYGTqfqDDZC0F4Vy-vlEiFlVmSYIRE030S0o2HTlY6U,https://drive.google.com/file/d/1MR6h43cbNLK5GnW17NmbL0hFBm1Mr98f/view,https://drive.google.com/file/d/1MR6h43cbNLK5GnW17NmbL0hFBm1Mr98f/view?usp=drivesdk,https://drive.google.com/file/d/1MR6h43cbNLK5GnW17NmbL0hFBm1Mr98f/view?usp=drive_link,https://drive.google.com/uc?id=1MR6h43cbNLK5GnW17NmbL0hFBm1Mr98f,https://drive.google.com/file/d/1N46S1E1IyUf39jYk_B9P71U3Ml8pmPE7/view?usp=sharing,https://drive.google.com/file/d/1MR6h43cbNLK5GnW17NmbL0hFBm1Mr98f/view?usp=share_link,https://drive.google.com/file/d/1YuZfEWfDDatNP29JXrhlD1wGnzp4BOad/view?usp=sharing,https://drive.google.com/file/d/1B2W6MEmoIPFy-7LR5vxKuQ_00-oYbPk0/view?usp=drive_link,,,,,,,,,,
+https://pixeldrain.com/u/u3CE7rg4,59,,Shadow,55216420,b8063017d5cf9c265fb27beafdbc840b,RVC v2,https://mega.nz/file/qaYVyRKI#GiaqJ31RwnQGtcB3n2FpH3KB8zIXL_VECiPR2XefncY,,,,,,,,,,,,,,,,,,
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+https://pixeldrain.com/u/uV8zLNSj,54,,thomyorke,55224197,9fc5a179aed068bfd512c058f512ba4c,RVC v2,https://drive.google.com/file/d/1jtqKE99CN1cB1IDvpez5zdEwWf9KAsbG/view?usp=drive_link,https://drive.google.com/file/d/18jWn_yLJ-9tEs-ISbGUT9BzoxVrG1JAq/view?usp=sharing,https://drive.google.com/file/d/1Dnm4NpzMGmKA2IFV4Z_L9hjRE0h64Bdo/view?usp=sharing,https://drive.google.com/file/d/1Gj1qUyOjSnxZobTUJvl4-f57a5pUkR0h/view?usp=drive_link,https://huggingface.co/analogspiderweb/analogspiderweb_RVC/resolve/main/thomyorke%20(1).zip,https://drive.google.com/file/d/1xCLnhyOiP-ePNeNH4mSaRuvRWHi6U83K/view?usp=sharing,,,,,,,,,,,,,
+https://drive.google.com/file/d/1Fk8UkVyUvg0TRQbztNdln31Nxhcev4lA/view?usp=drive_link,54,,lalisa,55019812,edbad62ea01b4237d420f1cb6ed09cdd,RVC v1,https://drive.google.com/file/d/16nbPZ7FzT_eFuHUXtyiS_xZvgKhn66Ls/view?usp=sharing,https://drive.google.com/file/d/16nbPZ7FzT_eFuHUXtyiS_xZvgKhn66Ls/view,https://drive.google.com/file/d/16nbPZ7FzT_eFuHUXtyiS_xZvgKhn66Ls/view?usp=sharing ,https://drive.google.com/file/d/1nobpR10T6WuYEzmLupRIxbbA1E8LgVbv/view?usp=sharing,https://drive.google.com/file/d/1lmglq22Pf0zHKIi988-StsXgcRgvavXz/view?usp=drivesdk,https://drive.google.com/file/d/16nbPZ7FzT_eFuHUXtyiS_xZvgKhn66Ls/view?usp=drivesdk,https://drive.google.com/file/d/1u7_I0VrhFeL-61iHucZptG9RNtQD8aug/view?usp=drivesdk,https://drive.google.com/file/d/16nbPZ7FzT_eFuHUXtyiS_xZvgKhn66Ls/view?usp=sharing ,https://drive.google.com/file/d/1vZhUoPWL-CX3mDgCo-edaOnIld8YXBnn/view?usp=drive_link,https://drive.google.com/file/d/1Bxo4dkse_QtmIZPSNjvrsop9vuxS9pHi/view?usp=drive_link,,,,,,,,,
+https://drive.google.com/file/d/16VhvBDlpbJdZ0WwYRxzofEgJLS5gf0q4/view?usp=sharing,54,,otamatone,55224197,51ed89bdbdb6f6d256e300a144a2e57e,RVC v2,https://drive.google.com/file/d/16VhvBDlpbJdZ0WwYRxzofEgJLS5gf0q4/view?usp=sharing,https://drive.google.com/file/d/16VhvBDlpbJdZ0WwYRxzofEgJLS5gf0q4/view,https://drive.google.com/file/d/16VhvBDlpbJdZ0WwYRxzofEgJLS5gf0q4/view?pli=1,https://drive.google.com/file/d/1j3sfy61OIjNBUxGPZe-TNpH-XDDV1Ax3/view?usp=sharing,https://drive.google.com/file/d/1j3sfy61OIjNBUxGPZe-TNpH-XDDV1Ax3/view,https://drive.google.com/file/d/1j3sfy61OIjNBUxGPZe-TNpH-XDDV1Ax3/view?usp=sharing ,https://drive.google.com/file/d/1YvamI0RWZIqquzC63ABWWfWtKU_h-4_R/view?usp=sharing,,,,,,,,,,,,
+https://drive.google.com/file/d/1cBx_dMaUN5e4EcxolYSOglWooBVLuWfg/view?usp=drive_link,54,,eazye,55193241,8b962f54c736b02e9b206f3609f9e83e,RVC v2,https://drive.google.com/file/d/1cBx_dMaUN5e4EcxolYSOglWooBVLuWfg/view,https://huggingface.co/1ski/1skiRVCModels/resolve/main/eazye.zip,,,,,,,,,,,,,,,,,
+https://mega.nz/file/92U3kLjA#Fqt0WaW51l0sjHIo9oiJsg0idSyXrLIZT1579jAV8Ms,54,,Broly,55193241,5e8f2b5e2401646eb00ff2b25593df85,RVC v2,https://mega.nz/file/4B41UQrZ#_Y6tVf00jkLGJ56oXbQzNYJTam7AF2HvUuj7oNtzqjo,Link: https://mega.nz/file/4B41UQrZ#_Y6tVf00jkLGJ56oXbQzNYJTam7AF2HvUuj7oNtzqjo ,https://drive.google.com/file/d/1QbwjTqzEuZz8COGmY0HR-5rYG21nbpLq/view?usp=drive_link,https://drive.google.com/file/d/16mnfEgn3nwbGSokSSXHIsLFcT5902Y0h/view?usp=sharing,Link: https://mega.nz/file/4B41UQrZ#_Y6tVf00jkLGJ56oXbQzNYJTam7AF2HvUuj7oNtzqjo,https://huggingface.co/SyberGen/DragonBallVModels/resolve/main/Broly.zip,https://mega.nz/file/gWMhxaLL#DRqcHeGHaUZFE8oq46hCVv4kj8J2ANiWvZ6q6DiH9kM,,,,,,,,,,,,
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+https://drive.google.com/file/d/1AuWfj0GccMVtzwf_2xMf9JeYiXmo2eEf/view?usp=drivesdk,54,,MARIOSUPERSTAR,55226492,d287d2850de283e0094f498fd5c32dfc,RVC v2,https://drive.google.com/file/d/1AuWfj0GccMVtzwf_2xMf9JeYiXmo2eEf/view,https://drive.google.com/file/d/1AuWfj0GccMVtzwf_2xMf9JeYiXmo2eEf/view?usp=sharing,https://mega.nz/file/lKFRFRhA#_xl1pOHrvo0d7JOvrAomZpKYEqdfCMrbzVhss1f42sk,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1_rfArEoUzGY77zF_Y-HEiLZLGZOUCZpY/view?usp=sharing,54,,gru_e70_s1890,55226033,73d083b8152f25e24318ca2d8e00da7b,RVC v2,https://drive.google.com/file/d/1_rfArEoUzGY77zF_Y-HEiLZLGZOUCZpY/view,https://drive.google.com/file/d/1_rfArEoUzGY77zF_Y-HEiLZLGZOUCZpY/view?usp=drive_link,https://drive.google.com/file/d/1tE2KVZAhLUaGDTJiSgEIVuPVFSe45Pug/view?usp=sharing,,,,,,,,,,,,,,,,
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+https://drive.google.com/file/d/1fjdJRqQ2fVin3Kqq57s1ULRPSVEaZhZR/view?usp=sharing ,54,,axlrose,55026095,2d68ddb2b044e760cb72ddc0a0ad79a0,RVC v1,https://drive.google.com/file/d/1fjdJRqQ2fVin3Kqq57s1ULRPSVEaZhZR/view?usp=sharing,https://drive.google.com/file/d/1fjdJRqQ2fVin3Kqq57s1ULRPSVEaZhZR/view,https://drive.google.com/file/d/16NzQesKcg_MSV7sqxYwIbUivznK1nHz3/view?usp=sharing,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1sE_F5pELzOLsa8QOaavbxyEdxLnhuUGP/view?usp=sharing,54,,Yeojin,55216420,1727eb0ca3ebe35e7343ef17ca8e2fc7,RVC v2,https://drive.google.com/file/d/1sE_F5pELzOLsa8QOaavbxyEdxLnhuUGP/view,https://drive.google.com/file/d/1sE_F5pELzOLsa8QOaavbxyEdxLnhuUGP/view?pli=1,https://drive.google.com/file/d/1sE_F5pELzOLsa8QOaavbxyEdxLnhuUGP/view?usp=sharing ,https://drive.google.com/file/d/1sE_F5pELzOLsa8QOaavbxyEdxLnhuUGP/view?usp=drive_link,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1EhqrrYv_v76wiKYQ8OJOzu1mrmI4u_8f/view?usp=sharing,54,,peni_parker,57584411,3bda13c538bb3a6946c8169b5ac40793,RVC v2,https://drive.google.com/file/d/1EhqrrYv_v76wiKYQ8OJOzu1mrmI4u_8f/view?usp=sharing ,https://drive.google.com/file/d/1EhqrrYv_v76wiKYQ8OJOzu1mrmI4u_8f/view,https://drive.google.com/file/d/1EhqrrYv_v76wiKYQ8OJOzu1mrmI4u_8f/view?,https://drive.google.com/file/d/1EhqrrYv_v76wiKYQ8OJOzu1mrmI4u_8f/view?usp=drive_link,,,,,,,,,,,,,,,
+https://mega.nz/file/ofkyDAYa#uQAhqw1gXwLuWWDLB_ylTcjqik5jfxcLnj3vMTpgA6Q,54,,GmanV2,57575716,9a96bd12f486329fcd2e4ba8e36a0d95,RVC v2,https://mega.nz/file/5IY3RAiR#uQAhqw1gXwLuWWDLB_ylTcjqik5jfxcLnj3vMTpgA6Q,https://www.dropbox.com/s/bd5hbm3r69lrbj3/GmanV2.zip?dl=1,https://drive.google.com/file/d/1JIKdk-xfFMaogYmejaYKGMHiOCOn8pYm/view?usp=drive_link,https://drive.google.com/file/d/1GSbsZSeEnYlr_3I7BiSq62KXTy8Y49Lh/view?usp=sharing,https://huggingface.co/realGamer/G-Man-RVC/resolve/main/GmanV2.zip,https://drive.google.com/file/d/1eJ2ZkloQqOIedMqzRCSf8XXO1WW2m8HI/view?usp=sharing,,,,,,,,,,,,,
+https://drive.google.com/file/d/1rzfsGtYEh1OJ8ql1XmamA1ttC_DKuc7v/view,54,,twov32333333,54996633,826dd3d7af3bb633e0bc71bb91eb3cf0,RVC v1,https://drive.google.com/file/d/1rzfsGtYEh1OJ8ql1XmamA1ttC_DKuc7v/view?usp=sharing,https://drive.google.com/file/d/1rzfsGtYEh1OJ8ql1XmamA1ttC_DKuc7v/view?pli=sharing,https://drive.google.com/file/d/1rzfsGtYEh1OJ8ql1XmamA1ttC_DKuc7v/view?usp=sharinghttps://drive.google.com/file/d/1A_Ae8mk4saCStnWLy9PUUyBA6blLu0dy/view?usp=sharinghttps://drive.google.com/file/d/12xcW8VczPsN0w4lPNzgvXzlapgS6BZpS/view?usp=sharing, https://drive.google.com/file/d/1rzfsGtYEh1OJ8ql1XmamA1ttC_DKuc7v/view?usp=sharing,https://drive.google.com/file/d/1rzfsGtYEh1OJ8ql1XmamA1ttC_DKuc7v/view?,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1HyYs0eNOTPHz7kJ-HUT-bXOquFUHLxOF/view?usp=sharing,54,,GwenStacy,57583493,de41a961173868c8692b24e48e800368,RVC v2, https://drive.google.com/file/d/1HyYs0eNOTPHz7kJ-HUT-bXOquFUHLxOF/view?usp=sharing,https://drive.google.com/file/d/1HyYs0eNOTPHz7kJ-HUT-bXOquFUHLxOF/view,https://mega.nz/file/imAHCaDb#Wm0CKmVVnRblYb7RAb-JtE1xzv1-4-pVwMyofxgMAus,https://drive.google.com/file/d/1HyYs0eNOTPHz7kJ-HUT-bXOquFUHLxOF/view?pli=1,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1Ea9G_gHaFPDHHdRN4O-eCS4pBjtPTrio/view?usp=sharing,54,,21savage,55223738,7e205f44bf0a2c47e718cf3f5a4eaaaf,RVC v2,https://huggingface.co/1ski/1skiRVCModels/resolve/main/21savage.zip,https://drive.google.com/file/d/1Yj9lVfw1DKD0RvEu0QSmHHrmYIQjXa76/view?usp=sharing,https://drive.google.com/file/d/1b96LKpUzG1Ixya-kDcMktqZWlX4Vgp9h/view?usp=sharing,https://drive.google.com/file/d/1Ea9G_gHaFPDHHdRN4O-eCS4pBjtPTrio/view,,,,,,,,,,,,,,,
+https://mega.nz/file/QX4hELLR#fyMySADgLSz9luv9WbQDX50W5HUbasERw6M28UgjNRk,54,,shuichiSaihara_e200_s16000,55232064,d05c6933256f8a7d0e6ee951428943ee,RVC v2,https://drive.google.com/file/d/1pUUhFnshTSft5JYpNGtmCAsT2MH3AEbF/view?usp=sharing,https://drive.google.com/file/d/1fh8JR5h5lp6zgfml_rA_ch-uNyNY5jqI/view?usp=sharing,https://drive.google.com/file/d/1W8cLqVo27yiCmE7g4153JYcJ_cL6TXt5/view?usp=drive_link, https://mega.nz/file/QX4hELLR#fyMySADgLSz9luv9WbQDX50W5HUbasERw6M28UgjNRk,https://mega.nz/file/IjFiwDLI#H1hcxVZUIS65lzoV7yLHJymptJ2kkzCQBGU8pfx9Wfk,https://drive.google.com/file/d/14IJZecSdAEvXLVDl74aU7WeSOgrvwgCC/view?usp=sharing,https://drive.google.com/file/d/1JH2DbCV525e4-hBjJBukmi7OXY07chm0/view?usp=sharing,,,,,,,,,,,,
+https://drive.google.com/file/d/1-EiCGbvRTUw6O87LvyC_Dy3RRbV5EuGc/view?usp=share_link,54,,plankton2333333,55027130,c3a313171dcd4d6d82005c0af8683d6d,RVC v1,https://drive.google.com/file/d/14BkHn0bRdWQgDMZlSJPbTkHPDsjLb2HY/view?usp=drive_link,https://drive.google.com/file/d/1-EiCGbvRTUw6O87LvyC_Dy3RRbV5EuGc/view?usp=drive_link,https://drive.google.com/file/d/1gfektM5gh7qTwCGBi7ETCM47zy1NUcub/view?usp=drive_link,https://drive.google.com/file/d/1-EiCGbvRTUw6O87LvyC_Dy3RRbV5EuGc/view,https://huggingface.co/QuickWick/Music-AI-Voices/resolve/main/Plankton%20(From%20SpongeBob)%20(RVC)%20500%20Epoch/Plankton%20(From%20SpongeBob)%20(RVC)%20500%20Epoch.zip, https://drive.google.com/file/d/1-EiCGbvRTUw6O87LvyC_Dy3RRbV5EuGc/view?usp=share_link,https://drive.google.com/file/d/1rpWjiN3QnCY8lxpAaBItxcic1ob8lejY/view?usp=sharing,https://drive.google.com/file/d/1rpWjiN3QnCY8lxpAaBItxcic1ob8lejY/view?usp=drive_link,,,,,,,,,,,
+https://drive.google.com/file/d/1mbA0Di9tqGPGOsyGPrMlk6pJ2RDqheaQ/view?usp=sharing,54,,Anuel,55193241,3c24ff67b06e731a1836a7bad59fe1ca,RVC v2,https://drive.google.com/file/d/1mbA0Di9tqGPGOsyGPrMlk6pJ2RDqheaQ/view,https://drive.google.com/file/d/1mbA0Di9tqGPGOsyGPrMlk6pJ2RDqheaQ/view?usp=sharing ,https://drive.google.com/file/d/1mbA0Di9tqGPGOsyGPrMlk6pJ2RDqheaQ/view?usp=drive_link,https://huggingface.co/Dragonite/Anuel_AA/resolve/main/Anuel%20(1).zip,https://huggingface.co/juuxn/RVCModels/resolve/main/Anuel_AA_(RVC_V2_[Harvest]_100_Epochs).zip, https://drive.google.com/file/d/1mbA0Di9tqGPGOsyGPrMlk6pJ2RDqheaQ/view?usp=sharing,https://drive.google.com/file/d/1yiqhWq2W0oPuFaReeYW77IztarP6mQp9/view?usp=drive_link,,,,,,,,,,,,
+https://drive.google.com/file/d/1-1cvB9cl21fMk8SSJ1k6BRtcjtLj3xR0/view?usp=sharing,54,,MissaSinfonia,55226033,208d1211b38101346ea5a8d2aafd7d90,RVC v2,https://huggingface.co/AndresLoquendo0/MissaSinfonia/resolve/main/MissaSinfonia.zip,https://drive.google.com/file/d/152P-fiVk4Xdg9nW99b6FNRUOIcOR8hoq/view?usp=drive_link,https://drive.google.com/file/d/1A7tBxRcwATQbCX826W3F65Laf6-pW9A6/view?usp=sharing,https://drive.google.com/file/d/1qkT5aHV7DyCfSTaEey-4egd5jSS5MJxv/view?usp=drive_link,https://drive.google.com/file/d/1zYKGtqVigCmsXRpgF8LEfGfRHSU3vVDC/view?usp=sharing,,,,,,,,,,,,,,
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+https://drive.google.com/file/d/1CgI6DcyB_QBXeP_lEUb_l5e7FdD4HFyf/view?usp=drive_link,38,,AxlRose90s,55224656,e74896df29068eb3ba791261840f72aa,RVC v2,https://drive.google.com/file/d/1CgI6DcyB_QBXeP_lEUb_l5e7FdD4HFyf/view?usp=sharing,https://drive.google.com/file/d/1CgI6DcyB_QBXeP_lEUb_l5e7FdD4HFyf/view, https://drive.google.com/file/d/1CgI6DcyB_QBXeP_lEUb_l5e7FdD4HFyf/view?usp=sharing,,,,,,,,,,,,,,,,
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+https://drive.google.com/file/d/1C8HH-jZCgwh_Z1Sxo4BbRjMMoF4ntxZV/view?usp=drivesdk,37,,TeddyBloxpin,55225574,4d3050ca164d7bac4bd5d68cb128861a,RVC v2,https://mega.nz/file/lw9DiL5B#9LmFLxCW0cItMoOdYvA9OAr-4LrJ9EwMeDe0C1vRoaY,https://drive.google.com/file/d/1dVsoVmiszQW7qFHpHWkGrAbc70kVIWuz/view?usp=drive_link,ttps://mega.nz/file/lw9DiL5B#9LmFLxCW0cItMoOdYvA9OAr-4LrJ9EwMeDe0C1vRoaY,,,,,,,,,,,,,,,,
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+https://drive.google.com/file/d/1iJxxp9Z3N50O_n_dsn3BuWmZxj1AoAXb/view?usp=sharing,37,,Ryder-Mixmods.com.br,55193241,fc738fb3b13290f3a09c8795477e12bf,RVC v2,https://mega.nz/file/guZ2zQgQ#6jsn-sOkvGZaui-jE5NYLrOD16Y6HPHBcorMc358xKg,https://drive.google.com/file/d/1iJxxp9Z3N50O_n_dsn3BuWmZxj1AoAXb/view,https://drive.google.com/file/d/1iJxxp9Z3N50O_n_dsn3BuWmZxj1AoAXb/edit,https://drive.google.com/file/d/1-4NFP0UeJfPYD-6fVpaU9s7CSiJTfmxG/view?usp=sharing,https://huggingface.co/RafaG/ModelsAIvoice/resolve/main/Ryder-Mixmods.com.br.zip,,,,,,,,,,,,,,
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+https://huggingface.co/JTeam/MissionControl/resolve/main/MissionControl.zip,31,,missioncontrol_e100_s10100,55232064,277da4a3041330803d0187f01db57f7c,RVC v2,https://mega.nz/file/hnFBFIbb#1VeKSJK4hv_IeCSFHVqd6P6yilUi_djIxklaGj7HIeo,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1E42uQ5sOAenx64V5MA5wz3Ee_7lczh2l/view?usp=sharing,31,,HertaJP,55228328,4f1bfd69512182bd1f77f50b348764c1,RVC v2,https://huggingface.co/Thereallo/RVCModels/resolve/main/HertaJP.zip,https://drive.google.com/file/d/1TQDiKquR7yXFieimx24N3pqntxZ69NIN/view?usp=drivesdk,https://drive.google.com/file/d/1mUfLtV7oNjm0ppqETUkvC6kVSl_WrHOy/view?usp=sharing,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1IFzgVaCnxBBoVVX5A_x9I8U1K_WdFP4k/view?usp=sharing,31,,Neeko,55193241,f96d059de881a62f16a66e345881d9f6,RVC v2,https://drive.google.com/file/d/1IFzgVaCnxBBoVVX5A_x9I8U1K_WdFP4k/view,https://drive.google.com/file/d/1-Ohmrj5A90rA9VtFohXIMDWCAZwcFLfq/view?usp=drive_link,https://drive.google.com/file/d/1IFzgVaCnxBBoVVX5A_x9I8U1K_WdFP4k/view?usp=drive_link,https://drive.google.com/file/d/1IFzgVaCnxBBoVVX5A_x9I8U1K_WdFP4k/view?pli=1,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1ASiG3CXnc3RI2Pxwgmt0-mT2ecnV8chM/view?usp=drive_link,31,,ButtHead,55027130,646c3a52e23f3fc1b48742040f1650d9,RVC v1,https://drive.google.com/file/d/1b79md8aFn5RDV9fZFAG0_G3M5L5OYIfn/view?usp=drive_link,https://drive.google.com/file/d/1SbXHJSGPescFLyL4OThmfPr2RWAYkczO/view,https://drive.google.com/file/d/1SbXHJSGPescFLyL4OThmfPr2RWAYkczO/view?usp=share_link,https://drive.google.com/file/d/1rCT4UXBIk8gXj9ES7W0JlomlRPr39RKF/view?usp=sharing,https://drive.google.com/file/d/1SbXHJSGPescFLyL4OThmfPr2RWAYkczO/view?usp=drivesdk,https://drive.google.com/file/d/1b79md8aFn5RDV9fZFAG0_G3M5L5OYIfn/view,https://huggingface.co/QuickWick/Music-AI-Voices/resolve/main/Butt-Head%20(Beavis%20%26%20Butt-Head)%20(RVC)%201K%20Epochs%2044K%20Steps/Butt-Head%20(Beavis%20%26%20Butt-Head)%20(RVC)%201K%20Epochs%2044K%20Steps.zip,https://drive.google.com/file/d/1ASiG3CXnc3RI2Pxwgmt0-mT2ecnV8chM/view,,,,,,,,,,,
+https://huggingface.co/raptarantino/frankocean325e/resolve/main/frankoceancrepe.zip,31,,frankoceancrepe,57591819,b1fae58d33385ae3b02a5e02a28887d4,RVC v2,https://cdn-lfs.huggingface.co/repos/85/69/856978a76895024fde9d2f57d86b928b631edc234ad6baea517ffa4ec3bd67f2/7325949b1308bda1f31f3a543e307731f06a6aa927f4bf0b13e690f1b362b584?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27frankoceancrepe.zip%3B+filename%3D%22frankoceancrepe.zip%22%3B&response-content-type=application%2Fzip&Expires=1688873273&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzg1LzY5Lzg1Njk3OGE3Njg5NTAyNGZkZTlkMmY1N2Q4NmI5MjhiNjMxZWRjMjM0YWQ2YmFlYTUxN2ZmYTRlYzNiZDY3ZjIvNzMyNTk0OWIxMzA4YmRhMWYzMWYzYTU0M2UzMDc3MzFmMDZhNmFhOTI3ZjRiZjBiMTNlNjkwZjFiMzYyYjU4ND9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2ODg4NzMyNzN9fX1dfQ__&Signature=NurRznYjJtITOGNnZ4Coyry-eS8XWs7P17dXc6NDJJn5GnV027a8XLeFTkWL98gph3NKmrScVAW8Zq4l4cO33Ynj4ooqkN2XeYIeFpUPDFKnlpQMQZXOZNL-l0fb-hC6KYICb4gPqHj4B5vnIEKGBSqKjQ-%7EkoBLcFwemGV1A6QTJFvQpKSFcexnbFzCa01zQFDmYl5YIwARDuQzIRZPgX72vqvmr2oX7qlKOauis8zzD00fVOTXJEaTYwkP0TjIVrOLkp%7Ek975A4KVN54Qia8l7XAuaE-bSSH8djMEcdUX8rP6BzPsgbCm3W-PcetENk6sD-Jcd2sBz22MLGLliCg__&Key-Pair-Id=KVTP0A1DKRTAX,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1lJAmYgl97NpGl1WiUkvAeUIXVOnota-I/view?usp=sharing,31,,ebonivon model,55228787,f2edcd20f1fefde86fb109c520a89797,RVC v2,https://drive.google.com/file/d/1lJAmYgl97NpGl1WiUkvAeUIXVOnota-I/view?usp=sharing ,https://drive.google.com/file/d/1lJAmYgl97NpGl1WiUkvAeUIXVOnota-I/view,,,,,,,,,,,,,,,,,
+https://huggingface.co/nauticalman22/MollyMcGeeRVC2/resolve/main/MollyMcGeeRVC2.zip,31,,MollyMcGeeRVC2,57590901,dafac6d905a3962a4b1cc5823d3ad87e,RVC v2,https://drive.google.com/file/d/1q737ldhhqto2JocLtHDWfZT62_wWmQeU/view?usp=sharing,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/139GAK5gCO34BroTq2_T-WESv7AmmXa9X/view?usp=drivesdk,31,,Jennie,55216420,83054b8883fbbfba1497bc033e304a61,RVC v2,https://drive.google.com/file/d/139GAK5gCO34BroTq2_T-WESv7AmmXa9X/view,https://drive.google.com/file/d/1BMvvAJo8HBlWGJMC-tHeOE_5lBoKMIWw/view?usp=sharing,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1LlzToPRdDgjRZ8pZSZNUfbkQGM_pXBvd/view?usp=sharing,31,,oguzsasi,55223738,6fc7840e81d67c01771c619e0f434fe2,RVC v2,https://huggingface.co/ailabturkiye/oguzsasi/resolve/main/added_IVF1385_Flat_nprobe_1_oguzsasi_v2.zip,,,,,,,,,,,,,,,,,,
+https://mega.nz/file/oWkgGCiC#6rP3y3jcj8e8GFcv0qTODq_h8dqPhuocOkHvN2wp8UA,30,,Speed-o-Sound-SonicRVCv2harvest_e225_s1350,55239472,6b0f9ec4237ae6e854fefbcc8d56e1ff,RVC v2,https://mega.nz/file/oWkgGCiC#6rP3y3jcj8e8GFcv0qTODq_h8dqPhuocOkHvN2wp8UA ,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1j3ahDfEpOgXSBY4luaIX5F_fwnTMQ0BA/view?usp=share_link,30,,Dora_e460,55224197,fbf9c4af19066817dc5d3c94f23c5689,RVC v2,https://drive.google.com/file/d/1j3ahDfEpOgXSBY4luaIX5F_fwnTMQ0BA/view?usp=share_link,https://drive.google.com/file/d/1j3ahDfEpOgXSBY4luaIX5F_fwnTMQ0BA/view,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/14ZLA6SUQ6zZvxvwkEp9thC5FNEQTtQFM/view?usp=drivesdk,30,,mugi2333333,54996174,0c0e996aa30309772c00c2497913c6ea,RVC v1,https://drive.google.com/file/d/14ZLA6SUQ6zZvxvwkEp9thC5FNEQTtQFM/view,https://drive.google.com/u/0/uc?id=14ZLA6SUQ6zZvxvwkEp9thC5FNEQTtQFM,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1EP3sl8BRwK_ps_GOAqqwoTy1dL8RNbBj/view?usp=drive_link,30,,Winwin,55216420,d1d9d3f5a4aaf74c93d230d8910750f6,RVC v2,https://drive.google.com/file/d/1EP3sl8BRwK_ps_GOAqqwoTy1dL8RNbBj/view,https://drive.google.com/file/d/1qMxFdpCy0VKSYW9hU_zAnol9DfvqEolJ/view?usp=drive_link,https://drive.google.com/file/d/1guDaDcVga4VGVz8yc7syVFeFVydDFwLm/view?usp=drive_link,https://drive.google.com/uc?id=1EP3sl8BRwK_ps_GOAqqwoTy1dL8RNbBj,,,,,,,,,,,,,,,
+https://pixeldrain.com/u/DGg29o5x,30,,luna,54996174,0334ee2d6af46cf4030218821a25eda1,RVC v1,https://pixeldrain.com/u/DGg29o5x,https://drive.google.com/file/d/1SYW7hr_fZqXdCsbG7QlAIjsDOkyDJ7zC/view?usp=drive_link,https://drive.google.com/file/d/1vlItk7SHI8fI_mRlmQ4Pe-aY_65Of9Z1/view?usp=sharing,https://huggingface.co/megaaziib/my-rvc-models-collection/resolve/main/luna.zip,https://drive.google.com/file/d/1HeDNH4zQqZaWKbCfVTMwrbKQ4Yj0lVXW/view?usp=sharing,https://mega.nz/file/HkMRwSCb#4N5hoszFOhtMIdGNPH-VHNgv7a8sfXV7yuHybF2a7qA,,,,,,,,,,,,,
+https://drive.google.com/file/d/10ZEpiAm9mYwEx20iAYJUuqqoK8zFhSJy/view,30,,summerwalker,55127639,84a9f80f6b0f7aaaddff9c1ee2a02076,RVC v1,https://drive.google.com/file/d/10ZEpiAm9mYwEx20iAYJUuqqoK8zFhSJy/view,https://drive.google.com/file/d/10ZEpiAm9mYwEx20iAYJUuqqoK8zFhSJy/view?usp=drivesdk,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1CwqV5KvwCKai6lOS_aC34zCIW-f4_cjJ/view?usp=sharing,30,,gandhi,55216420,c4f0a79f5c0fb2db78e82573bed5838d,RVC v2,https://drive.google.com/file/d/1CwqV5KvwCKai6lOS_aC34zCIW-f4_cjJ/view,https://drive.google.com/file/d/1CwqV5KvwCKai6lOS_aC34zCIW-f4_cjJ/view?usp=sharing/view?usp=drivesdk,,,,,,,,,,,,,,,,,
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+https://drive.google.com/file/d/11SvZWpFTHUZqkbZlczUBx7r0aKj_qHCk/view?usp=drive_link,30,,ChiefKeef,55224197,404f754207a7b94e1fd440c891a8e39f,RVC v2,https://drive.google.com/file/d/11SvZWpFTHUZqkbZlczUBx7r0aKj_qHCk/view,https://mega.nz/file/x6dGnS7a#pfshpu9wHVuq6Eq4Y2EkpqFxYb4K2rCeaIhHGKgpAf8,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1Eo68lDx5332iJIMuiQRNiotEbZ80MJEX/view?fbclid=IwAR0C45NHAJdb7AtDm7wDal_Dpy058fNLMpbXfEVANMCZL6pa5xwsZNnM-eI,30,,Slavcho,55222703,e803c029863edc73e2b9e99b910ae833,RVC v2,https://drive.google.com/file/d/1Eo68lDx5332iJIMuiQRNiotEbZ80MJEX/view?usp=sharing,https://drive.google.com/file/d/1Eo68lDx5332iJIMuiQRNiotEbZ80MJEX/view, https://drive.google.com/file/d/1Eo68lDx5332iJIMuiQRNiotEbZ80MJEX/view?usp=sharing,https://drive.google.com/file/d/1Eo68lDx5332iJIMuiQRNiotEbZ80MJEX/view?fbclid=IwAR2R28zGZLTxHO5XwlL7sDKiCJ8K_t2MVcCZOo9jqcJuN5fl567XgnjUNIU,https://drive.google.com/file/d/1Eo68lDx5332iJIMuiQRNiotEbZ80MJEX/view?fbclid=IwAR3ed-GcjzzNvDIHZu2URUuDLScbsg9TguWC7PT0tV1Bl_7Avy-xuTVGJhY,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1WgrSAOnsSfC6UTh2fNLVup1z0InMKEjf/view,30,,Ademo8002333333,55027130,c2c810f18bed6785ebddf8b677d5fd7d,RVC v1,https://drive.google.com/file/d/1XAw_6ge04CucMV8KUWWUCVgrZnzHsdKM/view?usp=drive_link,https://drive.google.com/file/d/1XAw_6ge04CucMV8KUWWUCVgrZnzHsdKM/view?usp=drive_link,https://drive.google.com/file/d/1kwgAnFh8S76-_SGmgp937KdE1GYhywZb/view?usp=sharing,https://drive.google.com/file/d/1WgrSAOnsSfC6UTh2fNLVup1z0InMKEjf/view?usp=sharing,https://drive.google.com/file/d/1NfasTy4COzV0GTWfBWrP9CohBiMbPUIZ/view?usp=drive_link,https://drive.google.com/file/d/15hLaxmdMbilc3X9f7kOfaLv6rkLUILBV/view?usp=drivesdk,https://drive.google.com/uc?id=1NfasTy4COzV0GTWfBWrP9CohBiMbPUIZ,https://drive.google.com/file/d/1NfasTy4COzV0GTWfBWrP9CohBiMbPUIZ/view?usp=sharing,https://drive.google.com/file/d/1If1bkPYTknXaGs2n7DnNQrUxB1su9dkf/view?usp=drive_link,,,,,,,,,,
+https://drive.google.com/file/d/1UFXoW4nbNlvV8dDd-uXfdf-ctnKfh4pS/view?usp=drive_link,30,,diego,55193241,ab8aca5070c48a576423a8b1f93159c4,RVC v2,https://drive.google.com/file/d/1UFXoW4nbNlvV8dDd-uXfdf-ctnKfh4pS/view?usp=sharing,https://drive.google.com/file/d/1UFXoW4nbNlvV8dDd-uXfdf-ctnKfh4pS/view?usp=drivesdk,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1HmstGs-GUaK7iirwdrIRhRzUI3zEIrNG/view?usp=sharing,30,,Tankman,55222703,282e66368cb352bd53a2da7a30133898,RVC v2,https://drive.google.com/file/d/1HmstGs-GUaK7iirwdrIRhRzUI3zEIrNG/view?usp=drive_link,https://drive.google.com/file/d/1HmstGs-GUaK7iirwdrIRhRzUI3zEIrNG/view,https://mega.nz/file/cYRBVZwZ#EzSGqtrGataKIgApbddIXOa60jJBlWoTXIFczQ9akr8,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1dPcyvcs7t3BwcAO2U1GQ69xP3I-1jo3g/view?usp=sharing,30,,KyokoKirigiri,55226033,96ac1a05914acb950a027f53f9468255,RVC v2,https://drive.google.com/file/d/17ZyQA-mgYZeJnscbYOK_u_DBLlV-W8ON/view?usp=drive_link,https://drive.google.com/file/d/17ZyQA-mgYZeJnscbYOK_u_DBLlV-W8ON/view,https://drive.google.com/file/d/17ZyQA-mgYZeJnscbYOK_u_DBLlV-W8ON/view?usp=sharing,https://drive.google.com/file/d/1dPcyvcs7t3BwcAO2U1GQ69xP3I-1jo3g/view?usp=drive_link,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1Ef9frWAW72OFHWpIdHUBuIza2r0tS_uR/view?usp=sharing,30,,CeltyKew,55223738,36fa24a48bcf7d92ab5d601251cf6d21,RVC v2,,,,,,,,,,,,,,,,,,,
+https://mega.nz/file/8SMyyQBZ#reANgzlUV11Q8Xd1EsPxD9xfifqg_3ceUbX9hmQBdcM,30,,Valentine_Skullgirls_e210_s2520,57593655,75467321ee3cd592e8a0046564a1331e,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-7e3Tnz9k6g2nH7wDLFweDEu7OVPBDKc/view,30,,ElrichMC2333333,55027130,6183c99dd19c3617a4ae9b930ece0c99,RVC v1,https://drive.google.com/file/d/1-7e3Tnz9k6g2nH7wDLFweDEu7OVPBDKc/view?usp=sharing,https://drive.google.com/file/d/1-7e3Tnz9k6g2nH7wDLFweDEu7OVPBDKc/view?usp=sharing,https://drive.google.com/uc?id=1-7e3Tnz9k6g2nH7wDLFweDEu7OVPBDKc,https://drive.google.com/file/d/1yzyPj5svEic4RNV9-G_ggZqMSvLgvvep/view?usp=sharing,https://drive.google.com/file/d/1yzyPj5svEic4RNV9-G_ggZqMSvLgvvep/view?usp=drive_link,https://drive.google.com/file/d/1-7e3Tnz9k6g2nH7wDLFweDEu7OVPBDKc/edit,,,,,,,,,,,,,
+https://drive.google.com/file/d/1SfdFJSUw10NAopNFw8bmtm0R6v9dD6G-/view?usp=share_link,30,,Cena226000,54996633,a5f8c375f0c69e7a06b4e3257bfd93c1,RVC v1,https://drive.google.com/file/d/1SfdFJSUw10NAopNFw8bmtm0R6v9dD6G-/view,https://drive.google.com/file/d/1-hDfdh8WmboLJt_P1VUNj_p7aZur7GLT/view?usp=sharing,https://drive.google.com/file/d/10AfwUOa78go3AQ_XWvjDdDAK_2ib7F68/view?usp=sharing,https://drive.google.com/file/d/1SfdFJSUw10NAopNFw8bmtm0R6v9dD6G-/view?usp=sharing,,,,,,,,,,,,,,,
+https://mega.nz/file/l8ggwYRQ#PCqnZBLt5dBp4ZgecnzKR6iVSOWc0-hn6Au6LUdexYc,30,,dababy,55019812,f660949bf8e5f5815f5e133b804aa454,RVC v1,https://huggingface.co/QuickWick/Music-AI-Voices/resolve/main/DaBaby%20(RVC)%201k%20Epoch%2070k%20Steps/DaBaby%20(RVC)%201k%20Epoch%2070k%20Steps.zip,https://mega.nz/file/l8ggwYRQ#PCqnZBLt5dBp4ZgecnzKR6iVSOWc0-hn6Au6LUdexYc DATASET: https://mega.nz/file/9lQS1TJQ#MjrQX0yXPcCuI6e7RvhgmoPP9pVlkcclEdRdlXOlb9M,https://drive.google.com/file/d/1xEtSSV_g02ihGm405i_5T0JDkZE3Yk9m/view?usp=share_link,,,,,,,,,,,,,,,,
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+https://huggingface.co/ailabturkiye/warwick/resolve/main/warwick.zip,30,,warwick,55222703,b3b2fd0b93e30aab235d83a2778ddf30,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1hVyQ4zNBcD34Zwwbtoy5_PnDBms28grt/view?usp=sharing,30,,biggie,57575716,92767c832ea509447b8a2c444d284fb3,RVC v2,https://huggingface.co/datasets/fckmylife/notorious_big/resolve/main/Notorious_BIG.zip,,,,,,,,,,,,,,,,,,
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+https://drive.google.com/file/d/12mFJIUXsUAIEOTTd2fLxpryrtYDgeeo6/view?usp=drive_link,29,,SonicDarkEra,55225574,f2ea0eb7f59fb315b19a337186a18518,RVC v2,https://drive.google.com/file/d/1JiGWFQoeKeIRCE-KPD0cbJn_h3oaFJoU/view?usp=sharing,https://drive.google.com/file/d/12mFJIUXsUAIEOTTd2fLxpryrtYDgeeo6/view,,,,,,,,,,,,,,,,,
+https://mega.nz/file/tU0yASBb#--BQiCad794Vvt2f76wjKxZKrn_vQKKyLiWmzUjyUqQ,29,,KKSlider_e200_s1000,57588083,ff2f340ff2b4a1ae8c8753aa94d96b77,RVC v2,https://mega.nz/file/tU0yASBb#--BQiCad794Vvt2f76wjKxZKrn_vQKKyLiWmzUjyUqQ ,//mega.nz/file/tU0yASBb#--BQiCad794Vvt2f76wjKxZKrn_vQKKyLiWmzUjyUqQ,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1021PgWU2N0VYqVIllsyY8750TVUSPpnv/view,29,,haram,55193241,0fbd29ac0772044b9eb0ba5a2b7ffe22,RVC v2,https://drive.google.com/file/d/1021PgWU2N0VYqVIllsyY8750TVUSPpnv/view?usp=sharing,https://drive.google.com/file/d/1021PgWU2N0VYqVIllsyY8750TVUSPpnv/view?pli=1,https://huggingface.co/kaengpil/Haram/resolve/main/haram.zip,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/15IMbL8cpsiNemz8JGUO51pi4-R0lmwBa/view?usp=sharing,29,,mc,55191736,1e9ed727115659b1ca3eda19348b6714,RVC v2,https://drive.google.com/file/d/15IMbL8cpsiNemz8JGUO51pi4-R0lmwBa/view,https://drive.google.com/file/d/1jC-PJSfB4-S5iHsZGHKNS1UkSNTzesqt/view?usp=sharing,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1iicZY5vi5hXHULj5k0JcKLH2A_O1sAg8/view?usp=drive_link,29,,UtatanePiko,55225115,3e348165445e7b16730d91a67b7262bb,RVC v2,https://drive.google.com/file/d/1-SCytKaqBVx2TSKL-WmcMhNlR05orKp-/view,https://drive.google.com/file/d/1-SCytKaqBVx2TSKL-WmcMhNlR05orKp-/view?usp=sharing,https://drive.google.com/file/d/1iicZY5vi5hXHULj5k0JcKLH2A_O1sAg8/view?usp=drive_link,https://drive.google.com/file/d/1-SCytKaqBVx2TSKL-WmcMhNlR05orKp-/view?usp=drivesdk,https://drive.google.com/file/d/1_vqD5fyhKteWTjHGx2mJ6EGKNdqacleF/view?usp=sharing,https://drive.google.com/file/d/1iicZY5vi5hXHULj5k0JcKLH2A_O1sAg8/view,,,,,,,,,,,,,
+https://drive.google.com/file/d/1hjmfac9r0Z8frPkrPLW9OFqde9g7byiW/view?usp=sharing,29,,andre,55193241,42bad823b9545122fec8f8a22efac6b5,RVC v2,https://drive.google.com/file/d/1hjmfac9r0Z8frPkrPLW9OFqde9g7byiW/view?usp=sharing,https://drive.google.com/file/d/1hjmfac9r0Z8frPkrPLW9OFqde9g7byiW/view,https://drive.google.com/file/d/1HAvgd0qJOD2q52OkWbHCVc7PUrTP4WOi/view?usp=sharing,https://drive.google.com/file/d/1Xq5JFq8emzd0UySKbA07zVIOhGnB9l56/view?usp=sharing,,,,,,,,,,,,,,,
+https://huggingface.co/QuickWick/Music-AI-Voices/resolve/main/Kid%20Cudi%20(RVC)%201k%20Epoch%2040k%20Steps/cudi.zip,29,,cudimodel,55027589,9f8c85905edd788b6cae81f8e7580d72,RVC v1,https://drive.google.com/uc?id=10YN70HsAEpmUi1WW7JqdlSCwaSpWDJJG,https://drive.google.com/file/d/10YN70HsAEpmUi1WW7JqdlSCwaSpWDJJG/view?usp=share_link,https://drive.google.com/file/d/10YN70HsAEpmUi1WW7JqdlSCwaSpWDJJG/view,https://mega.nz/file/tPFggYpQ#spUVo1eDlxdEd9yrhzO0_5WoMr14K1HBL1DZ5ayoTu8,,,,,,,,,,,,,,,
+ https://drive.google.com/file/d/1te0eeCiQaAQIslTwRpqcvstqVpSscLhU/view?usp=sharing,29,,kira150,54996174,e66b00eef98ebba8565014cca504306b,RVC v1,https://drive.google.com/file/d/1te0eeCiQaAQIslTwRpqcvstqVpSscLhU/view?usp=sharing,https://drive.google.com/file/d/1te0eeCiQaAQIslTwRpqcvstqVpSscLhU/view?usp=drive_link,https://drive.google.com/file/d/1te0eeCiQaAQIslTwRpqcvstqVpSscLhU/view,https://drive.google.com/file/d/1USvKL04oE0NWLaSacnfM9QKpQ40S686J/view?usp=sharing,,,,,,,,,,,,,,,
+https://mega.nz/file/CKBEWIwL#--d7hIXGCyIyG-ohotjqDSiffEhUxh1M9ULq7vmvPcA,29,,Goofball-BrainDump,55228328,ac491b48ceb06c4a1084cc29361c430c,RVC v2,https://drive.google.com/file/d/183OiSIsRKUHmzb6VLJMPLo0V3Z1PmAIb/view?usp=sharing,https://drive.google.com/file/d/183OiSIsRKUHmzb6VLJMPLo0V3Z1PmAIb/view,https://huggingface.co/FoxBlocks/Goofball-BrainDump/resolve/main/Goofball-BrainDump.zip,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1--B0ZEb2lOg6IFQYo0eQI9BI5dy86cbZ/view?usp=sharing,29,,tabimodel2333333,55027589,86b302ecbc7f84386de2ae342d635ee1,RVC v1,https://drive.google.com/file/d/1--B0ZEb2lOg6IFQYo0eQI9BI5dy86cbZ/view?usp=sharing,https://drive.google.com/file/d/1--B0ZEb2lOg6IFQYo0eQI9BI5dy86cbZ/view,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-AAggYM3JTxfoPcj22QG15laKCqqffhv/view?usp=drivesdk,29,,rarity,55019812,6902b07494267690902533ba82449a9b,RVC v1,https://drive.google.com/file/d/1-AAggYM3JTxfoPcj22QG15laKCqqffhv/view,https://drive.google.com/file/d/1fh634OHiqFC56l454QaYZx8tHLB-iEtB/view?usp=drive_link,https://drive.google.com/file/d/1tiBX4fTddI3m7wplIskuE9d88NUx-zTN/view?usp=sharing,https://drive.google.com/file/d/1EvQus8EZd82bb3PgfqD6ZTqh-_dT_UKm/view?usp=sharing,https://drive.google.com/file/d/1EvQus8EZd82bb3PgfqD6ZTqh-_dT_UKm/view?usp=drive_link,https://huggingface.co/MusicBox27/MyLittlePonyRVC/resolve/main/rarity.zip,,,,,,,,,,,,,
+https://mega.nz/file/gF5SFSJJ#wd8_Px_tj73XbXZLQX5Q6wK0F_PfTTqE-FQkGa57QHA,29,,KidGohanOrGoku,55226492,ea2cbc3ca25901df554bb35d9ecb8f39,RVC v2,https://drive.google.com/file/d/1bw_W87Q5EEkqQDbUwttlWQQEK6epneNj/view?usp=sharing,https://huggingface.co/SyberGen/DragonBallVModels/resolve/main/KidGohanOrGoku.zip,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1mcTNPOaAQ5xCjLOCIUAzNpkCFnw7fTuT/view,29,,sandro,55216420,63449cabce4be2ae489726cf76838edb,RVC v2,https://drive.google.com/file/d/1mcTNPOaAQ5xCjLOCIUAzNpkCFnw7fTuT/view?usp=drive_link,https://drive.google.com/file/d/1mcTNPOaAQ5xCjLOCIUAzNpkCFnw7fTuT/view?usp=sharing,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1tC7WxmUr19cJvxdkUQzxamkts8clqXaZ/view?usp=sharing,29,,Rare_Wubbox_e400_s800,57589001,4200e7e6b664bde4242178e028126c9c,RVC v2,https://drive.google.com/file/d/1tC7WxmUr19cJvxdkUQzxamkts8clqXaZ/view?usp=drive_link,https://drive.google.com/file/d/1tC7WxmUr19cJvxdkUQzxamkts8clqXaZ/view,https://drive.google.com/file/d/1ng7gbuWicsSMFLAxBmMrlXrFPvhUoiRk/view?usp=sharing,https://huggingface.co/Dr-Ivo-Robotnik/Rare_Wubbox/resolve/main/Rare_Wubbox.zip,https://drive.google.com/file/d/1tC7WxmUr19cJvxdkUQzxamkts8clqXaZ/view?usp=drivesdk,,,,,,,,,,,,,,
+https://drive.google.com/file/d/142aMPczsAfpnIeKnByLxliro1seKdXxb/view?usp=sharing,29,,secTOR,55216420,8976594ab373ae45fa2b746b32ffe1ed,RVC v2,https://drive.google.com/file/d/142aMPczsAfpnIeKnByLxliro1seKdXxb/view?usp=drive_link,,,,,,,,,,,,,,,,,,
+https://huggingface.co/ShadowLure/ClashRoyaleKing/resolve/main/ClashRoyaleKing.zip,29,,ClashRoyaleKing_e560_s2800,57591360,2926ae8165d15bd0555ca050daaf708d,RVC v2,https://drive.google.com/file/d/1SHiGwtzxe_1oOCdPGl0O-vrdCwxFqsTN/view?usp=drive_link,https://drive.google.com/file/d/15TrnznSvZAmhiJa9npusdItcbv9Pq0g4/view?usp=sharing,https://drive.google.com/file/d/13gUlvamUe-lk2vKT-kLw9j_TcPZxZYLN/view?usp=sharing,,,,,,,,,,,,,,,,
+https://mega.nz/file/3DQzkLZA#8OP4v9IQWpn0lUAYSbVzvFSg89KdGc9GWwqck0rp0ZU,29,,WallaceBreen500,55030343,a551a1b66401f8a378b01349941c0d55,RVC v1,https://mega.nz/file/3DQzkLZA#8OP4v9IQWpn0lUAYSbVzvFSg89KdGc9GWwqck0rp0ZU,https://huggingface.co/QuickWick/Music-AI-Voices/resolve/main/Wallace%20Breen%20(RVC)%20500%20Epoch/Wallace%20Breen%20(RVC)%20500%20Epoch.zip,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1O0YO3-0QbQ0qTzdqOQgfJn44N7ce3qnf/view?usp=sharing,29,,Rusherking,55224656,3faaeb60e878a35d3e49f1e4523dfbc7,RVC v2,https://drive.google.com/file/d/1O0YO3-0QbQ0qTzdqOQgfJn44N7ce3qnf/view,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1xM6SjUfpZ83LRuesiUF7MGOuOd4F2DrR/view?usp=drive_link,29,,KanonAnkharV2,55226033,2040b585ca88efc64c7600dfa78a8f55,RVC v2,https://drive.google.com/file/d/1xM6SjUfpZ83LRuesiUF7MGOuOd4F2DrR/view?fbclid=IwAR1ey8WAz5gSPKYNmVnraNayC-Jk5Nn_jPxDfAXBjouDBxWIASB027Sbx8I,https://drive.google.com/file/d/1xM6SjUfpZ83LRuesiUF7MGOuOd4F2DrR/view,,,,,,,,,,,,,,,,,
+https://mega.nz/file/Fk5DBBBI#bAIP5n_Ro4VfU5X0aQtuXhTFHau_GEylpnKHTo5ztr0,29,,nle_choppa_1000,55030343,7c0f30d95345f05b015c0e5a676893fc,RVC v1,https://mega.nz/file/Fk5DBBBI#bAIP5n_Ro4VfU5X0aQtuXhTFHau_GEylpnKHTo5ztr0,https://huggingface.co/QuickWick/Music-AI-Voices/resolve/main/NLE%20Choppa%20(RVC)%201000%20epochs%2051k/NLE%20Choppa%20(RVC)%201000%20epochs%2051k.zip,https://drive.google.com/file/d/1hqnZ_I2Xnu7Lf6-h4sBqtgQIhWDhYs2T/view?usp=sharing,https://drive.google.com/file/d/1pcJDZod62BDrhlN_ZhxxyjsE8oxfQ1OB/view?usp=sharing,https://mega.nz/file/Fk5DBBBI#bAIP5n_Ro4VfU5X0aQtuXhTFHau_GEylpnKHTo5ztr0 Dataset: https://mega.nz/file/I1Z0jTwR#t8ddqPFDgD9HCSfB-4cavCYR_vxM28OagWd2eKYALxs,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-9cRa9dMdo9GoRD5z4s_WUkcChcJjU8p/view,29,,Iida2333333,54996174,a9a9b7bf1b3db41724c5dc3d270f48a7,RVC v1,https://drive.google.com/file/d/1-9cRa9dMdo9GoRD5z4s_WUkcChcJjU8p/view?usp=sharing, https://drive.google.com/file/d/1-9cRa9dMdo9GoRD5z4s_WUkcChcJjU8p/view,https://drive.google.com/file/d/1-9cRa9dMdo9GoRD5z4s_WUkcChcJjU8p/view?usp=drive_link,https://drive.google.com/file/d/1-9cRa9dMdo9GoRD5z4s_WUkcChcJjU8p/view?usp=sharing ,,,,,,,,,,,,,,,
+https://huggingface.co/Coleereer/EGadd/resolve/main/Kirby.zip,29,,Kirby,55193241,bb0b4d3945f1c3e61c96d213c40e590d,RVC v2,https://drive.google.com/file/d/10VqOu-UbcUGBsTBWRaIZMrRjPDu5FHc3/view?usp=sharing,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1Zws6eHl0eic2JCIas6h703JmNzMiPra-/view?usp=sharing,29,,zorov2_e280_s13440,55228328,6d21a69a9bac698bfb2272542ec95757,RVC v2,https://mega.nz/file/xNAFgZoY#Y1u8PezyhbDNNsP0DrP16rUUtJhcmMJzPME4nfFrKhs,https://drive.google.com/file/d/1Zws6eHl0eic2JCIas6h703JmNzMiPra-/view,,,,,,,,,,,,,,,,,
+https://huggingface.co/vladihavr/StroheimRVC2/resolve/main/Stroheim_v2_300e.zip,29,,Stroheim,55223738,b19fbcfdb8e9b35c547040afca8bb038,RVC v2,https://drive.google.com/file/d/1JZlceI99FdOJUxOjWMJx4Y4O-yGqQm4n/view?usp=sharing,https://drive.google.com/file/d/1JZlceI99FdOJUxOjWMJx4Y4O-yGqQm4n/view?usp=drive_link,https://mega.nz/file/tGMwyA7Y#5NqUhsH5XsYyBwwies6Ki-8Ot-Rh3OeDzClEjrYTrts,https://drive.google.com/file/d/1TbWnAFyPcBSPW8K0nW8YSwY0UKP4TxfG/view?usp=sharing,https://mega.nz/file/WRhTQRbQ#Ht0fR9Z_SievI5Ojds8nINqUeJlszLLOqEdED_g9MHg,,,,,,,,,,,,,,
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+https://huggingface.co/xbxomr/baldiback/resolve/main/baldibackk.zip,6,,baldibackk,55224656,63abcc0c8d66d90b7dfcd75c2e9c2b17,RVC v2,https://drive.google.com/file/d/1GeyZH78Ru1meIkILYsjH9G8bZgK1Fi6C/view?usp=sharing,,,,,,,,,,,,,,,,,,
+https://huggingface.co/dijei/dijeiRVC/resolve/main/DKV2.zip,6,,DKV2,55226951,26fed197b776af39528ff6e44ec91e6c,RVC v2,https://drive.google.com/file/d/1mxvaxngiLwSxdmZA1-4UAvUL0Yny5F2t/view?usp=drive_link,,,,,,,,,,,,,,,,,,
+https://huggingface.co/MrAK2006/RVCModels/resolve/main/incineroartylerbunch.zip,6,,incineroartylerbunch,55229246,ec22b3c92bf6c7a8110771015557bb3b,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1Xs-Hb8soReZRz3i5XIdJIwQWvkWjLUs9/view?usp=drivesdk,6,,KatoRoid,55223738,09a5e638f28218f0cd6fcc4aff051399,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1RaGT5RF6i6I-csZGNbpaokzhQ_Jnjl3R/view?usp=sharing,6,,onew,57552078,5bb42a5b229f6259ee19744121240f29,RVC v2,,,,,,,,,,,,,,,,,,,
+https://huggingface.co/EAraid12/Cartwright/resolve/main/JayModel.zip,6,,Copy of jay-test_e265_s1060,55228787,3f6b52084bcba2fc7ff9099cc6974f1b,RVC v2,,,,,,,,,,,,,,,,,,,
+https://huggingface.co/Orwennes/RVC/resolve/main/aya.zip,6,,aya_sing,57581999,58fb742d8d10244a6511bdf1f94c40da,RVC v2,,,,,,,,,,,,,,,,,,,
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+https://drive.google.com/file/d/117yHPwiABFNJIyjCbAulTGUnJRk42CZr/view?usp=drive_link,6,,BOOM_MaB_LION_RVC,55227869,e3d8b69e72d18fcf9791642531e586e2,RVC v2,https://drive.google.com/file/d/117yHPwiABFNJIyjCbAulTGUnJRk42CZr/view?usp=drive_link,,,,,,,,,,,,,,,,,,
+https://huggingface.co/QuickWick/Music-AI-Voices/resolve/main/Matt%20Bellamy%20(From%20Muse)%20(RVC)%20200%20Epoch%2061k%20Steps/Matt%20Bellamy%20(From%20Muse)%20(RVC)%20200%20Epoch%2061k%20Steps.zip,6,,mattbellamyRVC61400,55029884,eab644d94c76e6e17be059b871553109,RVC v1,https://drive.google.com/file/d/18rN65gFn4xYzLfirN_fBLUMh4iLRp_JA/view?usp=sharing,https://drive.google.com/file/d/1c8LyLwq6IyPxaA7uSr1tc-vSk1bkWlSG/view?usp=drive_link,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-0DLZ_Wuj08JDzUdQfese3zVA9BT_Ud4/view?usp=share_link,6,,milojj,55019812,02996cedeb26073fd6110c3f63e210b1,RVC v1,https://drive.google.com/file/d/1-0DLZ_Wuj08JDzUdQfese3zVA9BT_Ud4/view,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-TPfs-Dwy9xa8zeJwTGrIWNb2hCiagKb/view?usp=drive_link,6,,marcaudio,55027589,79ba49ff9574989f26e4a42cbeac3732,RVC v1,https://drive.google.com/file/d/1mInD3TF_bXZFn4gz-V___oHH1aDnYgvu/view?usp=sharing,https://drive.google.com/file/d/1mInD3TF_bXZFn4gz-V___oHH1aDnYgvu/view?usp=drive_link,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-4b3HZy1YwNu2K74Eo_2BSFfh9pifETg/view?usp=sharing,6,,jaydesv22333333,55027130,4560c6f5b68a87552fa385d8d4ae5b3a,RVC v1,https://drive.google.com/file/d/1-4b3HZy1YwNu2K74Eo_2BSFfh9pifETg/view?usp=drivesdk,https://drive.google.com/file/d/1-4b3HZy1YwNu2K74Eo_2BSFfh9pifETg/view,https://drive.google.com/file/d/1rfylz9cm088giaFALP53yp5OFCBz9PMn/view?usp=sharing,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1usTasimbuihKv89pjWAoqOoowPkT8wHg/view?usp=drive_link,6,,EdogawaConan,55225574,540008e3690c6f6bad8a215d75a9b843,RVC v2,https://drive.google.com/file/d/1usTasimbuihKv89pjWAoqOoowPkT8wHg/view?fbclid=IwAR26ES33NKn6cgzWUp4rWBhButlheEG6WjRKKNyVGv3s1jYAqeyayoWto1k,https://drive.google.com/file/d/1usTasimbuihKv89pjWAoqOoowPkT8wHg/view?fbclid=IwAR0ZLZ_M7r03xw8KCgS_cBPiYoRFLc2l86PklDssHWvpTo7Fmdsjn1gqGTA,https://drive.google.com/file/d/1usTasimbuihKv89pjWAoqOoowPkT8wHg/view?fbclid=IwAR3pPEAth3PGQhrd6VuKTzMk7l233qYbYS3o7nWuAkmC6JUTmJe93SQGQAU,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1TQ7gcCyL3WKTqC4FpOjBgHCx8YaxZ2x5/view?usp=drive_link,6,,BoV2_e200_s13800,55227410,56a56fc333e10be63f47f4b1b9cd0d24,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/17W6rCKS91DQPuScZCjxdTmeskMppleON/view?usp=drive_link,6,,dub,57551491,53a94c4b9e926ffcb3b099932efff736,RVC v2,,,,,,,,,,,,,,,,,,,
+https://mega.nz/file/Xpk1iAwR#g771SsQ5baAyU2mydfF16JYkutEKJvG_npwtU7IFonU,6,,mw2valeria_e220_s15180,55230228,bd77edfc859a18b67191281260631e0c,RVC v2,https://huggingface.co/Munlight/ValeriaMW2/resolve/main/mw2valeria.zip,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1--VFpfpniBYAW1ypkt8OZrqTGySQ22uI/view,6,,antclemons2333333,55028048,8d0431c4f210457eabdc1523d5aa686b,RVC v1,https://drive.google.com/file/d/1--VFpfpniBYAW1ypkt8OZrqTGySQ22uI/view?usp=share_link,https://drive.google.com/file/d/1WGBq7HCRokmYx7KBVuZvW5UHwRp-Ztk3/view?usp=drive_link,,,,,,,,,,,,,,,,,
+https://mega.nz/file/WT4XxIpS#tqiiMLfYq4aeJm9SxMxboPZAALSxuV_v6GOCK5A6G_U,6,,oaktest,55026095,6a54c40e78b2ba706ff44298f9638770,RVC v1, https://mega.nz/file/WT4XxIpS#tqiiMLfYq4aeJm9SxMxboPZAALSxuV_v6GOCK5A6G_U,,,,,,,,,,,,,,,,,,
+https://huggingface.co/DeputyRipper/Jim_Carrey_RVC_V2_520_Epochs/resolve/main/Jim%20Carrey%20(RVC%20V2)(520%20Epochs).zip,6,,JimCarrey_e520_s9360,57588542,0157d673f7b820998ac8e3ba58f24039,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1kmd4KQs4MFoRCcl36TRx8aUSl18kSlWJ/view?usp=drive_link,6,,Ahyeon,55216420,2b6298adfe84294a803cb8a4d82dc437,RVC v2,,,,,,,,,,,,,,,,,,,
+https://mega.nz/file/FjdmUIhT#Pq1hEHAc3-oqOPMSE9n6y-nwODn_uDY6IsZHEngLWJI,6,,raiden-jp,57589001,3b52ca711dd0bb6df3d7f66cb42160aa,RVC v2,https://huggingface.co/ArkanDash/rvc-genshin-impact/resolve/main/prezipped/v2/raiden-jp%20100%20epochs%2048k%20v2.zip,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/13MMaBO6n0izmct67QMDZQoEW5Z25Sheu/view?usp=sharing,6,,Bigby,55193241,48bc6f88aa6234713886e3b5f0d3a64b,RVC v2,https://drive.google.com/file/d/13MMaBO6n0izmct67QMDZQoEW5Z25Sheu/view?usp=drive_link,https://drive.google.com/file/d/13MMaBO6n0izmct67QMDZQoEW5Z25Sheu/view?usp=drivesdk,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1ORNJKjB3L3r2jCUDQRdf5WXHtcvmdPv2/view?usp=sharing,6,,nadejdaa,55223738,3f70ed72d131d95a70d990ff8abc2c70,RVC v2,https://drive.google.com/file/d/1ORNJKjB3L3r2jCUDQRdf5WXHtcvmdPv2/view?usp=drive_link,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/16bCfj73Vc4b883BVeVQ1LwBrulRp1RWm/view?usp=sharing,6,,PacMan,57575716,95079894e62cd73d6d1d34b86cdd2be8,RVC v2,https://huggingface.co/Dingusmcbingus/PacManRVC/resolve/main/PacMan.zip,,,,,,,,,,,,,,,,,,
+https://huggingface.co/Xhepyxopila/RyansWorldRVCModels/resolve/main/AlphaLexa.zip,6,,AlphaLexa,57583493,7fc7585fd906c90c89d858e01f10998d,RVC v2,,,,,,,,,,,,,,,,,,,
+https://huggingface.co/astrin0321/Shy_Lily/resolve/main/LiLi.zip,6,,ShyLily_Talking_Test_e360_s21240,57447557,c156f9f497ba07d2cac5d2a94a8b766e,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1H0L0ehWQ82SMFyhMiN1kyY5Z2tz7ypH-/view?usp=sharing,6,,MordecaiHeller,55226492,5a56b46f26974e039950ae10cc584d02,RVC v2,https://drive.google.com/file/d/1H0L0ehWQ82SMFyhMiN1kyY5Z2tz7ypH-/view?usp=drive_link,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1tUC0B_pgtNCWu8-pWxViVvEDLIdX5Uva/view?usp=drive_link,6,,IneichenModelFull,55031261,0db7ed99f48e0fe24d9ff0b6bffb82ce,RVC v1,https://drive.google.com/file/d/1tUC0B_pgtNCWu8-pWxViVvEDLIdX5Uva/view?usp=sharing,,,,,,,,,,,,,,,,,,
+https://huggingface.co/clydemystik325/ClydesRVCModels/resolve/main/DanielUK_200e.zip,6,,DanielUK_200e,55226033,c10e480357940436c1b359dc3ed3d922,RVC v2,,,,,,,,,,,,,,,,,,,
+https://huggingface.co/BloxTobalRBX/VoiceModels/resolve/main/HandUnit.zip,6,,HandUnit,55223738,bfb2738c08ca8d1b170be466aa8f7b09,RVC v2,https://huggingface.co/BloxTobalRBX/VoiceModel/resolve/main/HandUnit.zip,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/11Yp5wNgbaIiax5Qxi2Ri-ht8RpT8BJ_9/view?usp=drive_link,6,,YungstarAI,55229705,d4ac72a671bfcdd2bdba64d4049933d5,RVC v2,https://drive.google.com/file/d/11Yp5wNgbaIiax5Qxi2Ri-ht8RpT8BJ_9/view?usp=sharing,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1LRNiZBZNBpQrFU6OTtWb0WAYtJ2y7BMo/view?usp=drive_link,6,,vsauce1,55222703,f68975336a74fb26309aaf21780b69c7,RVC v2,https://huggingface.co/Scarlap/VsauceMicheal/resolve/main/vsauce1.zip,https://drive.google.com/file/d/1LRNiZBZNBpQrFU6OTtWb0WAYtJ2y7BMo/view?usp=sharing,https://drive.google.com/file/d/1pa_0KjrT285NI39G5xm-8Ci8jcL1fQcP/view?usp=drive_link,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1Khfxgi_Oxn93BuqvG6U-wta_U0j8ztaM/view?usp=sharing,6,,MiphaAOC,55223738,76c6719304a6b689d88287f78bcc52b7,RVC v2,https://drive.google.com/file/d/1Khfxgi_Oxn93BuqvG6U-wta_U0j8ztaM/view?usp=sharin,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1dPDj9-xuGh6qVBhgbtW5rU9bL_E5XB2V/view?usp=drive_link,6,,kokomi-jp,55027589,f14aceff103836e775b6507fc07c6290,RVC v1,https://mega.nz/file/M6d0XJKZ#0ptzWNmezb2oEacuAa96SmPIzJ2FE0Ic7yFETC3xsWo,https://drive.google.com/file/d/1-I3HT4hKlgisN5Xa7gBEnICazdRNafq0/view?usp=drive_link,https://huggingface.co/ArkanDash/rvc-genshin-impact/resolve/main/prezipped/v1/kokomi-jp%20100%20epochs%2040k.zip,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1eBkeae8SHyEW6e-RsSAsuymjq0AScXnO/view?usp=sharing,6,,SaxtonHale_e140_s980,55229246,ff8793caca423cc870a38d63f876b329,RVC v2,https://drive.google.com/file/d/1eBkeae8SHyEW6e-RsSAsuymjq0AScXnO/view?usp=drive_link, https://drive.google.com/file/d/1eBkeae8SHyEW6e-RsSAsuymjq0AScXnO/view?usp=sharing,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1jlXBsFKx7-bar3PtTyQ4iRimhY0zxE-d/view,6,,Kurtains,55125567,cb9af07a377be0f86aee1bb5a7dcc632,RVC v1,https://drive.google.com/file/d/1jlXBsFKx7-bar3PtTyQ4iRimhY0zxE-d/view?usp=sharing,,,,,,,,,,,,,,,,,,
+https://mega.nz/file/c1FUzTjZ#ktJIaZ5iykLYCXe1dmtPimfnbNhksOoBLaTCtBKllE0,6,,Wuant,55193241,dc2405a6e9db2cbba9f920ce94dceaca,RVC v2,https://huggingface.co/Matt99/Matt99models/resolve/main/Wuant.zip,,,,,,,,,,,,,,,,,,
+https://huggingface.co/soulnull/rvcmodels/resolve/main/reviewbrah-330e.zip,6,,reviewbrah_e330_s100980,57589983,0e1177be36d1fff2804c20f21316e257,RVC v2,https://deadfrog.org/reviewbrah-330e.zip,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1Jjn9g5YlzVe74XvaaDf5f5Rk2x0ymrmX/view?usp=sharing,6,,jhayco,55210020,aa42c09772d4b69c79ef5f18a2233a72,RVC v2,https://drive.google.com/file/d/1fzl5yNkYiDQlJlIVqgVKNoCIRXwe64qV/view?usp=sharing,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1jGnR3fBqQVHlwdICpagSILWO7LoOd0Y8/view?usp=sharing,6,,MadonnaBlue,55225115,411a1e8b1bcd9e07d2f430b757affddc,RVC v2,https://drive.google.com/file/d/1jGnR3fBqQVHlwdICpagSILWO7LoOd0Y8/view?usp=drive_link,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/18A0ck-LoKEqPWTXHUhFKo7fcKmYPFJEt/view?usp=drive_link,6,,Ruiqi,55193241,8a0e6dad3644a51cf4659d0c7ac280d4,RVC v2,https://drive.google.com/file/d/10RMwEurLBuq-5TArXorfhNU9BgrQ8NHN/view?usp=drive_link,https://drive.google.com/file/d/18A0ck-LoKEqPWTXHUhFKo7fcKmYPFJEt/view?usp=drive_link%20If,https://drive.google.com/file/d/18A0ck-LoKEqPWTXHUhFKo7fcKmYPFJEt/view,,,,,,,,,,,,,,,,
+https://huggingface.co/Xhepyxopila/MarkusRVC/resolve/main/FishyBoopkins.zip,6,,Boopkins,57583034,a3b63444a6a74590facefcf479a5e975,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-GQLZcC535pNe8F9Qox1_7IXUGZdK1Kp/view?usp=sharing ,6,,Soojin,55216420,230f5555e7eaa933bcc54d08462a7fd9,RVC v2,https://drive.google.com/file/d/1-GQLZcC535pNe8F9Qox1_7IXUGZdK1Kp/view,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1j3HRiNrwIwFlX9vKaIguLRgq2B6rkiYJ/view?usp=sharing,6,,Axl-rosevoice,55226033,28ebef832c7b84976a1de5b5b3a067e7,RVC v2,https://drive.google.com/file/d/1j3HRiNrwIwFlX9vKaIguLRgq2B6rkiYJ/view?usp=drive_link,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1ZivVxFwp4ARP6BmhLZCl70yT10a7d6hB/view?usp=drive_link,6,,harun-tekin,55229705,c46bd621bdc617eab0c1b317f68a14e9,RVC v2,https://huggingface.co/ailabturkiye/haruntekin/resolve/main/Harun%20Tekin%20RVC.zip,,,,,,,,,,,,,,,,,,
+https://huggingface.co/FannToonsTheSilliest/nailyv2/resolve/main/stapy_FAKE.zip,6,,stapy_real_omfg,55226951,91b9c9e31c112edd035978fc4ef6e219,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1nNGz-Jo3OZKWQ9k7oMbcS6iQffv0mVzY/view?usp=drive_link,6,,rukanew2,55223738,6f27b812f8a572fc1bea7e6c09ee03c4,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-0IkWmb_Nko6nen887LxmDJGMkahXgNU/view?usp=sharing,6,,tvnautaxd,55224197,a3d96446d84481f49cf86f828fcc3b91,RVC v2,https://drive.google.com/file/d/1-0IkWmb_Nko6nen887LxmDJGMkahXgNU/view?usp=drive_link,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-MLCSlzEhJb9IclBF4Yj6-hV4gkaxO-n/view?usp=share_link,6,,herbiehancock,57585329,0a6968b06aaa02a974c2f720fbc5b664,RVC v2,https://drive.google.com/file/d/1-MLCSlzEhJb9IclBF4Yj6-hV4gkaxO-n/view,,,,,,,,,,,,,,,,,,
+https://huggingface.co/Astronomical/Tiktok-Memes/resolve/main/Spectacular-Bleeding-Sponge.zip,6,,Spectacular-Bleeding-Sponge,55232523,54a78fe759695320485a057877f95d7f,RVC v2,,,,,,,,,,,,,,,,,,,
+https://rvc-model-hosting.s3.amazonaws.com/my-models/TurnSignal.zip,6,,TurnSignal,55224656,bff888ef884cf18fa2a4694d60ad286c,RVC v2,https://huggingface.co/00a/TurnSignal/resolve/main/TurnSignal.zip,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1sJYpyl_-TmDsUjoAC0yBxf1hvgttLJ9B/view?usp=sharing,6,,NemoSila,55223738,2b065869cdb4f225c69e4b52871176f7,RVC v2,https://drive.google.com/file/d/1sJYpyl_-TmDsUjoAC0yBxf1hvgttLJ9B/view?usp=drive_link,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/12uIX7Ys2_Nuw63Az6qcDXmv2xEZitPhT/view?usp=sharing,6,,dio150,57575716,ff02797d5e5866aa7841b405f6e36dd4,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1DBXC29NzycJx5d_Y_9Rmkq_nDKuod15A/view?usp=sharing,6,,me,55191736,5d7bcaa5b944245273ba34f3819131ef,RVC v2,,,,,,,,,,,,,,,,,,,
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+https://huggingface.co/amiedan/DankiGenki/resolve/main/CTRNFCrunchBandicoot.zip,5,,crunch,55216420,11aef6af0cecde167aeb840a94ef8dc9,RVC v2,,,,,,,,,,,,,,,,,,,
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+https://drive.google.com/file/d/19slm58LNqUs_VE6kUDWoNvywGw4VaOrt/view?usp=sharing,1,,moti_e130_s650,55226492,a9bcd8c513bdb3ec302bec6645f39e46,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1sqATV6jke-2h_E6fq2yOnLo0RUesAtJ8/view?usp=drive_link,1,,akin,55192782,9184de6559ccead882bb9fe8091d7c3e,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-3oCxeyJzelBKrDRD9J64sLS7sLu6oy2/view?usp=drive_link,1,,Alisa300,54996633,cd26c8445c943ccb6d60a3a968c6f2d3,RVC v1,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1GqzBCad0xzEca1rkiqX8blMWqozL9erh/view?usp=sharing,1,,Valiant,55222703,0328a8dc7b389404b3aba0e30eb756cb,RVC v2,,,,,,,,,,,,,,,,,,,
+https://mega.nz/file/4SlnwAqB#2IstovSVpXaMnxO7BBUryzOYhsqR5BANnazfNWvmKOM,1,,BelkaVadim,55224656,0c77828272b2d5a5bf010b0e10edb770,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1QrZc4CYKCMi73778aUu2sTZ11xMJt24R/view?usp=drivesdk,1,,volk2,55193241,149f42ca7da6b3bd210443933fa678ea,RVC v2,,,,,,,,,,,,,,,,,,,
+https://huggingface.co/ailabturkiye/jotarokujo_jojo/resolve/main/Jotaro.zip,1,,Jotaro,55216420,880f5607a1d5c8ade6332aa22094a319,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1gVwydrG_9w7RXv3cnI-Cr-iUWDIF_XGB/view?usp=drive_link,1,,Shakeel_Aggressive_e500,55228328,68fc31ce115a82f6c739ef0a89c10556,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-02GXSWAHrUTAxRoVYAzfnl0-kGlwT0S/view?usp=drive_link,1,,Darksito,55223738,52629b51beb07bb1b4c2ad83d6bbc30c,RVC v2,,,,,,,,,,,,,,,,,,,
+https://huggingface.co/MrSpringtroll/frenchwhisper/resolve/main/whisper.zip,1,,whisper,55222703,94875e29c7690fbf32adfdb307216345,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1N9BwYUk1no8NujbWeA9apqVhE73gBtI_/view?usp=sharing,1,,News,55192782,94085b52bae1963b6c039ff8a1e6a8d7,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1jgLyPe9qJK2VMdTlIJbyeoli4AAvz3Fb/view?usp=drivesdk,1,,leahkate,55228787,5f8ef1f43fc0052b2b3a87d32998f4fb,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1SHeNl1FXhtMTzoxsPD_8NgwSKjM5ekzr/view?usp=drive_link,1,,firstfiverr,55028507,613903846d04f8952e366082265c4291,RVC v1,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1iQPyqwjwG3RYRh6DWhERajc1uvK1n_h-/view?usp=sharing,1,,fatmaerkal_e150_s1800,55229705,de57a6d0cd00cdf6074c8a9318d578e6,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1z5vydCDTPZAB3goLIvtsKnpbpbl8IgY4/view?usp=drive_link,1,,Shakeel_Aggressive_e300P,55228328,06f38ec5efd4d5ecb5e230f957cb43a3,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1NpYOlfo2_Bim1WEzVnOB6FQYg84MK6Ck/view?usp=sharing,1,,martha_e500_s5000,55227869,f12683334d49dce5459c46bf6043041b,RVC v2,,,,,,,,,,,,,,,,,,,
+https://huggingface.co/HungryBobo/OliSykes_48khz/resolve/main/OliSykes_48khz.zip,1,,Oliver_48k_e350_s36050,57589524,0c1b7130586655a245a94ee14fab56bb,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1FawA4XG1GLGRlELUimiD5kbnN1nRWep6/view?usp=drivesdk,1,,arianathankunext,55227410,a739ad712c1945566c9d3b1fe66903cd,RVC v2,,,,,,,,,,,,,,,,,,,
+https://mega.nz/file/RbsRDCzK#nWdmEwxiMYWWn6Mv7wIGOFkHLs8QnUwm_DBCeQ2I5J8,1,,Warlicia,55223738,29b7c1a21e74a2fbbf40c99dda9e7e8a,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1nE98pJahr19ImWuDN7MPj4mAGJNnXa3f/view?usp=sharing,1,,Shakeel_Aggressive_e700,55228328,a354e8749944636a283314b7b372faaa,RVC v2,,,,,,,,,,,,,,,,,,,
+https://huggingface.co/Kipsalo/DrNick/resolve/main/dr%20nick%20ai.zip,1,,DrNick,55216420,3b65c75392d8a0ef0c3288856da394ca,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1TtNitAAMgj446SEqZI6lx6caODjAohzF/view?usp=sharing,1,,haechanv2model,55226492,db67494f00e4bd25912918f27e608145,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1OQKcBTFpLGMN2thP5jotj5bXfAjxmA3P/view?usp=drive_link,1,,LMM,55192195,5eb4a5652d2a74fa3ddf3f25b63d798f,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/138c3qBwLsIhmEM6fOO3l7v4B2apqHTSV/view?usp=sharing,1,,Newss,55193241,073404880d8e753a168b0b5b76ae5377,RVC v2,,,,,,,,,,,,,,,,,,,
+https://huggingface.co/Camthekirby/My-Funky-Models/resolve/main/Pat-Fusty.zip,1,,Pat-Fusty,55224197,4e20fff97cecf45cfc89d9f59fcab9f9,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1-9QyfMblLiC4bxIMIYmomYXsrAhWNK5X/view?usp=sharing,1,,mili,55192782,4db01d412456799e1bea3cf069b23809,RVC v2,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1xPtJ4ovmn0Al-qsGTSeWRAbNYjQWvo1Q/view?usp=drive_link,1,,Olya300,54996174,c124e727b473b30c874ea89d406f7465,RVC v1,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1fHEK0kwGDt9s_mTkXxvT4stR2aAqvrGF/view?usp=sharing,1,,LAYLAM2,55026095,f8018424c32ad6c65f35cc0ab4c43d95,RVC v1,,,,,,,,,,,,,,,,,,,
+https://drive.google.com/file/d/1UXFAciwI5JBmO1f_UJUWLSJZIDDchqsj/view?usp=sharing,1,,Smack2,55216420,8063580f36636b6bd92a82fe0c1aa34c,RVC v2,,,,,,,,,,,,,,,,,,,
diff --git a/assets/weights/.gitattributes b/assets/weights/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/assets/zluda/patch-zluda-hip64.bat b/assets/zluda/patch-zluda-hip64.bat
new file mode 100644
index 0000000000000000000000000000000000000000..e51215e891968613137c91e320520a093bb8a477
--- /dev/null
+++ b/assets/zluda/patch-zluda-hip64.bat
@@ -0,0 +1,21 @@
+rmdir /S /q zluda
+curl -s -L https://github.com/lshqqytiger/ZLUDA/releases/download/rel.854c58e1565c3597e17046d7cc2b1eab96fcdf55/ZLUDA-windows-amd64.zip > zluda.zip
+tar -xf zluda.zip
+del zluda.zip
+
+if exist "runtime" (
+ copy runtime\Lib\site-packages\torch\lib\nvrtc64_112_0.dll runtime\Lib\site-packages\torch\lib\nvrtc_cuda.dll /y
+ copy zluda\cublas.dll runtime\Lib\site-packages\torch\lib\cublas64_11.dll /y
+ copy zluda\cusparse.dll runtime\Lib\site-packages\torch\lib\cusparse64_11.dll /y
+ copy zluda\nvrtc.dll runtime\Lib\site-packages\torch\lib\nvrtc64_112_0.dll /y
+ copy zluda\cufft.dll runtime\Lib\site-packages\torch\lib\cufft64_10.dll /y
+ copy zluda\cufftw.dll runtime\Lib\site-packages\torch\lib\cufftw64_10.dll /y
+) else (
+ copy env\Lib\site-packages\torch\lib\nvrtc64_112_0.dll env\Lib\site-packages\torch\lib\nvrtc_cuda.dll /y
+ copy zluda\cublas.dll env\Lib\site-packages\torch\lib\cublas64_11.dll /y
+ copy zluda\cusparse.dll env\Lib\site-packages\torch\lib\cusparse64_11.dll /y
+ copy zluda\nvrtc.dll env\Lib\site-packages\torch\lib\nvrtc64_112_0.dll /y
+ copy zluda\cufft.dll env\Lib\site-packages\torch\lib\cufft64_10.dll /y
+ copy zluda\cufftw.dll env\Lib\site-packages\torch\lib\cufftw64_10.dll /y
+)
+pause
\ No newline at end of file
diff --git a/assets/zluda/path-zluda-hip57.bat b/assets/zluda/path-zluda-hip57.bat
new file mode 100644
index 0000000000000000000000000000000000000000..acbb0231f9b8725688002c5c107d1e244a84e0f3
--- /dev/null
+++ b/assets/zluda/path-zluda-hip57.bat
@@ -0,0 +1,14 @@
+rmdir /S /q zluda
+curl -s -L https://github.com/lshqqytiger/ZLUDA/releases/download/rel.c0804ca624963aab420cb418412b1c7fbae3454b/ZLUDA-windows-rocm5-amd64.zip > zluda.zip
+tar -xf zluda.zip
+del zluda.zip
+
+if exist "runtime" (
+ copy zluda\cublas.dll runtime\Lib\site-packages\torch\lib\cublas64_11.dll /y
+ copy zluda\cusparse.dll runtime\Lib\site-packages\torch\lib\cusparse64_11.dll /y
+ copy zluda\nvrtc.dll runtime\Lib\site-packages\torch\lib\nvrtc64_112_0.dll /y
+) else (
+ copy zluda\cublas.dll env\Lib\site-packages\torch\lib\cublas64_11.dll /y
+ copy zluda\cusparse.dll env\Lib\site-packages\torch\lib\cusparse64_11.dll /y
+ copy zluda\nvrtc.dll env\Lib\site-packages\torch\lib\nvrtc64_112_0.dll /y
+)
\ No newline at end of file
diff --git a/assets/zluda/path-zluda-hip61.bat b/assets/zluda/path-zluda-hip61.bat
new file mode 100644
index 0000000000000000000000000000000000000000..6a699133103ff6124f53f30c45f21ffdd1adc7a2
--- /dev/null
+++ b/assets/zluda/path-zluda-hip61.bat
@@ -0,0 +1,14 @@
+rmdir /S /q zluda
+curl -s -L https://github.com/lshqqytiger/ZLUDA/releases/download/rel.c0804ca624963aab420cb418412b1c7fbae3454b/ZLUDA-windows-rocm6-amd64.zip > zluda.zip
+tar -xf zluda.zip
+del zluda.zip
+
+if exist "runtime" (
+ copy zluda\cublas.dll runtime\Lib\site-packages\torch\lib\cublas64_11.dll /y
+ copy zluda\cusparse.dll runtime\Lib\site-packages\torch\lib\cusparse64_11.dll /y
+ copy zluda\nvrtc.dll runtime\Lib\site-packages\torch\lib\nvrtc64_112_0.dll /y
+) else (
+ copy zluda\cublas.dll env\Lib\site-packages\torch\lib\cublas64_11.dll /y
+ copy zluda\cusparse.dll env\Lib\site-packages\torch\lib\cusparse64_11.dll /y
+ copy zluda\nvrtc.dll env\Lib\site-packages\torch\lib\nvrtc64_112_0.dll /y
+)
\ No newline at end of file
diff --git a/assets/zluda/path-zluda-hip62.bat b/assets/zluda/path-zluda-hip62.bat
new file mode 100644
index 0000000000000000000000000000000000000000..85d32237070b340dc13ab1ab28ade5b5caedcbca
--- /dev/null
+++ b/assets/zluda/path-zluda-hip62.bat
@@ -0,0 +1,21 @@
+rmdir /S /q zluda
+curl -s -L https://github.com/lshqqytiger/ZLUDA/releases/download/rel.5e717459179dc272b7d7d23391f0fad66c7459cf/ZLUDA-windows-rocm6-amd64.zip > zluda.zip
+tar -xf zluda.zip
+del zluda.zip
+
+if exist "runtime" (
+ copy runtime\Lib\site-packages\torch\lib\nvrtc64_112_0.dll runtime\Lib\site-packages\torch\lib\nvrtc_cuda.dll /y
+ copy zluda\cublas.dll runtime\Lib\site-packages\torch\lib\cublas64_11.dll /y
+ copy zluda\cusparse.dll runtime\Lib\site-packages\torch\lib\cusparse64_11.dll /y
+ copy zluda\nvrtc.dll runtime\Lib\site-packages\torch\lib\nvrtc64_112_0.dll /y
+ copy zluda\cufft.dll runtime\Lib\site-packages\torch\lib\cufft64_10.dll /y
+ copy zluda\cufftw.dll runtime\Lib\site-packages\torch\lib\cufftw64_10.dll /y
+) else (
+ copy env\Lib\site-packages\torch\lib\nvrtc64_112_0.dll env\Lib\site-packages\torch\lib\nvrtc_cuda.dll /y
+ copy zluda\cublas.dll env\Lib\site-packages\torch\lib\cublas64_11.dll /y
+ copy zluda\cusparse.dll env\Lib\site-packages\torch\lib\cusparse64_11.dll /y
+ copy zluda\nvrtc.dll env\Lib\site-packages\torch\lib\nvrtc64_112_0.dll /y
+ copy zluda\cufft.dll env\Lib\site-packages\torch\lib\cufft64_10.dll /y
+ copy zluda\cufftw.dll env\Lib\site-packages\torch\lib\cufftw64_10.dll /y
+)
+pause
\ No newline at end of file
diff --git a/assets/zluda/run_app.bat b/assets/zluda/run_app.bat
new file mode 100644
index 0000000000000000000000000000000000000000..a16f023163c2eeb9594cf3e241cdd3a6cdb73311
--- /dev/null
+++ b/assets/zluda/run_app.bat
@@ -0,0 +1,18 @@
+@echo off
+chcp 65001 > nul
+
+setlocal
+title Vietnamese RVC By Anh [ZLUDA]
+
+set HIP_VISIBLE_DEVICES="0"
+set ZLUDA_COMGR_LOG_LEVEL=1
+SET DISABLE_ADDMM_CUDA_LT=1
+
+if exist "runtime\\python.exe" (
+ zluda\zluda.exe -- runtime\\python.exe main\\app\\app.py --open --allow_all_disk
+) else if exist "env\\Scripts\\python.exe" (
+ zluda\zluda.exe -- env\\Scripts\\python.exe main\\app\\app.py --open --allow_all_disk
+)
+
+echo.
+pause
\ No newline at end of file
diff --git a/audios/.gitattributes b/audios/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/dataset/.gitattributes b/dataset/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/main/app/app.py b/main/app/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cb39b91bd517eee935f6d33007d8eaf541600b0
--- /dev/null
+++ b/main/app/app.py
@@ -0,0 +1,237 @@
+import os
+import io
+import ssl
+import sys
+import time
+import codecs
+import logging
+import warnings
+
+import gradio as gr
+
+try:
+ import spaces
+ @spaces.GPU
+ def dummpy():
+ pass
+ dummpy()
+
+ import json
+ configs = json.load(open(os.path.join("main", "configs", "config.json"), "r"))
+ configs["create_and_training_tab"] = False
+ configs["cpu_mode"] = True
+
+ json.dump(configs, open(os.path.join("main", "configs", "config.json"), "w"), indent=4)
+except ImportError:
+ pass
+
+sys.path.append(os.getcwd())
+# Track application startup time performance
+start_time = time.time()
+
+from main.app.variables import logger, config, translations, theme, font, configs, language, allow_disk, gradio_version
+
+from main.app.core.realtime import js_code
+from main.app.tabs.extra.extra import extra_tab
+from main.app.tabs.editing.editing import editing_tab
+from main.app.tabs.training.training import training_tab
+from main.app.tabs.realtime.realtime import realtime_tab
+from main.app.tabs.downloads.downloads import download_tab
+from main.app.tabs.inference.inference import inference_tab
+from main.configs.rpc import connect_discord_ipc, send_discord_rpc
+
+# Global SSL Fix: Bypass SSL verification for model/file downloads via HTTPS
+ssl._create_default_https_context = ssl._create_unverified_context
+
+if not config.debug_mode:
+ warnings.filterwarnings("ignore")
+ for l in ["httpx", "gradio", "uvicorn", "httpcore", "urllib3"]:
+ logging.getLogger(l).setLevel(logging.ERROR)
+
+# Windows Platform Hotfix: Prevent crashing on 'ConnectionResetError' within asyncio proactor events
+if sys.platform == "win32":
+ import asyncio.proactor_events as _pe
+
+ _orig_ccl = _pe._ProactorBasePipeTransport._call_connection_lost
+
+ def _ccl_patched(self, exc):
+ try:
+ _orig_ccl(self, exc)
+ except ConnectionResetError:
+ pass
+
+ _pe._ProactorBasePipeTransport._call_connection_lost = _ccl_patched
+
+# Dynamic CSS generation for custom fonts injecting into the Gradio UI frontend
+css = """
+
+""".format(
+ font_urls=font or "https://fonts.googleapis.com/css2?family=Saira&display=swap",
+ fonts=(
+ font or "https://fonts.googleapis.com/css2?family=Saira&display=swap"
+ ).replace("https://fonts.googleapis.com/css2?family=", "").replace("+", " ").split(":")[0].split("&")[0]
+
+)
+
+cpu_title = {
+ "vi-VN": "Hiện tại, nó đang chạy trên CPU thay vì ZeroGPU thực sự, muốn hiệu năng tốt nhát hãy sử dụng [Google Colab](https://colab.research.google.com/github/PhamHuynhAnh16/Vietnamese-RVC-ipynb/blob/main/Vietnamese-RVC.ipynb)",
+ "en-US": "Currently, it is running on the CPU instead of the actual ZeroGPU; for the best performance, please use [Google Colab](https://colab.research.google.com/github/PhamHuynhAnh16/Vietnamese-RVC-ipynb/blob/main/Vietnamese-RVC.ipynb)."
+}[configs.get("language", "vi-VN")]
+
+# Check if the application is running in Client Mode based on CLI arguments
+client_mode = True # "--client" in sys.argv
+
+# Configure UI operational parameters (JS scripts handling depends on Gradio core version)
+gr_params = {
+ "js": (
+ ("() => {\n" + js_code + "\n}")
+ if gradio_version else
+ js_code
+ ) if client_mode else None,
+ "theme": theme,
+ "css": css
+}
+
+# Construct the Main Gradio Web UI Layout
+with gr.Blocks(
+ title="📱 Vietnamese-RVC GUI",
+ **gr_params if gradio_version else {}
+) as app:
+ # Render Application Header
+ gr.HTML("🎵VIETNAMESE RVC🎵 ", padding=True)
+ gr.HTML(f"{translations['title']} ", padding=True)
+ gr.HTML(f"{cpu_title} ", padding=True)
+
+ # Render Functional Navigation Tabs dynamically based on user configuration settings
+ with gr.Tabs():
+ if configs.get("inference_tab", True): inference_tab()
+ if configs.get("editing_tab", True): editing_tab()
+ if configs.get("realtime_tab", True) and not config.is_zluda: realtime_tab()
+ if configs.get("create_and_training_tab", True): training_tab()
+ if configs.get("downloads_tab", True): download_tab()
+ if configs.get("extra_tab", True): extra_tab(app)
+
+ # This thing is useless; it's just there to make someone curious and get trolled.
+ with gr.Row():
+ gr.Markdown(
+ translations["rick_roll"].format(
+ rickroll=codecs.decode('uggcf://jjj.lbhghor.pbz/jngpu?i=qDj4j9JtKpD', 'rot13')
+ )
+ )
+
+ # Footer Section: Terms of Service info
+ with gr.Row():
+ gr.Markdown(translations["terms_of_use"])
+
+ # Footer Section: Exemption/Disclaimer agreements
+ with gr.Row():
+ gr.Markdown(translations["exemption"])
+
+ # Application Startup Sequence
+ if __name__ == "__main__":
+ # Hardware acceleration environment mapping (DirectML, ZLUDA AMD, ROCm)
+ device = config.device.replace("privateuseone", "dml")
+ if config.is_zluda: device = device.replace("cuda", "hip")
+ if config.hip_version is not None: device = device.replace("cuda", "rocm")
+
+ # Log system capabilities, execution backend, models compilation status, and computing precision
+ logger.info(f"Pytorch: {device} | Onnxruntime: {config.providers[0][0].replace('Dml', 'Ocl') if device.startswith('ocl') else config.providers[0][0].replace('Dml', 'ROCM') if config.hip_version and device.startswith('cuda') else config.providers[0][0]}")
+ if config.compile_all: logger.info(translations["compile_model"].format(compile_mode=config.compile_mode))
+ logger.info(f'{translations["precision"]}: {("BF16" if config.brain else "FP16") if config.is_half else "FP32"}')
+ logger.info(translations["start_app"])
+ logger.info(translations["set_lang"].format(lang=language))
+
+ # Retrieve server network configuration targets from settings
+ port = configs.get("app_port", 7860)
+ server_name = configs.get("server_name", "0.0.0.0")
+ share = "--share" in sys.argv
+
+ # Temporarily redirect standard output streams to suppress redundant engine logs during launch
+ original_stdout = sys.stdout
+ sys.stdout = io.StringIO()
+
+ # Retry loop to find an available network port if the designated one is occupied
+ for i in range(configs.get("num_of_restart", 5)):
+ try:
+ gradio_app, _, share_url = app.queue().launch(
+ favicon_path=configs["ico_path"],
+ server_name=server_name,
+ server_port=port,
+ show_error=configs.get("app_show_error", False),
+ inbrowser="--open" in sys.argv,
+ share=share,
+ allowed_paths=allow_disk,
+ prevent_thread_lock=True,
+ quiet=not config.debug_mode,
+ **gr_params if not gradio_version else {}
+ )
+ break # Port successfully bound, break retry cycle
+ except OSError:
+ logger.debug(translations["port"].format(port=port))
+ port -= 1 # Shift port downwards and retry
+ except Exception as e:
+ logger.error(translations["error_occurred"].format(e=e))
+ sys.exit(1)
+
+ # Client Mode Routing: Mount custom FastAPI microservices under Gradio framework
+ if client_mode:
+ logger.debug(translations["mount_fastapi"])
+
+ from main.inference.realtime.client import app as fastapi_app
+ gradio_app.mount("/api", fastapi_app)
+
+ # Flush buffered engine startup logs back to standard debugging console output
+ logger.debug("\n" + sys.stdout.getvalue())
+ sys.stdout = original_stdout
+
+ # Discord Integration: Establish a pipeline for active Rich Presence status updates
+ if configs.get("discord_presence", True) and "--no_discord" not in sys.argv:
+ pipe = connect_discord_ipc()
+ if pipe:
+ try:
+ logger.info(translations["start_rpc"])
+ send_discord_rpc(pipe)
+ except KeyboardInterrupt:
+ logger.info(translations["stop_rpc"])
+ pipe.close()
+
+ # Display server URL endpoints
+ logger.info(f"{translations['running_local_url']}: {server_name}:{port}")
+ if share: logger.info(f"{translations['running_share_url']}: {share_url}")
+
+ # Optional: Initialize and run background TensorBoard analytics instances if requested
+ if "--tensorboard" in sys.argv:
+ logger.info(translations["run_tensorboard"])
+
+ from main.app.run_tensorboard import launch_tensorboard
+ launch_tensorboard()
+
+ # Log total initialization performance benchmark metric
+ logger.info(f"{translations['gradio_start']}: {(time.time() - start_time):.2f}s")
+
+ # Keep the main process thread alive to maintain active web application instances
+ while 1:
+ time.sleep(5)
\ No newline at end of file
diff --git a/main/app/core/csrt.py b/main/app/core/csrt.py
new file mode 100644
index 0000000000000000000000000000000000000000..823759a99ea9a9ac89b8e3b9992eb7d036783003
--- /dev/null
+++ b/main/app/core/csrt.py
@@ -0,0 +1,170 @@
+import os
+import gc
+import sys
+
+sys.path.append(os.getcwd())
+
+from main.app.core.ui import gr_info, gr_warning, process_output
+from main.app.variables import config, translations, configs, logger
+from main.library.utils import check_spk_diarization, clear_gpu_cache
+
+def whisper_process(
+ model_size,
+ input_audio,
+ configs,
+ device,
+ out_queue,
+ language="vi"
+):
+ """
+ Loads the Whisper model and transcribes the input audio within an isolated process.
+
+ This function is designed to run inside a separate multiprocessing pool/process
+ to isolate heavy GPU usage and safely clear VRAM upon completion.
+
+ Args:
+ model_size (str): Size/name of the Whisper model to load (e.g., 'base', 'large-v3').
+ input_audio (str): Path to the source audio file.
+ configs (dict): Transcription configuration settings (e.g., fp16 options).
+ device (str): Target device for computation ('cuda' or 'cpu').
+ out_queue (multiprocessing.Queue): Inter-process queue to return results or exceptions.
+ language (str, optional): ISO code for target transcription language. Defaults to "vi".
+ """
+
+ # Lazy import to prevent heavy model dependencies loading in the parent process
+ from main.library.speaker_diarization.whisper import load_model
+
+ try:
+ # Initialize and load the target Whisper model onto the specified device
+ model = load_model(model_size, device=device)
+
+ # Optimize performance via torch.compile if enabled in global configuration
+ if config.compile_all:
+ import torch
+
+ model.encoder = torch.compile(model.encoder, mode=config.compile_mode)
+ model.decoder = torch.compile(model.decoder, mode=config.compile_mode)
+
+ # Execute the transcription process with word-level timestamps
+ segments = model.transcribe(
+ input_audio,
+ fp16=configs.get("fp16", False),
+ word_timestamps=True,
+ language=language
+ )
+
+ # Send the successfully extracted audio segments back to the main process
+ out_queue.put(segments["segments"])
+ except Exception as e:
+ # Pass any runtime exception to the main process for proper handling
+ out_queue.put(e)
+ finally:
+ # Strict memory management: Flush GPU cache and delete references to free VRAM
+ clear_gpu_cache()
+ del segments
+ gc.collect()
+
+def create_srt(
+ model_size,
+ input_audio,
+ output_file,
+ language
+):
+ """
+ Validates paths, runs Whisper transcription in a separate process, and generates an SRT subtitle file.
+
+ Args:
+ model_size (str): Size of the Whisper model to be deployed.
+ input_audio (str): File path of the audio to transcribe.
+ output_file (str): Target file path where the SRT file will be saved.
+ language (str): Target transcription language.
+
+ Returns:
+ list: A list containing a Gradio UI update dictionary object and the raw SRT text string.
+ Returns [None, None] if any input or output path validation fails.
+ """
+
+ import multiprocessing as mp
+
+ if not input_audio or not os.path.exists(input_audio) or os.path.isdir(input_audio):
+ gr_warning(translations["input_not_valid"])
+ return [None]*2
+
+ # Enforce standard SubRip Subtitle format extension
+ if not output_file.endswith(".srt"): output_file += ".srt"
+
+ if not output_file:
+ gr_warning(translations["output_not_valid"])
+ return [None]*2
+
+ # Ensure target directory tree exists before writing
+ output_dir = os.path.dirname(output_file)
+ if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True)
+
+ info = ""
+ output_file = process_output(output_file)
+ # Pre-execution environment/model checks
+ check_spk_diarization(model_size, speechbrain=False)
+ gr_info(translations["csrt"])
+
+ # Using 'spawn' context to guarantee a clean memory state, critical for CUDA operations
+ ctx = mp.get_context("spawn")
+ queue = ctx.Queue()
+
+ process = ctx.Process(
+ target=whisper_process,
+ args=(
+ model_size,
+ input_audio,
+ configs,
+ config.device,
+ queue,
+ language
+ )
+ )
+
+ process.start()
+ segments = queue.get() # Halts execution until the child process pushes data into the queue
+ process.join()
+
+ with open(output_file, "w", encoding="utf-8") as f:
+ for i, segment in enumerate(segments):
+ start = segment["start"]
+ end = segment["end"]
+ text = segment["text"].strip()
+ # Formatting block structures matching standard SRT syntax specifications
+ index = f"{i+1}\n"
+ timestamp = f"{format_timestamp(start)} --> {format_timestamp(end)}\n"
+ text1 = f"{text}\n\n"
+ # Write directly to disk
+ f.write(index)
+ f.write(timestamp)
+ f.write(text1)
+ # Keep a localized log string copy for display/logging
+ info = info + index + timestamp + text1
+ logger.info(info)
+
+ gr_info(translations["success"])
+ # Return a UI state update payload along with the log text raw string
+ return [{"value": output_file, "visible": True, "__type__": "update"}, info]
+
+def format_timestamp(seconds):
+ """
+ Converts a duration in raw floating-point seconds into a standardized SRT timecode string.
+
+ Format follows: HH:MM:SS,mmm
+
+ Args:
+ seconds (float): Total time duration represented in seconds.
+
+ Returns:
+ str: Format matched timecode string (e.g., '01:23:45,678').
+ """
+
+ hours = int(seconds // 3600)
+ minutes = int((seconds % 3600) // 60)
+
+ seconds = int(seconds % 60)
+ miliseconds = int(round((seconds % 1) * 1000))
+
+ return f"{hours:02}:{minutes:02}:{seconds:02},{miliseconds:03}"
\ No newline at end of file
diff --git a/main/app/core/downloads.py b/main/app/core/downloads.py
new file mode 100644
index 0000000000000000000000000000000000000000..21911941133964c69a2f567791818539d6d1f21d
--- /dev/null
+++ b/main/app/core/downloads.py
@@ -0,0 +1,459 @@
+import os
+import re
+import sys
+import json
+import codecs
+import shutil
+import yt_dlp
+import warnings
+import requests
+
+from bs4 import BeautifulSoup
+
+sys.path.append(os.getcwd())
+
+from main.tools import huggingface, gdown, meganz, mediafire, pixeldrain
+from main.app.variables import logger, translations, model_options, configs
+from main.app.core.process import move_files_from_directory, fetch_pretrained_data, extract_name_model
+from main.app.core.ui import gr_info, gr_warning, gr_error, process_output, replace_url, replace_modelname
+
+def download_url(url = None):
+ """
+ Downloads audio from a given URL (YouTube, etc.) and converts it to a standard WAV file.
+
+ Args:
+ url (str, optional): The target video or audio URL. Defaults to None.
+
+ Returns:
+ List[Union[str, None]]: A list containing the absolute audio path twice
+ and a success message indicator. Returns [None, None, None] if validation fails.
+ """
+
+ if not url:
+ gr_warning(translations["provide_url"])
+ return [None]*3
+
+ # Ensure the destination directory for audio storage exists
+ if not os.path.exists(configs["audios_path"]): os.makedirs(configs["audios_path"], exist_ok=True)
+
+ # Suppress internal library warnings from yt_dlp during runtime execution
+ with warnings.catch_warnings():
+ warnings.filterwarnings("ignore")
+
+ # Define yt-dlp processing parameters tailored for highest quality audio extraction
+ ydl_opts = {
+ "format": "bestaudio/best",
+ "postprocessors": [{
+ "key": "FFmpegExtractAudio",
+ "preferredcodec": "wav",
+ "preferredquality": "192"
+ }],
+ "quiet": True,
+ "no_warnings": True,
+ "noplaylist": True,
+ "verbose": False
+ }
+
+ gr_info(translations["start_download"])
+ audio_output = ""
+
+ # Dry-run execution to safely fetch the video title and format a secure filename
+ with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+ # Replace concurrent whitespaces with a single uniform hyphen delimiter
+ audio_output = process_output(os.path.join(
+ configs["audios_path"],
+ re.sub(
+ r'\s+', '-',
+ # Extract title and sanitize it by removing special regex patterns across languages
+ re.sub(
+ r'[^\w\s\u4e00-\u9fff\uac00-\ud7af\u0400-\u04FF\u1100-\u11FF]',
+ '',
+ ydl.extract_info(url, download=False).get('title', 'video')
+ ).strip()
+ )
+ ) + ".wav")
+
+ audio_output_dir = audio_output.replace(".wav", "")
+ # Clear temporary or existing conflicting directories with the same target signature
+ if os.path.exists(audio_output_dir):
+ shutil.rmtree(audio_output_dir, ignore_errors=True)
+
+ # Assign output template mapping configuration path dynamically
+ ydl_opts['outtmpl'] = audio_output_dir
+
+ # Execute active download stream session via standard context container pipeline
+ with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+ ydl.download([url])
+
+ gr_info(translations["success"])
+
+ return [
+ audio_output,
+ audio_output,
+ translations["success"]
+ ]
+
+def move_file(
+ file,
+ download_dir,
+ model,
+ use_orig_weight_name=False
+):
+ """
+ Unpacks zip archives if required and transfers model weights/logs to their designated directories.
+
+ Args:
+ file (str): Absolute file path of the downloaded asset.
+ download_dir (str): Sandbox extraction tracking path directory.
+ model (str): Target model naming profile identifier.
+ use_orig_weight_name (bool, optional): Retain original weights filename format flag. Defaults to False.
+ """
+
+ weights_dir = configs["weights_path"]
+ logs_dir = configs["logs_path"]
+
+ # Secure asset storage directory trees infrastructure structures
+ if not os.path.exists(weights_dir): os.makedirs(weights_dir, exist_ok=True)
+ if not os.path.exists(logs_dir): os.makedirs(logs_dir, exist_ok=True)
+
+ # Automatically extract archived packages prior to organizing internal architecture assets
+ if file.endswith(".zip"): shutil.unpack_archive(file, download_dir)
+ # Delegate standard classification mapping file placement migration processing pipelines
+ move_files_from_directory(download_dir, weights_dir, logs_dir, model, use_orig_weight_name)
+
+def download_model(url=None, model=None):
+ """
+ Routes arbitrary hosting URLs to provider-specific wrappers to fetch and process RVC models.
+
+ Supports HuggingFace, Google Drive, MediaFire, PixelDrain, and Mega.nz URLs.
+
+ Args:
+ url (str, optional): Target provider download address hyperlink routing endpoint. Defaults to None.
+ model (str, optional): Overriding label custom nomenclature naming flag identifier. Defaults to None.
+
+ Returns:
+ str: Context matched localization notification string state tracking execution output.
+ """
+
+ if not url: return gr_warning(translations["provide_url"])
+ logger.debug(model + ": " + url)
+
+ url = replace_url(url)
+ download_dir = "download_model"
+ use_orig_weight_name = False
+
+ os.makedirs(download_dir, exist_ok=True)
+
+ try:
+ gr_info(translations["start_download"])
+
+ # Service provider dynamic platform interface router mapping allocation
+ if "huggingface.co" in url:
+ file = huggingface.HF_download_file(
+ url,
+ download_dir
+ )
+ elif "google.com" in url:
+ file = gdown.gdown_download(
+ url,
+ download_dir
+ )
+ elif "mediafire.com" in url:
+ file = mediafire.Mediafire_Download(
+ url,
+ download_dir
+ )
+ elif "pixeldrain.com" in url:
+ file = pixeldrain.pixeldrain(
+ url,
+ download_dir
+ )
+ elif "mega.nz" in url:
+ file = meganz.mega_download_url(
+ url,
+ download_dir
+ )
+ else:
+ gr_warning(translations["not_support_url"])
+ return translations["not_support_url"]
+
+ # Deduce missing explicit naming contexts by reading core filename variants safely
+ if not model:
+ use_orig_weight_name = True
+ modelname = os.path.basename(file)
+
+ # Extract fallback labels from index references or base string naming schemas
+ model = (
+ extract_name_model(modelname)
+ if modelname.endswith(".index") else
+ os.path.splitext(modelname)[0]
+ )
+
+ if model is None: model = os.path.splitext(modelname)[0]
+
+ # Normalize naming syntax formats safely before dispatching local migration operations
+ model = replace_modelname(model)
+ move_file(file, download_dir, model, use_orig_weight_name)
+
+ gr_info(translations["success"])
+ return translations["success"]
+ except Exception as e:
+ gr_error(message=translations["error_occurred"].format(e=e))
+
+ return translations["error_occurred"].format(e=e)
+ finally:
+ # Clear localized runtime sandbox scratch-pad operational work directories safely
+ shutil.rmtree(download_dir, ignore_errors=True)
+
+def download_pretrained_model(
+ choices,
+ model,
+ sample_rate
+):
+ """
+ Handles retrieval of official or custom tracking pretrained foundation baseline model checkpoints.
+
+ Args:
+ choices (str): Selected interface operation execution path mode context.
+ model (str): Pretrained identifier name or direct hyperlink access reference value pattern.
+ sample_rate (str): Sample rate identifier tag matching targeting resolution limits.
+
+ Returns:
+ Union[str, List[None]]: Returns localization strings or lists based on specific routing configurations.
+ """
+
+ pretraineds_custom_path = configs["pretrained_custom_path"]
+
+ if choices == translations["list_model"]:
+ paths = fetch_pretrained_data()[model][sample_rate]
+ if not os.path.exists(pretraineds_custom_path):
+ os.makedirs(pretraineds_custom_path, exist_ok=True)
+
+ # Deobfuscate internal storage project links protected via lightweight ROT13 obfuscation
+ url = codecs.decode(
+ "uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/cergenvarq_phfgbz/",
+ "rot13"
+ ) + paths
+
+ gr_info(translations["download_pretrain"])
+
+ file = huggingface.HF_download_file(
+ replace_url(url),
+ os.path.join(pretraineds_custom_path, paths)
+ )
+
+ # Automatically unpack standard distribution compressed zip archives to clean target dependencies
+ if file.endswith(".zip"):
+ shutil.unpack_archive(file, pretraineds_custom_path)
+ os.remove(file)
+
+ gr_info(translations["success"])
+ return translations["success"]
+ elif choices == translations["download_url"]:
+ # Inline state evaluation mapping checking whether endpoints link to compressed structures
+ pretrain_is_zip = (
+ model.endswith(".zip") or
+ model.endswith(".zip?download=true") or
+ sample_rate.endswith(".zip") or
+ sample_rate.endswith(".zip?download=true")
+ )
+
+ urls = []
+ # Enforce validation checking to protect processing loops from missing arguments
+ if not model and not pretrain_is_zip:
+ gr_warning(translations["provide_pretrain"].format(dg="D"))
+ return [None]*2
+
+ if not sample_rate and not pretrain_is_zip:
+ gr_warning(translations["provide_pretrain"].format(dg="G"))
+ return [None]*2
+
+ gr_info(translations["download_pretrain"])
+
+ if model: urls.append(model)
+ if sample_rate: urls.append(sample_rate)
+
+ # Iterate structural collection mapping lists sequentially
+ for url in urls:
+ url = replace_url(url)
+ # External decentralized cloud network file extraction architecture interfaces routing
+ if "huggingface.co" in url:
+ file = huggingface.HF_download_file(
+ url,
+ pretraineds_custom_path
+ )
+ elif "google.com" in url:
+ file = gdown.gdown_download(
+ url,
+ pretraineds_custom_path
+ )
+ elif "mediafire.com" in url:
+ file = mediafire.Mediafire_Download(
+ url,
+ pretraineds_custom_path
+ )
+ elif "pixeldrain.com" in url:
+ file = pixeldrain.pixeldrain(
+ url,
+ pretraineds_custom_path
+ )
+ elif "mega.nz" in url:
+ file = meganz.mega_download_url(
+ url,
+ pretraineds_custom_path
+ )
+ else:
+ gr_warning(translations["not_support_url"])
+ return translations["not_support_url"], translations["not_support_url"]
+
+ # Post-download structural checking extraction phase operations optimization
+ if file.endswith(".zip"):
+ shutil.unpack_archive(file, pretraineds_custom_path)
+ if os.path.exists(file): os.remove(file)
+
+ gr_info(translations["success"])
+ return translations["success"], translations["success"]
+
+def is_empty_table(html):
+ """
+ Checks if the returned scraped HTML string represents an empty response placeholder layout structure.
+
+ Args:
+ html (str): Raw string containing target HTML elements snippets.
+
+ Returns:
+ bool: True if matched to an empty layout design component signature block, else False.
+ """
+
+ return (' Execute Batch Mode
+ gr_info(translations["is_folder"])
+
+ # Verify that the directory contains at least one supported audio file format
+ if not [
+ f for f in os.listdir(input_path)
+ if f.lower().endswith(tuple(file_types))
+ ]:
+ gr_warning(translations["not_found_in_folder"])
+ return return_none
+
+ gr_info(translations["batch_convert"])
+ output_dir = os.path.dirname(output_path) or output_path
+ # Direct directory references down to trigger internal conversion iterations
+ _convert_audio(input_path, output_dir)
+ gr_info(translations["batch_convert_success"])
+
+ return return_none
+ else: # Scenario B2: Input target is a standard single file path
+ output_dir = os.path.dirname(output_path) or output_path
+
+ if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True)
+ output_path = process_output(output_path)
+
+ gr_info(translations["convert_vocal"])
+ # Direct voice conversion run
+ _convert_audio(input_path, output_path, f0_file)
+ gr_info(translations["convert_success"])
+
+ return_none[0] = output_path
+ return return_none
+
+def convert_selection(
+ clean_audio=False,
+ autotune=False,
+ use_separate_audio=False,
+ use_original=False,
+ convert_backing=False,
+ not_merge_backing=False,
+ merge_instrument=False,
+ pitch=0,
+ clean_strength=0.5,
+ model=None,
+ index=None,
+ index_rate=0.5,
+ input_path=None,
+ output_path=None,
+ export_format="wav",
+ f0_method="rmvpe",
+ hybrid_method="hybrid[rmvpe+harvest]",
+ hop_length=160,
+ embedders="hubert_base",
+ custom_embedders=None,
+ resample_sr=0,
+ filter_radius=3,
+ rms_mix_rate=1.0,
+ protect=0.33,
+ split_audio=False,
+ f0_autotune_strength=1.0,
+ checkpointing=False,
+ predictor_onnx=False,
+ formant_shifting=False,
+ formant_qfrency=1.0,
+ formant_timbre=1.0,
+ f0_file=None,
+ embedders_mode="fairseq",
+ proposal_pitch=False,
+ proposal_pitch_threshold=255.0,
+ audio_processing=False,
+ alpha=0.5,
+ sid=0,
+ embedders_mix=False,
+ embedders_mix_layers=9,
+ embedders_mix_ratio=0.5,
+ noise_scale=0.35,
+ nprobe=1,
+ audio_upscaler=False
+):
+ """
+ Pre-evaluates separated folder choices before routing arguments to convert_audio.
+
+ This function coordinates Gradio UI components behavior based on isolated directory
+ searches. If multiple separate folders exist, it builds selections interactive elements;
+ if exactly 1 exists, it automatically executes the conversions instantly.
+
+ Returns:
+ list: A series of Gradio target component `.update()` dictionaries and execution payloads.
+ """
+
+ # Evaluate separate audio directories context paths if flag condition is met
+ if use_separate_audio:
+ gr_info(translations["search_separate"])
+
+ # Scan and filter out separate stems output folders inside the target directory path
+ choice = [
+ f for f in os.listdir(configs["audios_path"])
+ if os.path.isdir(os.path.join(configs["audios_path"], f))
+ ] if config.debug_mode else [
+ f for f in os.listdir(configs["audios_path"])
+ if (
+ os.path.isdir(os.path.join(configs["audios_path"], f)) and any(
+ file.lower().endswith(tuple(file_types))
+ for file in os.listdir(os.path.join(configs["audios_path"], f))
+ )
+ )
+ ]
+
+ gr_info(translations["found_choice"].format(choice=len(choice)))
+
+ if len(choice) == 0: # Case 1: No valid separated audio source stems are available
+ gr_warning(translations["separator==0"])
+
+ return [
+ {
+ "choices": [],
+ "value": "",
+ "interactive": False,
+ "visible": False if gradio_version else "hidden",
+ "__type__": "update"
+ },
+ None,
+ None,
+ None,
+ None,
+ None,
+ {
+ "visible": True,
+ "__type__": "update"
+ },
+ {
+ "visible": False if gradio_version else "hidden",
+ "__type__": "update"
+ }
+ ]
+ elif len(choice) == 1: # Case 2: Exactly 1 isolated source folder matches -> Trigger auto execution immediately
+ convert_output = convert_audio(
+ clean_audio,
+ autotune,
+ use_separate_audio,
+ use_original,
+ convert_backing,
+ not_merge_backing,
+ merge_instrument,
+ pitch,
+ clean_strength,
+ model,
+ index,
+ index_rate,
+ None,
+ None,
+ export_format,
+ f0_method,
+ hybrid_method,
+ hop_length,
+ embedders,
+ custom_embedders,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ choice[0], # Automatically binds the single located target directory folder name
+ checkpointing,
+ predictor_onnx,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ f0_file,
+ embedders_mode,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ audio_processing,
+ alpha,
+ sid,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ noise_scale,
+ nprobe,
+ audio_upscaler
+ )
+
+ # Return structural updates showing output file variables and updating layouts UI elements
+ return [
+ {
+ "choices": [],
+ "value": "",
+ "interactive": False,
+ "visible": False if gradio_version else "hidden",
+ "__type__": "update"
+ },
+ convert_output[0],
+ convert_output[1],
+ convert_output[2],
+ convert_output[3],
+ convert_output[4],
+ {
+ "visible": True,
+ "__type__": "update"
+ },
+ {
+ "visible": False if gradio_version else "hidden",
+ "__type__": "update"
+ }
+ ]
+ else: # Case 3: Multiple choice directories found -> Pop up the dropdown selector list in UI
+ return [
+ {
+ "choices": choice,
+ "value": choice[0],
+ "interactive": True,
+ "visible": True,
+ "__type__": "update"
+ },
+ None,
+ None,
+ None,
+ None,
+ None,
+ {
+ "visible": False if gradio_version else "hidden",
+ "__type__": "update"
+ },
+ {
+ "visible": True,
+ "__type__": "update"
+ }
+ ]
+ else: # If separate audio stems processing option is unchecked -> Run default single standard pipeline path
+ main_convert = convert_audio(
+ clean_audio,
+ autotune,
+ use_separate_audio,
+ use_original,
+ convert_backing,
+ not_merge_backing,
+ merge_instrument,
+ pitch,
+ clean_strength,
+ model,
+ index,
+ index_rate,
+ input_path,
+ output_path,
+ export_format,
+ f0_method,
+ hybrid_method,
+ hop_length,
+ embedders,
+ custom_embedders,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ None,
+ checkpointing,
+ predictor_onnx,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ f0_file,
+ embedders_mode,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ audio_processing,
+ alpha,
+ sid,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ noise_scale,
+ nprobe,
+ audio_upscaler
+ )
+
+ return [
+ {
+ "choices": [],
+ "value": "",
+ "interactive": False,
+ "visible": False if gradio_version else "hidden",
+ "__type__": "update"
+ },
+ main_convert[0],
+ None,
+ None,
+ None,
+ None,
+ {
+ "visible": True,
+ "__type__": "update"
+ },
+ {
+ "visible": False if gradio_version else "hidden",
+ "__type__": "update"
+ }
+ ]
+
+def vad_diarization(input_audio=None, num_spk=2, vad_sensitivity=3, vad_frame_ms=30, queue=None):
+ """
+ Performs Voice Activity Detection (VAD) and Speaker Diarization on an audio file.
+
+ This function detects speech segments using a VAD processor, extracts speaker
+ embeddings for each detected speech segment using a pretrained SpeechBrain model,
+ and clusters those embeddings to distinguish between different speakers (who spoke when).
+ Finally, it merges consecutive segments belonging to the same speaker and sends
+ the result back via a multiprocessing Queue.
+
+ Args:
+ input_audio (str, optional): Path to the input audio file. Defaults to None.
+ num_spk (int, optional): Number of expected speakers to cluster. Defaults to 2.
+ vad_sensitivity (int, optional): VAD sensitivity mode (typically 0-3, 3 being the most sensitive). Defaults to 3.
+ vad_frame_ms (int, optional): Duration of a VAD frame in milliseconds. Defaults to 30.
+ queue (multiprocessing.Queue, optional): Queue used to safely send results or errors back to the parent process. Defaults to None.
+
+ Returns:
+ None: Results are put directly into the provided `queue`.
+ """
+
+ try:
+ # Importing heavy machine learning libraries inside the function to save
+ # memory if this function is spawned in a separate multiprocessing context.
+ from sklearn.cluster import AgglomerativeClustering
+
+ from main.library.audio.audio import load_audio
+ from main.library.speaker_diarization.audio import Audio
+ from main.inference.realtime.vad_utils import VADProcessor
+ from main.library.speaker_diarization.segment import Segment
+ from main.library.utils import clear_gpu_cache, check_spk_diarization
+ from main.library.speaker_diarization.embedding import SpeechBrainPretrainedSpeakerEmbedding
+
+ # Load the input audio file, forcing a high-quality sample rate of 48kHz
+ y, sr = load_audio(input_audio, sample_rate=48000, return_sr=True)
+
+ # Initialize the VAD processor with specified sensitivity, frame duration, and sample rate
+ vad_processor = VADProcessor(sensitivity_mode=vad_sensitivity, frame_duration_ms=vad_frame_ms, sample_rate=sr)
+ # Extract timestamp segments where actual speech is detected
+ segments = vad_processor.get_speech(y)
+
+ # If no speech is found in the entire audio, send a warning and exit early
+ if not segments:
+ queue.put({"status": "warning", "message": "speech_not_in_segments"})
+ return
+
+ audio = Audio()
+ merged_segments = []
+ # Ensure speaker diarization assets are valid/downloaded
+ check_spk_diarization(model_size=None)
+
+ # Initialize the pretrained SpeechBrain embedding model on the designated hardware (CPU/GPU)
+ embedding_model = SpeechBrainPretrainedSpeakerEmbedding(
+ embedding=os.path.join(configs["speaker_diarization_path"], "models", "speechbrain"),
+ device=config.device
+ )
+
+ # Allocate a numpy array to store a 192-dimensional vector embedding for each speech segment
+ embeddings = np.zeros(shape=(len(segments), 192))
+ # Iterate over each VAD-detected segment to extract its speaker signature
+ for i, segment in enumerate(segments):
+ # Crop the audio to match the current segment's start and end times
+ waveform, _ = audio.crop(input_audio, Segment(segment["start"], segment["end"]))
+ # Format the waveform to fit the SpeechBrain model expectation (handle stereo vs mono channels)
+ # and extract the speaker identity embedding
+ embeddings[i] = embedding_model(waveform.mean(dim=0, keepdim=True)[None] if waveform.shape[0] == 2 else waveform[None])
+
+ # Cluster the speaker embeddings using Agglomerative Clustering based on the target speaker count
+ # np.nan_to_num safely handles any NaN/Infinite values in the embeddings before clustering
+ labels = AgglomerativeClustering(num_spk).fit(np.nan_to_num(embeddings)).labels_
+
+ # Assign the clustered speaker labels back to their corresponding speech segments
+ for i in range(len(segments)):
+ segments[i]["speaker"] = f"SPEAKER {labels[i] + 1}"
+
+ # Merge contiguous timeline blocks if the same speaker continues talking
+ if segments:
+ curr = segments[0].copy()
+
+ for i in range(1, len(segments)):
+ # If the next segment belongs to the current speaker, just extend the 'end' timestamp
+ if segments[i]["speaker"] == curr["speaker"]: curr["end"] = segments[i]["end"]
+ else:
+ # If a new speaker starts, save the finished segment and switch tracking to the new one
+ merged_segments.append(curr)
+ curr = segments[i].copy()
+
+ # Append the very last processed segment to the final list
+ merged_segments.append(curr)
+
+ # Manually delete large objects, clear GPU cache, and force garbage collection
+ # to prevent severe VRAM leaks or out-of-memory crashes during multi-turn processing
+ del audio, embedding_model, segments, labels, embeddings
+ clear_gpu_cache()
+ gc.collect()
+
+ # Send the successful diarization data back to the main thread/process
+ queue.put({"status": "success", "data": merged_segments})
+ except Exception as e:
+ # Catch any unexpected runtime errors, extract the complete traceback log,
+ # and send it safely via the queue to avoid freezing the system execution
+
+ import traceback
+ queue.put({"status": "error", "message": str(e), "traceback": traceback.format_exc()})
+
+def get_formatted_stats(merged_segments):
+ """
+ Generates a beautifully formatted ASCII table representing speaker diarization statistics.
+
+ This utility processes individual speaker segments, calculates the duration of each speech
+ block, formats timestamps into human-readable hours:minutes:seconds formats, and dynamically
+ calculates column widths to draw an aligned text-based table grid.
+
+ Args:
+ merged_segments (list of dict): A list of consolidated speech segments, where each
+ dictionary contains "speaker", "start", and "end" keys.
+
+ Returns:
+ str: A multi-line string containing the drawn ASCII statistics table.
+ """
+
+ rows, lines = [], []
+ speaker_stats = {}
+
+ # Extract translated table column headers (expected format: "Speaker Time_Range Duration")
+ headers = translations["headers"].split(" ")
+
+ # Data Extraction and Preprocessing
+ for seg in merged_segments:
+ spk = seg["speaker"]
+ start_t = seg["start"]
+ end_t = seg["end"]
+ dur = end_t - start_t
+
+ # Convert raw float seconds into a standard HH:MM:SS timedelta string format
+ time_range = f"{str(datetime.timedelta(seconds=int(start_t)))} -> {str(datetime.timedelta(seconds=int(end_t)))}"
+ dur_str = f"{dur:.2f}s"
+
+ # Group data into rows for tabular rendering
+ rows.append([spk, time_range, dur_str])
+ # Keep track of cumulative speaking duration per unique speaker
+ speaker_stats[spk] = speaker_stats.get(spk, 0.0) + dur
+
+ # Dynamic Column Width Calculation
+ col_widths = []
+ for i in range(3):
+ # Determine the maximum text length for each column (comparing header and all values)
+ # Replacing hyphens with spaces ensures correct visual padding boundary logic
+ max_len = max(len(headers[i].replace("-", " ")), *(len(row[i]) for row in rows))
+ col_widths.append(max_len)
+
+ # Grid Drawing Helper Functions
+ def make_line(left, mid, right):
+ """Constructs a horizontal border line using standard box-drawing characters."""
+
+ return left + mid.join("─" * (w + 2) for w in col_widths) + right
+
+ def format_row(row):
+ """Formats a single row of data, left-aligning content based on pre-calculated widths."""
+
+ return "│ " + " │ ".join(f"{cell:<{col_widths[i]}}" for i, cell in enumerate(row)) + " │"
+
+ lines.append("\n")
+ lines.append(make_line("┌", "┬", "┐")) # Top border
+ lines.append(format_row(headers)) # Column headers
+ lines.append(make_line("├", "┼", "┤")) # Header separator
+
+ for row in rows:
+ lines.append(format_row(row)) # Data rows
+
+ lines.append(make_line("└", "┴", "┘")) # Bottom border
+ return "\n".join(lines)
+
+def merge_audio(
+ files_list,
+ time_stamps,
+ original_file_path,
+ output_path,
+ export_format
+):
+ """
+ Recombines processed individual audio segments into a single unified audio file.
+
+ This function pieces back together separate speaker audio segments while carefully inserting
+ precise durations of synthetic silence in gaps where no speech was detected. This architecture
+ preserves the absolute temporal alignment and total duration relative to the original file.
+
+ Args:
+ files_list (list of str): Paths to the fragmented, modified audio segment files.
+ time_stamps (list of tuple): Expected millisecond coordinates `(start_ms, end_ms)` corresponding chronologically to each file in the `files_list`.
+ original_file_path (str): Path to the source raw audio file used to determine total duration bounds.
+ output_path (str): Target filesystem destination path for the final exported audio.
+ export_format (str): Target container codec format extension (e.g., "wav", "mp3", "flac").
+
+ Returns:
+ str: The filesystem path pointing to the successfully exported composite audio file.
+ """
+
+ from pydub import AudioSegment
+
+ from main.library.audio.audio import pydub_load
+
+ def extract_number(filename):
+ """Helper to extract trailing indices from filenames for exact numerical sorting order."""
+
+ match = re.search(r'_(\d+)', filename)
+ return int(match.group(1)) if match else 0
+
+ # Load original track to capture exact global duration boundary constraints
+ total_duration = len(pydub_load(original_file_path))
+ combined = AudioSegment.empty()
+ current_position = 0
+
+ # Zip and iterate using sorted filenames to guarantee strict temporal execution order
+ for file, (start_i, end_i) in zip(sorted(files_list, key=extract_number), time_stamps):
+ # If there is a temporal gap between current tracker and next segment start, insert silence
+ if start_i > current_position:
+ combined += AudioSegment.silent(
+ duration=start_i - current_position
+ )
+
+ # Append the processed speaker segment audio
+ combined += pydub_load(file)
+ current_position = end_i
+
+ # Handle edge case where the final speaker stopped talking before the absolute physical track end
+ if current_position < total_duration:
+ combined += AudioSegment.silent(
+ duration=total_duration - current_position
+ )
+
+ # Export output file onto the disk storage and wipe container from volatile memory
+ combined.export(output_path, format=export_format)
+ del combined
+
+ return output_path
+
+def convert_with_vad(
+ num_spk=2,
+ cleaner=False,
+ clean_strength=0.5,
+ autotune=False,
+ f0_autotune_strength=1.0,
+ checkpointing=False,
+ model_1=None,
+ model_2=None,
+ model_index_1=None,
+ model_index_2=None,
+ pitch_1=0,
+ pitch_2=0,
+ index_strength_1=0.5,
+ index_strength_2=0.5,
+ export_format="wav",
+ input_audio=None,
+ output_audio=None,
+ predictor_onnx=False,
+ f0_method="rmvpe",
+ hybrid_method="hybrid[rmvpe+harvest]",
+ hop_length=160,
+ embed_mode="fairseq",
+ embedders="hubert_base",
+ custom_embedders=None,
+ resample_sr=0,
+ filter_radius=3,
+ rms_mix_rate=1.0,
+ protect=0.33,
+ formant_shifting=False,
+ formant_qfrency_1=1.0,
+ formant_timbre_1=1.0,
+ formant_qfrency_2=1.0,
+ formant_timbre_2=1.0,
+ proposal_pitch=False,
+ proposal_pitch_threshold=255.0,
+ audio_processing=False,
+ alpha=0.5,
+ sid_1=0,
+ sid_2=0,
+ embedders_mix=False,
+ embedders_mix_layers=9,
+ embedders_mix_ratio=0.5,
+ noise_scale_1=0.35,
+ noise_scale_2=0.35,
+ vad_sensitivity=3,
+ vad_frame_ms=30,
+ nprobe_1=1,
+ nprobe_2=1
+):
+ """
+ Executes a complete end-to-end multi-speaker (RVC/SVC) pipeline.
+
+ The execution chain operates as follows:
+ 1. Validates presence and structural patterns of inputs/RVC/SVC model checkpoints.
+ 2. Spawns an isolated Subprocess to execute VAD and Speaker Diarization safely without CUDA race conditions.
+ 3. Splits the source file into physical segment chunks sorted into distinct folder structures per speaker.
+ 4. Invokes RVC/SVC inference engine (`convert()`) targeting each specific speaker folder with unique settings.
+ 5. Re-assembles the converted speaker voices back into a singular composite file using `merge_audio()`.
+
+ Returns:
+ str or None: Path to the finalized multi-speaker converted audio track, or None if validation fails.
+ """
+
+ import multiprocessing as mp
+
+ from main.library.audio.audio import pydub_load
+
+ model_pth_1, model_pth_2 = (
+ os.path.join(configs["weights_path"], model_1) if not os.path.exists(model_1) else model_1,
+ os.path.join(configs["weights_path"], model_2) if not os.path.exists(model_2) else model_2
+ )
+
+ # Halt operation if neither model path 1 nor model path 2 represents a valid existing RVC model file
+ if (
+ not model_1 or
+ not os.path.exists(model_pth_1) or
+ os.path.isdir(model_pth_1) or
+ not model_pth_1.endswith((".pth", ".onnx"))
+ ) and (
+ not model_2 or
+ not os.path.exists(model_pth_2) or
+ os.path.isdir(model_pth_2) or
+ not model_pth_2.endswith((".pth", ".onnx"))
+ ):
+ gr_warning(translations["provide_model"])
+ return None
+
+ # Fallback assignment: if single speaker model is provided, duplicate it to fill both channels
+ if not model_1: model_pth_1 = model_pth_2
+ if not model_2: model_pth_2 = model_pth_1
+
+ # Verify input audio path presence on disk storage
+ if not input_audio or not os.path.exists(input_audio) or os.path.isdir(input_audio):
+ gr_warning(translations["input_not_valid"])
+ return None
+
+ if not output_audio:
+ gr_warning(translations["output_not_valid"])
+ return None
+
+ output_audio = process_output(output_audio)
+ gr_info(translations["start_vad"])
+
+ try:
+ # Utilizing 'spawn' context ensures a clean memory layout, avoiding parent CUDA state cloning bugs
+ ctx = mp.get_context("spawn")
+ queue = ctx.Queue()
+
+ # Allocate worker process specifically bound to target diarization operation
+ process = ctx.Process(
+ target=vad_diarization,
+ args=(input_audio, num_spk, vad_sensitivity, vad_frame_ms, queue)
+ )
+
+ process.start()
+ result = queue.get() # Blocking wait call until child pipeline delivers structural dictionary response
+ process.join()
+
+ # Parse worker state signals
+ if result["status"] == "warning":
+ gr_warning(translations[result["message"]])
+ return None
+ elif result["status"] == "error":
+ logger.debug(result["traceback"])
+ gr_error(result["message"])
+ return None
+
+ merged_segments = result["data"]
+
+ gr_info(translations["analysis_completed"].format(length=len(merged_segments)))
+ gr_info(translations["process_audio"])
+
+ pydub_audio = pydub_load(input_audio)
+ output_folder = "audios_temp"
+
+ # Wipe pre-existing cache directories to avoid crosstalk artifact leakage across execution runs
+ if os.path.exists(output_folder): shutil.rmtree(output_folder, ignore_errors=True)
+
+ # Build clean directory maps partitioning Speaker 1 and Speaker 2 distinct batches
+ for f in [
+ output_folder,
+ os.path.join(output_folder, "1"),
+ os.path.join(output_folder, "2")
+ ]:
+ os.makedirs(f, exist_ok=True)
+
+ time_stamps, processed_segments = [], []
+ for i, seg in enumerate(merged_segments):
+ # Parse numeric speaker ID tag to map segment distribution pattern alternately (Odd IDs -> Folder 1, Even IDs -> Folder 2)
+ model_id = 1 if int(seg["speaker"].split()[-1]) % 2 != 0 else 2
+ start_ms, end_ms = int(seg["start"] * 1000), int(seg["end"] * 1000)
+
+ # Export raw un-converted slice into speaker-specific storage space
+ target_path = os.path.join(output_folder, str(model_id), f"segment_{i+1}.wav")
+ pydub_audio[start_ms:end_ms].export(target_path, format="wav")
+
+ # Formulate hypothetical path string pattern where the core convert engine will spit out outputs
+ processed_segments.append(os.path.join(output_folder, str(model_id), f"segment_{i+1}_output.wav"))
+ time_stamps.append((start_ms, end_ms))
+
+ # Log formatted ASCII runtime status overview matrix to standard console logs
+ logger.info(get_formatted_stats(merged_segments))
+
+ # Re-map algorithm string flags to internal system keys
+ f0method, embedder_model = (
+ f0_method if f0_method != "hybrid" else hybrid_method,
+ embedders if embedders != "custom" else custom_embedders
+ )
+
+ gr_info(translations["process_done_start_convert"])
+ # Run conversion processing for Speaker 1 Folder Group
+ convert(
+ pitch_1,
+ filter_radius,
+ index_strength_1,
+ rms_mix_rate,
+ protect,
+ hop_length,
+ f0method,
+ os.path.join(output_folder, "1"),
+ output_folder,
+ model_pth_1,
+ model_index_1,
+ autotune,
+ cleaner,
+ clean_strength,
+ "wav",
+ embedder_model,
+ 48000 if resample_sr == 0 else resample_sr,
+ False,
+ f0_autotune_strength,
+ checkpointing,
+ predictor_onnx,
+ embed_mode,
+ formant_shifting,
+ formant_qfrency_1,
+ formant_timbre_1,
+ "",
+ proposal_pitch,
+ proposal_pitch_threshold,
+ audio_processing,
+ alpha,
+ sid_1,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ noise_scale_1,
+ nprobe_1,
+ False
+ )
+ # Run conversion processing for Speaker 2 Folder Group
+ convert(
+ pitch_2,
+ filter_radius,
+ index_strength_2,
+ rms_mix_rate,
+ protect,
+ hop_length,
+ f0method,
+ os.path.join(output_folder, "2"),
+ output_folder,
+ model_pth_2,
+ model_index_2,
+ autotune,
+ cleaner,
+ clean_strength,
+ "wav",
+ embedder_model,
+ 48000 if resample_sr == 0 else resample_sr,
+ False,
+ f0_autotune_strength,
+ checkpointing,
+ predictor_onnx,
+ embed_mode,
+ formant_shifting,
+ formant_qfrency_2,
+ formant_timbre_2,
+ "",
+ proposal_pitch,
+ proposal_pitch_threshold,
+ audio_processing,
+ alpha,
+ sid_2,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ noise_scale_2,
+ nprobe_2,
+ False
+ )
+
+ gr_info(translations["convert_success"])
+
+ return merge_audio(
+ processed_segments,
+ time_stamps,
+ input_audio,
+ replace_export_format(output_audio, export_format),
+ export_format
+ )
+ except Exception as e:
+ import traceback
+
+ gr_error(translations["error_occurred"].format(e=e))
+ logger.debug(traceback.format_exc())
+
+ return None
+ finally:
+ # Guarantee dynamic filesystem cleanup actions to preserve free storage blocks on server instances
+ if os.path.exists("audios_temp"):
+ shutil.rmtree("audios_temp", ignore_errors=True)
+
+def convert_tts(
+ clean_audio=False,
+ autotune=False,
+ pitch=0,
+ clean_strength=0.5,
+ model=None,
+ index=None,
+ index_rate=0.5,
+ input_path=None,
+ output_path=None,
+ export_format="wav",
+ method="rmvpe",
+ hybrid_method="hybrid[rmvpe+harvest]",
+ hop_length=160,
+ embedders="hubert_base",
+ custom_embedders=None,
+ resample_sr=0,
+ filter_radius=3,
+ rms_mix_rate=1.0,
+ protect=0.33,
+ split_audio=False,
+ f0_autotune_strength=1.0,
+ checkpointing=False,
+ predictor_onnx=False,
+ formant_shifting=False,
+ formant_qfrency=1.0,
+ formant_timbre=1.0,
+ f0_file=None,
+ embedders_mode="fairseq",
+ proposal_pitch=False,
+ proposal_pitch_threshold=255.0,
+ audio_processing=False,
+ alpha=0.5,
+ sid=0,
+ embedders_mix=False,
+ embedders_mix_layers=9,
+ embedders_mix_ratio=0.5,
+ noise_scale=0.35,
+ nprobe=1,
+ audio_upscaler=False
+):
+ """
+ Executes (RVC/SVC) specifically tailored for Text-to-Speech (TTS) outputs.
+
+ This function coordinates the post-processing pipeline for synthesized TTS audio. It handles
+ validation of voice weights (.pth/.onnx), scans folders or maps individual input/output audio files,
+ manages runtime target path formatting, and routes all analytical hyper-parameters cleanly down
+ to the main RVC/SVC `convert` inference core.
+
+ Returns:
+ str or None: Path to the finalized voice-converted TTS file, or None if runtime validation fails.
+ """
+
+ # Attempt to resolve the checkpoint path either relative to the weights storage workspace or as an absolute literal path
+ model_path = os.path.join(configs["weights_path"], model) if not os.path.exists(model) else model
+
+ # Validate model path structure, extensions, and asset integrity rules
+ if (
+ not model_path or
+ not os.path.exists(model_path) or
+ os.path.isdir(model_path) or
+ not model.endswith((".pth", ".onnx"))
+ ):
+ gr_warning(translations["provide_model"])
+ return None
+
+ if not input_path or not os.path.exists(input_path):
+ gr_warning(translations["input_not_valid"])
+ return None
+
+ # If the user passed a directory path instead of a file, scan it to automatically find the correct TTS file
+ if os.path.isdir(input_path):
+ input_audio = [
+ f for f in os.listdir(input_path)
+ if "tts" in f and f.lower().endswith(tuple(file_types))
+ ]
+
+ # Abort if no matching internal TTS file pattern matching system `file_types` exists
+ if not input_audio:
+ gr_warning(translations["not_found_in_folder"])
+ return None
+
+ # Pick the first discovered matching TTS track artifact as the active source input file
+ input_path = os.path.join(input_path, input_audio[0])
+
+ if not output_path:
+ gr_warning(translations["output_not_valid"])
+ return None
+
+ # Ensure correct target suffix structure layout matches specified export format requirements
+ output_path = replace_export_format(output_path, export_format)
+ # If output points to a folder path template, append a generic fallback output filename
+ if os.path.isdir(output_path): output_path = os.path.join(output_path, f"tts.{export_format}")
+
+ # Build underlying directory structure maps dynamically if they are missing
+ output_dir = os.path.dirname(output_path)
+ if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True)
+
+ # Post-process output path mapping layout structure (e.g., handling spaces/unicode sanitization)
+ output_path = process_output(output_path)
+
+ # Handle conditional fallbacks when 'hybrid' mode or 'custom' embedders are configured via UI controls
+ f0method, embedder_model = (
+ method if method != "hybrid" else hybrid_method,
+ embedders if embedders != "custom" else custom_embedders
+ )
+
+ gr_info(translations["convert_vocal"])
+
+ convert(
+ pitch,
+ filter_radius,
+ index_rate,
+ rms_mix_rate,
+ protect,
+ hop_length,
+ f0method,
+ input_path,
+ output_path,
+ model_path,
+ index,
+ autotune,
+ clean_audio,
+ clean_strength,
+ export_format,
+ embedder_model,
+ resample_sr,
+ split_audio,
+ f0_autotune_strength,
+ checkpointing,
+ predictor_onnx,
+ embedders_mode,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ f0_file,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ audio_processing,
+ alpha,
+ sid,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ noise_scale,
+ nprobe,
+ audio_upscaler
+ )
+
+ gr_info(translations["convert_success"])
+ return output_path
\ No newline at end of file
diff --git a/main/app/core/model_utils.py b/main/app/core/model_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1827fea1498d37405688a30648b6c2422894e02f
--- /dev/null
+++ b/main/app/core/model_utils.py
@@ -0,0 +1,382 @@
+import os
+import sys
+import json
+import torch
+import datetime
+
+sys.path.append(os.getcwd())
+
+from main.app.core.ui import gr_info, gr_warning, gr_error
+from main.app.variables import config, logger, translations, configs
+
+def fushion_model_pth(
+ name,
+ model_path_1,
+ model_path_2,
+ ratio
+):
+ """
+ Fuses two RVC (.pth) models together based on a specified blending ratio.
+
+ This function extracts the weights from both checkpoints, validates their architectures,
+ interpolates the weights linearly, and saves the new blended model.
+
+ Args:
+ name (str): The filename for the output fused model.
+ model_path_1 (str): Path or filename of the first model.
+ model_path_2 (str): Path or filename of the second model.
+ ratio (float): The blending weight for the first model (usually between 0.0 and 1.0).
+
+ Returns:
+ list: A list containing a status message (str) and the output file path (str or None).
+ """
+
+ # Enforce the correct file extension for the output model
+ if not name.endswith(".pth"): name = name + ".pth"
+ # Resolve absolute paths using the configured weights directory if paths do not exist directly
+ model_path_1 = os.path.join(configs["weights_path"], model_path_1) if not os.path.exists(model_path_1) else model_path_1
+ model_path_2 = os.path.join(configs["weights_path"], model_path_2) if not os.path.exists(model_path_2) else model_path_2
+
+ # Validate existence and format of the first model path
+ if (
+ not model_path_1 or
+ not os.path.exists(model_path_1) or
+ not model_path_1.endswith(".pth")
+ ):
+ gr_warning(translations["provide_model_idx"].format(idx="1"))
+ return [translations["provide_model_idx"].format(idx="1"), None]
+
+ # Validate existence and format of the second model path
+ if (
+ not model_path_2 or
+ not os.path.exists(model_path_2) or
+ not model_path_2.endswith(".pth")
+ ):
+ gr_warning(translations["provide_model_idx"].format(idx="2"))
+ return [translations["provide_model_idx"].format(idx="2"), None]
+
+ from collections import OrderedDict
+
+ def extract(ckpt):
+ """Helper function to extract clean model weights, ignoring recognition/encoder keys."""
+
+ a = ckpt["model"]
+ opt = OrderedDict()
+ opt["weight"] = {}
+
+ for key in a.keys():
+ # Skip encoder query keys to avoid redundant configurations during fusion
+ if "enc_q" in key: continue
+
+ opt["weight"][key] = a[key]
+
+ return opt
+
+ try:
+ # Load checkpoints onto CPU safely to avoid out-of-memory errors
+ ckpt1 = torch.load(model_path_1, map_location="cpu", weights_only=True)
+ ckpt2 = torch.load(model_path_2, map_location="cpu", weights_only=True)
+
+ # Ensure both models share the same sample rate (sr) before fusion
+ if ckpt1["sr"] != ckpt2["sr"]:
+ del ckpt1, ckpt2
+
+ gr_warning(translations["sr_not_same"])
+ return [translations["sr_not_same"], None]
+
+ # Extract structural metadata from the first checkpoint
+ cfg = ckpt1["config"]
+ cfg_f0 = ckpt1["f0"]
+ cfg_version = ckpt1["version"]
+ cfg_sr = ckpt1["sr"]
+
+ vocoder = ckpt1.get("vocoder", "Default")
+ # Normalize the internal structure to extract raw weights dictionary
+ ckpt1 = extract(ckpt1) if "model" in ckpt1 else ckpt1["weight"]
+ ckpt2 = extract(ckpt2) if "model" in ckpt2 else ckpt2["weight"]
+
+ # Structural sanity check: ensure both models contain the exact same layers
+ if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
+ gr_warning(translations["architectures_not_same"])
+
+ return [translations["architectures_not_same"], None]
+
+ gr_info(translations["start_fushion_model"])
+
+ opt = OrderedDict()
+ opt["weight"] = {}
+ # Loop through all layers and perform linear interpolation (weighted average)
+ for key in ckpt1.keys():
+ # Special handling for embedding layer if speaker counts or configurations differ
+ if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
+ min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
+ # Fuse up to the minimum matching dimensions
+ opt["weight"][key] = (
+ ratio * (ckpt1[key][:min_shape0].float()) + (1 - ratio) * (ckpt2[key][:min_shape0].float())
+ ).half()
+ else:
+ # Standard matrix interpolation: convert to float for precision, then back to half precision (FP16)
+ opt["weight"][key] = (
+ ratio * (ckpt1[key].float()) + (1 - ratio) * (ckpt2[key].float())
+ ).half()
+
+ # Re-attach original metadata properties to the fused dictionary
+ opt["config"] = cfg
+ opt["sr"] = cfg_sr
+ opt["f0"] = cfg_f0
+ opt["version"] = cfg_version
+ opt["vocoder"] = vocoder
+ # Store detailed tracking info regarding the fusion process parameters
+ opt["infos"] = translations["model_fushion_info"].format(
+ name=name,
+ model_path_1=model_path_1,
+ model_path_2=model_path_2,
+ ratio=ratio
+ )
+
+ # Prepare directory path and save the unified model checkpoint
+ output_model = configs["weights_path"]
+ if not os.path.exists(output_model): os.makedirs(output_model, exist_ok=True)
+
+ torch.save(opt, os.path.join(output_model, name))
+ # Free memory explicitly
+ del ckpt1, ckpt2, opt
+
+ gr_info(translations["success"])
+ return [translations["success"], os.path.join(output_model, name)]
+ except Exception as e:
+ gr_error(message=translations["error_occurred"].format(e=e))
+ return [e, None]
+
+def fushion_model(
+ modelname,
+ model_path_1,
+ model_path_2,
+ ratio
+):
+ """
+ Wrapper function that validates input names and formats before initiating fusion.
+
+ Args:
+ modelname (str): Chosen name for the output file.
+ model_path_1 (str): File path for model 1.
+ model_path_2 (str): File path for model 2.
+ ratio (float): Fusion weight ratio.
+
+ Returns:
+ list: Results from `fushion_model_pth` or status logs.
+ """
+
+ # Enforce that a name must be provided for saving the output
+ if not modelname:
+ gr_warning(translations["provide_name_is_save"])
+ return [translations["provide_name_is_save"], None]
+
+ # Ensure both inputs are standard PyTorch weight files (.pth)
+ if model_path_1.endswith(".pth") and model_path_2.endswith(".pth"):
+ return fushion_model_pth(
+ modelname,
+ model_path_1,
+ model_path_2,
+ ratio
+ )
+ else:
+ gr_warning(translations["format_not_valid"])
+ return [None, None]
+
+def onnx_export(model_path, is_half, int8_mode):
+ """
+ Exports a PyTorch RVC model (.pth) to the ONNX format.
+
+ Args:
+ model_path (str): Path to the source .pth model.
+ is_half (bool): If True, exports the model using FP16 precision.
+ int8_mode (bool): If True, applies INT8 quantization optimizations.
+
+ Returns:
+ str: The returned output status or object generated by the underlying `onnx_exporter`.
+ """
+
+ # Handle filename auto-completion and configuration directory joining
+ if not model_path.endswith(".pth"): model_path += ".pth"
+ model_path = os.path.join(configs["weights_path"], model_path) if not os.path.exists(model_path) else model_path
+
+ # Validate source path existence
+ if (
+ not model_path or
+ not os.path.exists(model_path) or
+ not model_path.endswith(".pth")
+ ):
+ return gr_warning(translations["provide_model"])
+
+ try:
+ gr_info(translations["start_onnx_export"])
+
+ # Deferred dynamic import to keep initialization speed fast
+ from main.library.onnx.onnx_export import onnx_exporter
+
+ # Execute ONNX compilation targeting the application's processing device
+ output = onnx_exporter(
+ model_path,
+ model_path.replace(".pth", ".onnx"),
+ is_half=is_half,
+ int8_mode=int8_mode,
+ device=config.device
+ )
+
+ gr_info(translations["success"])
+ return output
+ except Exception as e:
+ return gr_error(e)
+
+def svc_export(model_path, config_path, modelname, delete_when_success, is_half):
+ """
+ Converts/Extracts an So-Vits-SVC model format into an RVC-compatible checkpoint layout.
+
+ Args:
+ model_path (str): Path to the source SVC checkpoint (often with a .pth extension).
+ config_path (str): Path to the source SVC configuration .json file.
+ modelname (str): Final target name for the output RVC model.
+ delete_when_success (bool): If True, safely deletes the source SVC files after successful conversion.
+ is_half (bool): Use half-precision (FP16) formatting for the output model if checked.
+
+ Returns:
+ str: Output tracking information, or None if the input is not a valid SVC model or on failure.
+ """
+
+ # Format and check path existence metrics
+ if not model_path.endswith(".pth"): model_path += ".pth"
+ model_path = os.path.join(configs["weights_path"], model_path) if not os.path.exists(model_path) else model_path
+
+ if (
+ not model_path or
+ not os.path.exists(model_path) or
+ not model_path.endswith(".pth")
+ ):
+ gr_warning(translations["provide_model"])
+ return None
+
+ # Validate the structural layout configuration file (.json) from SVC
+ if (
+ not config_path or
+ not os.path.exists(config_path) or
+ not config_path.endswith(".json")
+ ):
+ gr_warning(translations["provide_config"])
+ return None
+
+ # Derive base filename parameters if not specified explicitly
+ if modelname is None: modelname = os.path.basename(model_path)
+ if not modelname.endswith(".pth"): modelname += ".pth"
+ output_path = os.path.join(configs["weights_path"], modelname) if not os.path.exists(modelname) else modelname
+
+ try:
+ gr_info(translations["start_convert_svc"])
+
+ # Dynamic import of the extraction/conversion tool
+ from main.tools.export_svc import svc_converter
+
+ # Execute conversion from SVC weights structure to RVC weights structure
+ output = svc_converter(
+ model_path,
+ config_path,
+ output_path,
+ is_half=is_half
+ )
+
+ # Intercept case where the input model is not actually an SVC model structure
+ if output == translations["not_svc"]:
+ gr_warning(output)
+ return None
+
+ # Clean up disk space by eliminating original SVC assets if flag is True
+ if delete_when_success: os.remove(model_path); os.remove(config_path)
+ gr_info(translations["success"])
+ return output
+ except Exception as e:
+ return gr_error(e)
+
+def prettify_date(date_str):
+ """Standardizes ISO formatted timestamps into a readable date string format.
+
+ Args:
+ date_str (str): Input string containing an ISO timestamp (e.g., '2026-07-12T15:23:45.000').
+
+ Returns:
+ str: A clean, human-readable date format string ('%Y-%m-%d %H:%M:%S').
+ """
+
+ if date_str is None:
+ return translations["not_found_create_time"]
+
+ try:
+ # Parse dynamic precision timestamp fractions and standardize structure output
+ return datetime.datetime.strptime(date_str, "%Y-%m-%dT%H:%M:%S.%f").strftime("%Y-%m-%d %H:%M:%S")
+ except ValueError as e:
+ logger.debug(e)
+ return translations["format_not_valid"]
+
+def model_info(model_path):
+ """
+ Reads, extracts, and parses metadata fields contained within a .pth or .onnx model file.
+
+ Args:
+ model_path (str): File path of the target model to inspect.
+
+ Returns:
+ str: A formatted block containing comprehensive metadata stats of the target model.
+ """
+
+ # Resolve directory paths against standard internal structural keys
+ model_path = os.path.join(configs["weights_path"], model_path) if not os.path.exists(model_path) else model_path
+ model_data = None
+
+ # Ensure targeted file path points directly to a valid data asset
+ if (
+ not model_path or
+ not os.path.exists(model_path) or
+ os.path.isdir(model_path) or
+ not model_path.endswith((".pth", ".onnx"))
+ ):
+ return gr_warning(translations["provide_model"])
+
+ gr_info(translations["read_info"])
+ # Extract info fields cleanly from internal PyTorch dictionary states
+ if model_path.endswith(".pth"): model_data = torch.load(model_path, map_location="cpu")
+ else:
+ # Parse embedded configuration structures directly out of ONNX metadata property lists
+ import onnx
+
+ model = onnx.load(model_path)
+ for prop in model.metadata_props:
+ if prop.key == "model_info":
+ model_data = json.loads(prop.value)
+ break
+
+ del model
+
+ # Normalize variable key names between alternate dataset standard keys
+ epochs = model_data.get("epoch", None)
+ if not epochs: epochs = model_data.get("info", None)
+ if not epochs: epochs = translations["not_found"].format(name="")
+
+ # Render a structural text visualization matching standard system localization configs
+ info = translations["model_info"].format(
+ model_name=model_data.get("model_name", translations["unregistered"]),
+ model_author=model_data.get("author", translations["not_author"]),
+ epochs=epochs,
+ steps=model_data.get("step", translations["not_found"].format(name="")),
+ version=model_data.get("version", "v1").upper(),
+ sr=model_data.get("sr", translations["not_found"].format(name="")),
+ pitch_guidance=translations["trained_f0" if model_data.get("f0", False) else "not_f0"],
+ model_hash=model_data.get("model_hash", translations["not_found"].format(name="")),
+ creation_date_str=prettify_date(model_data.get("creation_date", None)),
+ vocoder=model_data.get("vocoder", "Default"),
+ speakers_id=model_data.get("speakers_id", 1),
+ architecture=model_data.get("architecture", "RVC")
+ )
+
+ gr_info(translations["success"])
+ del model_data
+
+ return info
\ No newline at end of file
diff --git a/main/app/core/presets.py b/main/app/core/presets.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2fbc71ec3dc7919881b43aa63d9404771e120b8
--- /dev/null
+++ b/main/app/core/presets.py
@@ -0,0 +1,809 @@
+import os
+import sys
+import json
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations, configs
+from main.app.core.ui import gr_info, gr_warning, change_preset_choices, change_effect_preset_choices, change_realtime_preset_choices
+
+def load_presets(
+ presets,
+ cleaner,
+ autotune,
+ pitch,
+ clean_strength,
+ index_strength,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+):
+ """
+ Loads conversion presets from a JSON file. If a key is missing from the file,
+ it falls back to the current UI component values.
+
+ Returns:
+ list: A flat list containing the resolved configurations for UI updates.
+ """
+
+ # Trigger warning if no preset file selection is provided
+ if not presets: gr_warning(translations["provide_file_settings"])
+
+ file = {}
+ if presets:
+ # Resolve path and safely load the target JSON configuration
+ with open(os.path.join(configs["presets_path"], presets)) as f:
+ file = json.load(f)
+
+ # Notify the user via Gradio UI that loading was successful
+ gr_info(translations["load_presets"].format(presets=presets))
+
+ # Return values in the exact positional order expected by Gradio outputs
+ return [
+ file.get("cleaner", cleaner),
+ file.get("autotune", autotune),
+ file.get("pitch", pitch),
+ file.get("clean_strength", clean_strength),
+ file.get("index_strength", index_strength),
+ file.get("resample_sr", resample_sr),
+ file.get("filter_radius", filter_radius),
+ file.get("rms_mix_rate", rms_mix_rate),
+ file.get("protect", protect),
+ file.get("split_audio", split_audio),
+ file.get("f0_autotune_strength", f0_autotune_strength),
+ file.get("formant_shifting", formant_shifting),
+ file.get("formant_qfrency", formant_qfrency),
+ file.get("formant_timbre", formant_timbre),
+ file.get("proposal_pitch", proposal_pitch),
+ file.get("proposal_pitch_threshold", proposal_pitch_threshold),
+ file.get("embedders_mix", embedders_mix),
+ file.get("embedders_mix_layers", embedders_mix_layers),
+ file.get("embedders_mix_ratio", embedders_mix_ratio)
+ ]
+
+def save_presets(
+ name,
+ cleaner,
+ autotune,
+ pitch,
+ clean_strength,
+ index_strength,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ cleaner_checkbox,
+ autotune_checkbox,
+ pitch_checkbox,
+ index_strength_checkbox,
+ resample_sr_checkbox,
+ filter_radius_checkbox,
+ rms_mix_rate_checkbox,
+ protect_checkbox,
+ split_audio_checkbox,
+ formant_shifting_checkbox,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+):
+ """
+ Saves the selected conversion features into a JSON preset file based on active checkboxes.
+
+ Returns:
+ dict: Triggers a state refresh for the preset dropdown choices.
+ """
+
+ # Validation: Ensure a valid filename is provided
+ if not name: return gr_warning(translations["provide_filename_settings"])
+
+ # Validation: Ensure at least one feature group checkbox is checked
+ if not any([
+ cleaner_checkbox,
+ autotune_checkbox,
+ pitch_checkbox,
+ index_strength_checkbox,
+ resample_sr_checkbox,
+ filter_radius_checkbox,
+ rms_mix_rate_checkbox,
+ protect_checkbox,
+ split_audio_checkbox,
+ formant_shifting_checkbox
+ ]):
+ return gr_warning(translations["choose1"])
+
+ settings = {}
+ # Dynamically append configurations only if the activation condition is satisfied
+ for checkbox, data in [
+ (
+ cleaner_checkbox,
+ {"cleaner": cleaner, "clean_strength": clean_strength}
+ ),
+ (
+ autotune_checkbox,
+ {"autotune": autotune, "f0_autotune_strength": f0_autotune_strength}),
+ (
+ pitch_checkbox,
+ {"pitch": pitch}
+ ),
+ (
+ index_strength_checkbox,
+ {"index_strength": index_strength}
+ ),
+ (
+ resample_sr_checkbox,
+ {"resample_sr": resample_sr}
+ ),
+ (
+ filter_radius_checkbox,
+ {"filter_radius": filter_radius}
+ ),
+ (
+ rms_mix_rate_checkbox,
+ {"rms_mix_rate": rms_mix_rate}
+ ),
+ (
+ protect_checkbox,
+ {"protect": protect}
+ ),
+ (
+ split_audio_checkbox,
+ {"split_audio": split_audio}
+ ),
+ (
+ formant_shifting_checkbox,
+ {"formant_shifting": formant_shifting, "formant_qfrency": formant_qfrency, "formant_timbre": formant_timbre}
+ ),
+ (
+ proposal_pitch,
+ {"proposal_pitch": proposal_pitch, "proposal_pitch_threshold": proposal_pitch_threshold}
+ ),
+ (
+ embedders_mix,
+ {"embedders_mix_layers": embedders_mix_layers, "embedders_mix_ratio": embedders_mix_ratio}
+ )
+ ]:
+ if checkbox: settings.update(data)
+
+ # Export configuration payload to disk
+ with open(os.path.join(configs["presets_path"], name + ".conversion.json"), "w") as f:
+ json.dump(settings, f, indent=4)
+
+ gr_info(translations["export_settings"].format(name=name))
+ return change_preset_choices()
+
+def audio_effect_load_presets(
+ presets,
+ resample_checkbox,
+ audio_effect_resample_sr,
+ chorus_depth,
+ chorus_rate_hz,
+ chorus_mix,
+ chorus_center_delay_ms,
+ chorus_feedback,
+ distortion_drive_db,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_level,
+ reverb_dry_level,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_second,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold_db,
+ compressor_ratio,
+ compressor_attack_ms,
+ compressor_release_ms,
+ limiter_threshold_db,
+ limiter_release_ms,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold_db,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix,
+ bass_boost,
+ bass_frequency,
+ treble_boost,
+ treble_frequency,
+ fade_in,
+ fade_out,
+ chorus_check_box,
+ distortion_checkbox,
+ reverb_check_box,
+ delay_check_box,
+ compressor_check_box,
+ limiter,
+ gain_checkbox,
+ bitcrush_checkbox,
+ clipping_checkbox,
+ phaser_check_box,
+ bass_or_treble,
+ fade
+):
+ """
+ Loads audio DSP effect presets from a dedicated JSON configuration.
+ Falls back to current UI values if parameters are missing from the configuration.
+
+ Returns:
+ list: Structured list used to unpack and update all active UI audio effects.
+ """
+
+ if not presets: gr_warning(translations["provide_file_settings"])
+
+ file = {}
+ if presets:
+ with open(os.path.join(configs["presets_path"], presets)) as f:
+ file = json.load(f)
+
+ gr_info(translations["load_presets"].format(presets=presets))
+
+ # Collect and map values sequentially for structural assignment in UI
+ return [
+ file.get("resample_checkbox", resample_checkbox),
+ file.get("audio_effect_resample_sr", audio_effect_resample_sr),
+ file.get("chorus_depth", chorus_depth),
+ file.get("chorus_rate_hz", chorus_rate_hz),
+ file.get("chorus_mix", chorus_mix),
+ file.get("chorus_center_delay_ms", chorus_center_delay_ms),
+ file.get("chorus_feedback", chorus_feedback),
+ file.get("distortion_drive_db", distortion_drive_db),
+ file.get("reverb_room_size", reverb_room_size),
+ file.get("reverb_damping", reverb_damping),
+ file.get("reverb_wet_level", reverb_wet_level),
+ file.get("reverb_dry_level", reverb_dry_level),
+ file.get("reverb_width", reverb_width),
+ file.get("reverb_freeze_mode", reverb_freeze_mode),
+ file.get("pitch_shift_semitones", pitch_shift_semitones),
+ file.get("delay_second", delay_second),
+ file.get("delay_feedback", delay_feedback),
+ file.get("delay_mix", delay_mix),
+ file.get("compressor_threshold_db", compressor_threshold_db),
+ file.get("compressor_ratio", compressor_ratio),
+ file.get("compressor_attack_ms", compressor_attack_ms),
+ file.get("compressor_release_ms", compressor_release_ms),
+ file.get("limiter_threshold_db", limiter_threshold_db),
+ file.get("limiter_release_ms", limiter_release_ms),
+ file.get("gain_db", gain_db),
+ file.get("bitcrush_bit_depth", bitcrush_bit_depth),
+ file.get("clipping_threshold_db", clipping_threshold_db),
+ file.get("phaser_rate_hz", phaser_rate_hz),
+ file.get("phaser_depth", phaser_depth),
+ file.get("phaser_centre_frequency_hz", phaser_centre_frequency_hz),
+ file.get("phaser_feedback", phaser_feedback),
+ file.get("phaser_mix", phaser_mix),
+ file.get("bass_boost", bass_boost),
+ file.get("bass_frequency", bass_frequency),
+ file.get("treble_boost", treble_boost),
+ file.get("treble_frequency", treble_frequency),
+ file.get("fade_in", fade_in),
+ file.get("fade_out", fade_out),
+ file.get("chorus_check_box", chorus_check_box),
+ file.get("distortion_checkbox", distortion_checkbox),
+ file.get("reverb_check_box", reverb_check_box),
+ file.get("delay_check_box", delay_check_box),
+ file.get("compressor_check_box", compressor_check_box),
+ file.get("limiter", limiter),
+ file.get("gain_checkbox", gain_checkbox),
+ file.get("bitcrush_checkbox", bitcrush_checkbox),
+ file.get("clipping_checkbox", clipping_checkbox),
+ file.get("phaser_check_box", phaser_check_box),
+ file.get("bass_or_treble", bass_or_treble),
+ file.get("fade", fade)
+ ]
+
+def audio_effect_save_presets(
+ name,
+ resample_checkbox,
+ audio_effect_resample_sr,
+ chorus_depth,
+ chorus_rate_hz,
+ chorus_mix,
+ chorus_center_delay_ms,
+ chorus_feedback,
+ distortion_drive_db,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_level,
+ reverb_dry_level,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_second,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold_db,
+ compressor_ratio,
+ compressor_attack_ms,
+ compressor_release_ms,
+ limiter_threshold_db,
+ limiter_release_ms,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold_db,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix,
+ bass_boost,
+ bass_frequency,
+ treble_boost,
+ treble_frequency,
+ fade_in,
+ fade_out,
+ chorus_check_box,
+ distortion_checkbox,
+ reverb_check_box,
+ delay_check_box,
+ compressor_check_box,
+ limiter,
+ gain_checkbox,
+ bitcrush_checkbox,
+ clipping_checkbox,
+ phaser_check_box,
+ bass_or_treble,
+ fade
+):
+ """
+ Saves configured audio FX parameters into an effect preset JSON file.
+
+ Args:
+ name (str): Target filename for export.
+ ... (other args): Audio effect processing chains and metrics configurations.
+
+ Returns:
+ dict: Refreshes drop-down lists dedicated to FX presets.
+ """
+
+ if not name: return gr_warning(translations["provide_filename_settings"])
+
+ # Ensure at least one audio processor module or structural pitch modifier is enabled
+ if not any([
+ resample_checkbox,
+ chorus_check_box,
+ distortion_checkbox,
+ reverb_check_box,
+ delay_check_box,
+ compressor_check_box,
+ limiter,
+ gain_checkbox,
+ bitcrush_checkbox,
+ clipping_checkbox,
+ phaser_check_box,
+ bass_or_treble,
+ fade,
+ pitch_shift_semitones != 0
+ ]):
+ return gr_warning(translations["choose1"])
+
+ settings = {}
+ # Mapping structure for all DSP block parameters depending on structural feature validation
+ for checkbox, data in [
+ (resample_checkbox, {
+ "resample_checkbox": resample_checkbox,
+ "audio_effect_resample_sr": audio_effect_resample_sr
+ }),
+ (chorus_check_box, {
+ "chorus_check_box": chorus_check_box,
+ "chorus_depth": chorus_depth,
+ "chorus_rate_hz": chorus_rate_hz,
+ "chorus_mix": chorus_mix,
+ "chorus_center_delay_ms": chorus_center_delay_ms,
+ "chorus_feedback": chorus_feedback
+ }),
+ (distortion_checkbox, {
+ "distortion_checkbox": distortion_checkbox,
+ "distortion_drive_db": distortion_drive_db
+ }),
+ (reverb_check_box, {
+ "reverb_check_box": reverb_check_box,
+ "reverb_room_size": reverb_room_size,
+ "reverb_damping": reverb_damping,
+ "reverb_wet_level": reverb_wet_level,
+ "reverb_dry_level": reverb_dry_level,
+ "reverb_width": reverb_width,
+ "reverb_freeze_mode": reverb_freeze_mode
+ }),
+ (pitch_shift_semitones != 0, {
+ "pitch_shift_semitones": pitch_shift_semitones
+ }),
+ (delay_check_box, {
+ "delay_check_box": delay_check_box,
+ "delay_second": delay_second,
+ "delay_feedback": delay_feedback,
+ "delay_mix": delay_mix
+ }),
+ (compressor_check_box, {
+ "compressor_check_box": compressor_check_box,
+ "compressor_threshold_db": compressor_threshold_db,
+ "compressor_ratio": compressor_ratio,
+ "compressor_attack_ms": compressor_attack_ms,
+ "compressor_release_ms": compressor_release_ms
+ }),
+ (limiter, {
+ "limiter": limiter,
+ "limiter_threshold_db": limiter_threshold_db,
+ "limiter_release_ms": limiter_release_ms
+ }),
+ (gain_checkbox, {
+ "gain_checkbox": gain_checkbox,
+ "gain_db": gain_db
+ }),
+ (bitcrush_checkbox, {
+ "bitcrush_checkbox": bitcrush_checkbox,
+ "bitcrush_bit_depth": bitcrush_bit_depth
+ }),
+ (clipping_checkbox, {
+ "clipping_checkbox": clipping_checkbox,
+ "clipping_threshold_db": clipping_threshold_db
+ }),
+ (phaser_check_box, {
+ "phaser_check_box": phaser_check_box,
+ "phaser_rate_hz": phaser_rate_hz,
+ "phaser_depth": phaser_depth,
+ "phaser_centre_frequency_hz": phaser_centre_frequency_hz,
+ "phaser_feedback": phaser_feedback,
+ "phaser_mix": phaser_mix
+ }),
+ (bass_or_treble, {
+ "bass_or_treble": bass_or_treble,
+ "bass_boost": bass_boost,
+ "bass_frequency": bass_frequency,
+ "treble_boost": treble_boost,
+ "treble_frequency": treble_frequency
+ }),
+ (fade, {
+ "fade": fade,
+ "fade_in": fade_in,
+ "fade_out": fade_out
+ })
+ ]:
+ if checkbox: settings.update(data)
+
+ # Save the runtime dictionary into the local path ecosystem
+ with open(os.path.join(configs["presets_path"], name + ".effect.json"), "w") as f:
+ json.dump(settings, f, indent=4)
+
+ gr_info(translations["export_settings"].format(name=name))
+ return change_effect_preset_choices()
+
+def save_realtime_config(
+ name,
+ exclusive_mode,
+ vad_enabled,
+ chunk_size,
+ pitch,
+ index_strength,
+ predictor_onnx,
+ hop_length,
+ embedder_mode,
+ f0_autotune,
+ proposal_pitch,
+ f0_autotune_strength,
+ proposal_pitch_threshold,
+ rms_mix_rate,
+ protect,
+ filter_radius,
+ silent_threshold,
+ extra_convert_size,
+ cross_fade_overlap_size,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ post_process,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder,
+ chorus,
+ distortion,
+ reverb,
+ pitch_shift,
+ delay,
+ compressor,
+ limiter,
+ gain,
+ bitcrush,
+ clipping,
+ phaser,
+ chorus_depth,
+ chorus_rate,
+ chorus_mix,
+ chorus_center_delay,
+ chorus_feedback,
+ distortion_gain,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_gain,
+ reverb_dry_gain,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_seconds,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold,
+ compressor_ratio,
+ compressor_attack,
+ compressor_release,
+ limiter_threshold,
+ limiter_release_time,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix
+):
+ """
+ Dumps all current interface configurations for low-latency real-time
+ inference into a customized runtime file.
+
+ Returns:
+ dict: Component update handler for real-time drop-downs.
+ """
+
+ if not name: return gr_warning(translations["provide_filename_settings"])
+
+ # Construct the continuous key-value state for low-latency engine parameters
+ settings = {
+ "exclusive_mode": exclusive_mode,
+ "vad_enabled": vad_enabled,
+ "chunk_size": chunk_size,
+ "pitch": pitch,
+ "index_strength": index_strength,
+ "predictor_onnx": predictor_onnx,
+ "hop_length": hop_length,
+ "embedder_mode": embedder_mode,
+ "embedders_mix": embedders_mix,
+ "embedders_mix_layers": embedders_mix_layers,
+ "embedders_mix_ratio": embedders_mix_ratio,
+ "use_phase_vocoder": use_phase_vocoder,
+ "f0_autotune": f0_autotune,
+ "proposal_pitch": proposal_pitch,
+ "f0_autotune_strength": f0_autotune_strength,
+ "proposal_pitch_threshold": proposal_pitch_threshold,
+ "rms_mix_rate": rms_mix_rate,
+ "protect": protect,
+ "filter_radius": filter_radius,
+ "silent_threshold": silent_threshold,
+ "extra_convert_size": extra_convert_size,
+ "cross_fade_overlap_size": cross_fade_overlap_size,
+ "vad_sensitivity": vad_sensitivity,
+ "vad_frame_ms": vad_frame_ms,
+ "clean_audio": clean_audio,
+ "clean_strength": clean_strength,
+ "post_process": post_process,
+ "chorus": chorus,
+ "distortion": distortion,
+ "reverb": reverb,
+ "pitch_shift": pitch_shift,
+ "delay": delay,
+ "compressor": compressor,
+ "limiter": limiter,
+ "gain": gain,
+ "bitcrush": bitcrush,
+ "clipping": clipping,
+ "phaser": phaser,
+ "chorus_depth": chorus_depth,
+ "chorus_rate": chorus_rate,
+ "chorus_mix": chorus_mix,
+ "chorus_center_delay": chorus_center_delay,
+ "chorus_feedback": chorus_feedback,
+ "distortion_gain": distortion_gain,
+ "reverb_room_size": reverb_room_size,
+ "reverb_damping": reverb_damping,
+ "reverb_wet_gain": reverb_wet_gain,
+ "reverb_dry_gain": reverb_dry_gain,
+ "reverb_width": reverb_width,
+ "reverb_freeze_mode": reverb_freeze_mode,
+ "pitch_shift_semitones": pitch_shift_semitones,
+ "delay_seconds": delay_seconds,
+ "delay_feedback": delay_feedback,
+ "delay_mix": delay_mix,
+ "compressor_threshold": compressor_threshold,
+ "compressor_ratio": compressor_ratio,
+ "compressor_attack": compressor_attack,
+ "compressor_release": compressor_release,
+ "limiter_threshold": limiter_threshold,
+ "limiter_release_time": limiter_release_time,
+ "gain_db": gain_db,
+ "bitcrush_bit_depth": bitcrush_bit_depth,
+ "clipping_threshold": clipping_threshold,
+ "phaser_rate_hz": phaser_rate_hz,
+ "phaser_depth": phaser_depth,
+ "phaser_centre_frequency_hz": phaser_centre_frequency_hz,
+ "phaser_feedback": phaser_feedback,
+ "phaser_mix": phaser_mix
+ }
+
+ path = os.path.join(configs["presets_path"], name + ".realtime.json")
+ with open(path, "w") as f:
+ json.dump(settings, f, indent=4)
+
+ gr_info(translations["export_settings"].format(name=name))
+ return change_realtime_preset_choices()
+
+def load_realtime_config(
+ presets,
+ exclusive_mode,
+ vad_enabled,
+ chunk_size,
+ pitch,
+ index_strength,
+ predictor_onnx,
+ hop_length,
+ embedder_mode,
+ f0_autotune,
+ proposal_pitch,
+ f0_autotune_strength,
+ proposal_pitch_threshold,
+ rms_mix_rate,
+ protect,
+ filter_radius,
+ silent_threshold,
+ extra_convert_size,
+ cross_fade_overlap_size,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ post_process,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder,
+ chorus,
+ distortion,
+ reverb,
+ pitch_shift,
+ delay,
+ compressor,
+ limiter,
+ gain,
+ bitcrush,
+ clipping,
+ phaser,
+ chorus_depth,
+ chorus_rate,
+ chorus_mix,
+ chorus_center_delay,
+ chorus_feedback,
+ distortion_gain,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_gain,
+ reverb_dry_gain,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_seconds,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold,
+ compressor_ratio,
+ compressor_attack,
+ compressor_release,
+ limiter_threshold,
+ limiter_release_time,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix
+):
+ """
+ Parses and processes an active real-time low-latency configuration module layout.
+
+ Returns:
+ list: Extracted data array mirroring interface parameters layout natively.
+ """
+
+ if not presets: gr_warning(translations["provide_file_settings"])
+
+ file = {}
+ if presets:
+ with open(os.path.join(configs["presets_path"], presets)) as f:
+ file = json.load(f)
+
+ gr_info(translations["load_presets"].format(presets=presets))
+
+ # Compile explicit properties directly from schema mapping rules
+ return [
+ file.get("exclusive_mode", exclusive_mode),
+ file.get("vad_enabled", vad_enabled),
+ file.get("chunk_size", chunk_size),
+ file.get("pitch", pitch),
+ file.get("index_strength", index_strength),
+ file.get("predictor_onnx", predictor_onnx),
+ file.get("hop_length", hop_length),
+ file.get("embedder_mode", embedder_mode),
+ file.get("f0_autotune", f0_autotune),
+ file.get("proposal_pitch", proposal_pitch),
+ file.get("f0_autotune_strength", f0_autotune_strength),
+ file.get("proposal_pitch_threshold", proposal_pitch_threshold),
+ file.get("rms_mix_rate", rms_mix_rate),
+ file.get("protect", protect),
+ file.get("filter_radius", filter_radius),
+ file.get("silent_threshold", silent_threshold),
+ file.get("extra_convert_size", extra_convert_size),
+ file.get("cross_fade_overlap_size", cross_fade_overlap_size),
+ file.get("vad_sensitivity", vad_sensitivity),
+ file.get("vad_frame_ms", vad_frame_ms),
+ file.get("clean_audio", clean_audio),
+ file.get("clean_strength", clean_strength),
+ file.get("post_process", post_process),
+ file.get("embedders_mix", embedders_mix),
+ file.get("embedders_mix_layers", embedders_mix_layers),
+ file.get("embedders_mix_ratio", embedders_mix_ratio),
+ file.get("use_phase_vocoder", use_phase_vocoder),
+ file.get("chorus", chorus),
+ file.get("distortion", distortion),
+ file.get("reverb", reverb),
+ file.get("pitch_shift", pitch_shift),
+ file.get("delay", delay),
+ file.get("compressor", compressor),
+ file.get("limiter", limiter),
+ file.get("gain", gain),
+ file.get("bitcrush", bitcrush),
+ file.get("clipping", clipping),
+ file.get("phaser", phaser),
+ file.get("chorus_depth", chorus_depth),
+ file.get("chorus_rate", chorus_rate),
+ file.get("chorus_mix", chorus_mix),
+ file.get("chorus_center_delay", chorus_center_delay),
+ file.get("chorus_feedback", chorus_feedback),
+ file.get("distortion_gain", distortion_gain),
+ file.get("reverb_room_size", reverb_room_size),
+ file.get("reverb_damping", reverb_damping),
+ file.get("reverb_wet_gain", reverb_wet_gain),
+ file.get("reverb_dry_gain", reverb_dry_gain),
+ file.get("reverb_width", reverb_width),
+ file.get("reverb_freeze_mode", reverb_freeze_mode),
+ file.get("pitch_shift_semitones", pitch_shift_semitones),
+ file.get("delay_seconds", delay_seconds),
+ file.get("delay_feedback", delay_feedback),
+ file.get("delay_mix", delay_mix),
+ file.get("compressor_threshold", compressor_threshold),
+ file.get("compressor_ratio", compressor_ratio),
+ file.get("compressor_attack", compressor_attack),
+ file.get("compressor_release", compressor_release),
+ file.get("limiter_threshold", limiter_threshold),
+ file.get("limiter_release_time", limiter_release_time),
+ file.get("gain_db", gain_db),
+ file.get("bitcrush_bit_depth", bitcrush_bit_depth),
+ file.get("clipping_threshold", clipping_threshold),
+ file.get("phaser_rate_hz", phaser_rate_hz),
+ file.get("phaser_depth", phaser_depth),
+ file.get("phaser_centre_frequency_hz", phaser_centre_frequency_hz),
+ file.get("phaser_feedback", phaser_feedback),
+ file.get("phaser_mix", phaser_mix)
+ ]
\ No newline at end of file
diff --git a/main/app/core/process.py b/main/app/core/process.py
new file mode 100644
index 0000000000000000000000000000000000000000..995a9bd39bbc9fa8d95262b3690a217acdfbfb7f
--- /dev/null
+++ b/main/app/core/process.py
@@ -0,0 +1,296 @@
+import os
+import re
+import sys
+import shutil
+import codecs
+import zipfile
+import requests
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import logger, translations, configs
+from main.app.core.ui import gr_info, gr_warning, gr_error, process_output, replace_punctuation
+
+def read_docx_text(path):
+ """
+ Extracts and merges all text content from a Microsoft Word (.docx) file.
+
+ Args:
+ path (str): The absolute or relative path to the .docx file.
+
+ Returns:
+ str: A single string containing all paragraphs separated by newlines.
+ """
+
+ import xml.etree.ElementTree
+ # Open the docx container as a zip archive and read the main XML content
+ with zipfile.ZipFile(path) as docx:
+ with docx.open("word/document.xml") as document_xml:
+ xml_content = document_xml.read()
+
+ # The exact OpenXML schema URL encoded in ROT13 to hide it from plain text strings
+ WORD_NAMESPACE = codecs.decode(
+ "{uggc://fpurznf.bcrakzysbezngf.bet/jbeqcebprffvatzy/2006/znva}",
+ "rot13"
+ )
+ paragraphs = []
+ # Parse the XML structure and iterate through all paragraph nodes ('p')
+ for paragraph in xml.etree.ElementTree.XML(xml_content).iter(WORD_NAMESPACE + 'p'):
+ # Extract and join all text nodes ('t') within the current paragraph
+ texts = [node.text for node in paragraph.iter(WORD_NAMESPACE + 't') if node.text]
+ if texts: paragraphs.append(''.join(texts))
+
+ return '\n'.join(paragraphs)
+
+def process_input(file_path):
+ """
+ Reads the contents of a file based on its extension and triggers UI feedback.
+
+ Args:
+ file_path (str): The path to the uploaded file.
+
+ Returns:
+ str: The extracted text content from the file, or an empty string if reading fails.
+ """
+
+ # SRT files are initialized as empty (possibly handled in another workflow step)
+ if file_path.endswith(".srt"):
+ file_contents = ""
+ elif file_path.endswith(".docx"):
+ file_contents = read_docx_text(file_path)
+ else:
+ try:
+ # Fallback to standard UTF-8 plain text reading for other file formats
+ with open(file_path, "r", encoding="utf-8") as file:
+ file_contents = file.read()
+ except Exception as e:
+ # Handle reading exceptions gracefully and notify user via UI
+ gr_warning(translations["read_error"])
+ logger.debug(e)
+ file_contents = ""
+
+ # Notify user of successful upload/processing
+ gr_info(translations["upload_success"])
+ return file_contents
+
+def move_files_from_directory(
+ src_dir,
+ dest_weights,
+ dest_logs,
+ model_name,
+ use_orig_weight_name=False
+):
+ """
+ Walks through a directory to sort and move model weights, logs, and indices.
+
+ Args:
+ src_dir (str): Source directory containing the unzipped model files.
+ dest_weights (str): Target directory for .pth and .onnx weight files.
+ dest_logs (str): Target directory for model logs and .index files.
+ model_name (str): The standardized name used for naming or structuring folders.
+ use_orig_weight_name (bool, optional): If True, keeps the original file name
+ instead of renaming it to model_name. Defaults to False.
+ """
+
+ for root, _, files in os.walk(src_dir):
+ for file in files:
+ file_path = os.path.join(root, file)
+
+ if file.endswith(".index"):
+ # Case 1: Process index files (Retrieval-based Voice Conversion indexes)
+ model_log_dir = os.path.join(dest_logs, model_name)
+ os.makedirs(model_log_dir, exist_ok=True)
+
+ # Clean and sanitize the output file name
+ filepath = process_output(os.path.join(model_log_dir, replace_punctuation(file)))
+ shutil.move(file_path, filepath)
+ elif file.endswith(".pth") and not file.startswith("D_") and not file.startswith("G_"):
+ # Case 2: Process PyTorch weight files, excluding generator/discriminator training states (G_ / D_)
+ pth_path = process_output(os.path.join(dest_weights, model_name + ".pth"))
+ shutil.move(file_path, pth_path if not use_orig_weight_name else dest_weights)
+ elif file.endswith(".onnx") and not file.startswith("D_") and not file.startswith("G_"):
+ # Case 3: Process ONNX inference files, excluding training checkpoints
+ onnx_path = process_output(os.path.join(dest_weights, model_name + ".onnx"))
+ shutil.move(file_path, onnx_path if not use_orig_weight_name else dest_weights)
+
+def extract_name_model(filename):
+ """
+ Extracts the model name from a string using a regex pattern (e.g., '_modelname_v2').
+
+ Args:
+ filename (str): The filename or path string to parse.
+
+ Returns:
+ Optional[str]: The extracted model name if matched, otherwise None.
+ """
+
+ # Regex looks for a string pattern between an underscore and a version tag '_vX'
+ match = re.search(
+ r"_([A-Za-z0-9]+)(?=_v\d*)",
+ replace_punctuation(filename)
+ )
+
+ return match.group(1) if match else None
+
+def save_drop_model(dropboxs):
+ """
+ Processes, extracts, and organizes files dropped or uploaded into the workspace.
+ Handles zip archives, individual model weights (.pth, .onnx), and index files.
+
+ Args:
+ dropboxs (List[str]): List of file paths pointing to the dropped/uploaded items.
+ """
+
+ weight_folder = configs["weights_path"]
+ logs_folder = configs["logs_path"]
+ save_model_temp = "save_model_temp"
+
+ # Ensure all target and temporary directories exist
+ if not os.path.exists(weight_folder): os.makedirs(weight_folder, exist_ok=True)
+ if not os.path.exists(logs_folder): os.makedirs(logs_folder, exist_ok=True)
+ if not os.path.exists(save_model_temp): os.makedirs(save_model_temp, exist_ok=True)
+
+ try:
+ for dropbox in dropboxs:
+ # Move individual items to the temporary workspace first
+ shutil.move(dropbox, save_model_temp)
+ file_name = os.path.basename(dropbox)
+
+ if file_name.endswith(".zip"):
+ # Scenario A: Processing a bundled zip package
+ shutil.unpack_archive(
+ os.path.join(save_model_temp, file_name),
+ save_model_temp
+ )
+
+ # Sort out unpacked contents into weights and logs folders
+ move_files_from_directory(
+ save_model_temp,
+ weight_folder,
+ logs_folder,
+ file_name.replace(".zip", "")
+ )
+ elif file_name.endswith((".pth", ".onnx")):
+ # Scenario B: Processing a direct standalone model weight file
+ output_file = process_output(os.path.join(weight_folder, file_name))
+
+ shutil.move(os.path.join(save_model_temp, file_name), output_file)
+ elif file_name.endswith(".index"):
+ # Scenario C: Processing a direct index file
+
+ # Try to extract the model name from the index file name pattern
+ modelname = extract_name_model(file_name)
+ # Fallback to the filename without extension if regex fails
+ if modelname is None:
+ modelname = os.path.splitext(os.path.basename(file_name))[0]
+
+ model_logs = os.path.join(logs_folder, modelname)
+ if not os.path.exists(model_logs):
+ os.makedirs(model_logs, exist_ok=True)
+
+ shutil.move(
+ os.path.join(save_model_temp, file_name),
+ model_logs
+ )
+ else:
+ # Scenario D: Unsupported extension type
+ gr_warning(translations["unable_analyze_model"])
+ return None
+
+ gr_info(translations["upload_success"])
+ return None
+ except Exception as e:
+ # Catch unforeseen filesystem errors and inform the user
+ gr_error(message=translations["error_occurred"].format(e=e))
+ return None
+ finally:
+ # Always clean up the temporary directory to avoid storage leaks
+ shutil.rmtree(save_model_temp, ignore_errors=True)
+
+def zip_file(
+ modelname,
+ pth_name,
+ index_path
+):
+ """
+ Bundles specified model weights and index files into a structured downloadable zip.
+
+ Args:
+ modelname (str): Name of the model, used for directory mapping and target file name.
+ pth_name (str): Name of the model weight file (.pth or .onnx).
+ index_path (str): Absolute or relative path to the associated .index file.
+
+ Returns:
+ dict: A Gradio UI component configuration dictionary to update the view state, or a UI warning event if verification fails.
+ """
+
+ pth_path = os.path.join(configs["weights_path"], pth_name)
+
+ # Validate weight file existence and extension integrity before packing
+ if (
+ not pth_name or
+ not os.path.exists(pth_path) or
+ not pth_name.endswith((".pth", ".onnx"))
+ ):
+ return gr_warning(translations["provide_model"])
+
+ zip_file_path = os.path.join(configs["logs_path"], modelname, modelname + ".zip")
+ gr_info(translations["start_zip"])
+
+ # Create the zip archive and map internal structure cleanly (root-level files inside zip)
+ with zipfile.ZipFile(zip_file_path, 'w') as zipf:
+ zipf.write(pth_path, os.path.basename(pth_path))
+ if index_path: zipf.write(index_path, os.path.basename(index_path))
+
+ gr_info(translations["success"])
+ # Return structured dict formatted for Gradio dynamic component updates
+ return {
+ "visible": True,
+ "value": zip_file_path,
+ "__type__": "update"
+ }
+
+def fetch_pretrained_data():
+ """
+ Fetches custom pre-trained data models configuration mapping via a remote API.
+
+ Returns:
+ dict: Parsed JSON data from the repository config if successful, otherwise an empty dictionary.
+ """
+
+ try:
+ # GET request to a HuggingFace configuration JSON file disguised via ROT13
+ response = requests.get(
+ codecs.decode(
+ "uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/wfba/phfgbz_cergenvarq.wfba",
+ "rot13"
+ )
+ )
+ response.raise_for_status()
+
+ return response.json()
+ except:
+ # Fail silently with safe fallback layout to prevent runtime app crash
+ return {}
+
+def update_sample_rate_dropdown(model):
+ """
+ Updates the target sample rate dropdown selection menu based on the selected model metadata.
+
+ Args:
+ model (str): The name or identifier of the selected model.
+
+ Returns:
+ dict: A Gradio UI dictionary layout mapping the updated state for the dropdown options.
+ """
+
+ # Fetch remote mappings data mapping sample rates to specific models
+ data = fetch_pretrained_data()
+
+ # Prevent execution if structural edge-case indicators occur
+ if model != translations["success"]:
+ return {
+ "choices": list(data[model].keys()),
+ "value": list(data[model].keys())[0],
+ "__type__": "update"
+ }
\ No newline at end of file
diff --git a/main/app/core/realtime.py b/main/app/core/realtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..7770d53aba93339a3eab13d2c199c9932f080b0a
--- /dev/null
+++ b/main/app/core/realtime.py
@@ -0,0 +1,1415 @@
+import os
+import sys
+import time
+import queue
+
+import multiprocessing as mp
+
+sys.path.append(os.getcwd())
+
+from main.app.core.ui import gr_info, gr_warning
+from main.app.variables import translations, configs, config, logger
+
+running, realtime_process, ui_queue, config_queue = False, None, None, None
+
+PIPELINE_SAMPLE_RATE = 16000
+
+interactive_true = {"interactive": True, "__type__": "update"}
+interactive_false = {"interactive": False, "__type__": "update"}
+callbacks_kwargs = {}
+
+js_code = """
+window._activeStream = null;
+window._audioCtx = null;
+window._workletNode = null;
+window._playbackNode = null;
+window._ws = null;
+window.OutputAudioRoute = null;
+window.MonitorAudioRoute = null;
+window.lastSend = 0;
+window.responseMs = 0;
+
+// Function to display status
+function setStatus(msg, use_alert = true) {
+ const realtimeStatus = document.querySelector("#realtime-status-info h2.output-class"); // find status text box
+ if (use_alert) alert(msg); // Use alert instead of gr.Info
+
+ if (realtimeStatus) {
+ realtimeStatus.innerText = msg;
+ realtimeStatus.style.whiteSpace = "nowrap";
+ realtimeStatus.style.textAlign = "center";
+ }
+}
+
+async function addModuleFromString(ctx, codeStr) {
+ const blob = new Blob([codeStr], {type: 'application/javascript'});
+ const url = URL.createObjectURL(blob);
+
+ await ctx.audioWorklet.addModule(url);
+ URL.revokeObjectURL(url);
+};
+
+function createOutputRoute(audioCtx, playbackNode, sinkId, gainValue = 1.0) {
+ const dest = audioCtx.createMediaStreamDestination(); // Create a MediaStreamDestination Node
+ const gainNode = audioCtx.createGain(); // Create a GainNode (Volume Control Node)
+ gainNode.gain.value = gainValue; // Sets the initial gain (volume).
+
+ // Connect the Audio Nodes
+ playbackNode.connect(gainNode);
+ gainNode.connect(dest);
+
+ // Create and Configure the audio Element
+ const el = document.createElement('audio');
+ el.autoplay = true;
+ el.srcObject = dest.stream;
+ el.style.display = 'none';
+ document.body.appendChild(el);
+
+ if (el.setSinkId) el.setSinkId(sinkId).catch(err => console.error(err));
+ return { dest, gainNode, el }; // Returns the objects (destination node, gain node, and audio element)
+}
+
+const inputWorkletSource = `
+class InputProcessor extends AudioWorkletProcessor {
+ constructor() {
+ super();
+ this.bufferCapacity = 48000; // Stores up to 1 second of audio at 48kHz
+ this.buffer = new Float32Array(this.bufferCapacity);
+ this.writePointer = 0;
+ this.readPointer = 0;
+ this.availableSamples = 0;
+ this.block_frame = 960; // Standard layout processing slice size
+ // Handle dynamic frame configuration updates from the main main thread
+ this.port.onmessage = (e) => {
+ if (e.data && e.data.block_frame) this.block_frame = e.data.block_frame;
+ };
+ }
+
+ process(inputs) {
+ const input = inputs[0];
+ if (!input || !input[0]) return true; // Keep worklet alive if no stream is active
+
+ const frame = input[0];
+ const frameSize = frame.length;
+
+ // Push new incoming samples into the internal circular ring buffer
+ if (this.availableSamples + frameSize <= this.bufferCapacity) {
+ for (let i = 0; i < frameSize; i++) {
+ this.buffer[this.writePointer] = frame[i];
+ this.writePointer = (this.writePointer + 1) % this.bufferCapacity;
+ }
+
+ this.availableSamples += frameSize;
+ }
+
+ // Slice accumulated audio into uniform blocks and dispatch them to the main thread
+ while (this.availableSamples >= this.block_frame) {
+ const chunk = new Float32Array(this.block_frame);
+
+ for (let i = 0; i < this.block_frame; i++) {
+ chunk[i] = this.buffer[this.readPointer];
+ this.readPointer = (this.readPointer + 1) % this.bufferCapacity;
+ }
+
+ this.availableSamples -= this.block_frame;
+ this.port.postMessage({ chunk });
+ }
+ return true;
+ }
+}
+registerProcessor('input-processor', InputProcessor);
+`;
+
+const playbackWorkletSource = `
+class PlaybackProcessor extends AudioWorkletProcessor {
+ constructor(options) {
+ super(options);
+ const bufferSize = options.processorOptions && options.processorOptions.bufferSize ? options.processorOptions.bufferSize : 98304;
+ this.buffer = new Float32Array(bufferSize);
+ this.bufferCapacity = bufferSize;
+ this.writePointer = 0;
+ this.readPointer = 0;
+ this.availableSamples = 0;
+
+ // Listen for returned server-processed audio chunks and load them into the playback ring buffer
+ this.port.onmessage = (e) => {
+ if (e.data && e.data.chunk) {
+ const chunk = new Float32Array(e.data.chunk);
+ const chunkSize = chunk.length;
+
+ // Guard against ring buffer overflows (drop chunk if filled)
+ if (this.availableSamples + chunkSize > this.bufferCapacity) return;
+
+ // Handle standard inline write vs circular wrapping wrap-around logic
+ if (this.writePointer + chunkSize <= this.bufferCapacity) {
+ this.buffer.set(chunk, this.writePointer);
+ } else {
+ const firstPart = this.bufferCapacity - this.writePointer;
+ this.buffer.set(chunk.subarray(0, firstPart), this.writePointer);
+ this.buffer.set(chunk.subarray(firstPart), 0);
+ }
+
+ this.writePointer = (this.writePointer + chunkSize) % this.bufferCapacity;
+ this.availableSamples += chunkSize;
+ }
+ };
+ }
+
+ process(inputs, outputs) {
+ const output = outputs[0];
+ if (!output || !output[0]) return true;
+
+ const frame = output[0];
+ const frameSize = frame.length;
+
+ // Populate the hardware output frame if there are enough accumulated samples
+ if (this.availableSamples >= frameSize) {
+ if (this.readPointer + frameSize <= this.bufferCapacity) {
+ frame.set(this.buffer.subarray(this.readPointer, this.readPointer + frameSize));
+ } else {
+ const firstPart = this.bufferCapacity - this.readPointer;
+ frame.set(this.buffer.subarray(this.readPointer, this.bufferCapacity), 0);
+ frame.set(this.buffer.subarray(0, frameSize - firstPart), firstPart);
+ }
+
+ this.readPointer = (this.readPointer + frameSize) % this.bufferCapacity;
+ this.availableSamples -= frameSize;
+ } else {
+ // Underflow protection: fill buffer with silence (zeros) to prevent harsh digital crackling
+ frame.fill(0);
+ }
+
+ // Duplicate audio channel configurations for stereo topologies if supported
+ if (output.length > 1) output[1].set(output[0]);
+ return true;
+ }
+}
+registerProcessor('playback-processor', PlaybackProcessor);
+`;
+
+window.getAudioDevices = async function() {
+ if (!navigator.mediaDevices) {
+ // If somehow the browser does not support.
+ setStatus("__MEDIA_DEVICES__");
+ return {"inputs": {}, "outputs": {}};
+ }
+
+ try {
+ // Request audio permissions to the browser.
+ await navigator.mediaDevices.getUserMedia({ audio: true });
+ } catch (err) {
+ console.error(err);
+ setStatus("__MIC_INACCESSIBLE__")
+
+ return {"inputs": {}, "outputs": {}};
+ }
+
+ // Read the audio devices available on the browser and filter out the devices.
+ const devices = await navigator.mediaDevices.enumerateDevices();
+ const inputs = {};
+ const outputs = {};
+
+ for (const device of devices) {
+ if (device.kind === "audioinput") {
+ inputs[device.label] = device.deviceId
+ } else if (device.kind === "audiooutput") {
+ outputs[device.label] = device.deviceId
+ }
+ }
+
+ // Returns the audio devices
+ if (!Object.keys(inputs).length && !Object.keys(outputs).length) return {"inputs": {}, "outputs": {}};
+ return {"inputs": inputs, "outputs": outputs};
+};
+
+window.StreamAudioRealtime = async function(
+ monitor,
+ vad_enabled,
+ input_audio_device,
+ output_audio_device,
+ monitor_output_device,
+ input_audio_gain,
+ output_audio_gain,
+ monitor_audio_gain,
+ chunk_size,
+ pitch,
+ model_pth,
+ model_index,
+ index_strength,
+ predictor_onnx,
+ f0_method,
+ hop_length,
+ embed_mode,
+ embedders,
+ custom_embedders,
+ f0_autotune,
+ proposal_pitch,
+ f0_autotune_strength,
+ proposal_pitch_threshold,
+ rms_mix_rate,
+ protect,
+ filter_radius,
+ silent_threshold,
+ extra_convert_size,
+ cross_fade_overlap_size,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ exclusive_mode,
+ post_process,
+ sid,
+ noise_scale,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder,
+ chorus,
+ distortion,
+ reverb,
+ pitch_shift,
+ delay,
+ compressor,
+ limiter,
+ gain,
+ bitcrush,
+ clipping,
+ phaser,
+ chorus_depth,
+ chorus_rate,
+ chorus_mix,
+ chorus_center_delay,
+ chorus_feedback,
+ distortion_gain,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_gain,
+ reverb_dry_gain,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_seconds,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold,
+ compressor_ratio,
+ compressor_attack,
+ compressor_release,
+ limiter_threshold,
+ limiter_release_time,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix,
+ nprobe
+) {
+ const SampleRate = 48000;
+ const ReadChunkSize = Math.round(chunk_size * SampleRate / 1000 / 128);
+ const block_frame = parseInt(ReadChunkSize) * 128;
+ const ButtonState = { start_button: true, stop_button: false };
+ const devices = await window.getAudioDevices();
+
+ input_audio_device = devices["inputs"][input_audio_device];
+ output_audio_device = devices["outputs"][output_audio_device];
+ if (monitor && devices["outputs"][monitor_output_device]) monitor_output_device = devices["outputs"][monitor_output_device];
+
+ try {
+ if (!input_audio_device || !output_audio_device) {
+ setStatus("__PROVIDE_AUDIO_DEVICE__");
+ return ButtonState;
+ }
+
+ if (monitor && !monitor_output_device) {
+ setStatus("__PROVIDE_MONITOR_DEVICE__");
+ return ButtonState;
+ }
+
+ if (!model_pth) {
+ setStatus("__PROVIDE_MODEL__")
+ return ButtonState;
+ }
+
+ setStatus("__START_REALTIME__", false)
+
+ const stream = await navigator.mediaDevices.getUserMedia({
+ audio: {
+ deviceId: { exact: input_audio_device }, // audio: input_audio_device ? { deviceId: { exact: input_audio_device } } : true
+ channelCount: { exact: 1 },
+ sampleRate: { exact: SampleRate },
+ latency: { ideal: 0 },
+ // disable all browser processing (You can make it optional)
+ echoCancellation: !exclusive_mode,
+ noiseSuppression: !exclusive_mode,
+ autoGainControl: !exclusive_mode
+ }
+ });
+
+ let latencyHint = "playback";
+ if (exclusive_mode) latencyHint = "interactive";
+
+ window._activeStream = stream;
+ window._audioCtx = new AudioContext({ sampleRate: SampleRate, latencyHint: latencyHint });
+
+ // Load processing modules.
+ await addModuleFromString(window._audioCtx, inputWorkletSource);
+ await addModuleFromString(window._audioCtx, playbackWorkletSource);
+ // await window._audioCtx.audioWorklet.addModule('/input_processor.js');
+ // await window._audioCtx.audioWorklet.addModule('/playback_processor.js');
+
+ // Initialize audio web parts
+ const src = window._audioCtx.createMediaStreamSource(stream);
+ const inputNode = new AudioWorkletNode(window._audioCtx, 'input-processor');
+ const playbackNode = new AudioWorkletNode(window._audioCtx, 'playback-processor', {
+ processorOptions: {
+ bufferSize: block_frame * 2 // Double or more is recommended to avoid loss of sound.
+ }
+ });
+
+ inputNode.port.postMessage({ block_frame: block_frame });
+ src.connect(inputNode);
+
+ // Create audio and monitor output
+ window.OutputAudioRoute = createOutputRoute(window._audioCtx, playbackNode, output_audio_device, output_audio_gain / 100);
+ if (monitor && monitor_output_device) window.MonitorAudioRoute = createOutputRoute(window._audioCtx, playbackNode, monitor_output_device, monitor_audio_gain / 100);
+
+ // Configure path and websocket
+ const protocol = (location.protocol === "https:") ? "wss:" : "ws:";
+ const wsUrl = protocol + '//' + location.hostname + `:${location.port}` + '/api/ws-audio';
+ const ws = new WebSocket(wsUrl);
+
+ // Set new values of buttons to avoid users initiating multiple realtime threads
+ ButtonState.start_button = false;
+ ButtonState.stop_button = true;
+
+ ws.binaryType = "arraybuffer";
+ window._ws = ws;
+
+ ws.onopen = () => {
+ console.log("__WS_CONNECTED__")
+
+ // send all parameters to websocket realtime
+ ws.send(
+ JSON.stringify({
+ type: 'init',
+ chunk_size: ReadChunkSize,
+ embedders: embedders,
+ model_pth: model_pth,
+ custom_embedders: custom_embedders,
+ cross_fade_overlap_size: cross_fade_overlap_size,
+ extra_convert_size: extra_convert_size,
+ model_index: model_index,
+ f0_method: f0_method,
+ predictor_onnx: predictor_onnx,
+ embedders_mode: embed_mode,
+ hop_length: hop_length,
+ silent_threshold: silent_threshold,
+ vad_enabled: vad_enabled,
+ vad_sensitivity: vad_sensitivity,
+ vad_frame_ms: vad_frame_ms,
+ clean_audio: clean_audio,
+ clean_strength: clean_strength,
+ f0_up_key: pitch,
+ index_rate: index_strength,
+ protect: protect,
+ filter_radius: filter_radius,
+ rms_mix_rate: rms_mix_rate,
+ f0_autotune: f0_autotune,
+ f0_autotune_strength: f0_autotune_strength,
+ proposal_pitch: proposal_pitch,
+ proposal_pitch_threshold: proposal_pitch_threshold,
+ input_audio_gain: input_audio_gain,
+ post_process: post_process,
+ sid: sid,
+ noise_scale: noise_scale,
+ embedders_mix: embedders_mix,
+ embedders_mix_layers: embedders_mix_layers,
+ embedders_mix_ratio: embedders_mix_ratio,
+ use_phase_vocoder: use_phase_vocoder,
+ nprobe: nprobe,
+ kwargs: {
+ chorus: chorus,
+ distortion: distortion,
+ reverb: reverb,
+ pitch_shift: pitch_shift,
+ delay: delay,
+ compressor: compressor,
+ limiter: limiter,
+ gain: gain,
+ bitcrush: bitcrush,
+ clipping: clipping,
+ phaser: phaser,
+ chorus_depth: chorus_depth,
+ chorus_rate: chorus_rate,
+ chorus_mix: chorus_mix,
+ chorus_delay: chorus_center_delay,
+ chorus_feedback: chorus_feedback,
+ distortion_gain: distortion_gain,
+ reverb_room_size: reverb_room_size,
+ reverb_damping: reverb_damping,
+ reverb_wet_level: reverb_wet_gain,
+ reverb_dry_level: reverb_dry_gain,
+ reverb_width: reverb_width,
+ reverb_freeze_mode: reverb_freeze_mode,
+ pitch_shift_semitones: pitch_shift_semitones,
+ delay_seconds: delay_seconds,
+ delay_feedback: delay_feedback,
+ delay_mix: delay_mix,
+ compressor_threshold: compressor_threshold,
+ compressor_ratio: compressor_ratio,
+ compressor_attack: compressor_attack,
+ compressor_release: compressor_release,
+ limiter_threshold: limiter_threshold,
+ limiter_release: limiter_release_time,
+ gain_db: gain_db,
+ bitcrush_bit_depth: bitcrush_bit_depth,
+ clipping_threshold: clipping_threshold,
+ phaser_rate_hz: phaser_rate_hz,
+ phaser_depth: phaser_depth,
+ phaser_centre_frequency_hz: phaser_centre_frequency_hz,
+ phaser_feedback: phaser_feedback,
+ phaser_mix: phaser_mix
+ }
+ })
+ );
+ };
+
+ inputNode.port.onmessage = (e) => {
+ // send audio from node to websocket for realtime
+ const chunk = e.data && e.data.chunk;
+
+ if (!chunk) return;
+ if (ws.readyState === WebSocket.OPEN) {
+ window.lastSend = performance.now();
+ ws.send(chunk);
+ }
+ };
+
+ ws.onmessage = (ev) => {
+ // Read the string values sent back from the websocket
+ if (typeof ev.data === 'string') {
+ const msg = JSON.parse(ev.data);
+
+ // Show latency information in the status bar of the interface
+ if (msg.type === 'latency') setStatus(`__LATENCY__: ${msg.value.toFixed(2)} ms | __VOLUME__: ${msg.volume.toFixed(2)} dB | __RESPONSE__: ${window.responseMs.toFixed(2)} ms`, use_alert=false)
+ if (msg.type === 'warnings') {
+ setStatus(msg.value);
+ StopAudioStream();
+ }
+
+ return;
+ }
+
+ // Send audio to the playback node to the audio device
+ const ab = ev.data;
+ playbackNode.port.postMessage({ chunk: ab }, [ab]);
+ window.responseMs = performance.now() - window.lastSend;
+ };
+
+ ws.onclose = () => console.log("__WS_CLOSED__");
+ window._workletNode = inputNode;
+ window._playbackNode = playbackNode;
+
+ if (window._audioCtx.state === 'suspended') await window._audioCtx.resume();
+
+ console.log("__REALTIME_STARTED__");
+ return ButtonState;
+ } catch (err) {
+ console.error("__ERROR__" + err);
+ alert("__ERROR__" + err.message);
+ // stop realtime when error
+ return StopAudioStream();
+ }
+};
+
+window.ChangeConfig = async function(value, key, if_kwargs=false) {
+ if (key === "output_audio_gain") {
+ window.OutputAudioRoute.gainNode.gain.value = value / 100
+ } else if (key == "monitor_audio_gain") {
+ if (window.MonitorAudioRoute) window.MonitorAudioRoute.gainNode.gain.value = value / 100
+ } else {
+ const protocol = (location.protocol === "https:") ? "wss:" : "ws:";
+ const wsUrl = protocol + '//' + location.hostname + `:${location.port}` + '/api/change-config';
+ const ws = new WebSocket(wsUrl);
+
+ ws.binaryType = "arraybuffer";
+ ws.onopen = () => {
+ ws.send(
+ JSON.stringify({
+ type: 'init',
+ key: key,
+ value: value,
+ if_kwargs: if_kwargs
+ })
+ );
+
+ ws.close();
+ };
+ }
+}
+
+window.StopAudioStream = async function() {
+ try {
+ if (window._ws) {
+ window._ws.close();
+ window._ws = null;
+ }
+
+ if (window._activeStream) {
+ window._activeStream.getTracks().forEach(t => t.stop());
+ window._activeStream = null;
+ }
+
+ if (window._workletNode) {
+ window._workletNode.disconnect();
+ window._workletNode = null;
+ }
+
+ if (window._playbackNode) {
+ window._playbackNode.disconnect();
+ window._playbackNode = null;
+ }
+
+ if (window._audioCtx) {
+ await window._audioCtx.close();
+ window._audioCtx = null;
+ }
+
+ if (window.OutputAudioRoute) window.OutputAudioRoute = null;
+ if (window.MonitorAudioRoute) window.MonitorAudioRoute = null;
+
+ document.querySelectorAll('audio').forEach(a => a.remove());
+ setStatus("__REALTIME_HAS_STOP__", false);
+
+ return {"start_button": true, "stop_button": false};
+ } catch (e) {
+ setStatus(`__ERROR__${e}`);
+
+ return {"start_button": false, "stop_button": true}
+ }
+};
+
+window.SoundfileRecordAudio = async function (RecordButton, RecordAudioPath, ExportFormat) {
+ const protocol = (location.protocol === "https:") ? "https:" : "http:";
+ const url = protocol + '//' + location.hostname + `:${location.port}` + '/api/record';
+
+ const res = await fetch(url, {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json"
+ },
+ body: JSON.stringify({
+ record_button: RecordButton,
+ record_audio_path: RecordAudioPath,
+ export_format: ExportFormat
+ })
+ });
+
+ const msg = await res.json();
+
+ if (msg.type === "info" || msg.type === "warnings") {
+ alert(msg.value);
+
+ return {
+ "button": msg.button,
+ "path": msg.path
+ };
+ }
+};
+""".replace(
+ "__MEDIA_DEVICES__", translations["media_devices"]
+).replace(
+ "__MIC_INACCESSIBLE__", translations["mic_inaccessible"]
+).replace(
+ "__PROVIDE_AUDIO_DEVICE__", translations["provide_audio_device"]
+).replace(
+ "__PROVIDE_MONITOR_DEVICE__", translations["provide_monitor_device"]
+).replace(
+ "__START_REALTIME__", translations["start_realtime"]
+).replace(
+ "__LATENCY__", translations['latency']
+).replace(
+ "__WS_CONNECTED__", translations["ws_connected"]
+).replace(
+ "__WS_CLOSED__", translations["ws_closed"]
+).replace(
+ "__REALTIME_STARTED__", translations["realtime_is_ready"]
+).replace(
+ "__ERROR__", translations["error_occurred"].format(e="")
+).replace(
+ "__REALTIME_HAS_STOP__", translations["realtime_has_stop"]
+).replace(
+ "__PROVIDE_MODEL__", translations["provide_model"]
+).replace(
+ "__VOLUME__", translations["volume"]
+).replace(
+ "__RESPONSE__", translations["response"]
+)
+
+def realtime_worker(queue_out, config_queue, callbacks_kwargs, audio_manager_kwargs):
+ """
+ Worker process target that handles the background real-time audio processing loop.
+
+ This function initializes audio callbacks, configures the audio manager, and handles
+ runtime commands (START, STOP, UPDATE_CONFIG) dispatched from the main UI process.
+
+ Args:
+ queue_out (queue.Queue): Inter-process queue to send status/logs back to the UI.
+ config_queue (queue.Queue): Inter-process queue to receive dynamic configuration commands.
+ callbacks_kwargs (Dict[str, Any]): Initialization arguments for the AudioCallbacks engine.
+ audio_manager_kwargs (Dict[str, Any]): Initialization arguments for hardware audio streams.
+ """
+
+ callbacks, audio_manager = None, None
+
+ try:
+ # Lazy import to avoid loading heavy deep learning/audio frameworks prematurely in the main thread
+ from main.inference.realtime.callbacks import AudioCallbacks
+
+ # Initialize the audio pipeline processing core
+ callbacks = AudioCallbacks(**callbacks_kwargs)
+ audio_manager = callbacks.audio
+
+ def audio_manager_start(kwargs):
+ """Helper function to map configuration dictionary to hardware audio manager start parameters."""
+
+ audio_manager.start(
+ input_device_id=kwargs["input_device_id"],
+ output_device_id=kwargs["output_device_id"],
+ output_monitor_id=kwargs["output_monitor_id"],
+ exclusive_mode=kwargs["exclusive_mode"],
+ asio_input_channel=kwargs["input_asio_channels"],
+ asio_output_channel=kwargs["output_asio_channels"],
+ asio_output_monitor_channel=kwargs["monitor_asio_channels"],
+ read_chunk_size=kwargs["chunk_size"],
+ input_audio_sample_rate=kwargs["input_audio_sample_rate"],
+ output_audio_sample_rate=kwargs["output_audio_sample_rate"],
+ output_monitor_sample_rate=kwargs["monitor_audio_sample_rate"]
+ )
+
+ # Boot up hardware audio streaming devices on process startup
+ audio_manager_start(audio_manager_kwargs)
+ queue_out.put(("INFO", translations["realtime_is_ready"]))
+
+ # Infinite loop handling streaming telemetry and dynamic payload commands
+ while 1:
+ time.sleep(0.1)
+ # Periodically stream performance stats (latency and input amplitude volume metrics) to the UI
+ if hasattr(callbacks, "audio") and hasattr(callbacks.audio, "performance") and hasattr(callbacks.audio, "volume"):
+ queue_out.put(("STATUS", f"{translations['latency']}: {callbacks.audio.performance:.2f} ms | {translations['volume']}: {callbacks.audio.volume:.2f} dB"))
+
+ try:
+ # Check for asynchronous runtime configurations or lifestyle control commands
+ cmd, callbacks_kwargs = config_queue.get_nowait()
+
+ if cmd == "START":
+ callbacks_kwargs, audio_manager_kwargs = callbacks_kwargs
+ # Lazy instantiation if the process was kept alive but components were previously torn down
+ if callbacks is None:
+ callbacks = AudioCallbacks(**callbacks_kwargs)
+ audio_manager = callbacks.audio
+ audio_manager_start(audio_manager_kwargs)
+
+ queue_out.put(("INFO", translations["realtime_is_ready"]))
+ elif cmd == "STOP":
+ if callbacks is not None:
+ # Safely stop physical low-level hardware IO stream interfaces
+ audio_manager.stop()
+
+ # Clean up telemetry metrics references
+ if hasattr(callbacks.audio, "performance"): del callbacks.audio.performance
+ if hasattr(callbacks.audio, "volume"): del callbacks.audio.volume
+
+ # Explicitly break reference trees to release underlying neural pipelines & weights
+ del callbacks.vc.vc_model.pipeline, callbacks.vc.vc_model
+ del audio_manager, callbacks
+
+ # Reset states completely
+ audio_manager = callbacks = None
+
+ # Garbage collect and flush VRAM to prevent memory leaks in shared application state
+ import gc
+ from main.library.utils import clear_gpu_cache
+
+ gc.collect()
+ clear_gpu_cache()
+
+ queue_out.put(("STOP", ""))
+ elif cmd == "UPDATE_CONFIG":
+ if callbacks is None: continue
+
+ # Calculate overlapping structure size windows mapped against the target system sample rate
+ crossfade_frame = int(callbacks_kwargs.get("cross_fade_overlap_size", 0.1) * callbacks.input_sample_rate)
+ extra_frame = int(callbacks_kwargs.get("extra_convert_size", 0.5) * callbacks.input_sample_rate)
+
+ # Trigger memory reallocation on the underlying core if latency window sizes are altered
+ if (
+ callbacks.vc.crossfade_frame != crossfade_frame or
+ callbacks.vc.extra_frame != extra_frame
+ ):
+ # Force-release old arrays to prevent internal calculation leaks before reconfiguration
+ del (
+ callbacks.vc.fade_in_window,
+ callbacks.vc.fade_out_window,
+ callbacks.vc.sola_buffer
+ )
+
+ callbacks.vc.vc_model.realloc(
+ callbacks.vc.block_frame,
+ callbacks.vc.extra_frame,
+ callbacks.vc.crossfade_frame,
+ callbacks.vc.sola_search_frame,
+ )
+ callbacks.vc.generate_strength()
+
+ # Convert the dB floor threshold configuration parameter to standard linear amplitude values
+ callbacks.vc.vc_model.input_sensitivity = 10 ** (callbacks_kwargs.get("silent_threshold", -90) / 20)
+
+ # Voice Activity Detection (VAD) Engine Configuration Block
+ vad_enabled = callbacks_kwargs.get("vad_enabled", True)
+ sensitivity_mode = callbacks_kwargs.get("vad_sensitivity", 3)
+ vad_frame_ms = callbacks_kwargs.get("vad_frame_ms", 30)
+
+ if vad_enabled is False:
+ callbacks.vc.vc_model.vad = None
+ elif vad_enabled and callbacks.vc.vc_model.vad is None:
+ from main.inference.realtime.vad_utils import VADProcessor
+
+ callbacks.vc.vc_model.vad = VADProcessor(
+ sensitivity_mode=sensitivity_mode,
+ sample_rate=callbacks.vc.vc_model.sample_rate,
+ frame_duration_ms=vad_frame_ms
+ )
+
+ if callbacks.vc.vc_model.vad is not None:
+ callbacks.vc.vc_model.vad.vad.set_mode(sensitivity_mode)
+ callbacks.vc.vc_model.vad.frame_length = int(callbacks.vc.vc_model.sample_rate * (vad_frame_ms / 1000.0))
+
+ # Audio Clean Gate (TorchGate Noise Reduction) Block
+ clean_audio = callbacks_kwargs.get("clean_audio", False)
+ clean_strength = callbacks_kwargs.get("clean_strength", 0.5)
+
+ if clean_audio is False:
+ callbacks.vc.vc_model.tg = None
+ elif clean_audio and callbacks.vc.vc_model.tg is None:
+ from main.library.audio.noisereduce import TorchGate
+
+ callbacks.vc.vc_model.tg = (
+ TorchGate(
+ callbacks.vc.vc_model.pipeline.tgt_sr,
+ prop_decrease=clean_strength,
+ ).to(config.device)
+ )
+
+ if callbacks.vc.vc_model.tg is not None:
+ callbacks.vc.vc_model.tg.prop_decrease = clean_strength
+
+ # Pedalboard Effects Post-Processing Block
+ post_process = callbacks_kwargs.get("post_process", False)
+ kwargs = callbacks_kwargs.get("kwargs", {})
+
+ if post_process is False:
+ callbacks.vc.vc_model.board = None
+ callbacks.vc.vc_model.kwargs = None
+ elif post_process and callbacks.vc.vc_model.kwargs != kwargs:
+ new_board = callbacks.vc.vc_model.setup_pedalboard(**kwargs)
+ callbacks.vc.vc_model.board = new_board
+ callbacks.vc.vc_model.kwargs = kwargs.copy()
+
+ # Direct primitive assignment mapping for real-time pipeline hyper-parameters
+ callbacks.audio.f0_up_key = callbacks_kwargs.get("f0_up_key", 0)
+ callbacks.audio.index_rate = callbacks_kwargs.get("index_rate", 0.75)
+ callbacks.audio.protect = callbacks_kwargs.get("protect", 0.5)
+ callbacks.audio.rms_mix_rate = callbacks_kwargs.get("rms_mix_rate", 1)
+
+ # Autotune and pitch suggestion modules adjustments
+ callbacks.audio.f0_autotune = callbacks_kwargs.get("f0_autotune", False)
+ callbacks.audio.f0_autotune_strength = callbacks_kwargs.get("f0_autotune_strength", 1.0)
+ callbacks.audio.proposal_pitch = callbacks_kwargs.get("proposal_pitch", False)
+ callbacks.audio.proposal_pitch_threshold = callbacks_kwargs.get("proposal_pitch_threshold", 155.0)
+
+ # Hardware mixing pipeline digital gain structures
+ callbacks.audio.input_audio_gain = callbacks_kwargs.get("input_audio_gain", 1.0)
+ callbacks.audio.output_audio_gain = callbacks_kwargs.get("output_audio_gain", 1.0)
+ callbacks.audio.monitor_audio_gain = callbacks_kwargs.get("monitor_audio_gain", 1.0)
+
+ # Contextual embedding multi-layer blending structure configs
+ callbacks.audio.embedders_mix = callbacks_kwargs.get("embedders_mix", False)
+ callbacks.audio.embedders_mix_layers = callbacks_kwargs.get("embedders_mix_layers", 9)
+ callbacks.audio.embedders_mix_ratio = callbacks_kwargs.get("embedders_mix_ratio", 0.5)
+
+ callbacks.audio.use_phase_vocoder = callbacks_kwargs.get("use_phase_vocoder", True)
+
+ # Model Weight Reload / Architecture Swap Block
+ noise_scale = callbacks_kwargs.get("noise_scale", 0.35)
+ model_pth = callbacks_kwargs.get("model_path", callbacks.vc.vc_model.model_path)
+ model_pth = os.path.join(configs["weights_path"], model_pth) if not os.path.exists(model_pth) else model_pth
+
+ if model_pth and callbacks.vc.vc_model.model_path != model_pth:
+ import torch
+ import torchaudio.transforms as tat
+
+ callbacks.vc.vc_model.model_path = model_pth
+ callbacks.vc.vc_model.pipeline.vc.setup(model_pth, noise_scale)
+ callbacks.vc.vc_model.pipeline.use_f0 = callbacks.vc.vc_model.pipeline.vc.use_f0
+ callbacks.vc.vc_model.pipeline.tgt_sr = callbacks.vc.vc_model.pipeline.vc.tgt_sr
+ callbacks.vc.vc_model.pipeline.version = callbacks.vc.vc_model.pipeline.vc.version
+
+ # Re-initialize the resampling layer since the target sample rate might have changed
+ callbacks.vc.vc_model.resample_out = tat.Resample(
+ orig_freq=callbacks.vc.vc_model.pipeline.tgt_sr,
+ new_freq=callbacks.vc.vc_model.output_sample_rate,
+ dtype=torch.float32
+ ).to(config.device)
+
+ if clean_audio:
+ from main.library.audio.noisereduce import TorchGate
+
+ callbacks.vc.vc_model.tg = (
+ TorchGate(
+ callbacks.vc.vc_model.pipeline.tgt_sr,
+ prop_decrease=clean_strength,
+ ).to(config.device)
+ )
+
+ # Speaker Identity ID Configuration
+ sid = callbacks_kwargs.get("sid", callbacks.vc.vc_model.pipeline.sid)
+ if callbacks.vc.vc_model.pipeline.sid != sid:
+ import torch
+ callbacks.vc.vc_model.pipeline.torch_sid = torch.tensor(
+ [sid], device=callbacks.vc.vc_model.pipeline.device, dtype=torch.int64
+ )
+
+ # FAISS Index Tracking Path Search Block
+ index_path = callbacks_kwargs.get("index_path", None)
+ if index_path:
+ if callbacks.vc.vc_model.index_path != index_path:
+ from main.library.utils import load_faiss_index
+
+ nprobe = callbacks_kwargs.get("nprobe", 1)
+ # Sanitize paths to handle literal quote configurations securely
+ index, index_reconstruct = load_faiss_index(
+ index_path.strip().strip('"').strip("\n").strip('"').strip().replace("trained", "added"),
+ nprobe=nprobe
+ )
+
+ callbacks.vc.vc_model.pipeline.index = index
+ if callbacks.vc.vc_model.pipeline.index.index is not None: callbacks.vc.vc_model.pipeline.index.index.nprobe = nprobe
+ callbacks.vc.vc_model.pipeline.big_tsr = index_reconstruct
+ callbacks.vc.vc_model.index_path = index_path
+ else:
+ callbacks.vc.vc_model.pipeline.index = None
+ callbacks.vc.vc_model.pipeline.big_tsr = None
+ callbacks.vc.vc_model.index_path = None
+
+ # Pitch Predictor and Feature Embedder Extraction Block
+ f0_method = callbacks_kwargs.get("f0_method", callbacks.vc.vc_model.pipeline.f0_method)
+ predictor_onnx = callbacks_kwargs.get("predictor_onnx", callbacks.vc.vc_model.pipeline.predictor.predictor_onnx)
+ embedders = callbacks_kwargs.get("embedder_model", callbacks.vc.vc_model.embedder_model)
+ embedders_mode = callbacks_kwargs.get("embedders_mode", callbacks.vc.vc_model.embedders_mode)
+ custom_embedders = callbacks_kwargs.get("embedder_model_custom", None)
+ embedder_model = (embedders if embedders != "custom" else custom_embedders)
+
+ # Ensure files and parameters match required asset types
+ from main.library.utils import check_assets
+ check_assets(f0_method, embedders, predictor_onnx, embedders_mode)
+
+ # Hot-swap the pitch predictor backend structures if options are updated
+ if (
+ callbacks.vc.vc_model.pipeline.vc.use_f0 and (
+ callbacks.vc.vc_model.pipeline.f0_method != f0_method or
+ callbacks.vc.vc_model.pipeline.predictor.predictor_onnx != predictor_onnx
+ )
+ ):
+ old_predictor = callbacks.vc.vc_model.pipeline.predictor
+ del old_predictor.pw, old_predictor.fcpe, old_predictor.djcm, old_predictor.penn, old_predictor.pesto, old_predictor.swift, old_predictor.rmvpe, old_predictor.crepe, old_predictor.mangio_penn, old_predictor.mangio_crepe
+ del old_predictor
+
+ callbacks.vc.vc_model.pipeline.predictor = callbacks.vc.vc_model.pipeline.setup_predictor(PIPELINE_SAMPLE_RATE, callbacks_kwargs.get("hop_length", callbacks.vc.vc_model.pipeline.predictor.hop_length), predictor_onnx)
+ callbacks.vc.vc_model.pipeline.f0_method = f0_method
+ callbacks.vc.vc_model.pipeline.predictor.predictor_onnx = predictor_onnx
+
+ # Hot-swap Content Vec or Hubert feature extractors if modified
+ if embedder_model:
+ if (
+ callbacks.vc.vc_model.embedder_model != embedder_model or
+ callbacks.vc.vc_model.embedders_mode != embedders_mode
+ ):
+ old_embedder = callbacks.vc.vc_model.pipeline.embedder
+ del old_embedder
+
+ callbacks.vc.vc_model.pipeline.embedder = callbacks.vc.vc_model.pipeline.setup_embedder(embedder_model, embedders_mode)
+ callbacks.vc.vc_model.embedder_model = embedder_model
+ callbacks.vc.vc_model.embedders_mode = embedders_mode
+
+ if (
+ callbacks.vc.vc_model.pipeline.vc.architecture == "SVC" and
+ callbacks.vc.vc_model.pipeline.vc.net_g.noise_scale != noise_scale
+ ):
+ callbacks.vc.vc_model.pipeline.vc.net_g.noise_scale = noise_scale
+
+ # Direct Disk Sound Recording Pipeline Controller
+ record_audio = callbacks_kwargs.get("record_audio", False)
+ if callbacks.vc.record_audio != record_audio:
+ callbacks.vc.record_audio = record_audio
+ callbacks.vc.record_audio_path = callbacks_kwargs.get("record_audio_path", None)
+ callbacks.vc.export_format = callbacks_kwargs.get("export_format", "wav")
+ callbacks.vc.setup_soundfile_record()
+ except queue.Empty:
+ pass
+ except Exception as e:
+ import traceback
+
+ logger.error(e)
+ logger.debug(traceback.format_exc())
+
+ queue_out.put(("ERROR", translations["realtime_has_stop_with_error"]))
+
+def realtime_start(
+ monitor,
+ exclusive_mode,
+ vad_enabled,
+ input_audio_device,
+ output_audio_device,
+ monitor_output_device,
+ input_audio_gain,
+ output_audio_gain,
+ monitor_audio_gain,
+ input_asio_channels,
+ output_asio_channels,
+ monitor_asio_channels,
+ input_audio_sample_rate,
+ output_audio_sample_rate,
+ monitor_audio_sample_rate,
+ chunk_size,
+ pitch,
+ model_pth,
+ model_index,
+ index_strength,
+ predictor_onnx,
+ f0_method,
+ hop_length,
+ embed_mode,
+ embedders,
+ custom_embedders,
+ f0_autotune,
+ proposal_pitch,
+ f0_autotune_strength,
+ proposal_pitch_threshold,
+ rms_mix_rate,
+ protect,
+ filter_radius,
+ silent_threshold,
+ extra_convert_size,
+ cross_fade_overlap_size,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ post_process,
+ sid,
+ noise_scale,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder,
+ chorus,
+ distortion,
+ reverb,
+ pitch_shift,
+ delay,
+ compressor,
+ limiter,
+ gain,
+ bitcrush,
+ clipping,
+ phaser,
+ chorus_depth,
+ chorus_rate,
+ chorus_mix,
+ chorus_center_delay,
+ chorus_feedback,
+ distortion_gain,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_gain,
+ reverb_dry_gain,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_seconds,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold,
+ compressor_ratio,
+ compressor_attack,
+ compressor_release,
+ limiter_threshold,
+ limiter_release_time,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix,
+ nprobe = 1
+):
+ """
+ Prepares configurations, sanitizes input components, and spawns the real-time worker process.
+
+ This acts as a UI generator that sends system state changes up to the interface layer,
+ instantiates pipeline dictionary blocks, and coordinates cross-process tracking.
+ """
+
+ global running, realtime_process, ui_queue, config_queue, callbacks_kwargs
+ running = True
+
+ gr_info(translations["start_realtime"])
+
+ # Configure interface visual element states
+ _interactive_false = interactive_false.copy()
+ _interactive_false["value"] = translations["stop_realtime_button"]
+
+ yield (
+ translations["start_realtime"],
+ interactive_false,
+ _interactive_false
+ )
+
+ # Audio IO hardware validation guardrails
+ if not input_audio_device or not output_audio_device:
+ gr_warning(translations["provide_audio_device"])
+
+ yield (
+ translations["provide_audio_device"],
+ interactive_true,
+ interactive_false
+ )
+ return
+
+ if monitor and not monitor_output_device:
+ gr_warning(translations["provide_monitor_device"])
+
+ yield (
+ translations["provide_monitor_device"],
+ interactive_true,
+ interactive_false
+ )
+ return
+
+ # Check and sanitize voice cloning target neural model path locations
+ model_pth = os.path.join(configs["weights_path"], model_pth) if not os.path.exists(model_pth) else model_pth
+ embedder_model = (embedders if embedders != "custom" else custom_embedders)
+
+ if (
+ not model_pth or
+ not os.path.exists(model_pth) or
+ os.path.isdir(model_pth) or
+ not model_pth.endswith((".pth", ".onnx"))
+ ):
+ gr_warning(translations["provide_model"])
+
+ yield (
+ translations["provide_model"],
+ interactive_true,
+ interactive_false
+ )
+ return
+
+ # Audio host API state management context switching for ASIO support
+ input_is_asio = "ASIO" in input_audio_device
+ output_is_asio = "ASIO" in output_audio_device
+ monitor_is_asio = "ASIO" in monitor_output_device
+
+ if input_is_asio or output_is_asio or monitor_is_asio:
+ import main.library.audio.sounddevice as sd
+
+ sd.terminate()
+ sd.initialize()
+
+ import main.app.core.ui as ui
+
+ # Map selected hardware device strings back to direct OS index values
+ input_device_id = ui.input_channels_map[input_audio_device][0]
+ output_device_id = ui.output_channels_map[output_audio_device][0]
+ output_monitor_id = ui.output_channels_map[monitor_output_device][0] if monitor else None
+ # Rescale percentages from sliders down to raw linear gain metrics
+ input_audio_gain /= 100.0
+ output_audio_gain /= 100.0
+ monitor_audio_gain /= 100.0
+
+ # Calculate actual process block sizes based on window ms metrics against incoming sample rates
+ chunk_size = int(chunk_size * input_audio_sample_rate / 1000 / 128)
+
+ # Master structural pack for Voice Changer (VC) Callback engine configurations
+ callbacks_kwargs = {
+ "pass_through": False,
+ "read_chunk_size": chunk_size,
+ "cross_fade_overlap_size": cross_fade_overlap_size,
+ "input_sample_rate": input_audio_sample_rate,
+ "output_sample_rate": input_audio_sample_rate,
+ "extra_convert_size": extra_convert_size,
+ "model_path": model_pth,
+ "index_path": model_index,
+ "f0_method": f0_method,
+ "predictor_onnx": predictor_onnx,
+ "embedder_model": embedder_model,
+ "embedders_mode": embed_mode,
+ "sample_rate": PIPELINE_SAMPLE_RATE,
+ "hop_length": hop_length,
+ "silent_threshold": silent_threshold,
+ "f0_up_key": pitch,
+ "index_rate": index_strength,
+ "protect": protect,
+ "filter_radius": filter_radius,
+ "rms_mix_rate": rms_mix_rate,
+ "f0_autotune": f0_autotune,
+ "f0_autotune_strength": f0_autotune_strength,
+ "proposal_pitch": proposal_pitch,
+ "proposal_pitch_threshold": proposal_pitch_threshold,
+ "input_audio_gain": input_audio_gain,
+ "output_audio_gain": output_audio_gain,
+ "monitor_audio_gain": monitor_audio_gain,
+ "monitor": monitor,
+ "vad_enabled": vad_enabled,
+ "vad_sensitivity": vad_sensitivity,
+ "vad_frame_ms": vad_frame_ms,
+ "clean_audio": clean_audio,
+ "clean_strength": clean_strength,
+ "post_process": post_process,
+ "sid": sid,
+ "noise_scale": noise_scale,
+ "nprobe": nprobe,
+ "embedders_mix": embedders_mix,
+ "embedders_mix_layers": embedders_mix_layers,
+ "embedders_mix_ratio": embedders_mix_ratio,
+ "use_phase_vocoder": use_phase_vocoder,
+ "record_audio": False,
+ "record_audio_path": "",
+ "export_format": "wav",
+ "kwargs": {
+ "chorus": chorus,
+ "distortion": distortion,
+ "reverb": reverb,
+ "pitch_shift": pitch_shift,
+ "delay": delay,
+ "compressor": compressor,
+ "limiter": limiter,
+ "gain": gain,
+ "bitcrush": bitcrush,
+ "clipping": clipping,
+ "phaser": phaser,
+ "chorus_depth": chorus_depth,
+ "chorus_rate": chorus_rate,
+ "chorus_mix": chorus_mix,
+ "chorus_delay": chorus_center_delay,
+ "chorus_feedback": chorus_feedback,
+ "distortion_gain": distortion_gain,
+ "reverb_room_size": reverb_room_size,
+ "reverb_damping": reverb_damping,
+ "reverb_wet_level": reverb_wet_gain,
+ "reverb_dry_level": reverb_dry_gain,
+ "reverb_width": reverb_width,
+ "reverb_freeze_mode": reverb_freeze_mode,
+ "pitch_shift_semitones": pitch_shift_semitones,
+ "delay_seconds": delay_seconds,
+ "delay_feedback": delay_feedback,
+ "delay_mix": delay_mix,
+ "compressor_threshold": compressor_threshold,
+ "compressor_ratio": compressor_ratio,
+ "compressor_attack": compressor_attack,
+ "compressor_release": compressor_release,
+ "limiter_threshold": limiter_threshold,
+ "limiter_release": limiter_release_time,
+ "gain_db": gain_db,
+ "bitcrush_bit_depth": bitcrush_bit_depth,
+ "clipping_threshold": clipping_threshold,
+ "phaser_rate_hz": phaser_rate_hz,
+ "phaser_depth": phaser_depth,
+ "phaser_centre_frequency_hz": phaser_centre_frequency_hz,
+ "phaser_feedback": phaser_feedback,
+ "phaser_mix": phaser_mix
+ }
+ }
+
+ # Master structural pack for the physical low-level host Soundcard device configuration
+ audio_manager_kwargs = {
+ "input_device_id": input_device_id,
+ "output_device_id": output_device_id,
+ "output_monitor_id": output_monitor_id,
+ "exclusive_mode": exclusive_mode,
+ "input_asio_channels": input_asio_channels,
+ "output_asio_channels": output_asio_channels,
+ "monitor_asio_channels": monitor_asio_channels,
+ "chunk_size": chunk_size,
+ "input_audio_sample_rate": input_audio_sample_rate,
+ "output_audio_sample_rate": output_audio_sample_rate,
+ "monitor_audio_sample_rate": monitor_audio_sample_rate
+ }
+
+ # Force using "spawn" multi-processing context to isolate variables securely across platforms (Windows/Linux)
+ ctx = mp.get_context("spawn")
+ if ui_queue is None or config_queue is None:
+ ui_queue = ctx.Queue()
+ config_queue = ctx.Queue()
+
+ # Flush stale artifacts out of the UI messaging queue
+ while not ui_queue.empty():
+ try:
+ ui_queue.get_nowait()
+ except queue.Empty:
+ break
+
+ # Initialize background process structures or hot-reload via state queues if already active
+ if realtime_process is None:
+ realtime_process = ctx.Process(
+ target=realtime_worker,
+ args=(ui_queue, config_queue, callbacks_kwargs, audio_manager_kwargs)
+ )
+ realtime_process.daemon = True
+ realtime_process.start()
+ elif realtime_process.is_alive():
+ config_queue.put(("START", (callbacks_kwargs, audio_manager_kwargs)))
+
+ # Main orchestration messaging polling loop between UI context and background workers
+ while running and realtime_process.is_alive():
+ try:
+ msg_type, data = ui_queue.get(timeout=0.1)
+
+ if msg_type == "STATUS": yield (data, interactive_false, interactive_true)
+ elif msg_type == "ERROR":
+ realtime_stop(None)
+
+ yield (translations["realtime_has_stop_with_error"], interactive_true, interactive_false)
+ return
+ elif msg_type == "INFO": gr_info(data)
+ elif msg_type == "STOP": break
+ except queue.Empty:
+ continue
+
+ return (
+ translations["realtime_has_stop"],
+ interactive_true,
+ interactive_false
+ )
+
+def change_config(value, key, if_kwargs=False):
+ """
+ Hot-swaps tracking parameter values inside the background worker configurations at runtime.
+
+ Args:
+ value (Any): Target configuration value structure to inject.
+ key (str): Configuration key matching parameter configurations.
+ if_kwargs (bool): Target dictionary layout router. If True, maps into Pedalboard effects.
+ """
+
+ global callbacks_kwargs
+
+ if running and realtime_process is not None and realtime_process.is_alive():
+ if if_kwargs:
+ callbacks_kwargs["kwargs"][key] = value
+ else:
+ callbacks_kwargs[key] = value
+
+ # Push mutations directly to the background configuration channel
+ config_queue.put(("UPDATE_CONFIG", callbacks_kwargs))
+
+def realtime_stop(stop_realtime):
+ """
+ Dispatches termination signals or forcefully kills running streaming child processes.
+ """
+
+ global running, realtime_process, ui_queue, config_queue
+
+ if realtime_process is not None and realtime_process.is_alive():
+ running = False
+
+ if stop_realtime == translations["stop_realtime_button"]:
+ gr_info(translations["stop_realtime"])
+
+ # Pause real-time processing
+ config_queue.put(("STOP", callbacks_kwargs))
+ gr_info(translations["realtime_has_stop"])
+ time.sleep(1)
+
+ _interactive_true = interactive_true.copy()
+ _interactive_true["value"] = translations["terminate"]
+
+ return (
+ translations["realtime_has_stop"],
+ interactive_true,
+ _interactive_true
+ )
+ else:
+ # Interrupt and terminate the real-time worker to completely free up VRAM.
+
+ realtime_process.terminate()
+ realtime_process.join()
+ realtime_process = ui_queue = config_queue = None
+
+ _interactive_false = interactive_false.copy()
+ _interactive_false["value"] = translations["stop_realtime_button"]
+
+ gr_info(translations["realtime_has_terminate"])
+
+ return (
+ translations["realtime_has_terminate"],
+ interactive_true,
+ _interactive_false
+ )
+ else:
+ gr_warning(translations["realtime_not_found"])
+
+ _interactive_false = interactive_false.copy()
+ _interactive_false["value"] = translations["stop_realtime_button"]
+
+ return (
+ translations["realtime_not_found"],
+ interactive_true,
+ _interactive_false
+ )
+
+def soundfile_record_audio(
+ record_button,
+ record_audio_path = None,
+ export_format = "wav"
+):
+ """
+ Toggle the pipeline's recording function.
+ """
+
+ global callbacks_kwargs
+
+ if running and realtime_process.is_alive():
+ if record_button == translations["start_record"]:
+ gr_info(translations["starting_record"])
+
+ if not record_audio_path: # Setup fallbacks path mapping if explicit pathing parameters are absent
+ record_audio_path = os.path.join(configs["audios_path"], "record_audio.wav")
+
+ # Enable audio recording flags
+ callbacks_kwargs["record_audio"] = True
+ callbacks_kwargs["record_audio_path"] = record_audio_path
+ callbacks_kwargs["export_format"] = export_format
+
+ # Push configuration updates across the multi-processing IPC pipe
+ config_queue.put(("UPDATE_CONFIG", callbacks_kwargs))
+ return translations["stop_record"], None
+ else:
+ gr_info(translations["stopping_record"])
+
+ # Unset recording structures
+ callbacks_kwargs["record_audio"] = False
+ callbacks_kwargs["record_audio_path"] = None
+ callbacks_kwargs["export_format"] = None
+
+ config_queue.put(("UPDATE_CONFIG", callbacks_kwargs))
+ return translations["start_record"], record_audio_path
+
+ gr_warning(translations["realtime_not_found"])
+ return translations["start_record"], None
\ No newline at end of file
diff --git a/main/app/core/restart.py b/main/app/core/restart.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ef1c28451c5d9a802241fe29b821b6931562eea
--- /dev/null
+++ b/main/app/core/restart.py
@@ -0,0 +1,71 @@
+import os
+import sys
+import json
+import platform
+import subprocess
+
+sys.path.append(os.getcwd())
+
+from main.app.core.ui import gr_info, gpu_infos
+from main.app.variables import python, translations, configs_json
+
+def restart_app(app):
+ """
+ Terminates the current application instance and launches a new one.
+
+ This function notifies the user via the UI, clears the terminal screen
+ based on the operating system, closes the existing application interface,
+ and spawns a new process running the main application script while
+ preserving initial command-line arguments (excluding '--open').
+ """
+
+ # Notify the user via UI that the application is restarting
+ gr_info(translations["restart"])
+ # Clear the terminal screen (supports both Windows and Unix-like systems)
+ os.system("cls" if platform.system() == "Windows" else "clear")
+
+ # Close the current UI/App instance safely
+ app.close()
+
+ # Construct and run the command to restart the application
+ # It excludes the '--open' flag to prevent opening a new browser tab on restart
+ subprocess.run([
+ python,
+ os.path.join("main", "app", "app.py")
+ ] + [arg for arg in sys.argv[1:] if arg != "--open"])
+
+def restart(lang, theme, font, gpu, app):
+ """
+ Updates the configuration file with new settings and reboots the application.
+
+ This function reads the existing JSON configuration, checks if the incoming
+ parameters (language, theme, font, and GPU selection) differ from the stored
+ values, updates them if necessary, writes the changes back to the file, and
+ triggers the application restart sequence.
+
+ Args:
+ lang (str): The newly selected language code.
+ theme (str): The newly selected UI theme name.
+ font (str): The newly selected UI font name.
+ gpu (str): The string representation or name of the selected GPU.
+ app: The current application instance to be passed to `restart_app`.
+ """
+
+ # Load the current configurations from the JSON file
+ configs = json.load(open(configs_json, "r"))
+
+ # Update settings only if they have changed from the current configuration
+ if lang != configs["language"]: configs["language"] = lang
+ if theme != configs["theme"]: configs["theme"] = theme
+ if font != configs["font"]: configs["font"] = font
+
+ # Find the index of the selected GPU from the available GPU list
+ gpu_idx = int(gpu_infos.index(gpu))
+ if gpu_idx != configs["gpu_idx"]: configs["gpu_idx"] = gpu_idx
+
+ # Save the updated configurations back to the JSON file with clean formatting
+ with open(configs_json, "w") as f:
+ json.dump(configs, f, indent=4)
+
+ # Invoke the application restart routine
+ restart_app(app)
\ No newline at end of file
diff --git a/main/app/core/separate.py b/main/app/core/separate.py
new file mode 100644
index 0000000000000000000000000000000000000000..a160eb3ce1e2ba6a59f2d21ce4dfaba4e7725943
--- /dev/null
+++ b/main/app/core/separate.py
@@ -0,0 +1,122 @@
+import os
+import sys
+import subprocess
+
+sys.path.append(os.getcwd())
+
+from main.app.core.ui import gr_info, gr_warning
+from main.app.variables import python, translations, configs
+
+def separate_music(
+ input_path,
+ output_dirs,
+ export_format,
+ model_name,
+ karaoke_model,
+ reverb_model,
+ denoise_model,
+ sample_rate,
+ shifts,
+ batch_size,
+ overlap,
+ aggression,
+ hop_length,
+ window_size,
+ segments_size,
+ post_process_threshold,
+ enable_tta,
+ enable_denoise,
+ high_end_process,
+ enable_post_process,
+ separate_backing,
+ separate_reverb
+):
+ """
+ Executes the music separation process by invoking an external source separation script.
+
+ This function performs initial validation on input file paths and output directories,
+ triggers an asynchronous or blocking subprocess execution with comprehensive processing
+ parameters (such as models, audio configurations, and processing toggles), and finally
+ maps out the predicted file paths of the generated audio stems (Vocals, Instruments, etc.).
+
+ Returns:
+ list: A list containing exactly 4 elements representing paths to output audio files:
+ [0] -> Original Vocals path (with or without reverb).
+ [1] -> Isolated Instruments track path.
+ [2] -> Main Vocals path (or None if separate_backing is disabled).
+ [3] -> Backing Vocals path (or None if separate_backing is disabled).
+ Returns [None, None, None, None] if any path validation step fails.
+ """
+
+ # Normalize the output directory by resolving its parent directory context if applicable
+ output_dirs = os.path.dirname(output_dirs) or output_dirs
+
+ # Validate input: must exist, must not be empty, and cannot be a directory itself
+ if not input_path or not os.path.exists(input_path) or os.path.isdir(input_path):
+ gr_warning(translations["input_not_valid"])
+ return [None]*4
+
+ # Validate destination directory presence
+ if not os.path.exists(output_dirs):
+ gr_warning(translations["output_not_valid"])
+ return [None]*4
+
+ # Redundant backup check ensuring the output tree structure exists safely
+ if not os.path.exists(output_dirs): os.makedirs(output_dirs)
+ # Send an initial UI status message indicating the processing engine has started
+ gr_info(translations["start_separator_music"])
+
+ # Build argument array and execute the core separation script via subprocess
+ subprocess.run([
+ python,
+ configs["separate_path"],
+ "--input_path", input_path,
+ "--output_dirs", output_dirs,
+ "--export_format", export_format,
+ "--model_name", model_name,
+ "--karaoke_model", karaoke_model,
+ "--reverb_model", reverb_model,
+ "--denoise_model", denoise_model,
+ "--sample_rate", str(sample_rate),
+ "--shifts", str(shifts),
+ "--batch_size", str(batch_size),
+ "--overlap", str(overlap),
+ "--aggression", str(aggression),
+ "--hop_length", str(hop_length),
+ "--window_size", str(window_size),
+ "--segments_size", str(segments_size),
+ "--post_process_threshold", str(post_process_threshold),
+ "--enable_tta", str(enable_tta),
+ "--enable_denoise", str(enable_denoise),
+ "--high_end_process", str(high_end_process),
+ "--enable_post_process", str(enable_post_process),
+ "--separate_backing", str(separate_backing),
+ "--separate_reverb", str(separate_reverb),
+ ])
+
+ # Notify user via UI upon successful execution of the separation process
+ gr_info(translations["success"])
+
+ # Extract the original filename without extension to map the target subdirectory structure
+ filename, _ = os.path.splitext(os.path.basename(input_path))
+ output_dirs = os.path.join(output_dirs, filename)
+
+ # Re-verify if input is a file before mapping dynamic strings into the output structure
+ return [
+ os.path.join(
+ output_dirs,
+ f"Original_Vocals_No_Reverb.{export_format}" if separate_reverb else f"Original_Vocals.{export_format}"
+ ),
+ os.path.join(
+ output_dirs,
+ f"Instruments.{export_format}"
+ ),
+ os.path.join(
+ output_dirs,
+ f"Main_Vocals_No_Reverb.{export_format}" if separate_reverb else f"Main_Vocals.{export_format}"
+ ) if separate_backing else None,
+ os.path.join(
+ output_dirs,
+ f"Backing_Vocals.{export_format}"
+ ) if separate_backing else None
+ ] if os.path.isfile(input_path) else [None]*4
\ No newline at end of file
diff --git a/main/app/core/training.py b/main/app/core/training.py
new file mode 100644
index 0000000000000000000000000000000000000000..84a7b539559ded591777526cec7cc1d934122fcf
--- /dev/null
+++ b/main/app/core/training.py
@@ -0,0 +1,681 @@
+import os
+import sys
+import time
+import shutil
+import codecs
+import datetime
+import threading
+import subprocess
+
+sys.path.append(os.getcwd())
+
+from main.tools import huggingface
+from main.app.core.ui import gr_info, gr_warning
+from main.app.variables import python, translations, configs, file_types, logger
+
+def if_done(done, p):
+ """
+ Monitors a subprocess and updates a shared status flag upon completion.
+ """
+
+ while 1:
+ # Check if the process is still running (poll returns None if alive)
+ if p.poll() is None: time.sleep(0.5)
+ else: break
+
+ # Mark the process as completed
+ done[0] = True
+
+def log_read(done, name, start_time):
+ """
+ Generator that continuously yields new log entries matching specific keywords.
+
+ Reads from the global 'app.log' file periodically and filters entries by
+ timestamp and process relevance.
+
+ Yields:
+ str: Concatenated log lines generated since the start_time.
+ """
+
+ log_file = os.path.join(configs["logs_path"], "app.log")
+
+ def read_logs():
+ """Helper to scan the log file and filter rows based on conditions."""
+
+ logs = []
+ # Open log safely with UTF-8 encoding
+ with open(log_file, "r", encoding="utf-8") as f:
+ for line in f:
+ try:
+ # Skip noise: ignore DEBUG logs, lines without matching keywords, or empty lines
+ if ("DEBUG" in line or not any(n in line for n in name) or line.strip() == ""): continue
+
+ # Parse log timestamp (assumes format: "YYYY-MM-DD HH:MM:SS.ffffff | LOG_MESSAGE")
+ timestamp = datetime.datetime.strptime(line.split("|")[0].strip(), "%Y-%m-%d %H:%M:%S.%f")
+
+ # Keep logs generated during or after the current operation
+ if timestamp >= start_time: logs.append(line)
+ except ValueError:
+ # Gracefully skip lines that do not match the expected timestamp layout
+ continue
+
+ return "".join(logs)
+
+ # Main pooling loop for streaming logs
+ while 1:
+ yield read_logs()
+
+ time.sleep(1)
+ # Break out if the subprocess has finished tracking
+ if done[0]:
+ break
+
+ # Yield final logs one last time to capture trailing entries
+ yield read_logs()
+
+def create_dataset(
+ input_data,
+ output_dirs,
+ skip_seconds,
+ skip_start_audios,
+ skip_end_audios,
+ separate,
+ model_name,
+ reverb_model,
+ denoise_model,
+ sample_rate,
+ shifts,
+ batch_size,
+ overlap,
+ aggression,
+ hop_length,
+ window_size,
+ segments_size,
+ post_process_threshold,
+ enable_tta,
+ enable_denoise,
+ high_end_process,
+ enable_post_process,
+ separate_reverb,
+ clean_dataset,
+ clean_strength
+):
+ """
+ Executes the dataset creation process as an asynchronous external script.
+ Launches a dedicated subprocess to structure audio data, remove silent chunks,
+ isolate vocals/instruments, and clean up audio segments.
+
+ Yields:
+ str: Live execution console text pulled from application logs.
+ """
+
+ # Notify user interface that execution is starting
+ gr_info(translations["start_create_dataset"])
+ start_time = datetime.datetime.now()
+
+ # Launch script as an independent detached background task
+ p = subprocess.Popen([
+ python,
+ configs["create_dataset_path"],
+ "--input_data", input_data,
+ "--output_dirs", output_dirs,
+ "--skip_seconds", str(skip_seconds),
+ "--skip_start_audios", str(skip_start_audios),
+ "--skip_end_audios", str(skip_end_audios),
+ "--separate", str(separate),
+ "--model_name", model_name,
+ "--reverb_model", reverb_model,
+ "--denoise_model", denoise_model,
+ "--sample_rate", str(sample_rate),
+ "--shifts", str(shifts),
+ "--batch_size", str(batch_size),
+ "--overlap", str(overlap),
+ "--aggression", str(aggression),
+ "--hop_length", str(hop_length),
+ "--window_size", str(window_size),
+ "--segments_size", str(segments_size),
+ "--post_process_threshold", str(post_process_threshold),
+ "--enable_tta", str(enable_tta),
+ "--enable_denoise", str(enable_denoise),
+ "--high_end_process", str(high_end_process),
+ "--enable_post_process", str(enable_post_process),
+ "--separate_reverb", str(separate_reverb),
+ "--clean_dataset", str(clean_dataset),
+ "--clean_strength", str(clean_strength),
+ ])
+
+ # Shared thread controller status
+ done = [False]
+ # Spawn thread to watch over process life cycle
+ threading.Thread(target=if_done, args=(done, p)).start()
+
+ # Stream relevant log channels to the UI component
+ for log in log_read(done, ["create_dataset", "separate_music", "separator"], start_time):
+ yield log
+
+def create_reference(
+ audio_path,
+ reference_name,
+ pitch_guidance,
+ version,
+ embedder_model,
+ embedders_mode,
+ f0_method,
+ predictor_onnx,
+ f0_up_key,
+ filter_radius,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ alpha=0.5,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5
+):
+ """
+ Generates acoustic feature reference vectors from a target audio clip.
+
+ Yields:
+ str: Continuous execution stdout parsed through system logger pipelines.
+ """
+
+ gr_info(translations["start_create_reference"])
+ start_time = datetime.datetime.now()
+ # Fire subprocess executing the reference generator backend
+ p = subprocess.Popen([
+ python,
+ configs["create_reference_path"],
+ "--audio_path", audio_path,
+ "--reference_name", reference_name,
+ "--pitch_guidance", str(pitch_guidance),
+ "--version", version,
+ "--embedder_model", embedder_model,
+ "--embedders_mode", embedders_mode,
+ "--f0_method", f0_method,
+ "--predictor_onnx", str(predictor_onnx),
+ "--f0_up_key", str(f0_up_key),
+ "--filter_radius", str(filter_radius),
+ "--f0_autotune", str(f0_autotune),
+ "--f0_autotune_strength", str(f0_autotune_strength),
+ "--proposal_pitch", str(proposal_pitch),
+ "--proposal_pitch_threshold", str(proposal_pitch_threshold),
+ "--alpha", str(alpha),
+ "--embedders_mix", str(embedders_mix),
+ "--embedders_mix_layers", str(embedders_mix_layers),
+ "--embedders_mix_ratio", str(embedders_mix_ratio)
+ ])
+
+ done = [False]
+
+ threading.Thread(target=if_done, args=(done, p)).start()
+
+ for log in log_read(done, ["create_reference"], start_time):
+ yield log
+
+def preprocess(
+ model_name,
+ sample_rate,
+ cpu_core,
+ cut_preprocess,
+ process_effects,
+ dataset,
+ clean_dataset,
+ clean_strength,
+ chunk_len=3.0,
+ overlap_len=0.3,
+ normalization_mode="none",
+ architecture="RVC"
+):
+ """
+ Execute the training data preprocessing process before performing extraction.
+
+ Yields:
+ str: Streaming update content blocks generated from standard output logs.
+ """
+
+ # Strip the string suffix ('k') and convert shorthand (e.g., "40k") to integer frequency (e.g., 40000)
+ sr = int(float(sample_rate.rstrip("k")) * 1000)
+ # Validate that workspace naming context is not empty
+ if not model_name: return gr_warning(translations["provide_name"])
+
+ # Optional integrity verification checking if the source dataset contains valid non-empty files
+ if configs.get("check_data", False):
+ try:
+ found = False
+ if os.path.exists(dataset):
+ for root, _, files in os.walk(dataset):
+ for f in files:
+ # Identify match against registered format tables and confirm file size > 0 bytes
+ if f.lower().endswith(tuple(file_types)) and os.path.getsize(os.path.join(root, f)) > 0:
+ found = True
+ break
+
+ if found: break
+ if not found: return gr_warning(translations["not_found_data"])
+ except Exception:
+ return gr_warning(translations["not_found_data"])
+
+ start_time = datetime.datetime.now()
+ model_dir = os.path.join(configs["logs_path"], model_name)
+ # Flush existing artifact folders to enforce clean workspace operations
+ if os.path.exists(model_dir): shutil.rmtree(model_dir, ignore_errors=True)
+
+ # Initialize backend subprocess executing the audio slicing pipeline script
+ p = subprocess.Popen([
+ python,
+ configs["preprocess_path"],
+ "--model_name", model_name,
+ "--dataset_path", dataset,
+ "--sample_rate", str(sr),
+ "--cpu_cores", str(cpu_core),
+ "--cut_preprocess", cut_preprocess,
+ "--process_effects", str(process_effects),
+ "--clean_dataset", str(clean_dataset),
+ "--clean_strength", str(clean_strength),
+ "--chunk_len", str(chunk_len),
+ "--overlap_len", str(overlap_len),
+ "--normalization_mode", normalization_mode,
+ "--architecture", architecture
+ ])
+
+ done = [False]
+
+ threading.Thread(target=if_done, args=(done, p)).start()
+ # Pre-create directory layout to hold output data streams safely
+ os.makedirs(model_dir, exist_ok=True)
+
+ for log in log_read(done, ["preprocess"], start_time):
+ yield log
+
+def extract(
+ model_name,
+ version,
+ method,
+ pitch_guidance,
+ hop_length,
+ cpu_cores,
+ gpu,
+ sample_rate,
+ embedders,
+ custom_embedders,
+ predictor_onnx,
+ embedders_mode,
+ f0_autotune,
+ f0_autotune_strength,
+ hybrid_method,
+ alpha=0.5,
+ include_mutes=2,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5,
+ architecture = "RVC"
+):
+ """
+ Extract features and pitch from the previously preprocessed data.
+
+ Yields:
+ str: Continuous execution stdout strings generated during processing runtime.
+ """
+
+ # Choose hybrid method if core choice is flagged as 'hybrid', else map native selection
+ # Do the same validation check for custom embedding models overrides
+ f0_method, embedder_model = (
+ method if method != "hybrid" else hybrid_method,
+ embedders if embedders != "custom" else custom_embedders
+ )
+
+ sr = int(float(sample_rate.rstrip("k")) * 1000)
+
+ if not model_name: return gr_warning(translations["provide_name"])
+ model_dir = os.path.join(configs["logs_path"], model_name)
+
+ # Workspace directory integrity verification before allowing extraction tasks
+ if configs.get("check_data", False):
+ try:
+ # Confirm preprocess operations successfully output target slicing maps
+ if not any(
+ os.path.isfile(os.path.join(model_dir, "sliced_audios", f))
+ for f in os.listdir(os.path.join(model_dir, "sliced_audios"))
+ ) or not any(
+ os.path.isfile(os.path.join(model_dir, "sliced_audios_16k", f))
+ for f in os.listdir(os.path.join(model_dir, "sliced_audios_16k"))
+ ):
+ return gr_warning(translations["not_found_data_preprocess"])
+ except:
+ return gr_warning(translations["not_found_data_preprocess"])
+
+ start_time = datetime.datetime.now()
+ # Launch feature analysis script as a background process node
+ p = subprocess.Popen([
+ python,
+ configs["extract_path"],
+ "--model_name", model_name,
+ "--rvc_version", version,
+ "--f0_method", f0_method,
+ "--pitch_guidance", str(pitch_guidance),
+ "--hop_length", str(hop_length),
+ "--cpu_cores", str(cpu_cores),
+ "--gpu", str(gpu),
+ "--sample_rate", str(sr),
+ "--embedder_model", embedder_model,
+ "--predictor_onnx", str(predictor_onnx),
+ "--embedders_mode", embedders_mode,
+ "--f0_autotune", str(f0_autotune),
+ "--f0_autotune_strength", str(f0_autotune_strength),
+ "--alpha", str(alpha),
+ "--include_mutes", str(include_mutes),
+ "--embedders_mix", str(embedders_mix),
+ "--embedders_mix_layers", str(embedders_mix_layers),
+ "--embedders_mix_ratio", str(embedders_mix_ratio),
+ "--architecture", architecture
+ ])
+
+ done = [False]
+
+ threading.Thread(target=if_done, args=(done, p)).start()
+ os.makedirs(model_dir, exist_ok=True)
+
+ for log in log_read(done, ["extract", "embedding", "feature"], start_time):
+ yield log
+
+def create_index(
+ model_name,
+ rvc_version,
+ index_algorithm,
+ nprobe=1
+):
+ """
+ Builds a vector search index file from extracted audio embeddings.
+
+ Yields:
+ str: Continuous feedback log data streams from index compilation steps.
+ """
+
+ if not model_name: return gr_warning(translations["provide_name"])
+ model_dir = os.path.join(configs["logs_path"], model_name)
+
+ # Ensure vector source map directories exist and contain valid raw material
+ if configs.get("check_data", False):
+ try:
+ if not any(
+ os.path.isfile(os.path.join(model_dir, f"{rvc_version}_extracted", f))
+ for f in os.listdir(os.path.join(model_dir, f"{rvc_version}_extracted"))
+ ):
+ return gr_warning(translations["not_found_data_extract"])
+ except:
+ return gr_warning(translations["not_found_data_extract"])
+
+ start_time = datetime.datetime.now()
+ # Launch subprocess to index feature vectors
+ p = subprocess.Popen([
+ python,
+ configs["create_index_path"],
+ "--model_name", model_name,
+ "--rvc_version", rvc_version,
+ "--index_algorithm", index_algorithm,
+ "--nprobe", str(nprobe)
+ ])
+
+ done = [False]
+
+ threading.Thread(target=if_done, args=(done, p)).start()
+ os.makedirs(model_dir, exist_ok=True)
+
+ for log in log_read(done, ["create_index"], start_time):
+ yield log
+
+def training(
+ model_name,
+ rvc_version,
+ save_every_epoch,
+ save_only_latest,
+ save_every_weights,
+ total_epoch,
+ sample_rate,
+ batch_size,
+ gpu,
+ pitch_guidance,
+ not_pretrain,
+ custom_pretrained,
+ pretrain_g,
+ pretrain_d,
+ detector,
+ threshold,
+ clean_up,
+ cache,
+ model_author,
+ vocoder,
+ checkpointing,
+ deterministic,
+ benchmark,
+ optimizer,
+ custom_reference=False,
+ reference_name="",
+ multiscale_mel_loss=False,
+ embedders="hubert_base",
+ custom_embedders=None,
+ cosine_annealing_lr=False,
+ architecture="RVC"
+):
+ """
+ Initializes and runs the main model training subprocess.
+ Configures pre-trained models, structures file arguments, downloads checkpoints,
+ and runs the optimization script while tracking process status.
+
+ Yields:
+ str: Segmented slice of the application's trailing execution stdout lines.
+ """
+
+ # SVC architecture always requires pitch guidance enabled
+ if architecture == "SVC": pitch_guidance = True
+
+ sr = int(float(sample_rate.rstrip("k")) * 1000)
+ if not model_name: return gr_warning(translations["provide_name"])
+
+ model_dir = os.path.join(configs["logs_path"], model_name)
+ # Remove stale process ID files before launching a new session
+ if os.path.exists(os.path.join(model_dir, "train_pid.txt")):
+ os.remove(os.path.join(model_dir, "train_pid.txt"))
+
+ # Verify that the extraction directory contains files before starting training
+ if configs.get("check_data", False):
+ try:
+ if not any(
+ os.path.isfile(os.path.join(model_dir, f"{rvc_version}_extracted", f))
+ for f in os.listdir(os.path.join(model_dir, f"{rvc_version}_extracted"))
+ ):
+ return gr_warning(translations["not_found_data_extract"])
+ except:
+ return gr_warning(translations["not_found_data_extract"])
+
+ # Pre-trained base models initialization and download logic
+ if not not_pretrain:
+ if not custom_pretrained:
+ pretrain_dir = (
+ configs["pretrained_v2_path"]
+ if rvc_version == 'v2' else
+ configs["pretrained_v1_path"]
+ )
+
+ # ROT13 obfuscated URL decoding for the HuggingFace pre-trained models repository
+ download_version = codecs.decode(
+ f"uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/cergenvarq_",
+ "rot13"
+ ) + f"{rvc_version}/"
+
+ # Selection matrix mapping pitch guidance + sample rate to specific filenames
+ pretrained_selector = {
+ True: {
+ 24000: ("f0G24k.pth", "f0D24k.pth"),
+ 32000: ("f0G32k.pth", "f0D32k.pth"),
+ 40000: ("f0G40k.pth", "f0D40k.pth"),
+ 44100: ("f0G44k.pth", "f0D44k.pth"),
+ 48000: ("f0G48k.pth", "f0D48k.pth")
+ },
+ False: {
+ 24000: ("G24k.pth", "D24k.pth"),
+ 32000: ("G32k.pth", "D32k.pth"),
+ 40000: ("G40k.pth", "D40k.pth"),
+ 44100: ("G44k.pth", "D44k.pth"),
+ 48000: ("G48k.pth", "D48k.pth")
+ }
+ }
+
+ pg2, pd2 = "", ""
+ pg, pd = pretrained_selector[pitch_guidance][sr]
+
+ # Construct targeted pre-trained model filenames based on architectural parameters
+ if vocoder != 'Default': pg2, pd2 = pg2 + vocoder + "_", pd2 + vocoder + "_"
+ if embedders not in ["hubert_base", "contentvec_base"]: pg2, pd2 = pg2 + embedders + "_", pd2 + embedders + "_"
+ if architecture != "RVC": pg2, pd2 = architecture + "_" + pg2, architecture + "_" + pd2
+
+ pg2, pd2 = pg2 + pg, pd2 + pd
+ logger.debug("PG: " + pg2 + " PD: " + pd2)
+
+ pretrained_G, pretrained_D = (
+ os.path.join(
+ pretrain_dir,
+ pg2
+ ),
+ os.path.join(
+ pretrain_dir,
+ pd2
+ )
+ )
+
+ # Automatically fetch base files from Hugging Face if they are missing locally
+ try:
+ if not os.path.exists(pretrained_G):
+ gr_info(translations["download_pretrained"].format(dg="G", rvc_version=rvc_version))
+ huggingface.HF_download_file(
+ "".join(
+ [
+ download_version,
+ pg2
+ ]
+ ),
+ os.path.join(
+ pretrain_dir,
+ pg2
+ )
+ )
+
+ if not os.path.exists(pretrained_D):
+ gr_info(translations["download_pretrained"].format(dg="D", rvc_version=rvc_version))
+ huggingface.HF_download_file(
+ "".join(
+ [
+ download_version,
+ pd2
+ ]
+ ),
+ os.path.join(
+ pretrain_dir,
+ pd2
+ )
+ )
+ except:
+ gr_warning(translations["not_use_pretrain_error_download"])
+ pretrained_G = pretrained_D = None
+ else:
+ # Handle localized custom pre-trained models definitions
+ if not pretrain_g:
+ return gr_warning(translations["provide_pretrained"].format(dg="G"))
+
+ if not pretrain_d:
+ return gr_warning(translations["provide_pretrained"].format(dg="D"))
+
+ pg2, pd2 = pretrain_g, pretrain_d
+ pretrained_G, pretrained_D = (
+ os.path.join(configs["pretrained_custom_path"], pg2) if not os.path.exists(pg2) else pg2,
+ os.path.join(configs["pretrained_custom_path"], pd2) if not os.path.exists(pd2) else pd2
+ )
+
+ if not os.path.exists(pretrained_G):
+ return gr_warning(translations["not_found_pretrain"].format(dg="G"))
+
+ if not os.path.exists(pretrained_D):
+ return gr_warning(translations["not_found_pretrain"].format(dg="D"))
+ else:
+ pretrained_G = pretrained_D = None
+ gr_warning(translations["not_use_pretrain"])
+
+ # Resolve cross-reference paths if custom referencing mechanisms are toggled
+ if custom_reference:
+ embedder_model = embedders if embedders != "custom" else custom_embedders
+
+ reference_path = os.path.join(
+ configs["reference_path"],
+ "".join([
+ reference_name,
+ "_",
+ rvc_version,
+ "_",
+ embedder_model,
+ "_",
+ str(pitch_guidance)
+ ])
+ )
+
+ if not os.path.exists(reference_path):
+ gr_warning(translations["not_found_reference"])
+
+ custom_reference = False
+ reference_path = None
+ else: reference_path = None
+
+ start_time = datetime.datetime.now()
+ # Launch core neural network training module script
+ p = subprocess.Popen([
+ python,
+ configs["train_path"],
+ "--model_name", model_name,
+ "--rvc_version", rvc_version,
+ "--save_every_epoch", str(save_every_epoch),
+ "--save_only_latest", str(save_only_latest),
+ "--save_every_weights", str(save_every_weights),
+ "--total_epoch", str(total_epoch),
+ "--batch_size", str(batch_size),
+ "--gpu", str(gpu),
+ "--pitch_guidance", str(pitch_guidance),
+ "--overtraining_detector", str(detector),
+ "--overtraining_threshold", str(threshold),
+ "--cleanup", str(clean_up),
+ "--cache_data_in_gpu", str(cache),
+ "--g_pretrained_path", str(pretrained_G),
+ "--d_pretrained_path", str(pretrained_D),
+ "--model_author", model_author,
+ "--vocoder", vocoder,
+ "--checkpointing", str(checkpointing),
+ "--deterministic", str(deterministic),
+ "--benchmark", str(benchmark),
+ "--optimizer", optimizer,
+ "--use_custom_reference", str(custom_reference),
+ "--reference_path", str(reference_path),
+ "--multiscale_mel_loss", str(multiscale_mel_loss),
+ "--use_cosine_annealing_lr", str(cosine_annealing_lr),
+ "--architecture", architecture,
+ "--filelist_path", "None",
+ "--config_save_path", "None",
+ "--spec_dir", "None",
+ "--eval_dir", "None",
+ "--cache_spectrogram", str(True),
+ "--save_the_pid", str(True),
+ "--custom_training", str(False)
+ ])
+
+ done = [False]
+ # Save active Process ID tracking variable values to a text file for external management controls
+ with open(os.path.join(model_dir, "train_pid.txt"), "w") as pid_file:
+ pid_file.write(str(p.pid))
+
+ threading.Thread(target=if_done, args=(done, p)).start()
+ # Capture outputs, formatting long streaming outputs to show only the 50 most recent lines
+ for log in log_read(done, ["train", "synthesizers", "extract_model", "utils"], start_time):
+ lines = log.splitlines()
+
+ if len(lines) > 50:
+ log = "\n".join(lines[-50:])
+
+ yield log
\ No newline at end of file
diff --git a/main/app/core/tts.py b/main/app/core/tts.py
new file mode 100644
index 0000000000000000000000000000000000000000..e28bb47bc5609d6d63db8c0362f1f38bcf64db7b
--- /dev/null
+++ b/main/app/core/tts.py
@@ -0,0 +1,248 @@
+import os
+import sys
+import pysrt
+import codecs
+import asyncio
+import requests
+import tempfile
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations
+from main.app.core.ui import gr_info, gr_warning, gr_error
+
+def synthesize_tts(
+ prompt,
+ voice = "vi-VN-NamMinhNeural",
+ speed = 0,
+ output = "audios/tts.wav",
+ pitch = 0,
+ google = False
+):
+ """
+ Synthesizes text into speech using either Edge-TTS or Google Translate TTS.
+ """
+
+ if not google:
+ # Lazy import to avoid loading edge_tts unnecessarily if Google is selected
+ from edge_tts import Communicate
+
+ # Run the asynchronous edge_tts saving process synchronously
+ asyncio.run(
+ Communicate(
+ text=prompt,
+ voice=voice,
+ # Format rate and pitch strings according to edge_tts requirements (e.g., "+0%", "-5Hz")
+ rate=f"+{speed}%" if speed >= 0 else f"{speed}%",
+ pitch=f"+{pitch}Hz" if pitch >= 0 else f"{pitch}Hz"
+ ).save(output)
+ )
+ else:
+ # Request speech from Google Translate TTS API (Obfuscated via ROT13)
+ response = requests.get(
+ codecs.decode(
+ "uggcf://genafyngr.tbbtyr.pbz/genafyngr_ggf",
+ "rot13"
+ ),
+ params={
+ "ie": "UTF-8",
+ "q": prompt,
+ "tl": voice,
+ # Map speed percentage to a factor range between 0.24 and 4.0
+ "ttsspeed": max(0.24, min(4.0, 1 + speed / 100)),
+ "client": "tw-ob"
+ },
+ headers={
+ "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36"
+ },
+ timeout=15
+ )
+
+ if response.status_code == 200:
+ # Save the raw audio binary data
+ with open(output, "wb") as f:
+ f.write(response.content)
+
+ # Apply post-processing modifications since Google API doesn't support pitch adjustments natively
+ if pitch != 0 or speed != 0:
+ import librosa
+ import soundfile as sf
+
+ # Load the newly saved audio file while preserving its original sample rate
+ y, sr = librosa.load(output, sr=None)
+
+ # Shift audio pitch if specified
+ if pitch != 0:
+ y = librosa.effects.pitch_shift(
+ y,
+ sr=sr,
+ n_steps=pitch
+ )
+
+ # Stretch or compress time if speed deviates from normal
+ rate = max(0.01, 1 + speed / 100)
+ if rate != 1.0:
+ y = librosa.effects.time_stretch(
+ y,
+ rate=rate
+ )
+
+ sf.write(
+ file=output,
+ data=y,
+ samplerate=sr,
+ # Determine file format from extension and overwrite the file with adjusted audio
+ format=os.path.splitext(
+ os.path.basename(output)
+ )[-1].lower().replace('.', '')
+ )
+ else: gr_error(f"{response.status_code}, {response.text}") # Display UI error if API request fails
+
+def srt_tts(
+ srt_file,
+ out_file = "audios/tts.wav",
+ voice = "vi-VN-NamMinhNeural",
+ rate = 0,
+ sr = 24000,
+ google = False
+):
+ """
+ Converts a SubRip Subtitle (SRT) file into a synchronized full-length audio track.
+ """
+
+ import librosa
+ import numpy as np
+ import soundfile as sf
+
+ def time_stretch(y, sr, target_duration):
+ """Stretches or compresses audio to fit perfectly into a target duration slot."""
+
+ rate = (len(y) / sr) / target_duration
+
+ if rate != 1.0:
+ y = librosa.effects.time_stretch(
+ y=y.astype(np.float32),
+ rate=rate
+ )
+
+ # Pad with silence or truncate to guarantee precise sample alignment
+ n_target = int(round(target_duration * sr))
+ return np.pad(y, (0, n_target - len(y))) if len(y) < n_target else y[:n_target]
+
+ def pysrttime_to_seconds(t):
+ """Converts a pysrt SubRipTime timestamp object into total seconds."""
+
+ return (t.hours * 60 + t.minutes) * 60 + t.seconds + t.milliseconds / 1000
+
+ # Parse subtitle file
+ subs = pysrt.open(srt_file)
+ if not subs: raise ValueError(translations["srt"])
+
+ # Allocate the entire timeline buffer based on the end timestamp of the final segment
+ final_audio = np.zeros(
+ int(round(pysrttime_to_seconds(subs[-1].end) * sr)),
+ dtype=np.float32
+ )
+
+ # Use a secure temporary directory to cache individual segment audios
+ with tempfile.TemporaryDirectory() as tempdir:
+ for idx, seg in enumerate(subs):
+ wav_path = os.path.join(tempdir, f"seg_{idx}.wav")
+ # Generate individual audio clip for the current subtitle block
+ synthesize_tts(
+ # Flatten multi-line segment text into a single cohesive string line
+ " ".join(seg.text.splitlines()),
+ voice,
+ 0,
+ wav_path,
+ rate,
+ google
+ )
+
+ # Read back generated segment clip
+ audio, file_sr = sf.read(wav_path, dtype=np.float32)
+ # Resample audio if it doesn't match the target master sample rate
+ if file_sr != sr: audio = librosa.resample(audio, orig_sr=file_sr, target_sr=sr, res_type="soxr_vhq")
+
+ # Stretch segment audio to precisely match the timestamp duration allocated in the SRT
+ adjusted = time_stretch(
+ audio,
+ sr,
+ pysrttime_to_seconds(seg.duration)
+ )
+
+ # Calculate precise insertion index boundaries relative to the master timeline
+ start_sample = int(round(pysrttime_to_seconds(seg.start) * sr))
+ end_sample = start_sample + adjusted.shape[0]
+ # Prevent buffer overflow bounds issues near the timeline tail end
+ if end_sample > final_audio.shape[0]:
+ adjusted = adjusted[: final_audio.shape[0] - start_sample]
+ end_sample = final_audio.shape[0]
+
+ # Overlay segment audio onto the master track canvas
+ final_audio[start_sample:end_sample] += adjusted
+
+ # Write out the finalized synchronized track
+ sf.write(out_file, final_audio, sr)
+
+def TTS(
+ prompt,
+ voice = "vi-VN-NamMinhNeural",
+ speed = 0,
+ output = "audios/tts.wav",
+ pitch = 0,
+ google = False,
+ srt_input = ""
+):
+ """
+ High-level orchestration entry point managing validations and pipeline distribution.
+ """
+
+ # Standardize string inputs
+ if not srt_input: srt_input = ""
+
+ # Validate that at least one viable text target or script file is loaded
+ if not prompt and not srt_input.endswith(".srt"):
+ gr_warning(translations["enter_the_text"])
+ return None
+
+ # Validate voice model selection parameter
+ if not voice:
+ gr_warning(translations["choose_voice"])
+ return None
+
+ # Validate destination target configurations
+ if not output:
+ gr_warning(translations["output_not_valid"])
+ return None
+
+ # If the user passes a folder instead of a filename, append a default filename
+ if os.path.isdir(output): output = os.path.join(output, f"tts.wav")
+ gr_info(translations["converttext"])
+
+ # Ensure container directories exist safely before attempting file writes
+ output_dir = os.path.dirname(output) or output
+ if not os.path.exists(output_dir): os.makedirs(output_dir, exist_ok=True)
+
+ # Route logic workflows depending on whether file configuration is an SRT or standard prompt text
+ if srt_input.endswith(".srt"):
+ srt_tts(
+ srt_input,
+ output,
+ voice,
+ 0,
+ 24000,
+ google
+ )
+ else:
+ synthesize_tts(
+ prompt,
+ voice,
+ speed,
+ output,
+ pitch,
+ google
+ )
+
+ gr_info(translations["success"])
+ return output
\ No newline at end of file
diff --git a/main/app/core/ui.py b/main/app/core/ui.py
new file mode 100644
index 0000000000000000000000000000000000000000..640e04572b8605290f344fb44fc0a1b14f9dc732
--- /dev/null
+++ b/main/app/core/ui.py
@@ -0,0 +1,824 @@
+import os
+import re
+import sys
+import json
+import torch
+import shutil
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+import main.library.audio.sounddevice as sd
+
+from main.inference.realtime.audio import audio_device
+
+from main.app.variables import (
+ logger,
+ config,
+ configs,
+ edgetts,
+ method_f0,
+ vr_models,
+ mdx_models,
+ file_types,
+ configs_json,
+ translations,
+ demucs_models,
+ method_f0_full,
+ gradio_version,
+ google_tts_voice
+)
+
+# Initialize GPU and audio device maps from configuration settings
+ngpu, gpu_infos = config.get_gpu_list()
+input_channels_map, output_channels_map = audio_device()
+
+def gr_info(message=""):
+ """Displays an information message in the Gradio UI and logs it."""
+
+ gr.Info(message, duration=2)
+ logger.info(message)
+
+def gr_warning(message=""):
+ """Displays a warning message in the Gradio UI and logs it."""
+
+ gr.Warning(message, duration=2)
+ logger.warning(message)
+
+def gr_error(message=""):
+ """Displays an error message in the Gradio UI and logs it."""
+
+ gr.Error(message=message, duration=6)
+ logger.error(message)
+
+def get_gpu_info():
+ """Retrieves formatted GPU info string and total GPU count based on system status."""
+
+ return (("\n".join(gpu_infos) if len(gpu_infos) > 0 and not config.cpu_mode else translations["no_support_gpu"]), ngpu)
+
+def gpu_number_str():
+ """Generates a string representation of available GPU indices or fallback states."""
+
+ if config.cpu_mode: return "-"
+ return str("-".join(map(str, range(ngpu)))) if config.device.startswith("cuda") else str(config.pytorch_device_idx)
+
+def change_f0_choices():
+ """Scans the designated f0 directory to update available text choices in the UI."""
+
+ # Find all absolute paths of .txt files recursively inside the F0 path
+ f0_file = sorted([
+ os.path.abspath(os.path.join(root, f))
+ for root, _, files in os.walk(configs["f0_path"])
+ for f in files
+ if f.endswith(".txt")
+ ])
+
+ return {
+ "value": f0_file[0] if len(f0_file) >= 1 else "",
+ "choices": f0_file,
+ "__type__": "update"
+ }
+
+def change_audios_choices(input_audio=""):
+ """Scans the audios directory to dynamically populate audio file choices in the UI."""
+
+ # Fetch all files matching supported extension formats excluding output tags
+ audios = sorted([
+ os.path.abspath(os.path.join(root, f))
+ for root, _, files in os.walk(configs["audios_path"])
+ for f in files if os.path.splitext(f)[1].lower() in file_types and not f.startswith(("Convert", "output"))
+ ])
+
+ return {
+ "value": input_audio if input_audio != "" else (audios[0] if len(audios) >= 1 else ""),
+ "choices": audios,
+ "__type__": "update"
+ }
+
+def change_reference_choices():
+ """Scans the reference directory and clean up folder names using regex patterns."""
+
+ # Filter directories and remove suffix tracking tags like _v1_hubert_base_True
+ reference = sorted([
+ re.sub(r'_v\d+_(?:[A-Za-z0-9_]+?)_(True|False)$', '', name)
+ for name in os.listdir(configs["reference_path"])
+ if (
+ os.path.exists(os.path.join(configs["reference_path"], name)) and
+ os.path.isdir(os.path.join(configs["reference_path"], name))
+ )
+ ])
+
+ return {
+ "value": reference[0] if len(reference) >= 1 else "",
+ "choices": reference,
+ "__type__": "update"
+ }
+
+def change_models_choices():
+ """Gathers available weight files (.pth/.onnx) and index files from the logs folder."""
+
+ model, index = (
+ sorted([
+ model
+ for model in os.listdir(configs["weights_path"])
+ if model.endswith((".pth", ".onnx")) and not model.startswith("G_") and not model.startswith("D_")
+ ]),
+ sorted([
+ os.path.join(root, name)
+ for root, _, files in os.walk(configs["logs_path"], topdown=False)
+ for name in files if name.endswith(".index") and "trained" not in name
+ ])
+ )
+
+ return [
+ {
+ "value": model[0] if len(model) >= 1 else "",
+ "choices": model,
+ "__type__": "update"
+ },
+ {
+ "value": index[0] if len(index) >= 1 else "",
+ "choices": index,
+ "__type__": "update"
+ }
+ ]
+
+def change_pretrained_choices():
+ """Separates custom pretrained models into discriminator (D) and generator (G) sets."""
+
+ pretrainD = sorted([
+ model for model in os.listdir(configs["pretrained_custom_path"])
+ if model.endswith(".pth") and "D" in model
+ ])
+
+ pretrainG = sorted([
+ model for model in os.listdir(configs["pretrained_custom_path"])
+ if model.endswith(".pth") and "G" in model
+ ])
+
+ return [
+ {
+ "choices": pretrainD,
+ "value": pretrainD[0] if len(pretrainD) >= 1 else "",
+ "__type__": "update"
+ },
+ {
+ "choices": pretrainG,
+ "value": pretrainG[0] if len(pretrainG) >= 1 else "",
+ "__type__": "update"
+ }
+ ]
+
+def change_preset_choices():
+ """Fetches preset configuration files dedicated to file conversion."""
+
+ return {
+ "value": "",
+ "choices": sorted([
+ f for f in os.listdir(configs["presets_path"])
+ if f.endswith(".conversion.json")
+ ]),
+ "__type__": "update"
+ }
+
+def change_effect_preset_choices():
+ """Fetches preset configuration files dedicated to post-processing effects."""
+
+ return {
+ "value": "",
+ "choices": sorted([
+ f for f in os.listdir(configs["presets_path"])
+ if f.endswith(".effect.json")
+ ]),
+ "__type__": "update"
+ }
+
+def change_realtime_preset_choices():
+ """Fetches preset configuration files dedicated to realtime execution."""
+
+ return {
+ "value": "",
+ "choices": sorted([
+ f for f in os.listdir(configs["presets_path"])
+ if f.endswith(".realtime.json")
+ ]),
+ "__type__": "update"
+ }
+
+def change_tts_voice_choices(google=False):
+ """Updates Voice choices depending on whether Google TTS or Edge TTS is chosen."""
+
+ return {
+ "choices": google_tts_voice if google else edgetts,
+ "value": google_tts_voice[0] if google else edgetts[0],
+ "__type__": "update"
+ }
+
+def change_backing_choices(
+ backing=False,
+ not_merge=False
+):
+ """Controls interactive permissions of sub-elements based on merge and backing configurations."""
+
+ if backing or not_merge:
+ return {
+ "value": False,
+ "interactive": False,
+ "__type__": "update"
+ }
+ elif not backing or not not_merge:
+ return {
+ "interactive": True,
+ "__type__": "update"
+ }
+ else:
+ gr_warning(translations["option_not_valid"])
+
+def change_download_choices(select = translations["download_url"]):
+ """Toggles visible interface boxes inside the main downloading tab depending on the source selected."""
+
+ selects = [False]*10
+
+ # Index mapping definitions for visibility flags
+ if select == translations["download_url"]:
+ selects[0] = selects[1] = selects[2] = True
+ elif select == translations["download_from_csv"]:
+ selects[3] = selects[4] = True
+ elif select == translations["search_models"]:
+ selects[5] = selects[6] = True
+ elif select == translations["upload"]:
+ selects[9] = True
+ else:
+ gr_warning(translations["option_not_valid"])
+
+ return [
+ {
+ "visible": selects[i] if gradio_version else (selects[i] or "hidden"),
+ "__type__": "update"
+ }
+ for i in range(len(selects))
+ ]
+
+def change_download_pretrained_choices(select = translations["download_url"]):
+ """Toggles visibility parameters inside the pretrained model downloading panel section."""
+
+ selects = [False]*7
+
+ if select == translations["download_url"]:
+ selects[0] = selects[1] = selects[2] = True
+ elif select == translations["list_model"]:
+ selects[3] = selects[4] = selects[5] = True
+ elif select == translations["upload"]:
+ selects[6] = True
+ else:
+ gr_warning(translations["option_not_valid"])
+
+ return [
+ {
+ "visible": selects[i] if gradio_version else (selects[i] or "hidden"),
+ "__type__": "update"
+ }
+ for i in range(len(selects))
+ ]
+
+def get_index(model = ""):
+ """Auto-detects and matches the correct retrieval index file path belonging to a chosen model name."""
+
+ model = os.path.basename(model).split("_")[0]
+
+ return {
+ "value": next((
+ f for f in [
+ os.path.join(root, name)
+ for root, _, files in os.walk(configs["logs_path"], topdown=False)
+ for name in files
+ if name.endswith(".index") and "trained" not in name
+ ]
+ if model.split(".")[0] in f
+ ), ""),
+ "__type__": "update"
+ } if model else None
+
+def index_strength_show(index = ""):
+ """Reveals or masks Index processing intensity adjustments if a valid file target is allocated."""
+
+ index_strength_visible = (
+ index != "" and
+ index != None and
+ os.path.exists(index) and
+ os.path.isfile(index)
+ )
+
+ return [
+ {
+ "visible": index_strength_visible if gradio_version else (index_strength_visible or "hidden"),
+ "value": 0.5,
+ "__type__": "update"
+ },
+ {
+ "visible": index_strength_visible if gradio_version else (index_strength_visible or "hidden"),
+ "value": 9,
+ "__type__": "update"
+ }
+ ]
+
+def hoplength_show(
+ method="rmvpe",
+ hybrid_method=None
+):
+ """Determines whether the Hop Length parameter control needs to be visible based on pitch tracking methods."""
+
+ visible = False
+ # Hop length adjustment is necessary only for specific pitch estimation backends
+ for m in [
+ "mangio-crepe",
+ "yin",
+ "piptrack",
+ "mangio-penn"
+ ]:
+ if m in method: visible = True
+
+ if (
+ hybrid_method is not None and
+ m in hybrid_method
+ ):
+ visible = True
+
+ if visible: break
+ else: visible = False
+
+ return {
+ "visible": visible if gradio_version else (visible or "hidden"),
+ "__type__": "update"
+ }
+
+def visible(value=False):
+ """Generates direct layout display flag mappings based on component status."""
+
+ return {
+ "visible": value if gradio_version else (value or "hidden"),
+ "__type__": "update"
+ }
+
+def visibleFalse(value=False):
+ """Sets component visibility property alongside a forced initialization value of False."""
+
+ return {
+ "visible": value if gradio_version else (value or "hidden"),
+ "value": False,
+ "__type__": "update"
+ }
+
+def valueFalse_interactive(value=False):
+ """Enforces active component interactivity permissions accompanied with a cleared selection flag."""
+
+ return {
+ "value": False,
+ "interactive": value,
+ "__type__": "update"
+ }
+
+def interactive(value=False):
+ """Sets a component's interactive status."""
+
+ return {
+ "interactive": value,
+ "__type__": "update"
+ }
+
+def valueEmpty_visible1(value=False):
+ """Clears value contents to empty text strings while managing overall item display parameters."""
+
+ return {
+ "value": "",
+ "visible": value if gradio_version else (value or "hidden"),
+ "__type__": "update"
+ }
+
+def pitch_guidance_lock(vocoders = "Default"):
+ """Locks pitch guidance to True and restricts interactive changes if using non-default vocoders."""
+
+ return {
+ "value": True,
+ "interactive": vocoders == "Default",
+ "__type__": "update"
+ }
+
+def vocoders_lock(pitch=True):
+ """Blocks vocoder optimization configuration fields if Pitch calculation parameters are omitted."""
+
+ if pitch:
+ return gr.update(interactive=True)
+
+ return gr.update(
+ value="Default",
+ interactive=False
+ )
+
+def unlock_f0(value=False):
+ """Dynamically fills pitch processing selection choices based on expanded algorithm capabilities."""
+
+ return {
+ "choices": method_f0_full if value else method_f0,
+ "value": "rmvpe",
+ "__type__": "update"
+ }
+
+def change_fp(fp="fp32", bf16=False, tf32=False, int8=False):
+ """Validates hardware support and updates runtime compute tensor precision parameters in configs_json."""
+
+ global config
+
+ fp16 = fp == "fp16"
+ # Abort tracking state adjustments if target architecture does not support FP16 operations
+ if fp16 and not config.allow_is_half:
+ gr_warning(translations["fp16_not_support"])
+ return "fp32"
+ else:
+ gr_info(translations["start_update_precision"])
+ # Safely overwrite local states and dump serialized data structures back to file
+ configs = json.load(open(configs_json, "r"))
+ configs["fp16"] = config.is_half = fp16
+ configs["brain"] = config.brain = bf16
+ configs["tf32"] = config.tf32 = tf32
+ configs["int8"] = config.int8 = int8
+
+ with open(configs_json, "w") as f:
+ json.dump(configs, f, indent=4)
+
+ gr_info(translations["success"])
+ return "fp16" if fp16 else "fp32"
+
+def process_output(file_path = ""):
+ """Manages output tracking filenames by clearing existing items or appending indexing strings."""
+
+ if config.configs.get("delete_exists_file", True):
+ # Delete existing target files directly if overwriting is permitted
+ if os.path.exists(file_path) and os.path.isfile(file_path): os.remove(file_path)
+ return file_path
+ else:
+ # Append incremented indexing values sequentially until finding a safe path destination location
+ if not os.path.exists(file_path): return file_path
+ file = os.path.splitext(os.path.basename(file_path))
+
+ index = 1
+ while 1:
+ file_path = os.path.join(
+ os.path.dirname(file_path),
+ f"{file[0]}_{index}{file[1]}"
+ )
+
+ if not os.path.exists(file_path): return file_path
+ index += 1
+
+def shutil_move(input_path = "", output_path = ""):
+ """Safely relocates files across local directories using atomic collision checking hooks."""
+
+ output_path = (
+ os.path.join(output_path, os.path.basename(input_path))
+ if os.path.isdir(output_path) else
+ output_path
+ )
+
+ return (
+ shutil.move(input_path, process_output(output_path))
+ if os.path.exists(output_path) else
+ shutil.move(input_path, output_path)
+ )
+
+def separate_change(
+ model_name = "HT-Tuned",
+ karaoke_model = "MDX-Version-2",
+ reverb_model = "MDX-Reverb",
+ enable_post_process = False,
+ separate_backing = False,
+ separate_reverb = False,
+ enable_denoise = False
+):
+ """Evaluates user-selected stem separation model classes to dynamically configure visible UI sliders."""
+
+ # Detect algorithmic source framework variants
+ model_type = (
+ "vr" if model_name in list(vr_models.keys()) else "mdx"
+ if model_name in list(mdx_models.keys()) else "demucs"
+ if model_name in list(demucs_models.keys()) else ""
+ )
+
+ karaoke_type = ("vr" if karaoke_model.startswith("VR") else "mdx") if separate_backing else None
+ reverb_type = ("vr" if not reverb_model.startswith("MDX") else "mdx") if separate_reverb else None
+
+ all_types = {model_type, karaoke_type, reverb_type}
+
+ is_vr = "vr" in all_types
+ is_mdx = "mdx" in all_types
+ is_demucs = "demucs" in all_types
+
+ # Returns an array mapped to expected Gradio component tracking behaviors sequentially
+ return [
+ visible(separate_backing),
+ visible(separate_reverb),
+ visible(is_mdx or is_demucs),
+ visible(is_mdx or is_demucs),
+ visible(is_mdx),
+ visible(is_mdx or is_vr),
+ visible(is_demucs),
+ visible(is_vr),
+ visible(is_vr),
+ visible(is_vr and enable_post_process),
+ visible(is_vr and enable_denoise),
+ valueFalse_interactive(is_vr),
+ valueFalse_interactive(is_vr),
+ interactive(is_vr) if is_vr else valueFalse_interactive(is_vr)
+ ]
+
+def create_dataset_change(
+ model_name = "HT-Tuned",
+ reverb_model = "MDX-Reverb",
+ enable_post_process = False,
+ separate_reverb = False,
+ enable_denoise = False
+):
+ """Adapts dataset processing workflow variables dynamically based on running separation backends."""
+
+ model_type = (
+ "vr" if model_name in list(vr_models.keys()) else "mdx"
+ if model_name in list(mdx_models.keys()) else "demucs"
+ if model_name in list(demucs_models.keys()) else ""
+ )
+
+ reverb_type = ("vr" if not reverb_model.startswith("MDX") else "mdx") if separate_reverb else None
+ all_types = {model_type, reverb_type}
+
+ is_vr = "vr" in all_types
+ is_mdx = "mdx" in all_types
+ is_demucs = "demucs" in all_types
+
+ return [
+ visible(separate_reverb),
+ visible(is_mdx or is_demucs),
+ visible(is_mdx or is_demucs),
+ visible(is_mdx),
+ visible(is_mdx or is_vr),
+ visible(is_demucs),
+ visible(is_vr),
+ visible(is_vr),
+ visible(is_vr and enable_post_process),
+ visible(is_vr and enable_denoise),
+ valueFalse_interactive(is_vr),
+ valueFalse_interactive(is_vr),
+ interactive(is_vr) if is_vr else valueFalse_interactive(is_vr)
+ ]
+
+def update_audio_device(input_device = "", output_device = "", monitor_device = "", monitor = False):
+ """Initializes ASIO device channels layout mappings or falls back to traditional stereo sound rules."""
+
+ input_is_asio = "ASIO" in input_device if input_device else (False if gradio_version else "hidden")
+ output_is_asio = "ASIO" in output_device if output_device else (False if gradio_version else "hidden")
+ monitor_is_asio = "ASIO" in monitor_device if monitor_device else (False if gradio_version else "hidden")
+
+ # Reset structural sounddevice core drivers properties safely if ASIO is discovered
+ if input_is_asio or output_is_asio or monitor_is_asio:
+ sd.terminate()
+ sd.initialize()
+
+ try:
+ input_max_ch = input_channels_map.get(input_device, [])[1]
+ output_max_ch = output_channels_map.get(output_device, [])[1]
+ monitor_max_ch = output_channels_map.get(monitor_device, [])[1] if monitor else 128
+ except:
+ input_max_ch = output_max_ch = monitor_max_ch = 2
+ else: input_max_ch = output_max_ch = monitor_max_ch = 2
+
+ return [
+ visible(monitor),
+ visible(monitor),
+ visible(
+ input_is_asio or output_is_asio or monitor_is_asio
+ ),
+ gr.update(
+ visible=input_is_asio,
+ maximum=input_max_ch - 1
+ ),
+ gr.update(
+ visible=output_is_asio,
+ maximum=output_max_ch - 1
+ ),
+ gr.update(
+ visible=monitor_is_asio,
+ maximum=monitor_max_ch - 1
+ ),
+ gr.update(
+ value=resolve_sample_rate(input_channels_map[input_device][0]) if input_is_asio else 48000
+ ),
+ gr.update(
+ value=resolve_sample_rate(output_channels_map[output_device][0]) if output_is_asio else 48000
+ ),
+ gr.update(
+ visible=monitor,
+ value=resolve_sample_rate(output_channels_map[monitor_device][0]) if monitor_is_asio else 48000
+ ),
+ gr.update(
+ visible=output_is_asio,
+ value=True
+ ),
+ gr.update(
+ visible=monitor_is_asio,
+ value=True
+ ),
+ ]
+
+def change_audio_device_choices():
+ """Rescans host audio drivers to update input/output device options in Gradio selectors."""
+
+ global input_channels_map, output_channels_map
+
+ # Safely cycle operational status loops inside audio subsystems
+ sd.terminate()
+ sd.initialize()
+
+ input_channels_map, output_channels_map = audio_device()
+ input_channels_map_list, output_channels_map_list = list(input_channels_map.keys()), list(output_channels_map.keys())
+
+ return [
+ {
+ "value": input_channels_map_list[0] if len(input_channels_map_list) >= 1 else "",
+ "choices": input_channels_map_list,
+ "__type__": "update"
+ },
+ {
+ "value": output_channels_map_list[0] if len(output_channels_map_list) >= 1 else "",
+ "choices": output_channels_map_list,
+ "__type__": "update"
+ },
+ {
+ "value": output_channels_map_list[0] if len(output_channels_map_list) >= 1 else "",
+ "choices": output_channels_map_list,
+ "__type__": "update"
+ }
+ ]
+
+def resolve_sample_rate(input_device_id = 0):
+ """Queries hardware drivers to extract default internal operational sample rates values."""
+
+ if configs.get("asio_enabled", False): return 48000
+
+ try:
+ device = sd.query_devices(input_device_id)
+ return int(device["default_samplerate"])
+ except Exception:
+ return 48000
+
+def replace_punctuation(filename = ""):
+ """Sanitizes file titles by purging illegal operational symbols with safe underscore placeholders."""
+
+ return re.sub(r"_+", "_", re.sub(r"[ \-|]+", "_", filename.translate(str.maketrans("", "", '()[],"\'{}<>?*:/\\')))).strip("_")
+
+def replace_url(url = ""):
+ """Formats huggingface storage pathways links into direct execution links formats."""
+
+ return url.replace("/blob/", "/resolve/").replace("?download=true", "").strip()
+
+def replace_modelname(modelname = ""):
+ """Strips extension suffixes from common AI models to reveal pure speaker tracking titles."""
+
+ return replace_punctuation(
+ modelname.replace(".onnx", "").replace(".pth", "").replace(".index", "").replace(".zip", "")
+ )
+
+def replace_export_format(audio_path = "", export_format = "wav"):
+ """Alters structural configuration extensions targets mapped to specific encoding files parameters."""
+
+ export_format = f".{export_format}"
+
+ return (
+ audio_path
+ if audio_path.endswith(export_format) else
+ audio_path.replace(f".{os.path.basename(audio_path).split('.')[-1]}", export_format)
+ )
+
+def update_dropdowns_from_json(data = {}):
+ """Parses JSON data received from frontend JavaScript to dynamically update the choices and values of three audio device dropdowns."""
+ if not data:
+ return [
+ gr.update(choices=[], value=None),
+ gr.update(choices=[], value=None),
+ gr.update(choices=[], value=None)
+ ]
+
+ inputs = list(data.get("inputs", {}).keys())
+ outputs = list(data.get("outputs", {}).keys())
+
+ return [
+ gr.update(
+ choices=inputs,
+ value=inputs[0] if len(inputs) > 0 else None
+ ),
+ gr.update(
+ choices=outputs,
+ value=outputs[0] if len(outputs) > 0 else None
+ ),
+ gr.update(
+ choices=outputs,
+ value=outputs[0] if len(outputs) > 0 else None
+ ),
+ ]
+
+def update_button_from_json(data = {}):
+ """Updates the interactivity states (enabled/disabled) of workflow buttons based on the control state flags passed from the frontend JavaScript."""
+ if not data:
+ return [
+ gr.update(interactive=True),
+ gr.update(interactive=False)
+ ]
+
+ return [
+ gr.update(interactive=data.get("start_button", True)),
+ gr.update(interactive=data.get("stop_button", False))
+ ]
+
+def update_value_from_json(data = {}):
+ """Updates UI text labels and destination paths dynamically using the tracking information generated by the frontend JavaScript execution."""
+
+ if not data:
+ return [
+ gr.update(),
+ gr.update(value=None)
+ ]
+
+ return [
+ data.get("button", "Start"),
+ data.get("path", None),
+ ]
+
+def get_speakers_id_and_architecture(model = ""):
+ """Reads structural internal tensors data parameters fields inside saved model weights to parse capabilities."""
+
+ model_path = os.path.join(configs["weights_path"], model) if not os.path.exists(model) else model
+ # Validate file integrity conditions before performing serialization inspections steps
+ if not model or not os.path.exists(model_path) or os.path.isdir(model_path) or not model.endswith((".pth", ".onnx")):
+ return [
+ {
+ "visible": False if gradio_version else "hidden",
+ "value": 0,
+ "choices": [0],
+ "__type__": "update"
+ },
+ {
+ "visible": False if gradio_version else "hidden",
+ "value": 0.4,
+ "__type__": "update"
+ }
+ ]
+
+ try:
+ if model_path.endswith(".pth"):
+ # Inspect metadata fields from PyTorch checkpoints safely using cpu isolation configurations
+ model_data = torch.load(model_path, map_location="cpu", weights_only=True)
+ else:
+ import onnx
+
+ model_data = None
+ # Scan structural attributes lists within ONNX metadata wrappers pipelines structures
+ model = onnx.load(model_path)
+
+ for prop in model.metadata_props:
+ if prop.key == "model_info":
+ model_data = json.loads(prop.value)
+ break
+
+ del model
+
+ speakers_id = model_data.get("speakers_id", 1)
+ speakers_id_visible = speakers_id and speakers_id != 1
+ noise_scale_visible = model_data.get("architecture", "RVC") == "SVC"
+ del model_data
+
+ return [
+ {
+ "visible": speakers_id_visible if gradio_version else (speakers_id_visible or "hidden"),
+ "value": 0,
+ "choices": list(range(speakers_id)),
+ "__type__": "update"
+ },
+ {
+ "visible": noise_scale_visible if gradio_version else (noise_scale_visible or "hidden"),
+ "value": 0.4,
+ "__type__": "update"
+ }
+ ]
+ except Exception:
+ # Gracefully fall back to standard RVC configuration properties if internal validation parsing errors occur
+ return [
+ {
+ "visible": False if gradio_version else "hidden",
+ "value": 0,
+ "choices": [0],
+ "__type__": "update"
+ },
+ {
+ "visible": False if gradio_version else "hidden",
+ "value": 0.4,
+ "__type__": "update"
+ }
+ ]
\ No newline at end of file
diff --git a/main/app/core/utils.py b/main/app/core/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..03f751f5caff05905c9df8d86e8cd29989c4aac7
--- /dev/null
+++ b/main/app/core/utils.py
@@ -0,0 +1,179 @@
+import os
+import sys
+import time
+import json
+import signal
+import codecs
+import requests
+import subprocess
+
+sys.path.append(os.getcwd())
+
+from main.app.core.ui import gr_info, gr_warning
+from main.app.variables import translations, configs
+
+def alive(pid):
+ """
+ Checks whether a process with the given PID is currently running.
+
+ Args:
+ pid (int): The process ID to inspect.
+
+ Returns:
+ bool: True if the process is active/alive, False otherwise.
+ """
+
+ try:
+ if sys.platform == "win32":
+ # On Windows, use tasklist to check if the PID exists
+ subprocess.check_output(["tasklist", "/FI", f"PID eq {pid}"], stderr=subprocess.DEVNULL)
+ return True
+ else:
+ # On Unix-like systems, signal 0 queries the existence of the process
+ os.kill(pid, 0)
+ return True
+ except:
+ return False
+
+def pid_kill(pid):
+ """
+ Attempts to terminate a process gracefully, falling back to a forced kill if necessary.
+
+ Args:
+ pid (int): The process ID to terminate.
+
+ Returns:
+ bool: True if the process was successfully terminated, False if it survived.
+ """
+
+ if not alive(pid): return True
+
+ # Step 1: Attempt graceful termination (SIGINT or taskkill /T)
+ try:
+ if sys.platform == "win32": subprocess.run(["taskkill", "/PID", str(pid), "/T"], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+ else: os.kill(pid, signal.SIGINT)
+ except:
+ pass
+
+ # Wait up to 1.5 seconds for the process to exit gracefully
+ for _ in range(15):
+ if not alive(pid): return True
+ time.sleep(0.1)
+
+ # Step 2: Fallback to aggressive forced termination (SIGKILL or taskkill /F /T)
+ try:
+ if sys.platform == "win32": subprocess.run(["taskkill", "/PID", str(pid), "/F", "/T"], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+ else: os.kill(pid, signal.SIGKILL)
+ except:
+ pass
+
+ # Wait up to 1.0 second for the forced kill to take effect
+ for _ in range(10):
+ if not alive(pid): return True
+ time.sleep(0.1)
+
+ return False
+
+def stop_pid(
+ pid_file,
+ model_name=None,
+ train=False
+):
+ """
+ Stops processes recorded in a PID file and handles active background training PIDs.
+
+ Args:
+ pid_file (str): The name of the tracking text file containing process IDs.
+ model_name (str, optional): The name of the specific model directory inside logs. Defaults to None.
+ train (bool, optional): If True, triggers structural validation and cleans up training PIDs stored inside the model's config.json. Defaults to False.
+ """
+
+ try:
+ # Determine the file path location based on whether a model context is given
+ pid_file_path = os.path.join("assets", f"{pid_file}.txt") if model_name is None else os.path.join(configs["logs_path"], model_name, f"{pid_file}.txt")
+
+ gr_info(translations["stop_pid"])
+
+ if not os.path.exists(pid_file_path): return gr_warning(translations["not_found_pid"])
+ else:
+ # Read all recorded PIDs and terminate them sequentially
+ with open(pid_file_path, "r") as pid_file:
+ pids = [int(pid) for pid in pid_file.readlines()]
+
+ for pid in pids:
+ pid_kill(pid)
+
+ if os.path.exists(pid_file_path): os.remove(pid_file_path)
+
+ # Config JSON tracking handler for deep training pipelines
+ pid_file_path = os.path.join(configs["logs_path"], model_name, "config.json")
+
+ if train and os.path.exists(pid_file_path):
+ with open(pid_file_path, "r") as pid_file:
+ pid_data = json.load(pid_file)
+ pids = pid_data.get("process_pids", [])
+
+ # Clear out the process_pids array from config.json to reset state safely
+ with open(pid_file_path, "w") as pid_file:
+ pid_data.pop("process_pids", None)
+
+ json.dump(pid_data, pid_file, indent=4)
+
+ # Terminate background worker PIDs parsed from the JSON file
+ for pid in pids:
+ pid_kill(pid)
+
+ gr_info(translations["end_pid"])
+ except:
+ pass
+
+def google_translate(text, source='auto', target='vi'):
+ """
+ Translates a text block using the free Google Translate single API endpoint.
+
+ Splits the string safely into chunks under 5,000 characters to prevent payload overflows.
+
+ Args:
+ text (str): The raw input string containing the content to translate.
+ source (str, optional): Language code of the origin text. Defaults to 'auto'.
+ target (str, optional): Target destination language translation code. Defaults to 'vi'.
+
+ Returns:
+ str: The translated text string result, or the original text string if an error occurs.
+ """
+
+ if text == "": return gr_warning(translations["prompt_warning"])
+
+ try:
+ import textwrap
+
+ def translate_chunk(chunk):
+ response = requests.get(
+ # ROT13 decoding hides the plain-text URL string footprint from basic scanners
+ codecs.decode("uggcf://genafyngr.tbbtyrncvf.pbz/genafyngr_n/fvatyr", "rot13"),
+ params={
+ 'client': 'gtx',
+ 'sl': source,
+ 'tl': target,
+ 'dt': 't',
+ 'q': chunk
+ }
+ )
+
+ # Parse structural nested response payload and re-assemble sentence arrays
+ return ''.join([i[0] for i in response.json()[0]]) if response.status_code == 200 else chunk
+
+ translated_text = ''
+ # Wrap long text objects safely into chunks of up to 5000 chars without splitting structural words
+ for chunk in textwrap.wrap(
+ text,
+ 5000,
+ break_long_words=False,
+ break_on_hyphens=False
+ ):
+ translated_text += translate_chunk(chunk)
+
+ return translated_text
+ except:
+ # Gracefully fall back to raw input text if networking disruptions happen
+ return text
\ No newline at end of file
diff --git a/main/app/install.py b/main/app/install.py
new file mode 100644
index 0000000000000000000000000000000000000000..3fae074a648aaf9b7b061b0070b918285845e30d
--- /dev/null
+++ b/main/app/install.py
@@ -0,0 +1,570 @@
+import os
+import sys
+import subprocess
+
+sys.path.append(os.getcwd())
+
+os.makedirs(os.path.join("assets", ".uv"), exist_ok=True)
+os.environ["UV_CACHE_DIR"] = os.path.join("assets", ".uv")
+
+# Determine the correct Python executable path based on the operating system
+python_dir = os.path.join("runtime", "python.exe") if sys.platform == "win32" else os.path.join("venv", "bin", "python")
+
+def run_command(command):
+ """
+ Executes a shell command and catches any execution errors.
+
+ Args:
+ command (str): The shell command string to be executed.
+ """
+
+ try:
+ # Run command via shell and block until completion; raise exception on non-zero exit code
+ subprocess.run(command, shell=True, check=True)
+ except subprocess.CalledProcessError as e:
+ print(f"\nLỗi khi thực thi lệnh: {e}")
+
+def install_dependencies():
+ """
+ Installs core prerequisite packages using 'uv' for ultra-fast performance.
+ """
+
+ print("Cài đặt các thư viện cơ bản...")
+ # Bootstrap 'uv' into the current python environment
+ run_command(f"{python_dir} -m pip install uv")
+ # Install foundational utilities needed for setup and platform compatibility
+ run_command(f"{python_dir} -m uv pip install six packaging python-dateutil platformdirs wget")
+
+def install_ffmpeg():
+ """
+ Downloads static binaries of FFmpeg and FFprobe for Windows environment
+ from a remote repository if they do not exist locally.
+ """
+
+ # Download ffmpeg.exe if missing
+ if not os.path.exists("ffmpeg.exe"):
+ print("Cài đặt ffmpeg...")
+ run_command('curl -L -o ffmpeg.exe https://huggingface.co/AnhP/Vietnamese-RVC-Project/resolve/main/ffmpeg/ffmpeg.exe')
+
+ # Download ffprobe.exe if missing
+ if not os.path.exists("ffprobe.exe"):
+ print("Cài đặt ffprobe...")
+ run_command('curl -L -o ffprobe.exe https://huggingface.co/AnhP/Vietnamese-RVC-Project/resolve/main/ffmpeg/ffprobe.exe')
+
+def install_ffmpeg_linux():
+ """
+ Installs FFmpeg on Linux using the system's package manager (APT, DNF, or Pacman).
+ """
+
+ # Try apt (Debian/Ubuntu), fallback to dnf (Fedora/RHEL), fallback to pacman (Arch)
+ run_command("sudo apt update && sudo apt install ffmpeg -y || sudo dnf install ffmpeg -y || sudo pacman -S --noconfirm ffmpeg")
+
+def remove_onnxruntime():
+ """
+ Removes general 'onnxruntime' references from requirements.txt to prevent
+ conflicts before installing specialized hardware-specific versions.
+ """
+
+ if not os.path.exists("requirements.txt"): return
+
+ # Read all lines from the requirements file
+ with open("requirements.txt", "r", encoding="utf-8") as f:
+ lines = f.readlines()
+
+ # Rewrite the file filtering out the generic onnxruntime package
+ with open("requirements.txt", "w", encoding="utf-8") as f:
+ f.writelines(l for l in lines if "onnxruntime" not in l)
+
+def install_base_system_deps():
+ """
+ Installs Linux system level libraries required for compiling C++ extensions
+ and handling audio/video components.
+ """
+
+ print("\nĐang cập nhật hệ thống và cài đặt thư viện lõi...")
+ # Installs compilation tools, Python development headers, PortAudio, and system/rendering libraries
+ run_command(f"apt update && apt install -y build-essential python3-dev portaudio19-dev libsndfile1 libgomp1 libglib2.0-0")
+
+def install_cpu():
+ """
+ Installs CPU-only versions of PyTorch, standard ONNX Runtime,
+ and general project dependencies.
+ """
+
+ print("\nCài đặt dành cho CPU...")
+
+ install_dependencies()
+ remove_onnxruntime()
+
+ print("Cài đặt các gói thư viện cpu...")
+ # Fetch CPU optimized wheels from PyTorch's official repository
+ run_command(f"{python_dir} -m uv pip install torch torchaudio torchvision --index-url https://download.pytorch.org/whl/cpu")
+ run_command(f"{python_dir} -m uv pip install onnxruntime")
+ run_command(f"{python_dir} -m uv pip install -r requirements.txt")
+
+def install_cuda(version = "cu118"):
+ """
+ Installs NVIDIA CUDA-accelerated libraries based on the specified toolkit version.
+
+ Args:
+ version (str): The CUDA version string identifier (e.g., 'cu118', 'cu121', 'cu128', 'cu130').
+ """
+
+ print("\nCài đặt dành cho CUDA...")
+
+ install_dependencies()
+ remove_onnxruntime()
+
+ print("Cài đặt các gói thư viện cuda...")
+ # Lock numpy and numba versions for older stable CUDA configurations to prevent ABI breakage
+ if version in ("cu118", "cu121"): run_command(f"{python_dir} -m uv pip install numpy==1.26.4 numba==0.61.0")
+
+ # Install specific PyTorch build matching the targeted CUDA API
+ if version == "cu128":
+ # This version is the most stable.
+ run_command(f"{python_dir} -m uv pip install torch==2.7.1 torchaudio==2.7.1 torchvision --index-url https://download.pytorch.org/whl/cu128")
+ else:
+ run_command(f"{python_dir} -m uv pip install torch torchaudio torchvision --index-url https://download.pytorch.org/whl/{version}")
+
+ # Install matching CUDA-optimized ONNX Runtime build
+ run_command(f"{python_dir} -m uv pip install onnxruntime-gpu" + ("==1.20.1" if version in ("cu118", "cu121") else "==1.22.0"))
+ run_command(f"{python_dir} -m uv pip install -r requirements.txt")
+
+ # Faiss-cpu is required as fallback/index handler for newer CUDA suites
+ if version in ("cu128", "cu130"): run_command(f"{python_dir} -m uv pip install faiss-cpu==1.13.2")
+
+def install_directml():
+ """
+ Installs DirectML-compatible backend packages for hardware-agnostic
+ GPU acceleration (ideal for older NVIDIA, AMD, or Intel cards on Windows).
+ """
+
+ print("\nCài đặt dành cho DIRECTML...")
+
+ install_dependencies()
+ remove_onnxruntime()
+
+ print("Cài đặt các gói thư viện directml...")
+
+ run_command(f"{python_dir} -m uv pip install numpy==1.26.4 numba==0.61.0")
+ run_command(f"{python_dir} -m uv pip install torch==2.4.1 torchaudio==2.4.1 torchvision")
+ # Install the DirectML device extension plug-in for PyTorch
+ run_command(f"{python_dir} -m uv pip install torch-directml==0.2.5.dev240914")
+ run_command(f"{python_dir} -m uv pip install onnxruntime-directml")
+ run_command(f"{python_dir} -m uv pip install -r requirements.txt")
+
+def install_opencl():
+ """
+ Installs OpenCL-backed extensions allowing GPU execution over cross-vendor hardware platforms.
+ """
+
+ print("\nCài đặt dành cho OPENCL...")
+
+ install_dependencies()
+ remove_onnxruntime()
+
+ print("Cài đặt các gói thư viện opencl...")
+
+ run_command(f"{python_dir} -m uv pip install numpy==1.26.4 numba==0.61.0")
+ run_command(f"{python_dir} -m uv pip install torch==2.6.0 torchaudio==2.6.0 torchvision")
+ # Pull pre-built OpenCL DLPrim implementation bridge wheel directly from GitHub releases
+ run_command(f"{python_dir} -m uv pip install https://github.com/artyom-beilis/pytorch_dlprim/releases/download/0.2.0/pytorch_ocl-0.2.0+torch2.6-cp312-none-win_amd64.whl")
+ run_command(f"{python_dir} -m uv pip install onnxruntime-directml")
+ run_command(f"{python_dir} -m uv pip install -r requirements.txt")
+
+def install_xpu(provider = "openvino"):
+ """
+ Installs Intel XPU acceleration packages targeting Intel Arc and integrated Iris Xe graphics.
+
+ Args:
+ provider (str): Execution provider for ONNX, either 'openvino' or 'directml'.
+ """
+
+ print("\nCài đặt dành cho XPU...")
+
+ install_dependencies()
+ remove_onnxruntime()
+
+ print("Cài đặt các gói thư viện xpu...")
+ # Pull Intel XPU-enabled Torch wheel
+ run_command(f"{python_dir} -m uv pip install torch torchaudio torchvision --index-url https://download.pytorch.org/whl/xpu")
+ run_command(f"{python_dir} -m uv pip install openvino==2025.4.1")
+ run_command(f"{python_dir} -m uv pip install triton" + "-windows" if sys.platform == "win32" else "")
+ # Dynamically pick the optimized inferencing runtime provider based on user preference
+ run_command(f"{python_dir} -m uv pip install " + ("onnxruntime-openvino==1.24.1" if provider == "openvino" else "onnxruntime-directml"))
+ run_command(f"{python_dir} -m uv pip install -r requirements.txt")
+ run_command(f"{python_dir} -m uv pip install faiss-cpu==1.13.2")
+
+def install_rocm(gfx_version, provider = "rocm"):
+ """
+ Installs AMD ROCm software stack dependencies mapped to specific GPU architectures.
+
+ Args:
+ gfx_version (str): The specific hardware ISA architecture code.
+ provider (str): Execution target flavor, defaults to 'rocm'.
+ """
+
+ print(f"\nCài đặt dành cho ROCm ({gfx_version})...")
+
+ install_dependencies()
+ remove_onnxruntime()
+
+ print("Cài đặt các gói thư viện rocm...")
+ # Fetch nightly/staging builds targeting specific AMD ISA architectures
+ run_command(f"{python_dir} -m uv pip install torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2-staging/{gfx_version}/")
+ run_command(f"{python_dir} -m uv pip install onnxruntime" + "-rocm" if provider == "rocm" else "-directml")
+ run_command(f"{python_dir} -m uv pip install -r requirements.txt")
+ run_command(f"{python_dir} -m uv pip install faiss-cpu==1.13.2")
+
+def install_requirements_win():
+ """
+ Interactive terminal menu to select and deploy hardware backends specifically on Windows OS.
+ """
+
+ print("\nCác phiên bản Backend:")
+ print("- 1. CPU (Đối với CPU).")
+ print("- 2. CUDA (Đối với GPU NVIDIA).")
+ print("- 3. DIRECTML (Đối với GPU NVIDIA (Dòng cũ như GTX 600 trở xuống) / AMD (Dòng cũ không hỗ trợ ROCM) / INTEL).")
+ print("- 4. OPENCL (Đối với GPU NVIDIA (Dòng cũ như GTX 600 trở xuống) / AMD (Dòng cũ không hỗ trợ ROCM) / INTEL).")
+ print("- 5. XPU (Đối với GPU INTEL Arc hoặc GPU hỗ trợ XPU).")
+ # ROCm is still not very stable.
+ # print("- 6. ROCM (Đối với GPU AMD hỗ trợ ROCM).")
+
+ # Primary selection loop
+ while 1:
+ selected = str(input("Hãy chọn phiên bản Backend (Nhập 1-5): ")).strip().upper()
+
+ if selected in ("1", "CPU"):
+ install_cpu()
+ break
+ elif selected in ("2", "CUDA"):
+ print("\nCác phiên bản CUDA (Lưu ý: Hãy chọn đúng phiên bản nếu như chọn sai có thể gây ra lỗi suy luận hoặc huấn luyện): \n 1. Phiên bản 118 dành cho các GPU GTX 10-Series trở lên. \n 2. Phiên bản 121 dành cho các GPU RTX 20-Series đến 30-Series trở lên. \n 3. Phiên bản 128 dành cho các GPU RTX 30-Series đến RTX 40-Series trở lên. \n 4. Phiên bản 130 dành cho các GPU RTX 50-Series trở lên.")
+
+ cuda_selected = None
+ # CUDA version sub-loop selection
+ while 1:
+ cuda_selected = str(input("Hãy chọn phiên bản CUDA (Nhập 1-4): ")).strip().upper()
+
+ if cuda_selected in ("1", "118"):
+ install_cuda("cu118")
+ break
+ elif cuda_selected in ("2", "121"):
+ install_cuda("cu121")
+ break
+ elif cuda_selected in ("3", "128"):
+ install_cuda("cu128")
+ break
+ elif cuda_selected in ("4", "130"):
+ install_cuda("cu130")
+ break
+ else:
+ print("\nVui lòng chọn phiên bản CUDA hợp lệ!")
+
+ # Optional high-level acceleration frameworks selection for modern CUDA versions
+ if cuda_selected in ("3", "4", "cu128", "cu130"):
+ print("\nCài đặt gói phụ thuộc TensorRT (không khuyên dùng với người dùng phổ thông)")
+ print("Lưu ý: Nếu cài đặt gói TensorRT, bạn sẽ cần thêm gói TensorRT từ Nvidia và thêm nó vào PATH hệ thống: https://developer.nvidia.com/tensorrt")
+
+ while 1:
+ tensorrt_selected = str(input("Bạn có muốn sử dụng TensorRT không (Y/N): ")).strip().upper()
+
+ if tensorrt_selected in ("Y", "YES"):
+ run_command(f"{python_dir} -m uv pip install tensorrt")
+ break
+ elif tensorrt_selected in ("N", "NO"):
+ break
+ else:
+ print("\nVui lòng chọn giá trị hợp lệ!")
+
+ print("\nCài đặt gói phụ thuộc Triton (không khuyên dùng với người dùng phổ thông)")
+
+ while 1:
+ triton_selected = str(input("Bạn có muốn sử dụng Triton không (Y/N): ")).strip().upper()
+
+ if triton_selected in ("Y", "YES"):
+ run_command(f"{python_dir} -m uv pip install triton-windows")
+ break
+ elif triton_selected in ("N", "NO"):
+ break
+ else:
+ print("\nVui lòng chọn giá trị hợp lệ!")
+ break
+ elif selected in ("3", "DIRECTML"):
+ install_directml()
+ break
+ elif selected in ("4", "OPENCL"):
+ install_opencl()
+ break
+ elif selected in ("5", "XPU"):
+ print("\nPhiên bản ONNXRUNTIME: \n 1. DIRECTML (Ổn định). \n 2. OPENVINO (Nhanh hơn).")
+
+ while 1:
+ provider_selected = str(input("Hãy chọn phiên bản ONNXRUNTIME (1-2): ")).strip().upper()
+
+ if provider_selected in ("1", "DIRECTML"):
+ install_xpu("directml")
+ break
+ elif provider_selected in ("2", "OPENVINO"):
+ install_xpu("openvino")
+ break
+ else:
+ print("\nVui lòng chọn giá trị hợp lệ!")
+
+ break
+ elif selected in ("6", "ROCM"):
+ print("\nLưu ý: ROCM có thể gặp vấn đề về hiệu suất hoặc lỗi trên các thiết bị chạy Windows và hãy chắc chắn GPU của bạn nằm trong danh sách hỗ trợ")
+
+ print("\nPhân loại ROCm:")
+ print(" 1. ROCM-DGPU (Dành cho GPU rời)")
+ print(" 2. ROCM-APU (Dành cho GPU tích hợp)")
+
+ while 1:
+ rocm_type = str(input("Hãy chọn loại GPU của bạn (Nhập 1 hoặc 2): ")).strip().upper()
+
+ if rocm_type in ("1", "ROCM-DGPU"):
+ print("\nCác phiên bản ROCM (Lưu ý: hãy chọn đúng phiên bản theo kiến trúc GPU của bạn, nếu không sẽ không thể nhận dạng được thiết bị)")
+ print(" 1. gfx900 -> Vega 56, Vega 64, Frontier Edition")
+ print(" 2. gfx906 -> Radeon VII, MI50, MI60")
+ print(" 3. gfx908 -> Instinct MI100")
+ print(" 4. gfx90a -> Instinct MI210, MI250, MI250X")
+ print(" 5. gfx101X-dgpu -> Dòng RDNA1 (RX 5500, RX 5600, RX 5700 XT...)")
+ print(" 6. gfx103X-dgpu -> Dòng RDNA2 (RX 6600, RX 6700 XT, RX 6800, RX 6900 XT...)")
+ print(" 7. gfx110X-dgpu -> Dòng RDNA3 (RX 7600, RX 7700 XT, RX 7800 XT, RX 7900 XTX...)")
+ print(" 8. gfx120X-all -> Dòng RDNA4 (RX 9060, RX 9060 XT, RX 9070 XT...)")
+
+ dgpu_mapping = {
+ "1": "gfx900",
+ "2": "gfx906",
+ "3": "gfx908",
+ "4": "gfx90a",
+ "5": "gfx101X-dgpu",
+ "6": "gfx103X-dgpu",
+ "7": "gfx110X-dgpu",
+ "8": "gfx120X-all"
+ }
+
+ while 1:
+ sub_select = str(input("Chọn số tương ứng với GPU rời của bạn (1-7): ")).strip()
+ if sub_select in dgpu_mapping:
+ install_rocm(dgpu_mapping[sub_select], "directml")
+ break
+ else:
+ print("\nVui lòng chọn đúng số từ 1 đến 7!")
+ break
+ elif rocm_type in ("2", "ROCM-APU"):
+ print("\nCác phiên bản ROCM (Lưu ý: hãy chọn đúng phiên bản theo kiến trúc APU của bạn, nếu không sẽ không thể nhận dạng được thiết bị)")
+ print(" 1. gfx103X-all -> APU tích hợp RDNA2 (Steam Deck, Ryzen 6000/7000 iGPU...)")
+ print(" 2. gfx110X-all -> APU tích hợp RDNA3 (Ryzen 7040/8040 iGPU, ROG Ally...)")
+ print(" 3. gfx1150 -> APU Ryzen AI 9 dòng 300 / Strix Point (Radeon 880M/890M)")
+ print(" 4. gfx1151 -> APU Strix Halo hoặc các biến thể đặc biệt")
+ print(" 5. gfx1152 -> Biến thể APU RDNA3.5 di động tiết kiệm điện")
+ print(" 6. gfx1153 -> Biến thể APU RDNA3.5 phân khúc phổ thông")
+ print(" 7. gfx120X-all -> Kiến trúc APU RDNA4 tích hợp mới nhất")
+
+ apu_mapping = {
+ "1": "gfx103X-all",
+ "2": "gfx110X-all",
+ "3": "gfx1150",
+ "4": "gfx1151",
+ "5": "gfx1152",
+ "6": "gfx1153",
+ "7": "gfx120X-all"
+ }
+
+ while 1:
+ sub_select = str(input("Chọn số tương ứng với APU của bạn (1-7): ")).strip()
+ if sub_select in apu_mapping:
+ install_rocm(apu_mapping[sub_select], "directml")
+ break
+ else:
+ print("\nVui lòng chọn đúng số từ 1 đến 7!")
+ break
+ else:
+ print("\nVui lòng chọn 1 hoặc 2!")
+ break
+ else:
+ print("\nVui lòng chọn phiên bản hợp lệ!")
+
+ print("\nHoàn tất quá trình cài đặt thư viện!")
+
+def install_requirements_linux():
+ """
+ Interactive terminal menu to select and deploy hardware backends specifically on Linux distributions.
+ """
+
+ print("\nCác phiên bản Backend:")
+ print("- 1. CPU (Đối với CPU).")
+ print("- 2. CUDA (Đối với GPU NVIDIA).")
+ print("- 3. XPU (Đối với GPU INTEL Arc hoặc GPU hỗ trợ XPU).")
+ print("- 4. ROCM (Đối với GPU AMD hỗ trợ ROCM).")
+
+ while 1:
+ selected = str(input("Hãy chọn phiên bản Backend (Nhập 1-5): ")).strip().upper()
+
+ if selected in ("1", "CPU"):
+ install_base_system_deps()
+ install_cpu()
+ break
+ elif selected in ("2", "CUDA"):
+ print("\nCác phiên bản CUDA (Lưu ý: Hãy chọn đúng phiên bản nếu như chọn sai có thể gây ra lỗi suy luận hoặc huấn luyện): \n 1. Phiên bản 118 dành cho các GPU GTX 10-Series trở lên. \n 2. Phiên bản 121 dành cho các GPU RTX 20-Series đến 30-Series trở lên. \n 3. Phiên bản 128 dành cho các GPU RTX 30-Series đến RTX 40-Series trở lên. \n 4. Phiên bản 130 dành cho các GPU RTX 50-Series trở lên.")
+
+ cuda_selected = None
+ run_command(f"apt update && apt install -y nvidia-cuda-toolkit")
+
+ while 1:
+ cuda_selected = str(input("Hãy chọn phiên bản CUDA (Nhập 1-4): ")).strip().upper()
+
+ if cuda_selected in ("1", "118"):
+ install_base_system_deps()
+ install_cuda("cu118")
+ break
+ elif cuda_selected in ("2", "121"):
+ install_base_system_deps()
+ install_cuda("cu121")
+ break
+ elif cuda_selected in ("3", "128"):
+ install_base_system_deps()
+ install_cuda("cu128")
+ break
+ elif cuda_selected in ("4", "130"):
+ install_base_system_deps()
+ install_cuda("cu130")
+ break
+ else:
+ print("\nVui lòng chọn phiên bản CUDA hợp lệ!")
+
+ if cuda_selected in ("3", "4", "cu128", "cu130"):
+ print("\nCài đặt gói phụ thuộc TensorRT (không khuyên dùng với người dùng phổ thông)")
+ print("Lưu ý: Nếu cài đặt gói TensorRT, bạn sẽ cần thêm gói TensorRT từ Nvidia và thêm nó vào PATH hệ thống: https://developer.nvidia.com/tensorrt")
+
+ while 1:
+ tensorrt_selected = str(input("Bạn có muốn sử dụng TensorRT không (Y/N): ")).strip().upper()
+
+ if tensorrt_selected in ("Y", "YES"):
+ run_command(f"{python_dir} -m uv pip install tensorrt")
+ break
+ elif tensorrt_selected in ("N", "NO"):
+ break
+ else:
+ print("\nVui lòng chọn giá trị hợp lệ!")
+
+ print("\nCài đặt gói phụ thuộc Triton (không khuyên dùng với người dùng phổ thông)")
+
+ while 1:
+ triton_selected = str(input("Bạn có muốn sử dụng Triton không (Y/N): ")).strip().upper()
+
+ if triton_selected in ("Y", "YES"):
+ run_command(f"{python_dir} -m uv pip install triton")
+ break
+ elif triton_selected in ("N", "NO"):
+ break
+ else:
+ print("\nVui lòng chọn giá trị hợp lệ!")
+ break
+ elif selected in ("3", "XPU"):
+ install_base_system_deps()
+ install_xpu("openvino")
+ break
+ elif selected in ("4", "ROCM"):
+ print("\nLưu ý: ROCM có thể gặp vấn đề về hiệu suất hoặc lỗi trên các thiết bị chạy Windows và hãy chắc chắn GPU của bạn nằm trong danh sách hỗ trợ")
+
+ print("\nPhân loại ROCm:")
+ print(" 1. ROCM-DGPU (Dành cho GPU rời)")
+ print(" 2. ROCM-APU (Dành cho GPU tích hợp)")
+
+ while 1:
+ rocm_type = str(input("Hãy chọn loại GPU của bạn (Nhập 1 hoặc 2): ")).strip().upper()
+
+ if rocm_type in ("1", "ROCM-DGPU"):
+ print("\nCác phiên bản ROCM (Lưu ý: hãy chọn đúng phiên bản theo kiến trúc GPU của bạn, nếu không sẽ không thể nhận dạng được thiết bị)")
+ print(" 1. gfx900 -> Vega 56, Vega 64, Frontier Edition")
+ print(" 2. gfx906 -> Radeon VII, MI50, MI60")
+ print(" 3. gfx908 -> Instinct MI100")
+ print(" 4. gfx90a -> Instinct MI210, MI250, MI250X")
+ print(" 5. gfx101X-dgpu -> Dòng RDNA1 (RX 5500, RX 5600, RX 5700 XT...)")
+ print(" 6. gfx103X-dgpu -> Dòng RDNA2 (RX 6600, RX 6700 XT, RX 6800, RX 6900 XT...)")
+ print(" 7. gfx110X-dgpu -> Dòng RDNA3 (RX 7600, RX 7700 XT, RX 7800 XT, RX 7900 XTX...)")
+ print(" 8. gfx120X-all -> Dòng RDNA4 (RX 9060, RX 9060 XT, RX 9070 XT...)")
+
+ dgpu_mapping = {
+ "1": "gfx900",
+ "2": "gfx906",
+ "3": "gfx908",
+ "4": "gfx90a",
+ "5": "gfx101X-dgpu",
+ "6": "gfx103X-dgpu",
+ "7": "gfx110X-dgpu",
+ "8": "gfx120X-all"
+ }
+
+ while 1:
+ sub_select = str(input("Chọn số tương ứng với GPU rời của bạn (1-7): ")).strip()
+ if sub_select in dgpu_mapping:
+ install_base_system_deps()
+ install_rocm(dgpu_mapping[sub_select], "rocm")
+ break
+ else:
+ print("\nVui lòng chọn đúng số từ 1 đến 7!")
+ break
+ elif rocm_type in ("2", "ROCM-APU"):
+ print("\nCác phiên bản ROCM (Lưu ý: hãy chọn đúng phiên bản theo kiến trúc APU của bạn, nếu không sẽ không thể nhận dạng được thiết bị)")
+ print(" 1. gfx103X-all -> APU tích hợp RDNA2 (Steam Deck, Ryzen 6000/7000 iGPU...)")
+ print(" 2. gfx110X-all -> APU tích hợp RDNA3 (Ryzen 7040/8040 iGPU, ROG Ally...)")
+ print(" 3. gfx1150 -> APU Ryzen AI 9 dòng 300 / Strix Point (Radeon 880M/890M)")
+ print(" 4. gfx1151 -> APU Strix Halo hoặc các biến thể đặc biệt")
+ print(" 5. gfx1152 -> Biến thể APU RDNA3.5 di động tiết kiệm điện")
+ print(" 6. gfx1153 -> Biến thể APU RDNA3.5 phân khúc phổ thông")
+ print(" 7. gfx120X-all -> Kiến trúc APU RDNA4 tích hợp mới nhất")
+
+ apu_mapping = {
+ "1": "gfx103X-all",
+ "2": "gfx110X-all",
+ "3": "gfx1150",
+ "4": "gfx1151",
+ "5": "gfx1152",
+ "6": "gfx1153",
+ "7": "gfx120X-all"
+ }
+
+ while 1:
+ sub_select = str(input("Chọn số tương ứng với APU của bạn (1-7): ")).strip()
+ if sub_select in apu_mapping:
+ install_base_system_deps()
+ install_rocm(apu_mapping[sub_select], "rocm")
+ break
+ else:
+ print("\nVui lòng chọn đúng số từ 1 đến 7!")
+ break
+ else:
+ print("\nVui lòng chọn 1 hoặc 2!")
+ break
+ else:
+ print("\nVui lòng chọn phiên bản hợp lệ!")
+
+ print("\nHoàn tất quá trình cài đặt thư viện!")
+
+
+if __name__ == "__main__":
+ # Validate the existence of the python runtime before starting any tasks
+ if not os.path.exists(python_dir):
+ print("\nKhông tìm thấy thời gian chạy python!")
+ sys.exit(1)
+
+ # Platform-specific routing logic
+ if sys.platform == "win32":
+ print("Lưu ý: Bạn cần chắc chắn đã cài đặt thư viện Visual C++ Redistributable Runtimes trước khi tiếp tục.")
+ input("Nhấn Enter để tiếp tục.")
+
+ install_requirements_win()
+ install_ffmpeg()
+ elif sys.platform == "linux":
+ # Linux environments require root/administrator privileges to install apt packages
+ if os.getuid() != 0:
+ print("❌ Lỗi: Bạn phải chạy với quyền sudo!")
+ sys.exit(1)
+
+ install_requirements_linux()
+ install_ffmpeg_linux()
+ else:
+ print("\nHiện chỉ hỗ trợ cài đặt trên Windows và Linux!")
+ sys.exit(1)
\ No newline at end of file
diff --git a/main/app/parser.py b/main/app/parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ed5c5121fa4e8e6423b6dd44e2dc7d5d09018e7
--- /dev/null
+++ b/main/app/parser.py
@@ -0,0 +1,422 @@
+import os
+import sys
+
+sys.path.append(os.getcwd())
+
+# Safely extract the primary command-line argument
+try:
+ argv = sys.argv[1]
+except IndexError:
+ argv = None
+
+# List of supported help flags for user guidance
+argv_help_is_allows = [
+ "--help_audio_effects",
+ "--help_convert",
+ "--help_create_dataset",
+ "--help_create_index",
+ "--help_extract",
+ "--help_preprocess",
+ "--help_separate_music",
+ "--help_train",
+ "--help_create_reference",
+ "--help"
+]
+
+# Mapping of execution flags to their corresponding internal module paths
+COMMANDS = {
+ "--audio_effects": "main.inference.audio_effects",
+ "--convert": "main.inference.conversion.convert",
+ "--create_dataset": "main.inference.create_dataset",
+ "--create_index": "main.inference.create_index",
+ "--extract": "main.inference.extracting.extract",
+ "--preprocess": "main.inference.preprocess.preprocess",
+ "--separate_music": "main.inference.separate_music",
+ "--train": "main.inference.training.train",
+ "--create_reference": "main.inference.create_reference",
+}
+
+# Base validation: Exit if no argument is provided, or if the argument is unrecognized and lacks a help flag
+if argv is None or argv not in list(COMMANDS) and "--help" not in argv:
+ print("Cú pháp không hợp lệ! Sử dụng --help để biết thêm")
+ sys.exit()
+
+if argv_help_is_allows[0] == argv:
+ print("""Các tham số của `--audio_effects`:
+ 1. Đường dẫn tệp:
+ - `--input_path` (bắt buộc): Đường dẫn đến tệp âm thanh đầu vào.
+ - `--output_path` (mặc định: `./audios/apply_effects.wav`): Đường dẫn lưu tệp đầu ra.
+ - `--export_format` (mặc định: `wav`): Định dạng xuất tệp (`wav`, `mp3`, ...).
+
+ 2. Lấy mẫu lại:
+ - `--resample` (mặc định: `False`): Có lấy mẫu lại hay không.
+ - `--resample_sr` (mặc định: `0`): Tần số lấy mẫu mới (Hz).
+
+ 3. Chorus:
+ - `--chorus`: Bật/tắt chorus.
+ - `--chorus_depth`, `--chorus_rate`, `--chorus_mix`, `--chorus_delay`, `--chorus_feedback`: Các thông số điều chỉnh chorus.
+
+ 4. Distortion:
+ - `--distortion`: Bật/tắt distortion.
+ - `--drive_db`: Mức độ méo âm thanh.
+
+ 5. Reverb:
+ - `--reverb`: Bật/tắt hồi âm.
+ - `--reverb_room_size`, `--reverb_damping`, `--reverb_wet_level`, `--reverb_dry_level`, `--reverb_width`, `--reverb_freeze_mode`: Điều chỉnh hồi âm.
+
+ 6. Pitchshift:
+ - `--pitchshift`: Bật/tắt thay đổi cao độ.
+ - `--pitch_shift`: Giá trị dịch cao độ.
+
+ 7. Delay:
+ - `--delay`: Bật/tắt delay.
+ - `--delay_seconds`, `--delay_feedback`, `--delay_mix`: Điều chỉnh thời gian trễ, phản hồi và hòa trộn.
+
+ 8. Compressor:
+ - `--compressor`: Bật/tắt compressor.
+ - `--compressor_threshold`, `--compressor_ratio`, `--compressor_attack_ms`, `--compressor_release_ms`: Các thông số nén.
+
+ 9. Limiter:
+ - `--limiter`: Bật/tắt giới hạn mức âm thanh.
+ - `--limiter_threshold`, `--limiter_release`: Ngưỡng giới hạn và thời gian nhả.
+
+ 10. Gain (Khuếch đại):
+ - `--gain`: Bật/tắt gain.
+ - `--gain_db`: Mức gain (dB).
+
+ 11. Bitcrush:
+ - `--bitcrush`: Bật/tắt hiệu ứng giảm độ phân giải.
+ - `--bitcrush_bit_depth`: Số bit của bitcrush.
+
+ 12. Clipping:
+ - `--clipping`: Bật/tắt cắt âm thanh.
+ - `--clipping_threshold`: Ngưỡng clipping.
+
+ 13. Phaser:
+ - `--phaser`: Bật/tắt hiệu ứng phaser.
+ - `--phaser_rate_hz`, `--phaser_depth`, `--phaser_centre_frequency_hz`, `--phaser_feedback`, `--phaser_mix`: Điều chỉnh hiệu ứng phaser.
+
+ 14. Boost bass & treble:
+ - `--treble_bass_boost`: Bật/tắt tăng cường âm bass và treble.
+ - `--bass_boost_db`, `--bass_boost_frequency`, `--treble_boost_db`, `--treble_boost_frequency`: Các thông số tăng bass và treble.
+
+ 15. Fade in & fade out:
+ - `--fade_in_out`: Bật/tắt hiệu ứng fade.
+ - `--fade_in_duration`, `--fade_out_duration`: Thời gian fade vào/ra.
+
+ 16. Kết hợp âm thanh:
+ - `--audio_combination`: Bật/tắt ghép nhiều tệp âm thanh.
+ - `--audio_combination_input`: Đường dẫn tệp âm thanh bổ sung.
+ - `--main_volume`: Âm lượng của âm thanh chính.
+ - `--combination_volume`:: Âm lượng của âm thanh cần kết hợp.
+ """)
+ sys.exit()
+elif argv_help_is_allows[1] == argv:
+ print("""Các tham số của --convert:
+ 1. Cấu hình xử lí giọng nói:
+ - `--pitch` (mặc định: `0`): Điều chỉnh cao độ.
+ - `--filter_radius` (mặc định: `3`): Độ mượt của đường F0.
+ - `--index_rate` (mặc định: `0.5`): Tỷ lệ sử dụng chỉ mục giọng nói.
+ - `--rms_mix_rate` (mặc định: `1`): Hệ số điều chỉnh biên độ âm lượng.
+ - `--protect` (mặc định: `0.33`): Bảo vệ phụ âm.
+ - `--hop_length` (mặc định: `64`): Bước nhảy khi xử lí âm thanh.
+ - `--sid` (mặc định: `0`): Mã số nhận dạng giọng nói đối với mô hình đa giọng.
+ - `--noise_scale` (mặc định: `0.35`): Tùy chỉnh đặc biệt cho mô hình SVC gây ảnh hưởng trực tiếp đến chất lượng của âm thanh, bạn không nên điều chỉnh nó.
+
+ 2. Cấu hình F0:
+ - `--f0_method` (mặc định: `rmvpe`): Phương pháp dự đoán F0 (`pm-ac`, `pm-cc`, `pm-shs`, `dio`, `dio-stonemask`, `mangio-crepe-tiny`, `mangio-crepe-small`, `mangio-crepe-medium`, `mangio-crepe-large`, `mangio-crepe-full`, `crepe-tiny`, `crepe-small`, `crepe-medium`, `crepe-large`, `crepe-full`, `fcpe`, `fcpe-legacy`, `fcpe-previous`, `rmvpe`, `rmvpe-medfilt`, `rmvpe-mix`, `rmvpe-mix-medfilt`, `hpa-rmvpe`, `hpa-rmvpe-medfilt`, `hpa-rmvpe-previous`, `hpa-rmvpe-previous-medfilt`, `harvest`, `harvest-stonemask`, `yin`, `pyin`, `swipe`, `swipe-stonemask`, `piptrack`, `penn`, `mangio-penn`, `djcm`, `djcm-medfilt`, `djcm-svs`, `djcm-svs-medfilt`, `swift`, `pesto`).
+ - `--f0_autotune` (mặc định: `False`): Có tự động điều chỉnh F0 hay không.
+ - `--f0_autotune_strength` (mặc định: `1`): Cường độ hiệu chỉnh tự động F0.
+ - `--f0_file` (mặc định: ``): Đường dẫn tệp F0 có sẵn.
+ - `--predictor_onnx` (mặc định: `False`): Có sử dụng chế độ tăng tốc bằng ONNX của trích xuất cao độ hay không.
+ - `--proposal_pitch` (mặc định: `False`): Đề xuất cao độ thay vì điều chỉnh thủ công.
+ - `--proposal_pitch_threshold` (mặc định: `0.0`): Ngưỡng tần số ước tính cao độ.
+ - `--alpha` (mặc định: `0.5`): Ngưỡng trộn cao độ khi ước tính cao độ hybrid.
+
+ 3. Mô hình nhúng:
+ - `--embedder_model` (mặc định: `hubert_base`): Mô hình nhúng sử dụng.
+ - `--embedders_mode` (mặc định: `fairseq`): Chế độ nhúng (`fairseq`, `transformers`, `onnx`).
+ - `--embedders_mix` (mặc định: `False`): Hòa trộn tầng.
+ - `--embedders_mix_layers` (mặc định: `9`): Tầng hòa trộn.
+ - `--embedders_mix_ratio` (mặc định: `0.5`): Tỉ lệ hòa trộn.
+
+ 4. Đường dẫn tệp:
+ - `--input_path` (bắt buộc): Đường dẫn tệp âm thanh đầu vào.
+ - `--output_path` (mặc định: `./audios/output.wav`): Đường dẫn lưu tệp đầu ra.
+ - `--export_format` (mặc định: `wav`): Định dạng xuất tệp.
+ - `--pth_path` (bắt buộc): Đường dẫn đến tệp mô hình `.pth`.
+ - `--index_path` (mặc định: `None`): Đường dẫn tệp chỉ mục (nếu có).
+
+ 5. Làm sạch âm thanh:
+ - `--clean_audio` (mặc định: `False`): Có áp dụng làm sạch âm thanh không.
+ - `--clean_strength` (mặc định: `0.7`): Mức độ làm sạch.
+
+ 6. Resampling & chia nhỏ âm thanh:
+ - `--resample_sr` (mặc định: `0`): Tần số lấy mẫu mới (0 nghĩa là giữ nguyên).
+ - `--split_audio` (mặc định: `False`): Có chia nhỏ audio trước khi xử lí không.
+
+ 7. Kiểm tra & tối ưu hóa:
+ - `--checkpointing` (mặc định: `False`): Bật/tắt checkpointing để tiết kiệm RAM.
+
+ 8. Dịch formant:
+ - `--formant_shifting` (mặc định: `False`): Có bật hiệu ứng dịch formant không.
+ - `--formant_qfrency` (mặc định: `0.8`): Hệ số dịch formant theo tần số.
+ - `--formant_timbre` (mặc định: `0.8`): Hệ số thay đổi màu sắc giọng.
+ """)
+ sys.exit()
+elif argv_help_is_allows[2] == argv:
+ print("""Các tham số của --create_dataset:
+ 1. Đường dẫn & cấu hình dataset:
+ - `--input_data` (bắt buộc): Đường dẫn liên kết đến âm thanh (Liên kết Youtube, có thể dùng dấu `,` để dùng nhiều liên kết).
+ - `--output_dirs` (mặc định: `./dataset`): Thư mục xuất dữ liệu đầu ra.
+ - `--sample_rate` (mặc định: `48000`): Tần số lấy mẫu cho âm thanh.
+
+ 2. Làm sạch dữ liệu:
+ - `--clean_dataset` (mặc định: `False`): Có áp dụng làm sạch dữ liệu hay không.
+ - `--clean_strength` (mặc định: `0.7`): Mức độ làm sạch dữ liệu.
+
+ 3. Tách giọng & hiệu ứng:
+ - `--separate` (mặc định: `True`): có tách nhạc hay không.
+ - `--separator_reverb` (mặc định: `False`): Có tách vang giọng không.
+ - `--model_name` (mặc định: `MDXNET_Main`): Mô hình tách nhạc ('Main_340', 'Main_390', 'Main_406', 'Main_427', 'Main_438', 'Inst_full_292', 'Inst_HQ_1', 'Inst_HQ_2', 'Inst_HQ_3', 'Inst_HQ_4', 'Inst_HQ_5', 'Kim_Vocal_1', 'Kim_Vocal_2', 'Kim_Inst', 'Inst_187_beta', 'Inst_82_beta', 'Inst_90_beta', 'Voc_FT', 'Crowd_HQ', 'MDXNET_9482', 'Inst_1', 'Inst_2', 'Inst_3', 'MDXNET_1_9703', 'MDXNET_2_9682', 'MDXNET_3_9662', 'Inst_Main', 'MDXNET_Main', 'HT-Tuned', 'HT-Normal', 'HD_MMI', 'HT_6S', 'HP-1', 'HP-2', 'HP-Vocal-1', 'HP-Vocal-2', 'HP2-1', 'HP2-2', 'HP2-3', 'SP-2B-1', 'SP-2B-2', 'SP-3B-1', 'SP-4B-1', 'SP-4B-2', 'SP-MID-1', 'SP-MID-2').
+ - `--reverb_model` (mặc định: `MDX-Reverb`): Mô hình tách nhạc ("MDX-Reverb", 'VR-Reverb', 'Echo-Aggressive', 'Echo-Normal').
+ - `--denoise_model` (mặc định: `Normal`): Mô hình tách nhạc ('Lite', 'Normal').
+
+ 4. Cấu hình xử lí âm thanh:
+ - `--shifts` (mặc định: `2`): Số lượng dự đoán.
+ - `--batch_size` (mặc định: `1`): Kích thước lô.
+ - `--overlap` (mặc định: `0.25`): Mức độ chồng lấn giữa các đoạn.
+ - `--aggression` (mặc định: `5`): Cường độ chiết xuất thân chính.
+ - `--hop_length` (mặc định: `1024`): Bước nhảy MDX khi xử lí.
+ - `--window_size` (mặc định: `512`): Kích thước cửa sổ.
+ - `--segments_size` (mặc định: `256`): Kích thước phân đoạn âm thanh.
+ - `--post_process_threshold` (mặc định: `0.2`): Mức độ xử lí hậu kỳ sau khi tách nhạc.
+
+ 5. Cấu hình xử lí âm thanh khác:
+ - `--enable_tta` (mặc định: `False`): Tăng cường suy luận.
+ - `--enable_denoise` (mặc định: `False`): Khữ tách nhạc.
+ - `--high_end_process` (mặc định: `False`): Xử lí dải cao.
+ - `--enable_post_process` (mặc định: `False`): Hậu xử lí.
+
+ 6. Bỏ qua phần âm thanh:
+ - `--skip_seconds` (mặc định: `False`): Có bỏ qua giây âm thanh nào không.
+ - `--skip_start_audios` (mặc định: `0`): Thời gian (giây) cần bỏ qua ở đầu audio.
+ - `--skip_end_audios` (mặc định: `0`): Thời gian (giây) cần bỏ qua ở cuối audio.
+ """)
+ sys.exit()
+elif argv_help_is_allows[3] == argv:
+ print("""Các tham số của --create_index:
+ 1. Thông tin mô hình:
+ - `--model_name` (bắt buộc): Tên mô hình.
+ - `--rvc_version` (mặc định: `v2`): Phiên bản (`v1`, `v2`).
+ - `--index_algorithm` (mặc định: `Auto`): Thuật toán index sử dụng (`Auto`, `Faiss`, `KMeans`).
+ """)
+ sys.exit()
+elif argv_help_is_allows[4] == argv:
+ print("""Các tham số của --extract:
+ 1. Thông tin mô hình:
+ - `--model_name` (bắt buộc): Tên mô hình.
+ - `--rvc_version` (mặc định: `v2`): Phiên bản mô hình (`v1`, `v2`).
+
+ 2. Cấu hình F0:
+ - `--f0_method` (mặc định: `rmvpe`): Phương pháp dự đoán F0 (`pm-ac`, `pm-cc`, `pm-shs`, `dio`, `dio-stonemask`, `mangio-crepe-tiny`, `mangio-crepe-small`, `mangio-crepe-medium`, `mangio-crepe-large`, `mangio-crepe-full`, `crepe-tiny`, `crepe-small`, `crepe-medium`, `crepe-large`, `crepe-full`, `fcpe`, `fcpe-legacy`, `fcpe-previous`, `rmvpe`, `rmvpe-medfilt`, `rmvpe-mix`, `rmvpe-mix-medfilt`, `hpa-rmvpe`, `hpa-rmvpe-medfilt`, `hpa-rmvpe-previous`, `hpa-rmvpe-previous-medfilt`, `harvest`, `harvest-stonemask`, `yin`, `pyin`, `swipe`, `swipe-stonemask`, `piptrack`, `penn`, `mangio-penn`, `djcm`, `djcm-medfilt`, `djcm-svs`, `djcm-svs-medfilt`, `swift`, `pesto`).
+ - `--predictor_onnx` (mặc định: `False`): Có sử dụng chế độ tăng tốc bằng ONNX của trích xuất cao độ hay không.
+ - `--pitch_guidance` (mặc định: `True`): Có sử dụng hướng dẫn cao độ hay không.
+ - `--f0_autotune` (mặc định: `False`): Có tự động điều chỉnh F0 hay không.
+ - `--f0_autotune_strength` (mặc định: `1`): Cường độ hiệu chỉnh tự động F0.
+ - `--alpha` (mặc định: `0.5`): Ngưỡng trộn cao độ khi ước tính cao độ hybrid.
+
+ 3. Cấu hình xử lí:
+ - `--hop_length` (mặc định: `128`): Độ dài bước nhảy trong quá trình xử lí.
+ - `--cpu_cores` (mặc định: `2`): Số lượng luồng CPU sử dụng.
+ - `--gpu` (mặc định: `-`): Chỉ định GPU sử dụng (ví dụ: `0` cho GPU đầu tiên, `-` để tắt GPU).
+ - `--sample_rate` (bắt buộc): Tần số lấy mẫu của âm thanh đầu vào.
+ - `--include_mutes` (mặc định: `2`): Số lượng tệp im lặng được sử dụng huấn luyện.
+
+ 4. Cấu hình nhúng:
+ - `--embedder_model` (mặc định: `hubert_base`): Tên mô hình nhúng.
+ - `--embedders_mode` (mặc định: `fairseq`): Chế độ nhúng (`fairseq`, `transformers`, `onnx`).
+ - `--embedders_mix` (mặc định: `False`): Hòa trộn tầng.
+ - `--embedders_mix_layers` (mặc định: `9`): Tầng hòa trộn.
+ - `--embedders_mix_ratio` (mặc định: `0.5`): Tỉ lệ hòa trộn.
+ """)
+ sys.exit()
+elif argv_help_is_allows[5] == argv:
+ print("""Các tham số của --preprocess:
+ 1. Thông tin mô hình:
+ - `--model_name` (bắt buộc): Tên mô hình.
+
+ 2. Cấu hình dữ liệu:
+ - `--dataset_path` (mặc định: `./dataset`): Đường dẫn thư mục chứa tệp dữ liệu.
+ - `--sample_rate` (bắt buộc): Tần số lấy mẫu của dữ liệu âm thanh.
+
+ 3. Cấu hình xử lí:
+ - `--cpu_cores` (mặc định: `2`): Số lượng luồng CPU sử dụng.
+ - `--cut_preprocess` (mặc định: `Automatic`): Cách cắt dữ liệu tiền xử lí (`Automatic`, `Simple`, `Skip`).
+ - `--process_effects` (mặc định: `False`): Có áp dụng tiền xử lí hay không.
+ - `--clean_dataset` (mặc định: `False`): Có làm sạch tệp dữ liệu hay không.
+ - `--clean_strength` (mặc định: `0.7`): Độ mạnh của quá trình làm sạch dữ liệu.
+
+ 4. Cấu hình khác:
+ - `--chunk_len` (mặc định: `3.0`): Độ dài của đoạn âm thanh cho phương pháp 'Simple'.
+ - `--overlap_len` (mặc định: `0.3`): Độ dài của phần chồng chéo giữa các lát cắt đối với phương pháp 'Simple'.
+ - `--normalization_mode` (mặc định: `none`): Có xử lí chuẩn hóa âm thanh không (`none`, `pre`, `post`)
+ """)
+ sys.exit()
+elif argv_help_is_allows[6] == argv:
+ print("""Các tham số của --separate_music:
+ 1. Cấu hình đầu vào, đầu ra:
+ - `--input_path` (bắt buộc): Đường dẫn tệp âm thanh đầu vào.
+ - `--output_dirs` (mặc định: `./audios`): Thư mục lưu tệp đầu ra.
+ - `--export_format` (mặc định: `wav`): Định dạng xuất tệp (`wav`, `mp3`,...).
+ - `--sample_rate` (mặc định: `44100`): Tần số lấy mẫu của âm thanh đầu ra.
+
+ 2. Cấu hình mô hình:
+ - `--model_name` (mặc định: `MDXNET_Main`): Mô hình tách nhạc ('Main_340', 'Main_390', 'Main_406', 'Main_427', 'Main_438', 'Inst_full_292', 'Inst_HQ_1', 'Inst_HQ_2', 'Inst_HQ_3', 'Inst_HQ_4', 'Inst_HQ_5', 'Kim_Vocal_1', 'Kim_Vocal_2', 'Kim_Inst', 'Inst_187_beta', 'Inst_82_beta', 'Inst_90_beta', 'Voc_FT', 'Crowd_HQ', 'MDXNET_9482', 'Inst_1', 'Inst_2', 'Inst_3', 'MDXNET_1_9703', 'MDXNET_2_9682', 'MDXNET_3_9662', 'Inst_Main', 'MDXNET_Main', 'HT-Tuned', 'HT-Normal', 'HD_MMI', 'HT_6S', 'HP-1', 'HP-2', 'HP-Vocal-1', 'HP-Vocal-2', 'HP2-1', 'HP2-2', 'HP2-3', 'SP-2B-1', 'SP-2B-2', 'SP-3B-1', 'SP-4B-1', 'SP-4B-2', 'SP-MID-1', 'SP-MID-2').
+ - `--karaoke_model` (mặc định: `MDX-Version-1`): Mô hình tách nhạc ('MDX-Version-1', 'MDX-Version-2', 'VR-Version-1', 'VR-Version-2').
+ - `--reverb_model` (mặc định: `MDX-Reverb`): Mô hình tách nhạc ("MDX-Reverb", 'VR-Reverb', 'Echo-Aggressive', 'Echo-Normal').
+ - `--denoise_model` (mặc định: `Normal`): Mô hình tách nhạc ('Lite', 'Normal').
+
+ 3. Cấu hình xử lí âm thanh:
+ - `--shifts` (mặc định: `2`): Số lượng dự đoán.
+ - `--batch_size` (mặc định: `1`): Kích thước lô.
+ - `--overlap` (mặc định: `0.25`): Mức độ chồng lấn giữa các đoạn.
+ - `--aggression` (mặc định: `5`): Cường độ chiết xuất thân chính.
+ - `--hop_length` (mặc định: `1024`): Bước nhảy MDX khi xử lí.
+ - `--window_size` (mặc định: `512`): Kích thước cửa sổ.
+ - `--segments_size` (mặc định: `256`): Kích thước phân đoạn âm thanh.
+ - `--post_process_threshold` (mặc định: `0.2`): Mức độ xử lí hậu kỳ sau khi tách nhạc.
+
+ 4. Cấu hình xử lí âm thanh khác:
+ - `--enable_tta` (mặc định: `False`): Tăng cường suy luận.
+ - `--enable_denoise` (mặc định: `False`): Khữ tách nhạc.
+ - `--high_end_process` (mặc định: `False`): Xử lí dải cao.
+ - `--enable_post_process` (mặc định: `False`): Hậu xử lí.
+ - `--separate_backing` (mặc định: `False`): Tách bè giọng.
+ - `--separate_reverb` (mặc định: `False`): Tách vang giọng.
+ """)
+ sys.exit()
+elif argv_help_is_allows[7] == argv:
+ print("""Các tham số của --train:
+ 1. Cấu hình mô hình:
+ - `--model_name` (bắt buộc): Tên mô hình.
+ - `--rvc_version` (mặc định: `v2`): Phiên bản mô hình (`v1`, `v2`).
+ - `--model_author` (tùy chọn): Tác giả của mô hình.
+
+ 2. Cấu hình lưu:
+ - `--save_every_epoch` (bắt buộc): Số kỷ nguyên giữa mỗi lần lưu.
+ - `--save_only_latest` (mặc định: `True`): Chỉ lưu điểm mới nhất.
+ - `--save_every_weights` (mặc định: `True`): Lưu tất cả trọng số của mô hình.
+
+ 3. Cấu hình huấn luyện:
+ - `--total_epoch` (mặc định: `300`): Tổng số kỷ nguyên huấn luyện.
+ - `--batch_size` (mặc định: `8`): Kích thước lô trong quá trình huấn luyện.
+
+ 4. Cấu hình thiết bị:
+ - `--gpu` (mặc định: `0`): Chỉ định GPU để sử dụng (số hoặc `-` nếu không dùng GPU).
+ - `--cache_data_in_gpu` (mặc định: `False`): Lưu dữ liệu vào GPU để tăng tốc.
+
+ 5. Cấu hình huấn luyện nâng cao:
+ - `--pitch_guidance` (mặc định: `True`): Sử dụng hướng dẫn cao độ.
+ - `--g_pretrained_path` (mặc định: ``): Đường dẫn đến trọng số G đã huấn luyện trước.
+ - `--d_pretrained_path` (mặc định: ``): Đường dẫn đến trọng số D đã huấn luyện trước.
+ - `--vocoder` (mặc định: `Default`): Bộ mã hóa được sử dụng (`Default`, `MRF-HiFi-GAN`, `RefineGAN`).
+ - `--architecture` (mặc định: `RVC`): Kiến trúc của mô hình giọng nói (`RVC`, `SVC`).
+
+ 6. Phát hiện huấn luyện quá mức:
+ - `--overtraining_detector` (mặc định: `False`): Bật/tắt chế độ phát hiện huấn luyện quá mức.
+ - `--overtraining_threshold` (mặc định: `50`): Ngưỡng để xác định huấn luyện quá mức.
+
+ 7. Xử lí dữ liệu:
+ - `--cleanup` (mặc định: `False`): Dọn dẹp tệp huấn luyện cũ để tiến hành huấn luyện lại từ đầu.
+
+ 8. Tối ưu:
+ - `--checkpointing` (mặc định: `False`): Bật/tắt checkpointing để tiết kiệm RAM.
+ - `--deterministic` (mặc định: `False`): Khi bật sẽ sử dụng các thuật toán có tính xác định cao, đảm bảo rằng mỗi lần chạy cùng một dữ liệu đầu vào sẽ cho kết quả giống nhau.
+ - `--benchmark` (mặc định: `False`): Khi bật sẽ thử nghiệm và chọn thuật toán tối ưu nhất cho phần cứng và kích thước cụ thể.
+ - `--optimizer` (mặc định: `AdamW`): Trình tối ưu hóa được sử dụng (`AdamW`, `RAdam`, `AnyPrecisionAdamW`, `AdaBelief`).
+ - `--multiscale_mel_loss` (mặc định: `False`): So sánh phổ Mel của âm thanh thật và âm thanh giả ở nhiều thang độ khác nhau. Giúp mô hình học được chi tiết âm sắc, độ sáng và cấu trúc tần số tốt hơn, từ đó cải thiện chất lượng và độ tự nhiên của giọng nói đầu ra.
+ - `--use_cosine_annealing_lr` (mặc định: `False`): Sử dụng phương pháp giảm LR bằng Cosine Annealing có thể giúp cải thiện chất lượng phát âm.
+
+ 9. Bộ tham chiếu:
+ - `--use_custom_reference` (mặc định: `False`): Có tùy chỉnh bộ tham chiếu hay không.
+ - `--reference_path` (mặc định: `False`): Đường dẫn đến bộ tham chiếu.
+
+ 10. Tùy chỉnh nâng cao:
+ - `--filelist_path` (mặc định: ""): Đường dẫn tệp chứa các đường dẫn tệp âm thanh, F0 và đặc tính âm thanh.
+ - `--config_save_path` (mặc định: ""): Đường dẫn tệp cấu hình huấn luyện.
+ - `--spec_dir` (mặc định: ""): Đường dẫn lưu trữ và tái sử dụng các tệp phổ.
+ - `--eval_dir` (mặc định: ""): Đường dẫn chứa tệp biểu đồ của Tensorboard.
+ - `--cache_spectrogram` (mặc định: `True`): Lưu các đoạn phổ âm thanh ra các tệp để sử dụng lại khi huấn luyện.
+ - `--save_the_pid` (mặc định: `True`): Lưu lại giá trị pid ra tệp cấu hình.
+ - `--custom_training` (mặc định: `False`): Sử dụng tùy chỉnh nâng cao. Thay đổi các đường dẫn tệp gốc.
+ """)
+ sys.exit()
+elif argv_help_is_allows[8] == argv:
+ print("""Các tham số của --create_reference:
+ 1. Đường dẫn tệp:
+ - `--audio_path` (bắt buộc): Đường dẫn tệp âm thanh đầu vào.
+ - `--reference_name` (mặc định: `reference`): Đường dẫn lưu bộ tham chiếu đầu ra.
+
+ 2. Cấu hình bộ tham chiếu:
+ - `--pitch_guidance` (mặc định: `True`): Sử dụng hướng dẫn cao độ.
+ - `--version` (mặc định: `v2`): Phiên bản mô hình (`v1`, `v2`).
+
+ 3. Cấu hình nhúng:
+ - `--embedder_model` (mặc định: `hubert_base`): Tên mô hình nhúng.
+ - `--embedders_mode` (mặc định: `fairseq`): Chế độ nhúng (`fairseq`, `transformers`, `onnx`).
+ - `--embedders_mix` (mặc định: `False`): Hòa trộn tầng.
+ - `--embedders_mix_layers` (mặc định: `9`): Tầng hòa trộn.
+ - `--embedders_mix_ratio` (mặc định: `0.5`): Tỉ lệ hòa trộn.
+
+ 4. Cấu hình F0:
+ - `--f0_method` (mặc định: `rmvpe`): Phương pháp dự đoán F0 (`pm-ac`, `pm-cc`, `pm-shs`, `dio`, `dio-stonemask`, `mangio-crepe-tiny`, `mangio-crepe-small`, `mangio-crepe-medium`, `mangio-crepe-large`, `mangio-crepe-full`, `crepe-tiny`, `crepe-small`, `crepe-medium`, `crepe-large`, `crepe-full`, `fcpe`, `fcpe-legacy`, `fcpe-previous`, `rmvpe`, `rmvpe-medfilt`, `rmvpe-mix`, `rmvpe-mix-medfilt`, `hpa-rmvpe`, `hpa-rmvpe-medfilt`, `hpa-rmvpe-previous`, `hpa-rmvpe-previous-medfilt`, `harvest`, `harvest-stonemask`, `yin`, `pyin`, `swipe`, `swipe-stonemask`, `piptrack`, `penn`, `mangio-penn`, `djcm`, `djcm-medfilt`, `djcm-svs`, `djcm-svs-medfilt`, `swift`, `pesto`).
+ - `--predictor_onnx` (mặc định: `False`): Có sử dụng chế độ tăng tốc bằng ONNX của trích xuất cao độ hay không.
+ - `--f0_up_key` (mặc định: `0`): Điều chỉnh cao độ.
+ - `--filter_radius` (mặc định: `3`): Độ mượt của đường F0.
+ - `--f0_autotune` (mặc định: `False`): Có tự động điều chỉnh F0 hay không.
+ - `--f0_autotune_strength` (mặc định: `1`): Cường độ hiệu chỉnh tự động F0.
+ - `--f0_file` (mặc định: ``): Đường dẫn tệp F0 có sẵn.
+ - `--proposal_pitch` (mặc định: `False`): Đề xuất cao độ thay vì điều chỉnh thủ công.
+ - `--proposal_pitch_threshold` (mặc định: `0.0`): Ngưỡng tần số ước tính cao độ.
+ - `--alpha` (mặc định: `0.5`): Ngưỡng trộn cao độ khi ước tính cao độ hybrid.
+ """)
+ sys.exit()
+elif argv_help_is_allows[9] == argv:
+ print("""Sử dụng:
+ 1. `--help_audio_effects`: Trợ giúp về phần thêm hiệu ứng âm thanh.
+ 2. `--help_convert`: Trợ giúp về chuyển đổi âm thanh.
+ 3. `--help_create_dataset`: Trợ giúp về tạo dữ liệu huấn luyện.
+ 4. `--help_create_index`: Trợ giúp về tạo chỉ mục.
+ 5. `--help_extract`: Trợ giúp về trích xuất dữ liệu huấn luyện.
+ 6. `--help_preprocess`: Trợ giúp về xử lí trước dữ liệu.
+ 7. `--help_separate_music`: Trợ giúp về tách nhạc.
+ 8. `--help_train`: Trợ giúp về huấn luyện mô hình.
+ 9. `--help_create_reference`: Trợ giúp về tạo bộ tham chiếu.
+ """)
+ sys.exit()
+elif argv in COMMANDS:
+ import importlib
+
+ # Dynamically import the target module mapping to the execution flag
+ module = importlib.import_module(COMMANDS[argv])
+ # Fetch the main executable callable from the imported module
+ main = module.main
+else:
+ # Fallback closure to prevent execution if an edge case bypasses the initial validation
+ main = lambda: print("Cú pháp không hợp lệ!"); sys.exit()
+
+if __name__ == "__main__":
+ import torch.multiprocessing as mp
+
+ try:
+ # Configure multiprocessing context based on execution requirements.
+ # RVC/SVC training and extraction tasks require the 'spawn' method for safe GPU/CUDA initialization.
+ if "--train" in argv: mp.set_start_method("spawn")
+ if "--preprocess" in argv or "--extract" in argv: mp.set_start_method("spawn", force=True)
+ except:
+ # Catch RuntimeError if the multiprocessing start method has already been set elsewhere
+ pass
+
+ # Fire the selected sub-module function
+ main()
\ No newline at end of file
diff --git a/main/app/run_tensorboard.py b/main/app/run_tensorboard.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c1c8c9c3b3e671c27ea44c52a680170138f4bcb
--- /dev/null
+++ b/main/app/run_tensorboard.py
@@ -0,0 +1,56 @@
+import os
+import sys
+import time
+import logging
+import warnings
+import webbrowser
+
+from tensorboard import program
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config, translations, logger
+
+def launch_tensorboard():
+ """
+ Configures and starts a background TensorBoard server using settings
+ defined in the application configuration files.
+
+ Returns:
+ str: The local host URL where the TensorBoard instance is running.
+ """
+
+ # Production Logging Configuration: Suppress verbose loggers from TensorBoard if debug mode is disabled
+ if not config.debug_mode:
+ warnings.filterwarnings("ignore")
+ for l in ["root", "tensorboard"]:
+ logging.getLogger(l).setLevel(logging.ERROR)
+
+ # Initialize the programmatic TensorBoard server instance
+ tb = program.TensorBoard()
+ # Configure arguments: setup the target log directory and bind to the designated system port
+ tb.configure(
+ argv=[
+ None,
+ "--logdir", config.configs["logs_path"],
+ f"--port={config.configs['tensorboard_port']}"
+ ]
+ )
+
+ # Fire up the background thread server and retrieve its operational URL
+ url = tb.launch()
+ # Log the successfully active TensorBoard endpoint destination
+ logger.info(f"{translations['tensorboard_url']}: {url}")
+
+ # Automate browser engagement: trigger local interface tab opening if '--open' argument exists
+ if "--open" in sys.argv: webbrowser.open(url)
+
+ return url
+
+if __name__ == "__main__":
+ # Boot up the tracking analytics platform
+ launch_tensorboard()
+
+ # Infinite loop to keep the main script thread alive for standalone service persistence
+ while 1:
+ time.sleep(5)
\ No newline at end of file
diff --git a/main/app/tabs/downloads/downloads.py b/main/app/tabs/downloads/downloads.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ea9e877a90ed9be964719a43d0c2f3cfdc2f103
--- /dev/null
+++ b/main/app/tabs/downloads/downloads.py
@@ -0,0 +1,330 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import (
+ models,
+ configs,
+ translations,
+ model_options
+)
+
+from main.app.core.downloads import (
+ search_models,
+ download_model,
+ download_pretrained_model
+)
+
+from main.app.core.ui import (
+ shutil_move,
+ change_download_choices,
+ change_download_pretrained_choices
+)
+
+from main.app.core.process import (
+ save_drop_model,
+ fetch_pretrained_data,
+ update_sample_rate_dropdown
+)
+
+def download_tab():
+ with gr.TabItem(
+ translations["downloads"]
+ ):
+ gr.Markdown(translations["download_markdown"])
+ with gr.Row():
+ gr.Markdown(translations["download_markdown_2"])
+ with gr.Row():
+ with gr.Accordion(
+ translations["model_download"],
+ open=True
+ ):
+ with gr.Row():
+ download_method = gr.Radio(
+ label=translations["model_download_select"],
+ choices=[
+ translations["download_url"],
+ translations["download_from_csv"],
+ translations["search_models"],
+ translations["upload"]
+ ],
+ interactive=True,
+ value=translations["download_url"]
+ )
+ with gr.Row():
+ gr.Markdown("___")
+ with gr.Column():
+ with gr.Row():
+ download_model_url = gr.Textbox(
+ label=translations["model_url"],
+ value="",
+ placeholder="https://...",
+ scale=6
+ )
+ download_model_name = gr.Textbox(
+ label=translations["modelname"],
+ value="",
+ placeholder=translations["modelname"],
+ scale=2
+ )
+ download_url_button = gr.Button(
+ value=translations["downloads"],
+ scale=2
+ )
+ with gr.Column():
+ download_csv_model = gr.Dropdown(
+ choices=models.keys(),
+ label=translations["model_warehouse"],
+ scale=8,
+ allow_custom_value=True,
+ visible=False
+ )
+ download_csv_button = gr.Button(
+ value=translations["get_model"],
+ scale=2,
+ variant="primary",
+ visible=False
+ )
+ with gr.Column():
+ search_model_name = gr.Textbox(
+ label=translations["name_to_search"],
+ placeholder=translations["modelname"],
+ interactive=True,
+ scale=8,
+ visible=False
+ )
+ search_button = gr.Button(
+ translations["search"],
+ scale=2,
+ visible=False
+ )
+ search_dropdown = gr.Dropdown(
+ label=translations["select_download_model"],
+ value="",
+ choices=[],
+ allow_custom_value=True,
+ interactive=False,
+ visible=False
+ )
+ search_download_model = gr.Button(
+ translations["downloads"],
+ variant="primary",
+ visible=False
+ )
+ with gr.Column():
+ upload_model_files = gr.Files(
+ label=translations["drop_model"],
+ file_types=[".pth", ".onnx", ".index", ".zip"],
+ visible=False
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["download_pretrained_2"],
+ open=True
+ ):
+ with gr.Row():
+ download_pretrain_method = gr.Radio(
+ label=translations["model_download_select"],
+ choices=[
+ translations["download_url"],
+ translations["list_model"],
+ translations["upload"]
+ ],
+ value=translations["download_url"],
+ interactive=True
+ )
+ with gr.Row():
+ gr.Markdown("___")
+ with gr.Column():
+ with gr.Row():
+ download_pretrain_disc_url = gr.Textbox(
+ label=translations["pretrained_url"].format(dg="D"),
+ value="",
+ placeholder="https://...",
+ interactive=True,
+ scale=4
+ )
+ download_pretrain_gen_url = gr.Textbox(
+ label=translations["pretrained_url"].format(dg="G"),
+ value="",
+ placeholder="https://...",
+ interactive=True,
+ scale=4
+ )
+ download_pretrain_button = gr.Button(
+ translations["downloads"],
+ scale=2,
+ visible=True
+ )
+ with gr.Column():
+ with gr.Row():
+ download_pretrain_choices = gr.Dropdown(
+ label=translations["select_pretrain"],
+ info=translations["select_pretrain_info"],
+ choices=list(fetch_pretrained_data().keys()),
+ value="Titan_Medium",
+ allow_custom_value=True,
+ interactive=True,
+ scale=6,
+ visible=False
+ )
+ download_pretrain_sample_rate = gr.Dropdown(
+ label=translations["pretrain_sr"],
+ info=translations["pretrain_sr"],
+ choices=["48k", "40k", "32k"],
+ value="48k",
+ interactive=True,
+ visible=False
+ )
+ download_pretrain_choices_button = gr.Button(
+ translations["downloads"],
+ scale=2,
+ variant="primary",
+ visible=False
+ )
+ with gr.Row():
+ upload_pretrains = gr.Files(
+ label=translations["drop_pretrain"].format(dg="G, D"),
+ file_types=[".pth"],
+ visible=False
+ )
+ with gr.Row():
+ download_url_button.click(
+ fn=download_model,
+ inputs=[
+ download_model_url,
+ download_model_name
+ ],
+ outputs=[download_model_url],
+ api_name="download_model"
+ )
+ download_csv_button.click(
+ fn=lambda model: download_model(
+ models[model],
+ model
+ ),
+ inputs=[
+ download_csv_model
+ ],
+ outputs=[
+ download_csv_model
+ ],
+ api_name="download_browser"
+ )
+ with gr.Row():
+ download_method.change(
+ fn=change_download_choices,
+ inputs=[
+ download_method
+ ],
+ outputs=[
+ download_model_url,
+ download_model_name,
+ download_url_button,
+ download_csv_model,
+ download_csv_button,
+ search_model_name,
+ search_button,
+ search_dropdown,
+ search_download_model,
+ upload_model_files
+ ]
+ )
+ search_button.click(
+ fn=search_models,
+ inputs=[
+ search_model_name
+ ],
+ outputs=[
+ search_dropdown,
+ search_download_model
+ ]
+ )
+ with gr.Row():
+ upload_model_files.upload(
+ fn=save_drop_model,
+ inputs=[
+ upload_model_files
+ ],
+ outputs=[
+ upload_model_files
+ ]
+ )
+ search_download_model.click(
+ fn=lambda model: download_model(
+ model_options[model],
+ model
+ ),
+ inputs=[
+ search_dropdown
+ ],
+ outputs=[
+ search_dropdown
+ ],
+ api_name="search_models"
+ )
+ with gr.Row():
+ download_pretrain_method.change(
+ fn=change_download_pretrained_choices,
+ inputs=[
+ download_pretrain_method
+ ],
+ outputs=[
+ download_pretrain_disc_url,
+ download_pretrain_gen_url,
+ download_pretrain_button,
+ download_pretrain_choices,
+ download_pretrain_sample_rate,
+ download_pretrain_choices_button,
+ upload_pretrains
+ ]
+ )
+ download_pretrain_choices.change(
+ fn=update_sample_rate_dropdown,
+ inputs=[
+ download_pretrain_choices
+ ],
+ outputs=[
+ download_pretrain_sample_rate
+ ]
+ )
+ with gr.Row():
+ download_pretrain_button.click(
+ fn=download_pretrained_model,
+ inputs=[
+ download_pretrain_method,
+ download_pretrain_disc_url,
+ download_pretrain_gen_url
+ ],
+ outputs=[
+ download_pretrain_disc_url,
+ download_pretrain_gen_url
+ ],
+ api_name="download_pretrain_link"
+ )
+ download_pretrain_choices_button.click(
+ fn=download_pretrained_model,
+ inputs=[
+ download_pretrain_method,
+ download_pretrain_choices,
+ download_pretrain_sample_rate
+ ],
+ outputs=[
+ download_pretrain_choices
+ ],
+ api_name="download_pretrain_choices"
+ )
+ upload_pretrains.upload(
+ fn=lambda upload_pretrains: [
+ shutil_move(pretrain.name, configs["pretrained_custom_path"])
+ for pretrain in upload_pretrains
+ ],
+ inputs=[
+ upload_pretrains
+ ],
+ outputs=[],
+ api_name="upload_pretrain"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/editing/child/audio_effects.py b/main/app/tabs/editing/child/audio_effects.py
new file mode 100644
index 0000000000000000000000000000000000000000..050ca6947c5f674dbc6cd6dfdcc9df3b4237eb1d
--- /dev/null
+++ b/main/app/tabs/editing/child/audio_effects.py
@@ -0,0 +1,1165 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.editing import audio_effects
+
+from main.app.core.presets import (
+ audio_effect_load_presets,
+ audio_effect_save_presets
+)
+
+from main.app.core.ui import (
+ visible,
+ shutil_move,
+ change_audios_choices,
+ change_effect_preset_choices
+)
+
+from main.app.variables import (
+ configs,
+ file_types,
+ translations,
+ audio_params,
+ paths_for_files,
+ sample_rate_choice,
+ export_format_choices,
+ audio_effect_presets_file
+)
+
+def audio_effects_tab():
+ with gr.Row():
+ gr.Markdown(translations["audio_effects_edit"])
+ with gr.Row():
+ with gr.Column():
+ with gr.Row():
+ reverb_check_box = gr.Checkbox(
+ label=translations["reverb"],
+ value=False,
+ interactive=True
+ )
+ chorus_check_box = gr.Checkbox(
+ label=translations["chorus"],
+ value=False,
+ interactive=True
+ )
+ delay_check_box = gr.Checkbox(
+ label=translations["delay"],
+ value=False,
+ interactive=True
+ )
+ phaser_check_box = gr.Checkbox(
+ label=translations["phaser"],
+ value=False,
+ interactive=True
+ )
+ compressor_check_box = gr.Checkbox(
+ label=translations["compressor"],
+ value=False,
+ interactive=True
+ )
+ more_options = gr.Checkbox(
+ label=translations["more_option"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["input_output"],
+ open=False
+ ):
+ with gr.Row():
+ upload_audio_files = gr.Files(
+ label=translations["drop_audio"],
+ file_types=file_types
+ )
+ with gr.Row():
+ audio_in_path = gr.Dropdown(
+ label=translations["input_audio"],
+ value="",
+ choices=paths_for_files,
+ info=translations["provide_audio"],
+ interactive=True,
+ allow_custom_value=True
+ )
+ audio_out_path = gr.Textbox(
+ label=translations["output_audio"],
+ value="audios/audio_effects.wav",
+ placeholder="audios/audio_effects.wav",
+ info=translations["provide_output"],
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Column():
+ audio_combination = gr.Checkbox(
+ label=translations["merge_instruments"],
+ value=False,
+ interactive=True
+ )
+ audio_combination_input = gr.Dropdown(
+ label=translations["input_audio"],
+ value="",
+ choices=paths_for_files,
+ info=translations["provide_audio"],
+ interactive=True,
+ allow_custom_value=True,
+ visible=False
+ )
+ with gr.Row():
+ main_volume = gr.Slider(
+ minimum=-80,
+ maximum=80,
+ label=translations["main_volume"],
+ info=translations["main_volume_info"],
+ value=-4,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ combine_volume = gr.Slider(
+ minimum=-80,
+ maximum=80,
+ label=translations["combination_volume"],
+ info=translations["combination_volume_info"],
+ value=-7,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ audio_effects_refresh = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ audio_output_format = gr.Radio(
+ label=translations["export_format"],
+ info=translations["export_info"],
+ choices=export_format_choices,
+ value="wav",
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["use_presets"],
+ open=False
+ ):
+ with gr.Row():
+ presets_name = gr.Dropdown(
+ label=translations["file_preset"],
+ choices=audio_effect_presets_file,
+ value=(
+ audio_effect_presets_file[0]
+ ) if len(audio_effect_presets_file) > 0 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ load_preset_button = gr.Button(
+ translations["load_file"],
+ variant="primary"
+ )
+ refresh_preset_button = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Accordion(
+ translations["export_file"],
+ open=False
+ ):
+ with gr.Row():
+ with gr.Column():
+ name_to_save_file = gr.Textbox(
+ label=translations["filename_to_save"]
+ )
+ save_file_button = gr.Button(
+ translations["export_file"]
+ )
+ with gr.Row():
+ upload_presets = gr.Files(
+ label=translations["upload_presets"],
+ file_types=[".effect.json"]
+ )
+ with gr.Row():
+ apply_effects_button = gr.Button(
+ translations["apply"],
+ variant="primary",
+ scale=2
+ )
+ with gr.Accordion(translations["audio_effects"], visible=False) as accordion:
+ with gr.Row():
+ with gr.Accordion(
+ translations["reverb"],
+ open=True,
+ visible=False
+ ) as reverb_accordion:
+ reverb_freeze_mode = gr.Checkbox(
+ label=translations["reverb_freeze"],
+ info=translations["reverb_freeze_info"],
+ value=False,
+ interactive=True
+ )
+ reverb_room_size = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.15,
+ label=translations["room_size"],
+ info=translations["room_size_info"],
+ interactive=True
+ )
+ reverb_damping = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.7,
+ label=translations["damping"],
+ info=translations["damping_info"],
+ interactive=True
+ )
+ reverb_wet_level = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.2,
+ label=translations["wet_level"],
+ info=translations["wet_level_info"],
+ interactive=True
+ )
+ reverb_dry_level = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.8,
+ label=translations["dry_level"],
+ info=translations["dry_level_info"],
+ interactive=True
+ )
+ reverb_width = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=1,
+ label=translations["width"],
+ info=translations["width_info"],
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["chorus"],
+ open=True,
+ visible=False
+ ) as chorus_accordion:
+ chorus_depth = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["chorus_depth"],
+ info=translations["chorus_depth_info"],
+ interactive=True
+ )
+ chorus_rate_hz = gr.Slider(
+ minimum=0.1,
+ maximum=10,
+ step=0.1,
+ value=1.5,
+ label=translations["chorus_rate_hz"],
+ info=translations["chorus_rate_hz_info"],
+ interactive=True
+ )
+ chorus_mix = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["chorus_mix"],
+ info=translations["chorus_mix_info"],
+ interactive=True
+ )
+ chorus_center_delay_ms = gr.Slider(
+ minimum=0,
+ maximum=50,
+ step=1,
+ value=10,
+ label=translations["chorus_center_delay_ms"],
+ info=translations["chorus_center_delay_ms_info"],
+ interactive=True
+ )
+ chorus_feedback = gr.Slider(
+ minimum=-1,
+ maximum=1,
+ step=0.01,
+ value=0,
+ label=translations["chorus_feedback"],
+ info=translations["chorus_feedback_info"],
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["delay"],
+ open=True,
+ visible=False
+ ) as delay_accordion:
+ delay_second = gr.Slider(
+ minimum=0,
+ maximum=5,
+ step=0.01,
+ value=0.5,
+ label=translations["delay_seconds"],
+ info=translations["delay_seconds_info"],
+ interactive=True
+ )
+ delay_feedback = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["delay_feedback"],
+ info=translations["delay_feedback_info"],
+ interactive=True
+ )
+ delay_mix = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["delay_mix"],
+ info=translations["delay_mix_info"],
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["phaser"],
+ open=True,
+ visible=False
+ ) as phaser_accordion:
+ phaser_depth = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["phaser_depth"],
+ info=translations["phaser_depth_info"],
+ interactive=True
+ )
+ phaser_rate_hz = gr.Slider(
+ minimum=0.1,
+ maximum=10,
+ step=0.1,
+ value=1,
+ label=translations["phaser_rate_hz"],
+ info=translations["phaser_rate_hz_info"],
+ interactive=True
+ )
+ phaser_mix = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["phaser_mix"],
+ info=translations["phaser_mix_info"],
+ interactive=True
+ )
+ phaser_centre_frequency_hz = gr.Slider(
+ minimum=50,
+ maximum=5000,
+ step=10,
+ value=1000,
+ label=translations["phaser_centre_frequency_hz"],
+ info=translations["phaser_centre_frequency_hz_info"],
+ interactive=True
+ )
+ phaser_feedback = gr.Slider(
+ minimum=-1,
+ maximum=1,
+ step=0.01,
+ value=0,
+ label=translations["phaser_feedback"],
+ info=translations["phaser_feedback_info"],
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["compressor"],
+ open=True,
+ visible=False
+ ) as compressor_accordion:
+ compressor_threshold_db = gr.Slider(
+ minimum=-60,
+ maximum=0,
+ step=1,
+ value=-20,
+ label=translations["compressor_threshold_db"],
+ info=translations["compressor_threshold_db_info"],
+ interactive=True
+ )
+ compressor_ratio = gr.Slider(
+ minimum=1,
+ maximum=20,
+ step=0.1,
+ value=1,
+ label=translations["compressor_ratio"],
+ info=translations["compressor_ratio_info"],
+ interactive=True
+ )
+ compressor_attack_ms = gr.Slider(
+ minimum=0.1,
+ maximum=100,
+ step=0.1,
+ value=10,
+ label=translations["compressor_attack_ms"],
+ info=translations["compressor_attack_ms_info"],
+ interactive=True
+ )
+ compressor_release_ms = gr.Slider(
+ minimum=10,
+ maximum=1000,
+ step=1,
+ value=100,
+ label=translations["compressor_release_ms"],
+ info=translations["compressor_release_ms_info"],
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["more_option"],
+ open=True,
+ visible=False
+ ) as more_accordion:
+ with gr.Row():
+ fade = gr.Checkbox(
+ label=translations["fade"],
+ value=False,
+ interactive=True
+ )
+ bass_or_treble = gr.Checkbox(
+ label=translations["bass_or_treble"],
+ value=False,
+ interactive=True
+ )
+ limiter = gr.Checkbox(
+ label=translations["limiter"],
+ value=False,
+ interactive=True
+ )
+ resample_checkbox = gr.Checkbox(
+ label=translations["resample"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ distortion_checkbox = gr.Checkbox(
+ label=translations["distortion"],
+ value=False,
+ interactive=True
+ )
+ gain_checkbox = gr.Checkbox(
+ label=translations["gain"],
+ value=False,
+ interactive=True
+ )
+ bitcrush_checkbox = gr.Checkbox(
+ label=translations["bitcrush"],
+ value=False,
+ interactive=True
+ )
+ clipping_checkbox = gr.Checkbox(
+ label=translations["clipping"],
+ value=False,
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["fade"],
+ open=True,
+ visible=False
+ ) as fade_accordion:
+ with gr.Row():
+ fade_in = gr.Slider(
+ minimum=0,
+ maximum=10000,
+ step=100,
+ value=0,
+ label=translations["fade_in"],
+ info=translations["fade_in_info"],
+ interactive=True
+ )
+ fade_out = gr.Slider(
+ minimum=0,
+ maximum=10000,
+ step=100,
+ value=0,
+ label=translations["fade_out"],
+ info=translations["fade_out_info"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["bass_or_treble"],
+ open=True,
+ visible=False
+ ) as bass_treble_accordion:
+ with gr.Row():
+ bass_boost = gr.Slider(
+ minimum=0,
+ maximum=20,
+ step=1,
+ value=0,
+ label=translations["bass_boost"],
+ info=translations["bass_boost_info"],
+ interactive=True
+ )
+ bass_frequency = gr.Slider(
+ minimum=20,
+ maximum=200,
+ step=10,
+ value=100,
+ label=translations["bass_frequency"],
+ info=translations["bass_frequency_info"],
+ interactive=True
+ )
+ with gr.Row():
+ treble_boost = gr.Slider(
+ minimum=0,
+ maximum=20,
+ step=1,
+ value=0,
+ label=translations["treble_boost"],
+ info=translations["treble_boost_info"],
+ interactive=True
+ )
+ treble_frequency = gr.Slider(
+ minimum=1000,
+ maximum=10000,
+ step=500,
+ value=3000,
+ label=translations["treble_frequency"],
+ info=translations["treble_frequency_info"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["limiter"],
+ open=True,
+ visible=False
+ ) as limiter_accordion:
+ with gr.Row():
+ limiter_threshold_db = gr.Slider(
+ minimum=-60,
+ maximum=0,
+ step=1,
+ value=-1,
+ label=translations["limiter_threshold_db"],
+ info=translations["limiter_threshold_db_info"],
+ interactive=True
+ )
+ limiter_release_ms = gr.Slider(
+ minimum=10,
+ maximum=1000,
+ step=1,
+ value=100,
+ label=translations["limiter_release_ms"],
+ info=translations["limiter_release_ms_info"],
+ interactive=True
+ )
+ with gr.Column():
+ pitch_shift_semitones = gr.Slider(
+ minimum=-20,
+ maximum=20,
+ step=1,
+ value=0,
+ label=translations["pitch"],
+ info=translations["pitch_info"],
+ interactive=True
+ )
+ audio_effect_resample_sr = gr.Radio(
+ choices=[0]+sample_rate_choice,
+ value=0,
+ label=translations["resample"],
+ info=translations["resample_info"],
+ interactive=True,
+ visible=False
+ )
+ distortion_drive_db = gr.Slider(
+ minimum=0,
+ maximum=50,
+ step=1,
+ value=20,
+ label=translations["distortion"],
+ info=translations["distortion_info"],
+ interactive=True,
+ visible=False
+ )
+ gain_db = gr.Slider(
+ minimum=-60,
+ maximum=60,
+ step=1,
+ value=0,
+ label=translations["gain"],
+ info=translations["gain_info"],
+ interactive=True,
+ visible=False
+ )
+ clipping_threshold_db = gr.Slider(
+ minimum=-60,
+ maximum=0,
+ step=1,
+ value=-1,
+ label=translations["clipping_threshold_db"],
+ info=translations["clipping_threshold_db_info"],
+ interactive=True,
+ visible=False
+ )
+ bitcrush_bit_depth = gr.Slider(
+ minimum=1,
+ maximum=24,
+ step=1,
+ value=16,
+ label=translations["bitcrush_bit_depth"],
+ info=translations["bitcrush_bit_depth_info"],
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ gr.Markdown(translations["output_audio"])
+ with gr.Row():
+ audio_play_input = gr.Audio(
+ interactive=False,
+ label=translations["input_audio"],
+ **audio_params
+ )
+ audio_play_output = gr.Audio(
+ interactive=False,
+ label=translations["output_audio"],
+ **audio_params
+ )
+ with gr.Row():
+ reverb_check_box.change(
+ fn=visible,
+ inputs=[
+ reverb_check_box
+ ],
+ outputs=[
+ reverb_accordion
+ ]
+ )
+ chorus_check_box.change(
+ fn=visible,
+ inputs=[
+ chorus_check_box
+ ],
+ outputs=[
+ chorus_accordion
+ ]
+ )
+ delay_check_box.change(
+ fn=visible,
+ inputs=[
+ delay_check_box
+ ],
+ outputs=[
+ delay_accordion
+ ]
+ )
+ with gr.Row():
+ compressor_check_box.change(
+ fn=visible,
+ inputs=[
+ compressor_check_box
+ ],
+ outputs=[
+ compressor_accordion
+ ]
+ )
+ phaser_check_box.change(
+ fn=visible,
+ inputs=[
+ phaser_check_box
+ ],
+ outputs=[
+ phaser_accordion
+ ]
+ )
+ more_options.change(
+ fn=visible,
+ inputs=[
+ more_options
+ ],
+ outputs=[
+ more_accordion
+ ]
+ )
+ with gr.Row():
+ fade.change(
+ fn=visible,
+ inputs=[
+ fade
+ ],
+ outputs=[
+ fade_accordion
+ ]
+ )
+ bass_or_treble.change(
+ fn=visible,
+ inputs=[
+ bass_or_treble
+ ],
+ outputs=[
+ bass_treble_accordion
+ ]
+ )
+ limiter.change(
+ fn=visible,
+ inputs=[
+ limiter
+ ],
+ outputs=[
+ limiter_accordion
+ ]
+ )
+ resample_checkbox.change(
+ fn=visible,
+ inputs=[
+ resample_checkbox
+ ],
+ outputs=[
+ audio_effect_resample_sr
+ ]
+ )
+ with gr.Row():
+ distortion_checkbox.change(
+ fn=visible,
+ inputs=[
+ distortion_checkbox
+ ],
+ outputs=[
+ distortion_drive_db
+ ]
+ )
+ gain_checkbox.change(
+ fn=visible,
+ inputs=[
+ gain_checkbox
+ ],
+ outputs=[
+ gain_db
+ ]
+ )
+ clipping_checkbox.change(
+ fn=visible,
+ inputs=[
+ clipping_checkbox
+ ],
+ outputs=[
+ clipping_threshold_db
+ ]
+ )
+ with gr.Row():
+ bitcrush_checkbox.change(
+ fn=visible,
+ inputs=[
+ bitcrush_checkbox
+ ],
+ outputs=[
+ bitcrush_bit_depth
+ ]
+ )
+ upload_audio_files.upload(
+ fn=lambda audio_in: [
+ shutil_move(audio.name, configs["audios_path"])
+ for audio in audio_in
+ ][0],
+ inputs=[
+ upload_audio_files
+ ],
+ outputs=[
+ audio_in_path
+ ]
+ )
+ audio_in_path.change(
+ fn=lambda audio: audio if audio else None,
+ inputs=[
+ audio_in_path
+ ],
+ outputs=[
+ audio_play_input
+ ]
+ )
+ with gr.Row():
+ audio_effects_refresh.click(
+ fn=lambda a, b: [
+ change_audios_choices(a),
+ change_audios_choices(b)
+ ],
+ inputs=[
+ audio_in_path,
+ audio_combination_input
+ ],
+ outputs=[
+ audio_in_path,
+ audio_combination_input
+ ]
+ )
+ audio_combination.change(
+ fn=visible,
+ inputs=[
+ audio_combination
+ ],
+ outputs=[
+ audio_combination_input
+ ]
+ )
+ audio_combination.change(
+ fn=lambda a: [
+ visible(a)
+ for _ in range(2)
+ ],
+ inputs=[
+ audio_combination
+ ],
+ outputs=[
+ main_volume,
+ combine_volume
+ ]
+ )
+ with gr.Row():
+ more_options.change(
+ fn=lambda: [False]*8,
+ inputs=[],
+ outputs=[
+ fade,
+ bass_or_treble,
+ limiter,
+ resample_checkbox,
+ distortion_checkbox,
+ gain_checkbox,
+ clipping_checkbox,
+ bitcrush_checkbox
+ ]
+ )
+ upload_presets.upload(
+ fn=lambda presets_in: [
+ shutil_move(preset.name, configs["presets_path"])
+ for preset in presets_in
+ ][0],
+ inputs=[
+ upload_presets
+ ],
+ outputs=[
+ presets_name
+ ]
+ )
+ refresh_preset_button.click(
+ fn=change_effect_preset_choices,
+ inputs=[],
+ outputs=[
+ presets_name
+ ]
+ )
+ with gr.Row():
+ reverb_check_box.change(
+ fn=lambda a, b, c, d, e, f: visible(max([a, b, c, d, e, f])),
+ inputs=[
+ reverb_check_box,
+ chorus_check_box,
+ delay_check_box,
+ phaser_check_box,
+ compressor_check_box,
+ more_options
+ ],
+ outputs=[accordion]
+ )
+ chorus_check_box.change(
+ fn=lambda a, b, c, d, e, f: visible(max([a, b, c, d, e, f])),
+ inputs=[
+ reverb_check_box,
+ chorus_check_box,
+ delay_check_box,
+ phaser_check_box,
+ compressor_check_box,
+ more_options
+ ],
+ outputs=[accordion]
+ )
+ delay_check_box.change(
+ fn=lambda a, b, c, d, e, f: visible(max([a, b, c, d, e, f])),
+ inputs=[
+ reverb_check_box,
+ chorus_check_box,
+ delay_check_box,
+ phaser_check_box,
+ compressor_check_box,
+ more_options
+ ],
+ outputs=[accordion]
+ )
+ with gr.Row():
+ phaser_check_box.change(
+ fn=lambda a, b, c, d, e, f: visible(max([a, b, c, d, e, f])),
+ inputs=[
+ reverb_check_box,
+ chorus_check_box,
+ delay_check_box,
+ phaser_check_box,
+ compressor_check_box,
+ more_options
+ ],
+ outputs=[accordion]
+ )
+ compressor_check_box.change(
+ fn=lambda a, b, c, d, e, f: visible(max([a, b, c, d, e, f])),
+ inputs=[
+ reverb_check_box,
+ chorus_check_box,
+ delay_check_box,
+ phaser_check_box,
+ compressor_check_box,
+ more_options
+ ],
+ outputs=[accordion]
+ )
+ more_options.change(
+ fn=lambda a, b, c, d, e, f: visible(max([a, b, c, d, e, f])),
+ inputs=[
+ reverb_check_box,
+ chorus_check_box,
+ delay_check_box,
+ phaser_check_box,
+ compressor_check_box,
+ more_options
+ ],
+ outputs=[accordion]
+ )
+ with gr.Row():
+ load_preset_button.click(
+ fn=audio_effect_load_presets,
+ inputs=[
+ presets_name,
+ resample_checkbox,
+ audio_effect_resample_sr,
+ chorus_depth,
+ chorus_rate_hz,
+ chorus_mix,
+ chorus_center_delay_ms,
+ chorus_feedback,
+ distortion_drive_db,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_level,
+ reverb_dry_level,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_second,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold_db,
+ compressor_ratio,
+ compressor_attack_ms,
+ compressor_release_ms,
+ limiter_threshold_db,
+ limiter_release_ms,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold_db,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix,
+ bass_boost,
+ bass_frequency,
+ treble_boost,
+ treble_frequency,
+ fade_in,
+ fade_out,
+ chorus_check_box,
+ distortion_checkbox,
+ reverb_check_box,
+ delay_check_box,
+ compressor_check_box,
+ limiter,
+ gain_checkbox,
+ bitcrush_checkbox,
+ clipping_checkbox,
+ phaser_check_box,
+ bass_or_treble,
+ fade
+ ],
+ outputs=[
+ resample_checkbox,
+ audio_effect_resample_sr,
+ chorus_depth,
+ chorus_rate_hz,
+ chorus_mix,
+ chorus_center_delay_ms,
+ chorus_feedback,
+ distortion_drive_db,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_level,
+ reverb_dry_level,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_second,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold_db,
+ compressor_ratio,
+ compressor_attack_ms,
+ compressor_release_ms,
+ limiter_threshold_db,
+ limiter_release_ms,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold_db,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix,
+ bass_boost,
+ bass_frequency,
+ treble_boost,
+ treble_frequency,
+ fade_in,
+ fade_out,
+ chorus_check_box,
+ distortion_checkbox,
+ reverb_check_box,
+ delay_check_box,
+ compressor_check_box,
+ limiter,
+ gain_checkbox,
+ bitcrush_checkbox,
+ clipping_checkbox,
+ phaser_check_box,
+ bass_or_treble,
+ fade
+ ],
+ )
+ save_file_button.click(
+ fn=audio_effect_save_presets,
+ inputs=[
+ name_to_save_file,
+ resample_checkbox,
+ audio_effect_resample_sr,
+ chorus_depth,
+ chorus_rate_hz,
+ chorus_mix,
+ chorus_center_delay_ms,
+ chorus_feedback,
+ distortion_drive_db,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_level,
+ reverb_dry_level,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_second,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold_db,
+ compressor_ratio,
+ compressor_attack_ms,
+ compressor_release_ms,
+ limiter_threshold_db,
+ limiter_release_ms,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold_db,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix,
+ bass_boost,
+ bass_frequency,
+ treble_boost,
+ treble_frequency,
+ fade_in,
+ fade_out,
+ chorus_check_box,
+ distortion_checkbox,
+ reverb_check_box,
+ delay_check_box,
+ compressor_check_box,
+ limiter,
+ gain_checkbox,
+ bitcrush_checkbox,
+ clipping_checkbox,
+ phaser_check_box,
+ bass_or_treble,
+ fade
+ ],
+ outputs=[
+ presets_name
+ ]
+ )
+ apply_effects_button.click(
+ fn=audio_effects,
+ inputs=[
+ audio_in_path,
+ audio_out_path,
+ resample_checkbox,
+ audio_effect_resample_sr,
+ chorus_depth,
+ chorus_rate_hz,
+ chorus_mix,
+ chorus_center_delay_ms,
+ chorus_feedback,
+ distortion_drive_db,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_level,
+ reverb_dry_level,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_second,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold_db,
+ compressor_ratio,
+ compressor_attack_ms,
+ compressor_release_ms,
+ limiter_threshold_db,
+ limiter_release_ms,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold_db,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix,
+ bass_boost,
+ bass_frequency,
+ treble_boost,
+ treble_frequency,
+ fade_in,
+ fade_out,
+ audio_output_format,
+ chorus_check_box,
+ distortion_checkbox,
+ reverb_check_box,
+ delay_check_box,
+ compressor_check_box,
+ limiter,
+ gain_checkbox,
+ bitcrush_checkbox,
+ clipping_checkbox,
+ phaser_check_box,
+ bass_or_treble,
+ fade,
+ audio_combination,
+ audio_combination_input,
+ main_volume,
+ combine_volume
+ ],
+ outputs=[
+ audio_play_output
+ ],
+ api_name="audio_effects"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/editing/child/quirk.py b/main/app/tabs/editing/child/quirk.py
new file mode 100644
index 0000000000000000000000000000000000000000..d010d8fb9dfc74ca2a244e711623a238985fdf11
--- /dev/null
+++ b/main/app/tabs/editing/child/quirk.py
@@ -0,0 +1,130 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.editing import apply_voice_quirk
+
+from main.app.core.ui import (
+ shutil_move,
+ change_audios_choices
+)
+
+from main.app.variables import (
+ configs,
+ file_types,
+ audio_params,
+ translations,
+ paths_for_files,
+ export_format_choices
+)
+
+def quirk_tab():
+ with gr.Row():
+ gr.Markdown(translations["quirk_markdown"])
+ with gr.Row():
+ input_audio_play = gr.Audio(
+ interactive=False,
+ label=translations["input_audio"],
+ **audio_params
+ )
+ with gr.Row():
+ quirk_choice = gr.Radio(
+ label=translations["quirk_label"],
+ info=translations["quirk_label_info"],
+ choices=list(translations["quirk_choice"].keys()),
+ interactive=True,
+ value=list(translations["quirk_choice"].keys())[0]
+ )
+ with gr.Row():
+ apply_quirk_button = gr.Button(
+ translations["apply"],
+ variant="primary"
+ )
+ with gr.Row():
+ with gr.Accordion(translations["input_output"], open=False):
+ with gr.Row():
+ quirk_upload_audio = gr.Files(
+ label=translations["drop_audio"],
+ file_types=file_types
+ )
+ with gr.Column():
+ quirk_export_format = gr.Radio(
+ label=translations["export_format"],
+ info=translations["export_info"],
+ choices=export_format_choices,
+ value="wav",
+ interactive=True
+ )
+ quirk_input_path = gr.Dropdown(
+ label=translations["audio_path"],
+ value="",
+ choices=paths_for_files,
+ info=translations["provide_audio"],
+ allow_custom_value=True,
+ interactive=True
+ )
+ quirk_output_path = gr.Textbox(
+ label=translations["output_path"],
+ value="audios/output.wav",
+ placeholder="audios/output.wav",
+ info=translations["output_path_info"],
+ interactive=True
+ )
+ with gr.Column():
+ quirk_refresh = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ output_audio_play = gr.Audio(
+ interactive=False,
+ label=translations["output_audio"],
+ **audio_params
+ )
+ with gr.Row():
+ quirk_upload_audio.upload(
+ fn=lambda audio_in: [
+ shutil_move(audio.name, configs["audios_path"])
+ for audio in audio_in
+ ][0],
+ inputs=[
+ quirk_upload_audio
+ ],
+ outputs=[
+ quirk_input_path
+ ]
+ )
+ quirk_input_path.change(
+ fn=lambda audio: audio if audio else None,
+ inputs=[
+ quirk_input_path
+ ],
+ outputs=[
+ input_audio_play
+ ]
+ )
+ quirk_refresh.click(
+ fn=change_audios_choices,
+ inputs=[
+ quirk_input_path
+ ],
+ outputs=[
+ quirk_input_path
+ ]
+ )
+ with gr.Row():
+ apply_quirk_button.click(
+ fn=apply_voice_quirk,
+ inputs=[
+ quirk_input_path,
+ quirk_choice,
+ quirk_output_path,
+ quirk_export_format
+ ],
+ outputs=[
+ output_audio_play
+ ],
+ api_name="quirk"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/editing/editing.py b/main/app/tabs/editing/editing.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb631732c7be16d6b2875ceb3d2e2aa8d69af4d4
--- /dev/null
+++ b/main/app/tabs/editing/editing.py
@@ -0,0 +1,27 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import (
+ configs,
+ translations
+)
+
+def editing_tab():
+ with gr.TabItem(translations["editing"]):
+ if configs.get("effects_tab", True):
+ from main.app.tabs.editing.child.audio_effects import audio_effects_tab
+
+ with gr.TabItem(translations["audio_effects"]):
+ gr.Markdown(translations["apply_audio_effects"])
+ audio_effects_tab()
+
+ if configs.get("quirk_tab", True):
+ from main.app.tabs.editing.child.quirk import quirk_tab
+
+ with gr.TabItem(translations["quirk"]):
+ gr.Markdown(translations["quirk_info"])
+ quirk_tab()
\ No newline at end of file
diff --git a/main/app/tabs/extra/child/analyzer.py b/main/app/tabs/extra/child/analyzer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7b851dc2a4c9f92c6b81b3a6f64056fb7dd4c4a
--- /dev/null
+++ b/main/app/tabs/extra/child/analyzer.py
@@ -0,0 +1,94 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.tools.analyzer import analyze_audio
+from main.app.core.ui import change_audios_choices, shutil_move
+from main.app.variables import translations, paths_for_files, audio_params, file_types, configs
+
+def analyzer_tab():
+ with gr.Row():
+ gr.Markdown(translations["analyzer_markdown_2"])
+ with gr.Row():
+ with gr.Accordion(
+ translations["input_output"],
+ open=False
+ ):
+ input_audio = gr.Dropdown(
+ label=translations["audio_path"],
+ value="",
+ choices=paths_for_files,
+ info=translations["provide_audio"],
+ allow_custom_value=True,
+ interactive=True
+ )
+ upload_audio_file = gr.Files(
+ label=translations["drop_audio"],
+ file_types=file_types
+ )
+ with gr.Row():
+ play_audio = gr.Audio(
+ interactive=False,
+ label=translations["input_audio"],
+ **audio_params
+ )
+ with gr.Row():
+ refresh_audio = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ get_info_button = gr.Button(
+ value=translations["read_audio"],
+ variant="primary"
+ )
+ with gr.Row():
+ output_info = gr.Textbox(
+ label=translations["output_information"],
+ value="",
+ max_lines=8,
+ interactive=False,
+ )
+ with gr.Row():
+ image_output = gr.Image(
+ interactive=False
+ )
+ with gr.Row():
+ get_info_button.click(
+ fn=analyze_audio,
+ inputs=[input_audio],
+ outputs=[output_info, image_output],
+ )
+ input_audio.change(
+ fn=lambda audio: audio if os.path.isfile(audio) else None,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ play_audio
+ ]
+ )
+ refresh_audio.click(
+ fn=change_audios_choices,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ with gr.Row():
+ upload_audio_file.upload(
+ fn=lambda audio_in: [
+ shutil_move(audio.name, configs["audios_path"])
+ for audio in audio_in
+ ][0],
+ inputs=[
+ upload_audio_file
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
\ No newline at end of file
diff --git a/main/app/tabs/extra/child/convert_model_onnx.py b/main/app/tabs/extra/child/convert_model_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a7b6f4c835c1d19ee193a726851f85286b5706b
--- /dev/null
+++ b/main/app/tabs/extra/child/convert_model_onnx.py
@@ -0,0 +1,106 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.model_utils import onnx_export
+
+from main.app.core.ui import (
+ visible,
+ shutil_move,
+ change_models_choices
+)
+
+from main.app.variables import (
+ configs,
+ model_name,
+ translations
+)
+
+def convert_onnx_model_tab():
+ with gr.Row():
+ gr.Markdown(translations["pytorch2onnx_markdown"])
+ with gr.Row():
+ is_half_weight = gr.Checkbox(
+ label=translations["is_half_weight"],
+ value=False,
+ interactive=True
+ )
+ int8_mode = gr.Checkbox(
+ label=translations["int8_mode"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ model_pth_upload = gr.File(
+ label=translations["drop_model"],
+ file_types=[".pth"]
+ )
+ with gr.Accordion(
+ label=translations["model_name"],
+ open=True
+ ):
+ with gr.Row():
+ model_pth_path = gr.Dropdown(
+ label=translations["model_name"],
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ refresh_model = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ convert_onnx_button = gr.Button(
+ translations["convert_model"],
+ variant="primary",
+ scale=2
+ )
+ with gr.Row():
+ output_model_file = gr.File(
+ label=translations["output_model_path"],
+ file_types=[".onnx"],
+ interactive=False,
+ visible=False
+ )
+ with gr.Row():
+ model_pth_upload.upload(
+ fn=lambda model_pth_upload: shutil_move(model_pth_upload.name, configs["weights_path"]),
+ inputs=[
+ model_pth_upload
+ ],
+ outputs=[
+ model_pth_path
+ ]
+ )
+ refresh_model.click(
+ fn=lambda: change_models_choices()[0],
+ inputs=[],
+ outputs=[
+ model_pth_path
+ ]
+ )
+ with gr.Row():
+ convert_onnx_button.click(
+ fn=onnx_export,
+ inputs=[
+ model_pth_path,
+ is_half_weight,
+ int8_mode
+ ],
+ outputs=[
+ output_model_file
+ ],
+ api_name="model_onnx_export"
+ )
+ convert_onnx_button.click(
+ fn=lambda: visible(True),
+ inputs=[],
+ outputs=[
+ output_model_file
+ ]
+ )
\ No newline at end of file
diff --git a/main/app/tabs/extra/child/convert_model_svc.py b/main/app/tabs/extra/child/convert_model_svc.py
new file mode 100644
index 0000000000000000000000000000000000000000..f72dbda903cd1ecc62ffff5b7bea3396b0f5a162
--- /dev/null
+++ b/main/app/tabs/extra/child/convert_model_svc.py
@@ -0,0 +1,134 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.model_utils import svc_export
+
+from main.app.core.ui import (
+ visible,
+ shutil_move,
+ change_models_choices
+)
+
+from main.app.variables import (
+ configs,
+ model_name,
+ translations
+)
+
+def convert_svc_model_tab():
+ with gr.Row():
+ gr.Markdown(translations["convert_model_svc_markdown_2"])
+ with gr.Row():
+ delete_when_success = gr.Checkbox(
+ label=translations["delete_when_success"],
+ value=True,
+ interactive=True
+ )
+ is_half_weight = gr.Checkbox(
+ label=translations["is_half_weight"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ model_pth_upload = gr.File(
+ label=translations["drop_model"],
+ file_types=[".pth"]
+ )
+ config_upload = gr.File(
+ label=translations["drop_json"],
+ file_types=[".json"]
+ )
+ with gr.Accordion(
+ label=translations["model_name"],
+ open=True
+ ):
+ with gr.Row():
+ model_pth_path = gr.Dropdown(
+ label=translations["model_name"],
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ config_path = gr.Textbox(
+ label=translations["config_path"],
+ value="",
+ placeholder="config.json",
+ interactive=True
+ )
+ with gr.Row():
+ modelname = gr.Textbox(
+ label=translations["modelname"],
+ value="",
+ placeholder=translations["modelname"],
+ interactive=True
+ )
+ with gr.Row():
+ refresh_model = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ convert_svc_button = gr.Button(
+ translations["convert_model_svc"],
+ variant="primary",
+ scale=2
+ )
+ with gr.Row():
+ output_model_file = gr.File(
+ label=translations["output_model_path"],
+ file_types=[".pth", ".onnx"],
+ interactive=False,
+ visible=False
+ )
+ with gr.Row():
+ model_pth_upload.upload(
+ fn=lambda model_pth_upload: shutil_move(model_pth_upload.name, configs["weights_path"]),
+ inputs=[
+ model_pth_upload
+ ],
+ outputs=[
+ model_pth_path
+ ]
+ )
+ config_upload.upload(
+ fn=lambda config: shutil_move(config.name, configs["weights_path"]),
+ inputs=[
+ config_upload
+ ],
+ outputs=[
+ config_path
+ ]
+ )
+ refresh_model.click(
+ fn=lambda: change_models_choices()[0],
+ inputs=[],
+ outputs=[
+ model_pth_path
+ ]
+ )
+ with gr.Row():
+ convert_svc_button.click(
+ fn=svc_export,
+ inputs=[
+ model_pth_path,
+ config_path,
+ modelname,
+ delete_when_success,
+ is_half_weight
+ ],
+ outputs=[
+ output_model_file
+ ],
+ api_name="model_svc_export"
+ )
+ convert_svc_button.click(
+ fn=lambda: visible(True),
+ inputs=[],
+ outputs=[
+ output_model_file
+ ]
+ )
\ No newline at end of file
diff --git a/main/app/tabs/extra/child/create_srt.py b/main/app/tabs/extra/child/create_srt.py
new file mode 100644
index 0000000000000000000000000000000000000000..550a00566824da035fcb3a8cc015123cd9c74754
--- /dev/null
+++ b/main/app/tabs/extra/child/create_srt.py
@@ -0,0 +1,145 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.csrt import create_srt
+
+from main.app.core.ui import (
+ shutil_move,
+ change_audios_choices
+)
+
+from main.app.variables import (
+ configs,
+ file_types,
+ translations,
+ audio_params,
+ whisper_model,
+ paths_for_files,
+ whisper_languages
+)
+
+def create_srt_tab():
+ with gr.Row():
+ gr.Markdown(translations["create_srt_markdown_2"])
+ with gr.Row():
+ with gr.Column():
+ srt_content = gr.Textbox(
+ label=translations["srt_content"],
+ value="",
+ lines=11,
+ max_lines=11,
+ interactive=False
+ )
+ with gr.Column():
+ with gr.Group():
+ language = gr.Dropdown(
+ label=translations["whisper_languages"],
+ value="vi",
+ choices=whisper_languages,
+ info=translations["whisper_languages_info"],
+ interactive=True
+ )
+ model_size = gr.Radio(
+ label=translations["model_size"],
+ info=translations["model_size_info"],
+ choices=whisper_model,
+ value=whisper_model[0],
+ interactive=True
+ )
+ with gr.Row():
+ convert_button = gr.Button(
+ translations["convert_audio"],
+ variant="primary"
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["input_output"],
+ open=False
+ ):
+ with gr.Column():
+ input_audio = gr.Dropdown(
+ label=translations["audio_path"],
+ value="",
+ choices=paths_for_files,
+ info=translations["provide_audio"],
+ allow_custom_value=True,
+ interactive=True
+ )
+ output_file = gr.Textbox(
+ label=translations["srt_path_output_file"],
+ value="srt/output.srt",
+ placeholder="srt/output.srt",
+ interactive=True
+ )
+ with gr.Column():
+ refresh = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ upload_audio_files = gr.Files(
+ label=translations["drop_audio"],
+ file_types=file_types
+ )
+ with gr.Row():
+ play_audio = gr.Audio(
+ interactive=False,
+ label=translations["input_audio"],
+ **audio_params
+ )
+ with gr.Row():
+ output_srt = gr.File(
+ label=translations["srt_output_file"],
+ file_types=[".srt"],
+ interactive=False,
+ visible=False
+ )
+ with gr.Row():
+ upload_audio_files.upload(
+ fn=lambda audio_in: [
+ shutil_move(audio.name, configs["audios_path"])
+ for audio in audio_in
+ ][0],
+ inputs=[
+ upload_audio_files
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ input_audio.change(
+ fn=lambda audio: audio if os.path.isfile(audio) else None,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ play_audio
+ ]
+ )
+ refresh.click(
+ fn=change_audios_choices,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ with gr.Row():
+ convert_button.click(
+ fn=create_srt,
+ inputs=[
+ model_size,
+ input_audio,
+ output_file,
+ language
+ ],
+ outputs=[
+ output_srt,
+ srt_content
+ ],
+ api_name="create_srt"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/extra/child/f0_extract.py b/main/app/tabs/extra/child/f0_extract.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8801809d44e6749517e0aae7d6f5fa4b15bace8
--- /dev/null
+++ b/main/app/tabs/extra/child/f0_extract.py
@@ -0,0 +1,225 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.f0_extract import f0_extract
+
+from main.app.core.ui import (
+ visible,
+ unlock_f0,
+ shutil_move,
+ change_audios_choices
+)
+
+from main.app.variables import (
+ configs,
+ method_f0,
+ file_types,
+ audio_params,
+ translations,
+ paths_for_files
+)
+
+def f0_extract_tab():
+ with gr.Row():
+ gr.Markdown(translations["f0_extractor_markdown_2"])
+ with gr.Row():
+ extractor_button = gr.Button(
+ translations["extract_button"].replace("2. ", ""),
+ variant="primary"
+ )
+ with gr.Row():
+ with gr.Column():
+ upload_audio_file = gr.Files(
+ label=translations["drop_audio"],
+ file_types=file_types
+ )
+ audioplay = gr.Audio(
+ interactive=False,
+ label=translations["input_audio"],
+ **audio_params
+ )
+ with gr.Column():
+ with gr.Accordion(
+ translations["f0_method"],
+ open=False
+ ):
+ with gr.Group():
+ with gr.Row():
+ predictor_onnx = gr.Checkbox(
+ label=translations["predictor_onnx"],
+ value=configs.get("int8", False),
+ interactive=True
+ )
+ unlock_full_method = gr.Checkbox(
+ label=translations["f0_unlock"],
+ value=False,
+ interactive=True
+ )
+ autotune = gr.Checkbox(
+ label=translations["autotune"],
+ value=False,
+ interactive=True
+ )
+ proposal_pitch = gr.Checkbox(
+ label=translations["proposal_pitch"],
+ value=False,
+ interactive=True
+ )
+ f0_method_extract = gr.Radio(
+ label=translations["f0_method"],
+ info=translations["f0_method_info"],
+ choices=[m for m in method_f0 if m != "hybrid"],
+ value="rmvpe",
+ interactive=True
+ )
+ pitch = gr.Slider(
+ minimum=-24,
+ maximum=24,
+ step=1,
+ info=translations["pitch_info"],
+ label=translations["pitch"],
+ value=0,
+ interactive=True
+ )
+ f0_autotune_strength = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["autotune_rate"],
+ info=translations["autotune_rate_info"],
+ value=1,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ proposal_pitch_threshold = gr.Slider(
+ minimum=50.0,
+ maximum=1200.0,
+ label=translations["proposal_pitch_threshold"],
+ info=translations["proposal_pitch_threshold_info"],
+ value=255.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ filter_radius = gr.Slider(
+ minimum=0,
+ maximum=7,
+ label=translations["filter_radius"],
+ info=translations["filter_radius_info"],
+ value=3,
+ step=1,
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["audio_path"],
+ open=True
+ ):
+ input_audio_path = gr.Dropdown(
+ label=translations["audio_path"],
+ value="",
+ choices=paths_for_files,
+ allow_custom_value=True,
+ interactive=True
+ )
+ refresh_audio_button = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ gr.Markdown("___")
+ with gr.Row():
+ file_output = gr.File(
+ label="",
+ file_types=[".txt"],
+ interactive=False
+ )
+ image_output = gr.Image(
+ label="",
+ interactive=False
+ )
+ with gr.Row():
+ upload_audio_file.upload(
+ fn=lambda audio_in: [
+ shutil_move(audio.name, configs["audios_path"])
+ for audio in audio_in
+ ][0],
+ inputs=[
+ upload_audio_file
+ ],
+ outputs=[
+ input_audio_path
+ ]
+ )
+ input_audio_path.change(
+ fn=lambda audio: audio if os.path.isfile(audio) else None,
+ inputs=[
+ input_audio_path
+ ],
+ outputs=[
+ audioplay
+ ]
+ )
+ refresh_audio_button.click(
+ fn=change_audios_choices,
+ inputs=[
+ input_audio_path
+ ],
+ outputs=[
+ input_audio_path
+ ]
+ )
+ with gr.Row():
+ autotune.change(
+ fn=visible,
+ inputs=[
+ autotune
+ ],
+ outputs=[
+ f0_autotune_strength
+ ]
+ )
+ proposal_pitch.change(
+ fn=visible,
+ inputs=[
+ proposal_pitch
+ ],
+ outputs=[
+ proposal_pitch_threshold
+ ]
+ )
+ with gr.Row():
+ unlock_full_method.change(
+ fn=lambda method: {
+ "choices": [m for m in unlock_f0(method)["choices"] if m != "hybrid"],
+ "value": "rmvpe",
+ "__type__": "update"
+ },
+ inputs=[
+ unlock_full_method
+ ],
+ outputs=[
+ f0_method_extract
+ ]
+ )
+ extractor_button.click(
+ fn=f0_extract,
+ inputs=[
+ input_audio_path,
+ f0_method_extract,
+ predictor_onnx,
+ pitch,
+ filter_radius,
+ autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold
+ ],
+ outputs=[
+ file_output,
+ image_output
+ ],
+ api_name="f0_extract"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/extra/child/fushion.py b/main/app/tabs/extra/child/fushion.py
new file mode 100644
index 0000000000000000000000000000000000000000..1585930b4e220edb71e3c2bc58eb87b6ffd5951b
--- /dev/null
+++ b/main/app/tabs/extra/child/fushion.py
@@ -0,0 +1,132 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.model_utils import fushion_model
+
+from main.app.core.ui import (
+ visible,
+ shutil_move,
+ change_models_choices
+)
+
+from main.app.variables import (
+ configs,
+ model_name,
+ translations
+)
+
+def fushion_tab():
+ with gr.Row():
+ gr.Markdown(translations["fushion_markdown_2"])
+ with gr.Row():
+ modelname = gr.Textbox(
+ label=translations["modelname"],
+ placeholder="Model.pth",
+ value="",
+ max_lines=2,
+ interactive=True
+ )
+ with gr.Row():
+ fushion_button = gr.Button(
+ translations["fushion"],
+ variant="primary",
+ scale=4
+ )
+ with gr.Column():
+ with gr.Row():
+ upload_model_1 = gr.File(
+ label=f"{translations['model_name']} 1",
+ file_types=[".pth"]
+ )
+ upload_model_2 = gr.File(
+ label=f"{translations['model_name']} 2",
+ file_types=[".pth"]
+ )
+ with gr.Row():
+ model_path_1 = gr.Dropdown(
+ label=translations["model_name"] + " 1",
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ model_path_2 = gr.Dropdown(
+ label=translations["model_name"] + " 2",
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ refresh_model = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ ratio = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["model_ratio"],
+ info=translations["model_ratio_info"],
+ value=0.5,
+ interactive=True
+ )
+ with gr.Row():
+ output_model = gr.File(
+ label=translations["output_model_path"],
+ file_types=[".pth", ".onnx"],
+ interactive=False,
+ visible=False
+ )
+ with gr.Row():
+ upload_model_1.upload(
+ fn=lambda model: shutil_move(model.name, configs["weights_path"]),
+ inputs=[
+ upload_model_1
+ ],
+ outputs=[
+ model_path_1
+ ]
+ )
+ upload_model_2.upload(
+ fn=lambda model: shutil_move(model.name, configs["weights_path"]),
+ inputs=[
+ upload_model_2
+ ],
+ outputs=[
+ model_path_2
+ ]
+ )
+ refresh_model.click(
+ fn=lambda: [change_models_choices()[0] for _ in range(2)],
+ inputs=[],
+ outputs=[
+ model_path_1,
+ model_path_2
+ ]
+ )
+ with gr.Row():
+ fushion_button.click(
+ fn=fushion_model,
+ inputs=[
+ modelname,
+ model_path_1,
+ model_path_2,
+ ratio
+ ],
+ outputs=[
+ modelname,
+ output_model
+ ],
+ api_name="fushion_model"
+ )
+ fushion_button.click(
+ fn=lambda: visible(True),
+ inputs=[],
+ outputs=[
+ output_model
+ ]
+ )
\ No newline at end of file
diff --git a/main/app/tabs/extra/child/read_model.py b/main/app/tabs/extra/child/read_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a9a298c3c1ad33e7db9d7182f2b9d6fdeb0ff8c
--- /dev/null
+++ b/main/app/tabs/extra/child/read_model.py
@@ -0,0 +1,83 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.model_utils import model_info
+
+from main.app.core.ui import (
+ shutil_move,
+ change_models_choices
+)
+
+from main.app.variables import (
+ configs,
+ model_name,
+ translations,
+)
+
+def read_model_tab():
+ with gr.Row():
+ gr.Markdown(translations["read_model_markdown_2"])
+ with gr.Row():
+ model = gr.File(
+ label=translations["drop_model"],
+ file_types=[".pth", ".onnx"]
+ )
+ with gr.Accordion(
+ label=translations["model_name"],
+ open=True
+ ):
+ model_path = gr.Dropdown(
+ label=translations["model_name"],
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ refresh_model = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ read_button = gr.Button(
+ translations["readmodel"],
+ variant="primary",
+ scale=2
+ )
+ with gr.Row():
+ output_info = gr.Textbox(
+ label=translations["modelinfo"],
+ value="",
+ interactive=False,
+ lines=10
+ )
+ with gr.Row():
+ model.upload(
+ fn=lambda model: shutil_move(model.name, configs["weights_path"]),
+ inputs=[
+ model
+ ],
+ outputs=[
+ model_path
+ ]
+ )
+ read_button.click(
+ fn=model_info,
+ inputs=[
+ model_path
+ ],
+ outputs=[
+ output_info
+ ],
+ api_name="read_model"
+ )
+ refresh_model.click(
+ fn=lambda: change_models_choices()[0],
+ inputs=[],
+ outputs=[
+ model_path
+ ]
+ )
\ No newline at end of file
diff --git a/main/app/tabs/extra/child/settings.py b/main/app/tabs/extra/child/settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..53ad2c9d03942cdaa7afa25d53491e76bf73e0cc
--- /dev/null
+++ b/main/app/tabs/extra/child/settings.py
@@ -0,0 +1,214 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.utils import stop_pid
+from main.app.core.ui import change_fp, gpu_infos
+
+from main.app.core.restart import restart
+
+from main.app.variables import (
+ font,
+ theme,
+ config,
+ configs,
+ language,
+ reload_js,
+ translations
+)
+
+
+def settings_tab(app):
+ with gr.Row():
+ gr.Markdown(translations["settings_markdown_2"])
+ with gr.Row():
+ toggle_button = gr.Button(
+ translations["change_light_dark"],
+ variant="secondary",
+ scale=2
+ )
+ with gr.Row():
+ with gr.Column():
+ language_dropdown = gr.Dropdown(
+ label=translations["lang"],
+ interactive=True,
+ info=translations["lang_restart"],
+ choices=configs.get("support_language", "vi-VN"),
+ value=language
+ )
+ with gr.Column():
+ theme_dropdown = gr.Dropdown(
+ label=translations["theme"],
+ interactive=True,
+ info=translations["theme_restart"],
+ choices=configs.get("themes", theme),
+ value=theme,
+ allow_custom_value=True
+ )
+ with gr.Column():
+ font_choice = gr.Textbox(
+ label=translations["font"],
+ info=translations["font_info"],
+ value=font,
+ interactive=True
+ )
+ with gr.Row():
+ gpu_idx = gr.Dropdown(
+ label=translations["gpu_idx"],
+ interactive=len(gpu_infos) > 0,
+ info=translations["gpu_idx_info"],
+ choices=gpu_infos if len(gpu_infos) > 0 else ["CPU"],
+ value=gpu_infos[config.pytorch_device_idx] if len(gpu_infos) > 0 else "CPU"
+ )
+ with gr.Row():
+ restart_button = gr.Button(
+ translations["restart_button"]
+ )
+ with gr.Row():
+ with gr.Column():
+ with gr.Group():
+ with gr.Row():
+ bf16_checkbox = gr.Checkbox(
+ label=translations["bf16"],
+ value=config.brain,
+ interactive=config.device.startswith(("cuda", "xpu")) and config.bf16_support
+ )
+ tf32_checkbox = gr.Checkbox(
+ label=translations["tf32"],
+ value=config.tf32,
+ interactive=config.device.startswith("cuda") and config.tf32_support
+ )
+ int8_checkbox = gr.Checkbox(
+ label=translations["int8_mode"],
+ value=config.int8,
+ interactive=True
+ )
+ fp_choice = gr.Radio(
+ choices=["fp16", "fp32"],
+ value="fp16" if configs.get("fp16", False) else "fp32",
+ label=translations["precision"],
+ info=translations["precision_info"],
+ interactive=config.allow_is_half
+ )
+ fp_button = gr.Button(
+ translations["update_precision"],
+ variant="primary",
+ scale=2
+ )
+ with gr.Row():
+ commands = gr.Textbox(
+ label=translations["commands"],
+ info=translations["commands_info"],
+ value="",
+ interactive=True
+ )
+ with gr.Row():
+ run_commands_button = gr.Button(
+ translations["run_commands"]
+ )
+ with gr.Row():
+ with gr.Column():
+ with gr.Accordion(translations["stop"], open=False, visible=True):
+ separate_stop = gr.Button(
+ translations["stop_separate"]
+ )
+ convert_stop = gr.Button(
+ translations["stop_convert"]
+ )
+ create_dataset_stop = gr.Button(
+ translations["stop_create_dataset"]
+ )
+ with gr.Accordion(translations["stop_training"], open=False):
+ model_name_stop = gr.Textbox(
+ label=translations["modelname"],
+ info=translations["training_model_name"],
+ value="",
+ placeholder=translations["modelname"],
+ interactive=True
+ )
+ preprocess_stop = gr.Button(
+ translations["stop_preprocess"]
+ )
+ extract_stop = gr.Button(
+ translations["stop_extract"]
+ )
+ train_stop = gr.Button(
+ translations["stop_training"]
+ )
+ with gr.Row():
+ toggle_button.click(
+ fn=None,
+ js="() => {document.body.classList.toggle('dark')}"
+ )
+ fp_button.click(
+ fn=change_fp,
+ inputs=[
+ fp_choice,
+ bf16_checkbox,
+ tf32_checkbox,
+ int8_checkbox
+ ],
+ outputs=[
+ fp_choice
+ ]
+ )
+ run_commands_button.click(
+ fn=os.system,
+ inputs=[
+ commands
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ restart_button.click(
+ fn=lambda lang, theme, font, gpu: restart(lang, theme, font, gpu, app),
+ inputs=[language_dropdown, theme_dropdown, font_choice, gpu_idx],
+ outputs=[]
+ )
+ restart_button.click(
+ fn=None,
+ js=reload_js,
+ inputs=[],
+ outputs=[]
+ )
+ with gr.Row():
+ separate_stop.click(
+ fn=lambda: stop_pid("separate_pid", None, False),
+ inputs=[],
+ outputs=[]
+ )
+ convert_stop.click(
+ fn=lambda: stop_pid("convert_pid", None, False),
+ inputs=[],
+ outputs=[]
+ )
+ create_dataset_stop.click(
+ fn=lambda: stop_pid("create_dataset_pid", None, False),
+ inputs=[],
+ outputs=[]
+ )
+ with gr.Row():
+ preprocess_stop.click(
+ fn=lambda model_name_stop: stop_pid("preprocess_pid", model_name_stop, False),
+ inputs=[
+ model_name_stop
+ ],
+ outputs=[]
+ )
+ extract_stop.click(
+ fn=lambda model_name_stop: stop_pid("extract_pid", model_name_stop, False),
+ inputs=[
+ model_name_stop
+ ],
+ outputs=[]
+ )
+ train_stop.click(
+ fn=lambda model_name_stop: stop_pid("train_pid", model_name_stop, True),
+ inputs=[
+ model_name_stop
+ ],
+ outputs=[]
+ )
\ No newline at end of file
diff --git a/main/app/tabs/extra/extra.py b/main/app/tabs/extra/extra.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd2b90542e3cf7df83795233f569c441b534482d
--- /dev/null
+++ b/main/app/tabs/extra/extra.py
@@ -0,0 +1,62 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import (
+ configs,
+ translations
+)
+
+from main.app.tabs.extra.child.fushion import fushion_tab
+from main.app.tabs.extra.child.analyzer import analyzer_tab
+from main.app.tabs.extra.child.settings import settings_tab
+from main.app.tabs.extra.child.read_model import read_model_tab
+from main.app.tabs.extra.child.f0_extract import f0_extract_tab
+from main.app.tabs.extra.child.create_srt import create_srt_tab
+from main.app.tabs.extra.child.convert_model_svc import convert_svc_model_tab
+from main.app.tabs.extra.child.convert_model_onnx import convert_onnx_model_tab
+
+def extra_tab(app):
+ with gr.TabItem(translations["extra"]):
+ if configs.get("fushion_tab", True):
+ with gr.TabItem(translations["fushion"]):
+ gr.Markdown(translations["fushion_markdown"])
+ fushion_tab()
+
+ if configs.get("read_tab", True):
+ with gr.TabItem(translations["read_model"]):
+ gr.Markdown(translations["read_model_markdown"])
+ read_model_tab()
+
+ if configs.get("onnx_tab", True):
+ with gr.TabItem(translations["convert_model"]):
+ gr.Markdown(translations["pytorch2onnx"])
+ convert_onnx_model_tab()
+
+ if configs.get("svc_tab", True):
+ with gr.TabItem(translations["convert_model_svc"]):
+ gr.Markdown(translations["convert_model_svc_markdown"])
+ convert_svc_model_tab()
+
+ if configs.get("f0_extractor_tab", True):
+ with gr.TabItem(translations["f0_extractor_tab"]):
+ gr.Markdown(translations["f0_extractor_markdown"])
+ f0_extract_tab()
+
+ if configs.get("create_srt_tab", True):
+ with gr.TabItem(translations["create_srt_tab"]):
+ gr.Markdown(translations["create_srt_markdown"])
+ create_srt_tab()
+
+ if configs.get("analyzer_tab", True):
+ with gr.TabItem(translations["analyzer_tab"]):
+ gr.Markdown(translations["analyzer_markdown"])
+ analyzer_tab()
+
+ if configs.get("settings_tab", True):
+ with gr.TabItem(translations["settings"]):
+ gr.Markdown(translations["settings_markdown"])
+ settings_tab(app)
\ No newline at end of file
diff --git a/main/app/tabs/inference/child/convert.py b/main/app/tabs/inference/child/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..290e4bc82eb86f14a9dd9cb4f5d082bb522ea4e6
--- /dev/null
+++ b/main/app/tabs/inference/child/convert.py
@@ -0,0 +1,1132 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.presets import (
+ load_presets,
+ save_presets
+)
+
+from main.app.core.inference import (
+ convert_audio,
+ convert_selection
+)
+
+from main.app.variables import (
+ configs,
+ f0_file,
+ method_f0,
+ file_types,
+ model_name,
+ index_path,
+ audio_params,
+ presets_file,
+ translations,
+ embedders_mode,
+ embedders_model,
+ paths_for_files,
+ sample_rate_choice,
+ hybrid_f0_method,
+ export_format_choices,
+)
+
+from main.app.core.ui import (
+ visible,
+ unlock_f0,
+ get_index,
+ shutil_move,
+ hoplength_show,
+ change_f0_choices,
+ index_strength_show,
+ change_models_choices,
+ change_preset_choices,
+ change_audios_choices,
+ valueFalse_interactive,
+ change_backing_choices,
+ get_speakers_id_and_architecture
+)
+
+def convert_tab():
+ with gr.Row():
+ gr.Markdown(translations["convert_info"])
+ with gr.Row():
+ with gr.Column():
+ with gr.Group():
+ with gr.Row():
+ cleaner = gr.Checkbox(
+ label=translations["clear_audio"],
+ value=False,
+ interactive=True
+ )
+ autotune = gr.Checkbox(
+ label=translations["autotune"],
+ value=False,
+ interactive=True
+ )
+ use_audio = gr.Checkbox(
+ label=translations["use_audio"],
+ value=False,
+ interactive=True
+ )
+ checkpointing = gr.Checkbox(
+ label=translations["memory_efficient_training"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ use_original = gr.Checkbox(
+ label=translations["convert_original"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ convert_backing = gr.Checkbox(
+ label=translations["convert_backing"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ not_merge_backing = gr.Checkbox(
+ label=translations["not_merge_backing"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ merge_instrument = gr.Checkbox(
+ label=translations["merge_instruments"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ pitch = gr.Slider(
+ minimum=-24,
+ maximum=24,
+ step=1,
+ info=translations["pitch_info"],
+ label=translations["pitch"],
+ value=0,
+ interactive=True
+ )
+ clean_strength = gr.Slider(
+ label=translations["clean_strength"],
+ info=translations["clean_strength_info"],
+ minimum=0,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ with gr.Column():
+ convert_select_audio = gr.Dropdown(
+ label=translations["select_separate"],
+ choices=[],
+ value="",
+ interactive=True,
+ allow_custom_value=True,
+ visible=False
+ )
+ convert_select_audio_button = gr.Button(
+ translations["convert_audio"],
+ visible=False
+ )
+ with gr.Row():
+ with gr.Column():
+ convert_audio_button = gr.Button(
+ translations["convert_audio"],
+ variant="primary"
+ )
+ with gr.Row():
+ with gr.Column():
+ drop_audio_file = gr.Files(
+ label=translations["drop_audio"],
+ file_types=file_types
+ )
+ play_audio = gr.Audio(
+ interactive=False,
+ label=translations["input_audio"],
+ **audio_params
+ )
+ with gr.Column():
+ with gr.Accordion(
+ translations["model_accordion"],
+ open=True
+ ):
+ with gr.Row():
+ model_pth = gr.Dropdown(
+ label=translations["model_name"],
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ model_index = gr.Dropdown(
+ label=translations["index_path"],
+ choices=index_path,
+ value=index_path[0] if len(index_path) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ refresh_model = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ index_strength = gr.Slider(
+ label=translations["index_strength"],
+ info=translations["index_strength_info"],
+ minimum=0,
+ maximum=1,
+ value=0.5,
+ step=0.01,
+ interactive=True,
+ visible=True
+ )
+ nprobe = gr.Slider(
+ label=translations["nprobe"],
+ info=translations["nprobe_info"],
+ minimum=1,
+ maximum=64,
+ value=9,
+ step=1,
+ interactive=True,
+ visible=True
+ )
+ with gr.Row():
+ sid_dict, architecture_dict = get_speakers_id_and_architecture(model_pth.value)
+ if sid_dict["visible"] == "hidden": sid_dict["visible"] = False
+ if architecture_dict["visible"] == "hidden": architecture_dict["visible"] = False
+
+ sids = gr.Dropdown(
+ label=translations["sids_label"],
+ info=translations["sids_info"],
+ choices=sid_dict["choices"],
+ value=sid_dict["value"],
+ visible=sid_dict["visible"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["input_output"],
+ open=False
+ ):
+ with gr.Column():
+ export_format = gr.Radio(
+ label=translations["export_format"],
+ info=translations["export_info"],
+ choices=export_format_choices,
+ value="wav",
+ interactive=True
+ )
+ input_audio = gr.Dropdown(
+ label=translations["audio_path"],
+ value="",
+ choices=paths_for_files,
+ info=translations["provide_audio"],
+ allow_custom_value=True,
+ interactive=True
+ )
+ output_audio = gr.Textbox(
+ label=translations["output_path"],
+ value="audios/output.wav",
+ placeholder="audios/output.wav",
+ info=translations["output_path_info"],
+ interactive=True
+ )
+ with gr.Column():
+ refresh_audio = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Accordion(
+ translations["setting"],
+ open=False
+ ):
+ with gr.Accordion(
+ translations["f0_method"],
+ open=False
+ ):
+ with gr.Group():
+ with gr.Row():
+ predictor_onnx = gr.Checkbox(
+ label=translations["predictor_onnx"],
+ info=translations["predictor_onnx_info"],
+ value=configs.get("int8", False),
+ interactive=True
+ )
+ unlock_full_method = gr.Checkbox(
+ label=translations["f0_unlock"],
+ info=translations["f0_unlock_info"],
+ value=False,
+ interactive=True
+ )
+ f0_method = gr.Radio(
+ label=translations["f0_method"],
+ info=translations["f0_method_info"],
+ choices=method_f0,
+ value="rmvpe",
+ interactive=True
+ )
+ hybrid_method = gr.Dropdown(
+ label=translations["f0_method_hybrid"],
+ info=translations["f0_method_hybrid_info"],
+ choices=hybrid_f0_method,
+ value=hybrid_f0_method[0],
+ interactive=True,
+ allow_custom_value=True,
+ visible=False
+ )
+ hop_length = gr.Slider(
+ label=translations['hop_length'],
+ info=translations["hop_length_info"],
+ minimum=64,
+ maximum=512,
+ value=160,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ alpha = gr.Slider(
+ label=translations["alpha_label"],
+ info=translations["alpha_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Accordion(
+ translations["f0_file"],
+ open=False
+ ):
+ upload_f0_file = gr.File(
+ label=translations["upload_f0"],
+ file_types=[".txt"]
+ )
+ f0_file_dropdown = gr.Dropdown(
+ label=translations["f0_file_2"],
+ value="",
+ choices=f0_file,
+ allow_custom_value=True,
+ interactive=True
+ )
+ refresh_f0_file = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Accordion(
+ translations["hubert_model"],
+ open=False
+ ):
+ embedders_mix = gr.Checkbox(
+ label=translations["embedders_mix"],
+ info=translations["embedders_mix_info"],
+ value=False,
+ interactive=True
+ )
+ embedder_mode = gr.Radio(
+ label=translations["embed_mode"],
+ info=translations["embed_mode_info"],
+ value="onnx" if configs.get("int8", False) else "fairseq",
+ choices=embedders_mode,
+ interactive=True,
+ visible=True
+ )
+ embedders = gr.Radio(
+ label=translations["hubert_model"],
+ info=translations["hubert_info"],
+ choices=embedders_model,
+ value="hubert_base",
+ interactive=True
+ )
+ custom_embedders = gr.Textbox(
+ label=translations["modelname"],
+ info=translations["modelname_info"],
+ value="",
+ placeholder="hubert_base",
+ interactive=True,
+ visible=False
+ )
+ with gr.Column(visible=False) as embedders_mix_column:
+ embedders_mix_layers = gr.Slider(
+ label=translations["embedders_mix_layers"],
+ info=translations["embedders_mix_layers_info"],
+ minimum=1,
+ maximum=12,
+ value=9,
+ step=1,
+ interactive=True
+ )
+ embedders_mix_ratio = gr.Slider(
+ label=translations["embedders_mix_ratio"],
+ info=translations["embedders_mix_ratio_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["use_presets"],
+ open=False
+ ):
+ with gr.Row():
+ presets_name = gr.Dropdown(
+ label=translations["file_preset"],
+ choices=presets_file,
+ value=presets_file[0] if len(presets_file) > 0 else '',
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ load_preset_button = gr.Button(
+ translations["load_file"],
+ variant="primary"
+ )
+ refresh_preset_button = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Accordion(
+ translations["export_file"],
+ open=False
+ ):
+ with gr.Row():
+ with gr.Column():
+ with gr.Group():
+ with gr.Row():
+ cleaner_checkbox = gr.Checkbox(
+ label=translations["save_clean"],
+ value=True,
+ interactive=True
+ )
+ autotune_checkbox = gr.Checkbox(
+ label=translations["save_autotune"],
+ value=True,
+ interactive=True
+ )
+ pitch_checkbox = gr.Checkbox(
+ label=translations["save_pitch"],
+ value=True,
+ interactive=True
+ )
+ index_strength_checkbox = gr.Checkbox(
+ label=translations["save_index_2"],
+ value=True,
+ interactive=True
+ )
+ resample_sr_checkbox = gr.Checkbox(
+ label=translations["save_resample"],
+ value=True,
+ interactive=True
+ )
+ filter_radius_checkbox = gr.Checkbox(
+ label=translations["save_filter"],
+ value=True,
+ interactive=True
+ )
+ rms_mix_rate_checkbox = gr.Checkbox(
+ label=translations["save_envelope"],
+ value=True,
+ interactive=True
+ )
+ protect_checkbox = gr.Checkbox(
+ label=translations["save_protect"],
+ value=True,
+ interactive=True
+ )
+ split_audio_checkbox = gr.Checkbox(
+ label=translations["save_split"],
+ value=True,
+ interactive=True
+ )
+ formant_shifting_checkbox = gr.Checkbox(
+ label=translations["formantshift"],
+ value=True,
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Column():
+ name_to_save_file = gr.Textbox(
+ label=translations["filename_to_save"]
+ )
+ save_file_button = gr.Button(
+ translations["export_file"]
+ )
+ with gr.Row():
+ upload_presets = gr.Files(
+ label=translations["upload_presets"],
+ file_types=[".conversion.json"]
+ )
+ with gr.Column():
+ with gr.Group():
+ with gr.Row():
+ split_audio = gr.Checkbox(
+ label=translations["split_audio"],
+ value=False,
+ interactive=True
+ )
+ formant_shifting = gr.Checkbox(
+ label=translations["formantshift"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ proposal_pitch = gr.Checkbox(
+ label=translations["proposal_pitch"],
+ value=False,
+ interactive=True
+ )
+ audio_processing = gr.Checkbox(
+ label=translations["audio_processing"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ audio_upscaler = gr.Checkbox(
+ label=translations["audio_upscaler"],
+ info=translations["audio_upscaler_info"],
+ value=False,
+ interactive=True
+ )
+ resample_sr = gr.Radio(
+ choices=[0]+sample_rate_choice,
+ label=translations["resample"],
+ info=translations["resample_info"],
+ value=0,
+ interactive=True
+ )
+ proposal_pitch_threshold = gr.Slider(
+ minimum=50.0,
+ maximum=1200.0,
+ label=translations["proposal_pitch_threshold"],
+ info=translations["proposal_pitch_threshold_info"],
+ value=255.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ f0_autotune_strength = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["autotune_rate"],
+ info=translations["autotune_rate_info"],
+ value=1,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ filter_radius = gr.Slider(
+ minimum=0,
+ maximum=7,
+ label=translations["filter_radius"],
+ info=translations["filter_radius_info"],
+ value=3,
+ step=1,
+ interactive=True
+ )
+ rms_mix_rate = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["rms_mix_rate"],
+ info=translations["rms_mix_rate_info"],
+ value=1,
+ step=0.1,
+ interactive=True
+ )
+ protect = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["protect"],
+ info=translations["protect_info"],
+ value=0.33,
+ step=0.01,
+ interactive=True
+ )
+ noise_scale = gr.Slider(
+ minimum=0.1,
+ maximum=1.0,
+ label=translations["noise_scale"],
+ info=translations["noise_scale_info"],
+ value=0.4,
+ step=0.01,
+ interactive=True,
+ visible=architecture_dict["visible"]
+ )
+ with gr.Row():
+ formant_qfrency = gr.Slider(
+ value=1.0,
+ label=translations["formant_qfrency"],
+ info=translations["formant_qfrency"],
+ minimum=0.0,
+ maximum=16.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ formant_timbre = gr.Slider(
+ value=1.0,
+ label=translations["formant_timbre"],
+ info=translations["formant_timbre"],
+ minimum=0.0,
+ maximum=16.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ gr.Markdown(translations["output_convert"])
+ with gr.Row():
+ main_convert = gr.Audio(
+ interactive=False,
+ label=translations["main_convert"],
+ visible=True,
+ **audio_params
+ )
+ backing_convert = gr.Audio(
+ interactive=False,
+ label=translations["convert_backing"],
+ visible=False,
+ **audio_params
+ )
+ main_backing = gr.Audio(
+ interactive=False,
+ label=translations["main_or_backing"],
+ visible=False,
+ **audio_params
+ )
+ with gr.Row():
+ original_convert = gr.Audio(
+ interactive=False,
+ label=translations["convert_original"],
+ visible=False,
+ **audio_params
+ )
+ vocal_instrument = gr.Audio(
+ interactive=False,
+ label=translations["voice_or_instruments"],
+ visible=False,
+ **audio_params
+ )
+ with gr.Row():
+ upload_f0_file.upload(
+ fn=lambda inp: shutil_move(inp.name, configs["f0_path"]),
+ inputs=[
+ upload_f0_file
+ ],
+ outputs=[
+ f0_file_dropdown
+ ]
+ )
+ refresh_f0_file.click(
+ fn=change_f0_choices,
+ inputs=[],
+ outputs=[
+ f0_file_dropdown
+ ]
+ )
+ unlock_full_method.change(
+ fn=unlock_f0,
+ inputs=[
+ unlock_full_method
+ ],
+ outputs=[
+ f0_method
+ ]
+ )
+ with gr.Row():
+ load_preset_button.click(
+ fn=load_presets,
+ inputs=[
+ presets_name,
+ cleaner,
+ autotune,
+ pitch,
+ clean_strength,
+ index_strength,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ ],
+ outputs=[
+ cleaner,
+ autotune,
+ pitch,
+ clean_strength,
+ index_strength,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ ]
+ )
+ refresh_preset_button.click(
+ fn=change_preset_choices,
+ inputs=[],
+ outputs=[
+ presets_name
+ ]
+ )
+ save_file_button.click(
+ fn=save_presets,
+ inputs=[
+ name_to_save_file,
+ cleaner,
+ autotune,
+ pitch,
+ clean_strength,
+ index_strength,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ cleaner_checkbox,
+ autotune_checkbox,
+ pitch_checkbox,
+ index_strength_checkbox,
+ resample_sr_checkbox,
+ filter_radius_checkbox,
+ rms_mix_rate_checkbox,
+ protect_checkbox,
+ split_audio_checkbox,
+ formant_shifting_checkbox,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ ],
+ outputs=[
+ presets_name
+ ]
+ )
+ with gr.Row():
+ upload_presets.upload(
+ fn=lambda presets_in: [
+ shutil_move(preset.name, configs["presets_path"])
+ for preset in presets_in
+ ][0],
+ inputs=[
+ upload_presets
+ ],
+ outputs=[
+ presets_name
+ ]
+ )
+ autotune.change(
+ fn=visible,
+ inputs=[
+ autotune
+ ],
+ outputs=[
+ f0_autotune_strength
+ ]
+ )
+ use_audio.change(
+ fn=lambda a: [
+ visible(a),
+ visible(a),
+ visible(a),
+ visible(a),
+ visible(a),
+ valueFalse_interactive(a),
+ valueFalse_interactive(a),
+ valueFalse_interactive(a),
+ valueFalse_interactive(a),
+ visible(not a),
+ visible(not a),
+ visible(not a),
+ visible(not a)
+ ],
+ inputs=[
+ use_audio
+ ],
+ outputs=[
+ main_backing,
+ use_original,
+ convert_backing,
+ not_merge_backing,
+ merge_instrument,
+ use_original,
+ convert_backing,
+ not_merge_backing,
+ merge_instrument,
+ input_audio,
+ output_audio,
+ drop_audio_file,
+ play_audio
+ ]
+ )
+ with gr.Row():
+ convert_backing.change(
+ fn=lambda a,b: [
+ change_backing_choices(a, b),
+ visible(a)
+ ],
+ inputs=[
+ convert_backing,
+ not_merge_backing
+ ],
+ outputs=[
+ use_original,
+ backing_convert
+ ]
+ )
+ use_original.change(
+ fn=lambda audio, original: [
+ visible(original),
+ visible(not original),
+ visible(audio and not original),
+ valueFalse_interactive(not original),
+ valueFalse_interactive(not original)
+ ],
+ inputs=[
+ use_audio,
+ use_original
+ ],
+ outputs=[
+ original_convert,
+ main_convert,
+ main_backing,
+ convert_backing,
+ not_merge_backing
+ ]
+ )
+ cleaner.change(
+ fn=visible,
+ inputs=[
+ cleaner
+ ],
+ outputs=[
+ clean_strength
+ ]
+ )
+ with gr.Row():
+ merge_instrument.change(
+ fn=visible,
+ inputs=[
+ merge_instrument
+ ],
+ outputs=[
+ vocal_instrument
+ ]
+ )
+ not_merge_backing.change(
+ fn=lambda audio, merge, cvb: [
+ visible(audio and not merge),
+ change_backing_choices(cvb, merge)
+ ],
+ inputs=[
+ use_audio,
+ not_merge_backing,
+ convert_backing
+ ],
+ outputs=[
+ main_backing,
+ use_original
+ ]
+ )
+ f0_method.change(
+ fn=lambda f0_method, hybrid: [
+ visible(f0_method == "hybrid"),
+ visible(f0_method == "hybrid"),
+ hoplength_show(f0_method, hybrid)
+ ],
+ inputs=[
+ f0_method,
+ hybrid_method
+ ],
+ outputs=[
+ hybrid_method,
+ alpha,
+ hop_length
+ ]
+ )
+ with gr.Row():
+ hybrid_method.change(
+ fn=hoplength_show,
+ inputs=[
+ f0_method,
+ hybrid_method
+ ],
+ outputs=[
+ hop_length
+ ]
+ )
+ refresh_model.click(
+ fn=change_models_choices,
+ inputs=[],
+ outputs=[
+ model_pth,
+ model_index
+ ]
+ )
+ model_pth.change(
+ fn=get_index,
+ inputs=[
+ model_pth
+ ],
+ outputs=[
+ model_index
+ ]
+ )
+ with gr.Row():
+ drop_audio_file.upload(
+ fn=lambda audio_in: [
+ shutil_move(audio.name, configs["audios_path"])
+ for audio in audio_in
+ ][0],
+ inputs=[
+ drop_audio_file
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ input_audio.change(
+ fn=lambda audio: audio if os.path.isfile(audio) else None,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ play_audio
+ ]
+ )
+ formant_shifting.change(
+ fn=lambda a: [
+ visible(a)
+ for _ in range(2)
+ ],
+ inputs=[
+ formant_shifting
+ ],
+ outputs=[
+ formant_qfrency,
+ formant_timbre
+ ]
+ )
+ with gr.Row():
+ embedders.change(
+ fn=lambda embedders: visible(embedders == "custom"),
+ inputs=[
+ embedders
+ ],
+ outputs=[
+ custom_embedders
+ ]
+ )
+ refresh_audio.click(
+ fn=change_audios_choices,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ model_index.change(
+ fn=index_strength_show,
+ inputs=[
+ model_index
+ ],
+ outputs=[
+ index_strength,
+ nprobe
+ ]
+ )
+ with gr.Row():
+ convert_audio_button.click(
+ fn=lambda: visible(False),
+ inputs=[],
+ outputs=[
+ convert_audio_button
+ ]
+ )
+ convert_select_audio_button.click(
+ fn=lambda: [
+ visible(False)
+ for _ in range(2)
+ ],
+ inputs=[],
+ outputs=[
+ convert_select_audio,
+ convert_select_audio_button
+ ]
+ )
+ with gr.Row():
+ proposal_pitch.change(
+ fn=visible,
+ inputs=[
+ proposal_pitch
+ ],
+ outputs=[
+ proposal_pitch_threshold
+ ]
+ )
+ model_pth.change(
+ fn=get_speakers_id_and_architecture,
+ inputs=[
+ model_pth
+ ],
+ outputs=[
+ sids,
+ noise_scale
+ ]
+ )
+ with gr.Row():
+ embedders_mix.change(
+ fn=visible,
+ inputs=[
+ embedders_mix
+ ],
+ outputs=[
+ embedders_mix_column
+ ]
+ )
+ convert_audio_button.click(
+ fn=convert_selection,
+ inputs=[
+ cleaner,
+ autotune,
+ use_audio,
+ use_original,
+ convert_backing,
+ not_merge_backing,
+ merge_instrument,
+ pitch,
+ clean_strength,
+ model_pth,
+ model_index,
+ index_strength,
+ input_audio,
+ output_audio,
+ export_format,
+ f0_method,
+ hybrid_method,
+ hop_length,
+ embedders,
+ custom_embedders,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ checkpointing,
+ predictor_onnx,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ f0_file_dropdown,
+ embedder_mode,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ audio_processing,
+ alpha,
+ sids,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ noise_scale,
+ nprobe,
+ audio_upscaler
+ ],
+ outputs=[
+ convert_select_audio,
+ main_convert,
+ backing_convert,
+ main_backing,
+ original_convert,
+ vocal_instrument,
+ convert_audio_button,
+ convert_select_audio_button
+ ],
+ api_name="convert_selection"
+ )
+ convert_select_audio_button.click(
+ fn=convert_audio,
+ inputs=[
+ cleaner,
+ autotune,
+ use_audio,
+ use_original,
+ convert_backing,
+ not_merge_backing,
+ merge_instrument,
+ pitch,
+ clean_strength,
+ model_pth,
+ model_index,
+ index_strength,
+ input_audio,
+ output_audio,
+ export_format,
+ f0_method,
+ hybrid_method,
+ hop_length,
+ embedders,
+ custom_embedders,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ convert_select_audio,
+ checkpointing,
+ predictor_onnx,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ f0_file_dropdown,
+ embedder_mode,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ audio_processing,
+ alpha,
+ sids,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ noise_scale,
+ nprobe,
+ audio_upscaler
+ ],
+ outputs=[
+ main_convert,
+ backing_convert,
+ main_backing,
+ original_convert,
+ vocal_instrument,
+ convert_audio_button
+ ],
+ api_name="convert_audio"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/inference/child/convert_tts.py b/main/app/tabs/inference/child/convert_tts.py
new file mode 100644
index 0000000000000000000000000000000000000000..06d081051c9ba7dd2ec04388e1f4feea14bbf947
--- /dev/null
+++ b/main/app/tabs/inference/child/convert_tts.py
@@ -0,0 +1,969 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.tts import TTS
+from main.app.core.process import process_input
+from main.app.core.inference import convert_tts
+from main.app.core.utils import google_translate
+
+from main.app.core.presets import (
+ save_presets,
+ load_presets
+)
+
+from main.app.variables import (
+ configs,
+ edgetts,
+ f0_file,
+ method_f0,
+ model_name,
+ index_path,
+ presets_file,
+ translations,
+ audio_params,
+ embedders_mode,
+ embedders_model,
+ google_tts_voice,
+ hybrid_f0_method,
+ sample_rate_choice,
+ export_format_choices
+)
+
+from main.app.core.ui import (
+ visible,
+ unlock_f0,
+ get_index,
+ shutil_move,
+ hoplength_show,
+ change_f0_choices,
+ index_strength_show,
+ change_models_choices,
+ change_preset_choices,
+ change_tts_voice_choices,
+ get_speakers_id_and_architecture
+)
+
+def convert_tts_tab():
+ with gr.Row():
+ gr.Markdown(translations["convert_text_markdown_2"])
+ with gr.Row():
+ with gr.Column():
+ with gr.Group():
+ with gr.Row():
+ use_txt = gr.Checkbox(
+ label=translations["input_txt"],
+ value=False,
+ interactive=True
+ )
+ google_tts_check_box = gr.Checkbox(
+ label=translations["googletts"],
+ value=False,
+ interactive=True
+ )
+ prompt = gr.Textbox(
+ label=translations["text_to_speech"],
+ value="",
+ placeholder="Hello Words",
+ lines=5,
+ max_lines=5
+ )
+ with gr.Column():
+ speed = gr.Slider(
+ label=translations["voice_speed"],
+ info=translations["voice_speed_info"],
+ minimum=-100,
+ maximum=100,
+ value=0,
+ step=1
+ )
+ pitch = gr.Slider(
+ minimum=-24,
+ maximum=24,
+ step=1,
+ info=translations["pitch_info"],
+ label=translations["pitch"],
+ value=0,
+ interactive=True
+ )
+ with gr.Row():
+ tts_button = gr.Button(
+ translations["tts_1"],
+ variant="primary",
+ scale=2
+ )
+ convert_button = gr.Button(
+ translations["tts_2"],
+ variant="secondary",
+ scale=2
+ )
+ with gr.Row():
+ with gr.Column():
+ txt_input = gr.File(
+ label=translations["drop_text"],
+ file_types=[".txt", ".srt", ".docx"],
+ visible=False
+ )
+ tts_voice = gr.Dropdown(
+ label=translations["voice"],
+ choices=edgetts,
+ interactive=True,
+ value="vi-VN-NamMinhNeural"
+ )
+ tts_pitch = gr.Slider(
+ minimum=-24,
+ maximum=24,
+ step=1,
+ info=translations["pitch_info_2"],
+ label=translations["pitch"],
+ value=0,
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["translate"],
+ open=False
+ ):
+ with gr.Row():
+ source_lang = gr.Dropdown(
+ label=translations["source_lang"],
+ choices=["auto"]+google_tts_voice,
+ interactive=True,
+ value="auto"
+ )
+ target_lang = gr.Dropdown(
+ label=translations["target_lang"],
+ choices=google_tts_voice,
+ interactive=True,
+ value="en"
+ )
+ translate_button = gr.Button(
+ translations["translate"]
+ )
+ with gr.Column():
+ with gr.Accordion(
+ translations["model_accordion"],
+ open=True
+ ):
+ with gr.Row():
+ model_pth = gr.Dropdown(
+ label=translations["model_name"],
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ model_index = gr.Dropdown(
+ label=translations["index_path"],
+ choices=index_path,
+ value=index_path[0] if len(index_path) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ refresh = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ index_strength = gr.Slider(
+ label=translations["index_strength"],
+ info=translations["index_strength_info"],
+ minimum=0,
+ maximum=1,
+ value=0.5,
+ step=0.01,
+ interactive=True,
+ visible=True
+ )
+ nprobe = gr.Slider(
+ label=translations["nprobe"],
+ info=translations["nprobe_info"],
+ minimum=1,
+ maximum=64,
+ value=9,
+ step=1,
+ interactive=True,
+ visible=True
+ )
+ with gr.Row():
+ sid_dict, architecture_dict = get_speakers_id_and_architecture(model_pth.value)
+ if sid_dict["visible"] == "hidden": sid_dict["visible"] = False
+ if architecture_dict["visible"] == "hidden": architecture_dict["visible"] = False
+
+ sids = gr.Dropdown(
+ label=translations["sids_label"],
+ info=translations["sids_info"],
+ choices=sid_dict["choices"],
+ value=sid_dict["value"],
+ visible=sid_dict["visible"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["output_path"],
+ open=False
+ ):
+ export_format = gr.Radio(
+ label=translations["export_format"],
+ info=translations["export_info"],
+ choices=export_format_choices,
+ value="wav",
+ interactive=True
+ )
+ output_audio_tts = gr.Textbox(
+ label=translations["output_tts"],
+ value="audios/tts.wav",
+ placeholder="audios/tts.wav",
+ info=translations["tts_output"],
+ interactive=True
+ )
+ output_audio = gr.Textbox(
+ label=translations["output_tts_convert"],
+ value="audios/tts-convert.wav",
+ placeholder="audios/tts-convert.wav",
+ info=translations["tts_output"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["setting"],
+ open=False
+ ):
+ with gr.Accordion(
+ translations["f0_method"],
+ open=False
+ ):
+ with gr.Group():
+ with gr.Row():
+ predictor_onnx = gr.Checkbox(
+ label=translations["predictor_onnx"],
+ info=translations["predictor_onnx_info"],
+ value=configs.get("int8", False),
+ interactive=True
+ )
+ unlock_full_method = gr.Checkbox(
+ label=translations["f0_unlock"],
+ info=translations["f0_unlock_info"],
+ value=False,
+ interactive=True
+ )
+ f0_method = gr.Radio(
+ label=translations["f0_method"],
+ info=translations["f0_method_info"],
+ choices=method_f0,
+ value="rmvpe",
+ interactive=True
+ )
+ hybrid_f0method = gr.Dropdown(
+ label=translations["f0_method_hybrid"],
+ info=translations["f0_method_hybrid_info"],
+ choices=hybrid_f0_method,
+ value=hybrid_f0_method[0],
+ interactive=True,
+ allow_custom_value=True,
+ visible=False
+ )
+ hop_length = gr.Slider(
+ label=translations['hop_length'],
+ info=translations["hop_length_info"],
+ minimum=64,
+ maximum=512,
+ value=160,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ alpha = gr.Slider(
+ label=translations["alpha_label"],
+ info=translations["alpha_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Accordion(
+ translations["f0_file"],
+ open=False
+ ):
+ upload_f0_file = gr.File(
+ label=translations["upload_f0"],
+ file_types=[".txt"]
+ )
+ f0_file_dropdown = gr.Dropdown(
+ label=translations["f0_file_2"],
+ value="",
+ choices=f0_file,
+ allow_custom_value=True,
+ interactive=True
+ )
+ refresh_f0_file = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Accordion(
+ translations["hubert_model"],
+ open=False
+ ):
+ embedders_mix = gr.Checkbox(
+ label=translations["embedders_mix"],
+ info=translations["embedders_mix_info"],
+ value=False,
+ interactive=True
+ )
+ embedder_mode = gr.Radio(
+ label=translations["embed_mode"],
+ info=translations["embed_mode_info"],
+ value="onnx" if configs.get("int8", False) else "fairseq",
+ choices=embedders_mode,
+ interactive=True,
+ visible=True
+ )
+ embedders = gr.Radio(
+ label=translations["hubert_model"],
+ info=translations["hubert_info"],
+ choices=embedders_model,
+ value="hubert_base",
+ interactive=True
+ )
+ custom_embedders = gr.Textbox(
+ label=translations["modelname"],
+ info=translations["modelname_info"],
+ value="",
+ placeholder="hubert_base",
+ interactive=True,
+ visible=False
+ )
+ with gr.Column(visible=False) as embedders_mix_column:
+ embedders_mix_layers = gr.Slider(
+ label=translations["embedders_mix_layers"],
+ info=translations["embedders_mix_layers_info"],
+ minimum=1,
+ maximum=12,
+ value=9,
+ step=1,
+ interactive=True
+ )
+ embedders_mix_ratio = gr.Slider(
+ label=translations["embedders_mix_ratio"],
+ info=translations["embedders_mix_ratio_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["use_presets"],
+ open=False
+ ):
+ with gr.Row():
+ presets_name = gr.Dropdown(
+ label=translations["file_preset"],
+ choices=presets_file,
+ value=presets_file[0] if len(presets_file) > 0 else '',
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ load_preset_button = gr.Button(
+ translations["load_file"],
+ variant="primary"
+ )
+ refresh_preset_button = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Accordion(
+ translations["export_file"],
+ open=False
+ ):
+ with gr.Row():
+ with gr.Column():
+ with gr.Group():
+ with gr.Row():
+ cleaner_checkbox = gr.Checkbox(
+ label=translations["save_clean"],
+ value=True,
+ interactive=True
+ )
+ autotune_checkbox = gr.Checkbox(
+ label=translations["save_autotune"],
+ value=True,
+ interactive=True
+ )
+ pitch_checkbox = gr.Checkbox(
+ label=translations["save_pitch"],
+ value=True,
+ interactive=True
+ )
+ index_strength_checkbox = gr.Checkbox(
+ label=translations["save_index_2"],
+ value=True,
+ interactive=True
+ )
+ resample_sr_checkbox = gr.Checkbox(
+ label=translations["save_resample"],
+ value=True,
+ interactive=True
+ )
+ filter_radius_checkbox = gr.Checkbox(
+ label=translations["save_filter"],
+ value=True,
+ interactive=True
+ )
+ rms_mix_rate_checkbox = gr.Checkbox(
+ label=translations["save_envelope"],
+ value=True,
+ interactive=True
+ )
+ protect_checkbox = gr.Checkbox(
+ label=translations["save_protect"],
+ value=True,
+ interactive=True
+ )
+ split_audio_checkbox = gr.Checkbox(
+ label=translations["save_split"],
+ value=True,
+ interactive=True
+ )
+ formant_shifting_checkbox = gr.Checkbox(
+ label=translations["formantshift"],
+ value=True,
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Column():
+ name_to_save_file = gr.Textbox(
+ label=translations["filename_to_save"]
+ )
+ save_file_button = gr.Button(
+ translations["export_file"]
+ )
+ with gr.Row():
+ upload_presets = gr.Files(
+ label=translations["upload_presets"],
+ file_types=[".conversion.json"]
+ )
+ with gr.Group():
+ with gr.Row():
+ audio_processing = gr.Checkbox(
+ label=translations["audio_processing"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ formant_shifting = gr.Checkbox(
+ label=translations["formantshift"],
+ value=False,
+ interactive=True
+ )
+ split_audio = gr.Checkbox(
+ label=translations["split_audio"],
+ value=False,
+ interactive=True
+ )
+ cleaner = gr.Checkbox(
+ label=translations["clear_audio"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ autotune = gr.Checkbox(
+ label=translations["autotune"],
+ value=False,
+ interactive=True
+ )
+ checkpointing = gr.Checkbox(
+ label=translations["memory_efficient_training"],
+ value=False,
+ interactive=True
+ )
+ proposal_pitch = gr.Checkbox(
+ label=translations["proposal_pitch"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ audio_upscaler = gr.Checkbox(
+ label=translations["audio_upscaler"],
+ info=translations["audio_upscaler_info"],
+ value=False,
+ interactive=True
+ )
+ with gr.Column():
+ resample_sr = gr.Radio(
+ choices=[0]+sample_rate_choice,
+ label=translations["resample"],
+ info=translations["resample_info"],
+ value=0,
+ interactive=True
+ )
+ proposal_pitch_threshold = gr.Slider(
+ minimum=50.0,
+ maximum=1200.0,
+ label=translations["proposal_pitch_threshold"],
+ info=translations["proposal_pitch_threshold_info"],
+ value=255.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ f0_autotune_strength = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["autotune_rate"],
+ info=translations["autotune_rate_info"],
+ value=1,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ clean_strength = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["clean_strength"],
+ info=translations["clean_strength_info"],
+ value=0.5,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ filter_radius = gr.Slider(
+ minimum=0,
+ maximum=7,
+ label=translations["filter_radius"],
+ info=translations["filter_radius_info"],
+ value=3,
+ step=1,
+ interactive=True
+ )
+ rms_mix_rate = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["rms_mix_rate"],
+ info=translations["rms_mix_rate_info"],
+ value=1,
+ step=0.1,
+ interactive=True
+ )
+ protect = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["protect"],
+ info=translations["protect_info"],
+ value=0.33,
+ step=0.01,
+ interactive=True
+ )
+ noise_scale = gr.Slider(
+ minimum=0.1,
+ maximum=1.0,
+ label=translations["noise_scale"],
+ info=translations["noise_scale_info"],
+ value=0.4,
+ step=0.01,
+ interactive=True,
+ visible=architecture_dict["visible"]
+ )
+ with gr.Row():
+ formant_qfrency = gr.Slider(
+ value=1.0,
+ label=translations["formant_qfrency"],
+ info=translations["formant_qfrency"],
+ minimum=0.0,
+ maximum=16.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ formant_timbre = gr.Slider(
+ value=1.0,
+ label=translations["formant_timbre"],
+ info=translations["formant_timbre"],
+ minimum=0.0,
+ maximum=16.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ gr.Markdown(translations["output_tts_markdown"])
+ with gr.Row():
+ tts_voice_audio = gr.Audio(
+ interactive=False,
+ label=translations["output_text_to_speech"],
+ **audio_params
+ )
+ tts_voice_convert = gr.Audio(
+ interactive=False,
+ label=translations["output_file_tts_convert"],
+ **audio_params
+ )
+ with gr.Row():
+ load_preset_button.click(
+ fn=load_presets,
+ inputs=[
+ presets_name,
+ cleaner,
+ autotune,
+ pitch,
+ clean_strength,
+ index_strength,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ ],
+ outputs=[
+ cleaner,
+ autotune,
+ pitch,
+ clean_strength,
+ index_strength,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ ]
+ )
+ save_file_button.click(
+ fn=save_presets,
+ inputs=[
+ name_to_save_file,
+ cleaner,
+ autotune,
+ pitch,
+ clean_strength,
+ index_strength,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ cleaner_checkbox,
+ autotune_checkbox,
+ pitch_checkbox,
+ index_strength_checkbox,
+ resample_sr_checkbox,
+ filter_radius_checkbox,
+ rms_mix_rate_checkbox,
+ protect_checkbox,
+ split_audio_checkbox,
+ formant_shifting_checkbox,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ ],
+ outputs=[
+ presets_name
+ ]
+ )
+ refresh_preset_button.click(
+ fn=change_preset_choices,
+ inputs=[],
+ outputs=[
+ presets_name
+ ]
+ )
+ with gr.Row():
+ proposal_pitch.change(
+ fn=visible,
+ inputs=[
+ proposal_pitch
+ ],
+ outputs=[
+ proposal_pitch_threshold
+ ]
+ )
+ upload_presets.upload(
+ fn=lambda presets_in: [
+ shutil_move(preset.name, configs["presets_path"])
+ for preset in presets_in
+ ][0],
+ inputs=[
+ upload_presets
+ ],
+ outputs=[
+ presets_name
+ ]
+ )
+ translate_button.click(
+ fn=google_translate,
+ inputs=[
+ prompt,
+ source_lang,
+ target_lang
+ ],
+ outputs=[
+ prompt
+ ],
+ api_name="google_translate"
+ )
+ with gr.Row():
+ unlock_full_method.change(
+ fn=unlock_f0,
+ inputs=[
+ unlock_full_method
+ ],
+ outputs=[
+ f0_method
+ ]
+ )
+ upload_f0_file.upload(
+ fn=lambda inp: shutil_move(inp.name, configs["f0_path"]),
+ inputs=[
+ upload_f0_file
+ ],
+ outputs=[
+ f0_file_dropdown
+ ]
+ )
+ refresh_f0_file.click(
+ fn=change_f0_choices,
+ inputs=[],
+ outputs=[
+ f0_file_dropdown
+ ]
+ )
+ with gr.Row():
+ autotune.change(
+ fn=visible,
+ inputs=[
+ autotune
+ ],
+ outputs=[
+ f0_autotune_strength
+ ]
+ )
+ model_pth.change(
+ fn=get_index,
+ inputs=[
+ model_pth
+ ],
+ outputs=[
+ model_index
+ ]
+ )
+ with gr.Row():
+ cleaner.change(
+ fn=visible,
+ inputs=[
+ cleaner
+ ],
+ outputs=[
+ clean_strength
+ ]
+ )
+ f0_method.change(
+ fn=lambda method, hybrid: [
+ visible(method == "hybrid"),
+ visible(method == "hybrid"),
+ hoplength_show(method, hybrid)
+ ],
+ inputs=[
+ f0_method,
+ hybrid_f0method
+ ],
+ outputs=[
+ hybrid_f0method,
+ alpha,
+ hop_length
+ ]
+ )
+ hybrid_f0method.change(
+ fn=hoplength_show,
+ inputs=[
+ f0_method,
+ hybrid_f0method
+ ],
+ outputs=[
+ hop_length
+ ]
+ )
+ with gr.Row():
+ refresh.click(
+ fn=change_models_choices,
+ inputs=[],
+ outputs=[
+ model_pth,
+ model_index
+ ]
+ )
+ embedders.change(
+ fn=lambda embedders: visible(embedders == "custom"),
+ inputs=[
+ embedders
+ ],
+ outputs=[
+ custom_embedders
+ ]
+ )
+ formant_shifting.change(
+ fn=lambda a: [
+ visible(a)
+ for _ in range(2)
+ ],
+ inputs=[
+ formant_shifting
+ ],
+ outputs=[
+ formant_qfrency,
+ formant_timbre
+ ]
+ )
+ with gr.Row():
+ model_index.change(
+ fn=index_strength_show,
+ inputs=[
+ model_index
+ ],
+ outputs=[
+ index_strength,
+ nprobe
+ ]
+ )
+ txt_input.upload(
+ fn=process_input,
+ inputs=[
+ txt_input
+ ],
+ outputs=[
+ prompt
+ ]
+ )
+ use_txt.change(
+ fn=visible,
+ inputs=[
+ use_txt
+ ],
+ outputs=[
+ txt_input
+ ]
+ )
+ use_txt.change(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[
+ txt_input
+ ]
+ )
+ with gr.Row():
+ google_tts_check_box.change(
+ fn=change_tts_voice_choices,
+ inputs=[
+ google_tts_check_box
+ ],
+ outputs=[
+ tts_voice
+ ]
+ )
+ model_pth.change(
+ fn=get_speakers_id_and_architecture,
+ inputs=[
+ model_pth
+ ],
+ outputs=[
+ sids,
+ noise_scale
+ ]
+ )
+ embedders_mix.change(
+ fn=visible,
+ inputs=[
+ embedders_mix
+ ],
+ outputs=[
+ embedders_mix_column
+ ]
+ )
+ with gr.Row():
+ tts_button.click(
+ fn=TTS,
+ inputs=[
+ prompt,
+ tts_voice,
+ speed,
+ output_audio_tts,
+ tts_pitch,
+ google_tts_check_box,
+ txt_input
+ ],
+ outputs=[
+ tts_voice_audio
+ ],
+ api_name="text-to-speech"
+ )
+ convert_button.click(
+ fn=convert_tts,
+ inputs=[
+ cleaner,
+ autotune,
+ pitch,
+ clean_strength,
+ model_pth,
+ model_index,
+ index_strength,
+ output_audio_tts,
+ output_audio,
+ export_format,
+ f0_method,
+ hybrid_f0method,
+ hop_length,
+ embedders,
+ custom_embedders,
+ resample_sr,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ split_audio,
+ f0_autotune_strength,
+ checkpointing,
+ predictor_onnx,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ f0_file_dropdown,
+ embedder_mode,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ audio_processing,
+ alpha,
+ sids,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ noise_scale,
+ nprobe,
+ audio_upscaler
+ ],
+ outputs=[
+ tts_voice_convert
+ ],
+ api_name="convert_tts"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/inference/child/convert_with_vad.py b/main/app/tabs/inference/child/convert_with_vad.py
new file mode 100644
index 0000000000000000000000000000000000000000..18a4136cc08e728c7586aa0938f7a13b42d2c392
--- /dev/null
+++ b/main/app/tabs/inference/child/convert_with_vad.py
@@ -0,0 +1,800 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.inference import convert_with_vad
+
+from main.app.core.ui import (
+ visible,
+ get_index,
+ unlock_f0,
+ shutil_move,
+ hoplength_show,
+ index_strength_show,
+ change_audios_choices,
+ change_models_choices,
+ get_speakers_id_and_architecture
+)
+
+from main.app.variables import (
+ configs,
+ method_f0,
+ model_name,
+ index_path,
+ file_types,
+ audio_params,
+ translations,
+ embedders_mode,
+ embedders_model,
+ paths_for_files,
+ hybrid_f0_method,
+ sample_rate_choice,
+ export_format_choices,
+)
+
+def convert_with_vad_tab():
+ with gr.Row():
+ gr.Markdown(translations["convert_with_vad_info"])
+ with gr.Row():
+ with gr.Column():
+ with gr.Group():
+ with gr.Row():
+ cleaner = gr.Checkbox(
+ label=translations["clear_audio"],
+ value=False,
+ interactive=True
+ )
+ autotune = gr.Checkbox(
+ label=translations["autotune"],
+ value=False,
+ interactive=True
+ )
+ checkpointing = gr.Checkbox(
+ label=translations["memory_efficient_training"],
+ value=False,
+ interactive=True
+ )
+ formant_shifting = gr.Checkbox(
+ label=translations["formantshift"],
+ value=False,
+ interactive=True
+ )
+ proposal_pitch = gr.Checkbox(
+ label=translations["proposal_pitch"],
+ value=False,
+ interactive=True
+ )
+ audio_processing = gr.Checkbox(
+ label=translations["audio_processing"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ num_spk = gr.Slider(
+ minimum=2,
+ maximum=8,
+ step=1,
+ info=translations["num_spk_info"],
+ label=translations["num_spk"],
+ value=2,
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Column():
+ convert_button = gr.Button(
+ translations["convert_audio"],
+ variant="primary"
+ )
+ with gr.Row():
+ with gr.Column():
+ with gr.Accordion(
+ translations["model_accordion"] + " 1",
+ open=True
+ ):
+ with gr.Row():
+ model_pth_1 = gr.Dropdown(
+ label=translations["model_name"],
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ model_index_1 = gr.Dropdown(
+ label=translations["index_path"],
+ choices=index_path,
+ value=index_path[0] if len(index_path) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ refresh_model_1 = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ pitch_1 = gr.Slider(
+ minimum=-24,
+ maximum=24,
+ step=1,
+ info=translations["pitch_info"],
+ label=translations["pitch"],
+ value=0,
+ interactive=True
+ )
+ index_strength_1 = gr.Slider(
+ label=translations["index_strength"],
+ info=translations["index_strength_info"],
+ minimum=0,
+ maximum=1,
+ value=0.5,
+ step=0.01,
+ interactive=True,
+ visible=True
+ )
+ with gr.Row():
+ nprobe_1 = gr.Slider(
+ label=translations["nprobe"],
+ info=translations["nprobe_info"],
+ minimum=1,
+ maximum=64,
+ value=9,
+ step=1,
+ interactive=True,
+ visible=True
+ )
+ with gr.Row():
+ sid_dict_1, architecture_dict_1 = get_speakers_id_and_architecture(model_pth_1.value)
+ if sid_dict_1["visible"] == "hidden": sid_dict_1["visible"] = False
+ if architecture_dict_1["visible"] == "hidden": architecture_dict_1["visible"] = False
+
+ sids_1 = gr.Dropdown(
+ label=translations["sids_label"],
+ info=translations["sids_info"],
+ choices=sid_dict_1["choices"],
+ value=sid_dict_1["value"],
+ visible=sid_dict_1["visible"],
+ interactive=True
+ )
+ with gr.Row():
+ noise_scale_1 = gr.Slider(
+ minimum=0.1,
+ maximum=1.0,
+ label=translations["noise_scale"],
+ info=translations["noise_scale_info"],
+ value=0.4,
+ step=0.01,
+ interactive=True,
+ visible=architecture_dict_1["visible"]
+ )
+ with gr.Accordion(
+ translations["input_output"],
+ open=False
+ ):
+ with gr.Column():
+ export_format = gr.Radio(
+ label=translations["export_format"],
+ info=translations["export_info"],
+ choices=export_format_choices,
+ value="wav",
+ interactive=True
+ )
+ input_audio = gr.Dropdown(
+ label=translations["audio_path"],
+ value="",
+ choices=paths_for_files,
+ info=translations["provide_audio"],
+ allow_custom_value=True,
+ interactive=True
+ )
+ output_audio = gr.Textbox(
+ label=translations["output_path"],
+ value="audios/output.wav",
+ placeholder="audios/output.wav",
+ info=translations["output_path_info"],
+ interactive=True
+ )
+ with gr.Column():
+ refresh_audio = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ drop_audio = gr.Files(
+ label=translations["drop_audio"],
+ file_types=file_types
+ )
+ with gr.Column():
+ with gr.Accordion(
+ translations["model_accordion"] + " 2",
+ open=True
+ ):
+ with gr.Row():
+ model_pth_2 = gr.Dropdown(
+ label=translations["model_name"],
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ model_index_2 = gr.Dropdown(
+ label=translations["index_path"],
+ choices=index_path,
+ value=index_path[0] if len(index_path) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ refresh_model_2 = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ pitch_2 = gr.Slider(
+ minimum=-24,
+ maximum=24,
+ step=1,
+ info=translations["pitch_info"],
+ label=translations["pitch"],
+ value=0,
+ interactive=True
+ )
+ index_strength_2 = gr.Slider(
+ label=translations["index_strength"],
+ info=translations["index_strength_info"],
+ minimum=0,
+ maximum=1,
+ value=0.5,
+ step=0.01,
+ interactive=True,
+ visible=True
+ )
+ with gr.Row():
+ nprobe_2 = gr.Slider(
+ label=translations["nprobe"],
+ info=translations["nprobe_info"],
+ minimum=1,
+ maximum=64,
+ value=9,
+ step=1,
+ interactive=True,
+ visible=True
+ )
+ with gr.Row():
+ sid_dict_2, architecture_dict_2 = get_speakers_id_and_architecture(model_pth_2.value)
+ if sid_dict_2["visible"] == "hidden": sid_dict_2["visible"] = False
+ if architecture_dict_2["visible"] == "hidden": architecture_dict_2["visible"] = False
+
+ sids_2 = gr.Dropdown(
+ label=translations["sids_label"],
+ info=translations["sids_info"],
+ choices=sid_dict_2["choices"],
+ value=sid_dict_2["value"],
+ visible=sid_dict_2["visible"],
+ interactive=True
+ )
+ with gr.Row():
+ noise_scale_2 = gr.Slider(
+ minimum=0.1,
+ maximum=1.0,
+ label=translations["noise_scale"],
+ info=translations["noise_scale_info"],
+ value=0.4,
+ step=0.01,
+ interactive=True,
+ visible=architecture_dict_2["visible"]
+ )
+ with gr.Accordion(
+ translations["setting"],
+ open=False
+ ):
+ with gr.Accordion(
+ translations["f0_method"],
+ open=False
+ ):
+ with gr.Group():
+ with gr.Row():
+ predictor_onnx = gr.Checkbox(
+ label=translations["predictor_onnx"],
+ info=translations["predictor_onnx_info"],
+ value=configs.get("int8", False),
+ interactive=True
+ )
+ unlock_full_method = gr.Checkbox(
+ label=translations["f0_unlock"],
+ info=translations["f0_unlock_info"],
+ value=False,
+ interactive=True
+ )
+ f0_method = gr.Radio(
+ label=translations["f0_method"],
+ info=translations["f0_method_info"],
+ choices=method_f0,
+ value="rmvpe",
+ interactive=True
+ )
+ hybrid_f0method = gr.Dropdown(
+ label=translations["f0_method_hybrid"],
+ info=translations["f0_method_hybrid_info"],
+ choices=hybrid_f0_method,
+ value=hybrid_f0_method[0],
+ interactive=True,
+ allow_custom_value=True,
+ visible=False
+ )
+ hop_length = gr.Slider(
+ label=translations['hop_length'],
+ info=translations["hop_length_info"],
+ minimum=64,
+ maximum=512,
+ value=160,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ alpha = gr.Slider(
+ label=translations["alpha_label"],
+ info=translations["alpha_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Accordion(
+ translations["hubert_model"],
+ open=False
+ ):
+ embedders_mix = gr.Checkbox(
+ label=translations["embedders_mix"],
+ info=translations["embedders_mix_info"],
+ value=False,
+ interactive=True
+ )
+ embedder_mode = gr.Radio(
+ label=translations["embed_mode"],
+ info=translations["embed_mode_info"],
+ value="onnx" if configs.get("int8", False) else "fairseq",
+ choices=embedders_mode,
+ interactive=True,
+ visible=True
+ )
+ embedders = gr.Radio(
+ label=translations["hubert_model"],
+ info=translations["hubert_info"],
+ choices=embedders_model,
+ value="hubert_base",
+ interactive=True
+ )
+ custom_embedders = gr.Textbox(
+ label=translations["modelname"],
+ info=translations["modelname_info"],
+ value="",
+ placeholder="hubert_base",
+ interactive=True,
+ visible=False
+ )
+ with gr.Column(visible=False) as embedders_mix_column:
+ embedders_mix_layers = gr.Slider(
+ label=translations["embedders_mix_layers"],
+ info=translations["embedders_mix_layers_info"],
+ minimum=1,
+ maximum=12,
+ value=9,
+ step=1,
+ interactive=True
+ )
+ embedders_mix_ratio = gr.Slider(
+ label=translations["embedders_mix_ratio"],
+ info=translations["embedders_mix_ratio_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True
+ )
+ with gr.Column():
+ resample_sr3 = gr.Radio(
+ choices=[0]+sample_rate_choice,
+ label=translations["resample"],
+ info=translations["resample_info"],
+ value=0,
+ interactive=True
+ )
+ proposal_pitch_threshold = gr.Slider(
+ minimum=50.0,
+ maximum=1200.0,
+ label=translations["proposal_pitch_threshold"],
+ info=translations["proposal_pitch_threshold_info"],
+ value=255.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ clean_strength = gr.Slider(
+ label=translations["clean_strength"],
+ info=translations["clean_strength_info"],
+ minimum=0,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ f0_autotune_strength = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["autotune_rate"],
+ info=translations["autotune_rate_info"],
+ value=1,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ filter_radius = gr.Slider(
+ minimum=0,
+ maximum=7,
+ label=translations["filter_radius"],
+ info=translations["filter_radius_info"],
+ value=3,
+ step=1,
+ interactive=True
+ )
+ rms_mix_rate = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["rms_mix_rate"],
+ info=translations["rms_mix_rate_info"],
+ value=1,
+ step=0.1,
+ interactive=True
+ )
+ protect = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["protect"],
+ info=translations["protect_info"],
+ value=0.33,
+ step=0.01,
+ interactive=True
+ )
+ with gr.Row():
+ vad_sensitivity = gr.Slider(
+ minimum=0,
+ maximum=3,
+ label=translations["vad_sensitivity_label"],
+ info=translations["vad_sensitivity_info"],
+ value=3,
+ step=1,
+ interactive=True,
+ visible=True
+ )
+ vad_frame_ms = gr.Slider(
+ minimum=10,
+ maximum=30,
+ label=translations["vad_frame_ms_label"],
+ info=translations["vad_frame_ms_info"],
+ value=30,
+ step=10,
+ interactive=True,
+ visible=True
+ )
+ with gr.Row():
+ formant_qfrency = gr.Slider(
+ value=1.0,
+ label=translations["formant_qfrency"] + " 1",
+ info=translations["formant_qfrency"],
+ minimum=0.0,
+ maximum=16.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ formant_timbre = gr.Slider(
+ value=1.0,
+ label=translations["formant_timbre"] + " 1",
+ info=translations["formant_timbre"],
+ minimum=0.0,
+ maximum=16.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ formant_qfrency = gr.Slider(
+ value=1.0,
+ label=translations["formant_qfrency"] + " 2",
+ info=translations["formant_qfrency"],
+ minimum=0.0,
+ maximum=16.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ formant_timbre = gr.Slider(
+ value=1.0,
+ label=translations["formant_timbre"] + " 2",
+ info=translations["formant_timbre"],
+ minimum=0.0,
+ maximum=16.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ gr.Markdown(translations["input_output"])
+ with gr.Row():
+ play_input_audio = gr.Audio(
+ interactive=False,
+ label=translations["input_audio"],
+ **audio_params
+ )
+ play_output_audio = gr.Audio(
+ interactive=False,
+ label=translations["output_file_tts_convert"],
+ **audio_params
+ )
+ with gr.Row():
+ autotune.change(
+ fn=visible,
+ inputs=[
+ autotune
+ ],
+ outputs=[
+ f0_autotune_strength
+ ]
+ )
+ cleaner.change(
+ fn=visible,
+ inputs=[
+ cleaner
+ ],
+ outputs=[
+ clean_strength
+ ]
+ )
+ f0_method.change(
+ fn=lambda method, hybrid: [
+ visible(method == "hybrid"),
+ visible(method == "hybrid"),
+ hoplength_show(method, hybrid)
+ ],
+ inputs=[
+ f0_method,
+ hybrid_f0method
+ ],
+ outputs=[
+ hybrid_f0method,
+ alpha,
+ hop_length
+ ]
+ )
+ with gr.Row():
+ hybrid_f0method.change(
+ fn=hoplength_show,
+ inputs=[
+ f0_method,
+ hybrid_f0method
+ ],
+ outputs=[
+ hop_length
+ ]
+ )
+ refresh_model_1.click(
+ fn=change_models_choices,
+ inputs=[],
+ outputs=[
+ model_pth_1,
+ model_index_1
+ ]
+ )
+ model_pth_1.change(
+ fn=get_index,
+ inputs=[
+ model_pth_1
+ ],
+ outputs=[
+ model_index_1
+ ]
+ )
+ with gr.Row():
+ refresh_model_2.click(
+ fn=change_models_choices,
+ inputs=[],
+ outputs=[
+ model_pth_2,
+ model_index_2
+ ]
+ )
+ model_pth_2.change(
+ fn=get_index,
+ inputs=[
+ model_pth_2
+ ],
+ outputs=[
+ model_index_2
+ ]
+ )
+ drop_audio.upload(
+ fn=lambda audio_in: [
+ shutil_move(audio.name, configs["audios_path"])
+ for audio in audio_in
+ ][0],
+ inputs=[
+ drop_audio
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ with gr.Row():
+ input_audio.change(
+ fn=lambda audio: audio if os.path.isfile(audio) else None,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ play_input_audio
+ ]
+ )
+ formant_shifting.change(
+ fn=lambda a: [
+ visible(a)
+ for _ in range(4)
+ ],
+ inputs=[
+ formant_shifting
+ ],
+ outputs=[
+ formant_qfrency,
+ formant_timbre,
+ formant_qfrency,
+ formant_timbre
+ ]
+ )
+ embedders.change(
+ fn=lambda embedders: visible(embedders == "custom"),
+ inputs=[
+ embedders
+ ],
+ outputs=[
+ custom_embedders
+ ]
+ )
+ with gr.Row():
+ refresh_audio.click(
+ fn=change_audios_choices,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ model_index_1.change(
+ fn=index_strength_show,
+ inputs=[
+ model_index_1
+ ],
+ outputs=[
+ index_strength_1,
+ nprobe_1
+ ]
+ )
+ model_index_2.change(
+ fn=index_strength_show,
+ inputs=[
+ model_index_2
+ ],
+ outputs=[
+ index_strength_2,
+ nprobe_2
+ ]
+ )
+ with gr.Row():
+ unlock_full_method.change(
+ fn=unlock_f0,
+ inputs=[
+ unlock_full_method
+ ],
+ outputs=[
+ f0_method
+ ]
+ )
+ proposal_pitch.change(
+ fn=visible,
+ inputs=[
+ proposal_pitch
+ ],
+ outputs=[
+ proposal_pitch_threshold
+ ]
+ )
+ with gr.Row():
+ model_pth_1.change(
+ fn=get_speakers_id_and_architecture,
+ inputs=[
+ model_pth_1
+ ],
+ outputs=[
+ sids_1,
+ noise_scale_1
+ ]
+ )
+ model_pth_2.change(
+ fn=get_speakers_id_and_architecture,
+ inputs=[
+ model_pth_2
+ ],
+ outputs=[
+ sids_2,
+ noise_scale_2
+ ]
+ )
+ embedders_mix.change(
+ fn=visible,
+ inputs=[
+ embedders_mix
+ ],
+ outputs=[
+ embedders_mix_column
+ ]
+ )
+ with gr.Row():
+ convert_button.click(
+ fn=convert_with_vad,
+ inputs=[
+ num_spk,
+ cleaner,
+ clean_strength,
+ autotune,
+ f0_autotune_strength,
+ checkpointing,
+ model_pth_1,
+ model_pth_2,
+ model_index_1,
+ model_index_2,
+ pitch_1,
+ pitch_2,
+ index_strength_1,
+ index_strength_2,
+ export_format,
+ input_audio,
+ output_audio,
+ predictor_onnx,
+ f0_method,
+ hybrid_f0method,
+ hop_length,
+ embedder_mode,
+ embedders,
+ custom_embedders,
+ resample_sr3,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ formant_qfrency,
+ formant_timbre,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ audio_processing,
+ alpha,
+ sids_1,
+ sids_2,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ noise_scale_1,
+ noise_scale_2,
+ vad_sensitivity,
+ vad_frame_ms,
+ nprobe_1,
+ nprobe_2
+ ],
+ outputs=[
+ play_output_audio
+ ],
+ api_name="convert_with_vad"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/inference/child/separate.py b/main/app/tabs/inference/child/separate.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e02642239424f1433ef19e28cca727db48a8d65
--- /dev/null
+++ b/main/app/tabs/inference/child/separate.py
@@ -0,0 +1,600 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.downloads import download_url
+from main.app.core.separate import separate_music
+
+from main.app.core.ui import (
+ visible,
+ shutil_move,
+ separate_change,
+ change_audios_choices,
+ valueFalse_interactive
+)
+
+from main.app.variables import (
+ configs,
+ uvr_model,
+ vr_models,
+ mdx_models,
+ file_types,
+ audio_params,
+ translations,
+ reverb_models,
+ karaoke_models,
+ denoise_models,
+ paths_for_files,
+ sample_rate_choice,
+ export_format_choices
+)
+
+def separate_tab():
+ with gr.Row():
+ gr.Markdown(translations["4_part"])
+ with gr.Row():
+ with gr.Column():
+ with gr.Group():
+ with gr.Row():
+ enable_denoise = gr.Checkbox(
+ label=translations["denoise_mdx"],
+ value=False,
+ interactive=False
+ )
+ separate_backing = gr.Checkbox(
+ label=translations["separator_backing"],
+ value=False,
+ interactive=True
+ )
+ separate_reverb = gr.Checkbox(
+ label=translations["dereveb_audio"],
+ value=False,
+ interactive=True
+ )
+ enable_tta = gr.Checkbox(
+ label=translations["enable_tta"],
+ value=False,
+ interactive=False
+ )
+ high_end_process = gr.Checkbox(
+ label=translations["high_end_process"],
+ value=False,
+ interactive=False
+ )
+ enable_post_process = gr.Checkbox(
+ label=translations["enable_post_process"],
+ value=False,
+ interactive=False
+ )
+ with gr.Row():
+ model_name = gr.Dropdown(
+ label=translations["separator_model"],
+ value=uvr_model[0],
+ choices=uvr_model,
+ interactive=True
+ )
+ karaoke_model = gr.Dropdown(
+ label=translations["separator_backing_model"],
+ value=list(karaoke_models.keys())[0],
+ choices=list(karaoke_models.keys()),
+ interactive=True,
+ visible=False
+ )
+ reverb_model = gr.Dropdown(
+ label=translations["dereveb_model"],
+ value=list(reverb_models.keys())[0],
+ choices=list(reverb_models.keys()),
+ interactive=True,
+ visible=False
+ )
+ denoise_model = gr.Dropdown(
+ label=translations["denoise_model"],
+ value=list(denoise_models.keys())[0],
+ choices=list(denoise_models.keys()),
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ with gr.Column():
+ separate_button = gr.Button(
+ translations["separator_tab"],
+ variant="primary"
+ )
+ with gr.Row():
+ with gr.Column():
+ with gr.Group():
+ with gr.Row():
+ shifts = gr.Slider(
+ label=translations["shift"],
+ info=translations["shift_info"],
+ minimum=1,
+ maximum=20,
+ value=2,
+ step=1,
+ interactive=True
+ )
+ batch_size = gr.Slider(
+ label=translations["batch_size"],
+ info=translations["mdx_batch_size_info"],
+ minimum=1,
+ maximum=64,
+ value=1,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ segments_size = gr.Slider(
+ label=translations["segments_size"],
+ info=translations["segments_size_info"],
+ minimum=32,
+ maximum=3072,
+ value=256,
+ step=32,
+ interactive=True
+ )
+ aggression = gr.Slider(
+ label=translations['aggression'],
+ info=translations["aggression_info"],
+ minimum=1,
+ maximum=50,
+ value=5,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ drop_audio = gr.Files(
+ label=translations["drop_audio"],
+ file_types=file_types
+ )
+ with gr.Accordion(
+ translations["use_url"],
+ open=False
+ ):
+ url = gr.Textbox(
+ label=translations["url_audio"],
+ value="",
+ placeholder="https://www.youtube.com/...",
+ scale=6
+ )
+ download_button = gr.Button(
+ translations["downloads"]
+ )
+ with gr.Column():
+ with gr.Group():
+ with gr.Row():
+ overlap = gr.Radio(
+ label=translations["overlap"],
+ info=translations["overlap_info"],
+ choices=[
+ "0.25",
+ "0.5",
+ "0.75",
+ "0.99"
+ ],
+ value="0.25",
+ interactive=True
+ )
+ with gr.Row():
+ window_size = gr.Slider(
+ label=translations["window_size"],
+ info=translations["window_size_info"],
+ minimum=320,
+ maximum=1024,
+ value=512,
+ step=32,
+ interactive=True,
+ visible=False
+ )
+ hop_length = gr.Slider(
+ label=translations['hop_length'],
+ info=translations["hop_length_info"],
+ minimum=64,
+ maximum=8192,
+ value=1024,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ post_process_threshold = gr.Slider(
+ label=translations['post_process_threshold'],
+ info=translations["post_process_threshold_info"],
+ minimum=0.1,
+ maximum=0.3,
+ value=0.2,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ sample_rate = gr.Radio(
+ choices=sample_rate_choice,
+ value=44100,
+ label=translations["sr"],
+ info=translations["sr_info"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["input_output"],
+ open=False
+ ):
+ export_format = gr.Radio(
+ label=translations["export_format"],
+ info=translations["export_info"],
+ choices=export_format_choices,
+ value="wav",
+ interactive=True
+ )
+ input_audio = gr.Dropdown(
+ label=translations["audio_path"],
+ value="",
+ choices=paths_for_files,
+ allow_custom_value=True,
+ interactive=True
+ )
+ refresh_audio = gr.Button(
+ translations["refresh"]
+ )
+ output_dirs = gr.Textbox(
+ label=translations["output_folder"],
+ value="audios",
+ placeholder="audios",
+ info=translations["output_folder_info"],
+ interactive=True
+ )
+ audio_input = gr.Audio(
+ interactive=False,
+ label=translations["input_audio"],
+ **audio_params
+ )
+ with gr.Row():
+ gr.Markdown(translations["output_separator"])
+ with gr.Row():
+ instruments_audio = gr.Audio(
+ interactive=False,
+ label=translations["instruments"],
+ visible=True,
+ **audio_params
+ )
+ original_vocals = gr.Audio(
+ interactive=False,
+ label=translations["original_vocal"],
+ visible=True,
+ **audio_params
+ )
+ main_vocals = gr.Audio(
+ interactive=False,
+ label=translations["main_vocal"],
+ visible=False,
+ **audio_params
+ )
+ backing_vocals = gr.Audio(
+ interactive=False,
+ label=translations["backing_vocal"],
+ visible=False,
+ **audio_params
+ )
+ with gr.Row():
+ model_name.change(
+ fn=lambda a: valueFalse_interactive(a in list(mdx_models.keys()) + list(vr_models.keys())),
+ inputs=[
+ model_name
+ ],
+ outputs=[
+ enable_denoise
+ ]
+ )
+ separate_backing.change(
+ fn=lambda a, b: valueFalse_interactive(a or b),
+ inputs=[
+ separate_backing,
+ separate_reverb
+ ],
+ outputs=[
+ enable_denoise
+ ]
+ )
+ separate_reverb.change(
+ fn=lambda a, b: valueFalse_interactive(a or b),
+ inputs=[
+ separate_backing,
+ separate_reverb
+ ],
+ outputs=[
+ enable_denoise
+ ]
+ )
+ with gr.Row():
+ input_audio.change(
+ fn=lambda audio: audio if os.path.isfile(audio) else None,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ audio_input
+ ]
+ )
+ drop_audio.upload(
+ fn=lambda audio_in: [
+ shutil_move(audio.name, configs["audios_path"])
+ for audio in audio_in
+ ][0],
+ inputs=[
+ drop_audio
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ refresh_audio.click(
+ fn=change_audios_choices,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ with gr.Row():
+ separate_backing.change(
+ fn=lambda a: [
+ visible(a)
+ for _ in range(2)
+ ],
+ inputs=[
+ separate_backing
+ ],
+ outputs=[
+ main_vocals,
+ backing_vocals
+ ]
+ )
+ download_button.click(
+ fn=download_url,
+ inputs=[
+ url
+ ],
+ outputs=[
+ input_audio,
+ audio_input,
+ url
+ ],
+ api_name='download_url'
+ )
+ with gr.Row():
+ model_name.change(
+ fn=separate_change,
+ inputs=[
+ model_name,
+ karaoke_model,
+ reverb_model,
+ enable_post_process,
+ separate_backing,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ karaoke_model,
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ with gr.Row():
+ karaoke_model.change(
+ fn=separate_change,
+ inputs=[
+ model_name,
+ karaoke_model,
+ reverb_model,
+ enable_post_process,
+ separate_backing,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ karaoke_model,
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ separate_backing.change(
+ fn=separate_change,
+ inputs=[
+ model_name,
+ karaoke_model,
+ reverb_model,
+ enable_post_process,
+ separate_backing,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ karaoke_model,
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ with gr.Row():
+ reverb_model.change(
+ fn=separate_change,
+ inputs=[
+ model_name,
+ karaoke_model,
+ reverb_model,
+ enable_post_process,
+ separate_backing,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ karaoke_model,
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ separate_reverb.change(
+ fn=separate_change,
+ inputs=[
+ model_name,
+ karaoke_model,
+ reverb_model,
+ enable_post_process,
+ separate_backing,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ karaoke_model,
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ with gr.Row():
+ enable_denoise.change(
+ fn=separate_change,
+ inputs=[
+ model_name,
+ karaoke_model,
+ reverb_model,
+ enable_post_process,
+ separate_backing,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ karaoke_model,
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ enable_post_process.change(
+ fn=separate_change,
+ inputs=[
+ model_name,
+ karaoke_model,
+ reverb_model,
+ enable_post_process,
+ separate_backing,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ karaoke_model,
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ with gr.Row():
+ separate_button.click(
+ fn=separate_music,
+ inputs=[
+ input_audio,
+ output_dirs,
+ export_format,
+ model_name,
+ karaoke_model,
+ reverb_model,
+ denoise_model,
+ sample_rate,
+ shifts,
+ batch_size,
+ overlap,
+ aggression,
+ hop_length,
+ window_size,
+ segments_size,
+ post_process_threshold,
+ enable_tta,
+ enable_denoise,
+ high_end_process,
+ enable_post_process,
+ separate_backing,
+ separate_reverb
+ ],
+ outputs=[
+ original_vocals,
+ instruments_audio,
+ main_vocals,
+ backing_vocals
+ ],
+ api_name="separate_music"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/inference/inference.py b/main/app/tabs/inference/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..c477e6d777256245b87c328f6bf0cee3263578f0
--- /dev/null
+++ b/main/app/tabs/inference/inference.py
@@ -0,0 +1,41 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import (
+ configs,
+ translations
+)
+
+def inference_tab():
+ with gr.TabItem(translations["inference"]):
+ if configs.get("separator_tab", True):
+ from main.app.tabs.inference.child.separate import separate_tab
+
+ with gr.TabItem(translations["separator_tab"]):
+ gr.Markdown(f"## {translations['separator_tab']}")
+ separate_tab()
+
+ if configs.get("convert_tab", True):
+ from main.app.tabs.inference.child.convert import convert_tab
+
+ with gr.TabItem(translations["convert_audio"]):
+ gr.Markdown(f"## {translations['convert_audio']}")
+ convert_tab()
+
+ if configs.get("convert_with_vad_tab", True):
+ from main.app.tabs.inference.child.convert_with_vad import convert_with_vad_tab
+
+ with gr.TabItem(translations["convert_with_vad"]):
+ gr.Markdown(f"## {translations['convert_with_vad']}")
+ convert_with_vad_tab()
+
+ if configs.get("tts_tab", True):
+ from main.app.tabs.inference.child.convert_tts import convert_tts_tab
+
+ with gr.TabItem(translations["convert_text"]):
+ gr.Markdown(translations["convert_text_markdown"])
+ convert_tts_tab()
diff --git a/main/app/tabs/realtime/realtime.py b/main/app/tabs/realtime/realtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..a544c4b5709b0153a476bb90f99067e9af0a81a3
--- /dev/null
+++ b/main/app/tabs/realtime/realtime.py
@@ -0,0 +1,2468 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.realtime import (
+ realtime_stop,
+ change_config,
+ realtime_start,
+ soundfile_record_audio,
+)
+
+from main.app.variables import (
+ config,
+ configs,
+ method_f0,
+ model_name,
+ index_path,
+ audio_params,
+ translations,
+ embedders_mode,
+ embedders_model,
+ sample_rate_choice,
+ export_format_choices,
+ realtime_presets_file
+)
+
+from main.app.core.ui import (
+ visible,
+ get_index,
+ unlock_f0,
+ shutil_move,
+ visibleFalse,
+ hoplength_show,
+ input_channels_map,
+ output_channels_map,
+ update_audio_device,
+ index_strength_show,
+ change_models_choices,
+ update_value_from_json,
+ update_button_from_json,
+ update_dropdowns_from_json,
+ change_audio_device_choices,
+ change_realtime_preset_choices,
+ get_speakers_id_and_architecture
+)
+
+from main.app.core.presets import (
+ save_realtime_config,
+ load_realtime_config
+)
+
+client_mode = True # "--client" in sys.argv
+
+def realtime_tab():
+ with gr.TabItem(translations["realtime"]):
+ gr.Markdown(translations["realtime_markdown"])
+ with gr.Row():
+ gr.Markdown(translations["realtime_markdown_2"])
+ with gr.Row():
+ status = gr.Label(
+ label=translations["realtime_latency"],
+ value=translations["realtime_not_startup"],
+ elem_id="realtime-status-info"
+ )
+ with gr.Row():
+ monitor = gr.Checkbox(
+ label=translations["monitor"],
+ value=False,
+ interactive=True
+ )
+ exclusive_mode = gr.Checkbox(
+ label=translations["exclusive_mode"],
+ value=False,
+ interactive=True
+ )
+ vad_enabled = gr.Checkbox(
+ label=translations["vad_enabled"],
+ value=False,
+ interactive=True
+ )
+ clean_audio = gr.Checkbox(
+ label=translations["clear_audio"],
+ value=False,
+ interactive=True
+ )
+ use_phase_vocoder = gr.Checkbox(
+ label=translations["phase_vocoder"],
+ value=not config.device.startswith(("privateuseone", "ocl")),
+ interactive=not config.device.startswith(("privateuseone", "ocl"))
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["audio_device"],
+ open=True
+ ):
+ with gr.Row():
+ input_audio_device = gr.Dropdown(
+ label=translations["input_audio_device_label"],
+ info=translations["input_audio_device_info"],
+ choices=list(input_channels_map.keys()) if not client_mode else [],
+ value=(list(input_channels_map.keys())[0] if len(list(input_channels_map.keys())) >= 1 else "") if not client_mode else None,
+ interactive=True
+ )
+ output_audio_device = gr.Dropdown(
+ label=translations["output_audio_device_label"],
+ info=translations["output_audio_device_info"],
+ choices=list(output_channels_map.keys()) if not client_mode else [],
+ value=(list(output_channels_map.keys())[0] if len(list(output_channels_map.keys())) >= 1 else "") if not client_mode else None,
+ interactive=True
+ )
+ monitor_output_device = gr.Dropdown(
+ label=translations["monitor_output_device_label"],
+ info=translations["monitor_output_device_info"],
+ choices=list(output_channels_map.keys()) if not client_mode else [],
+ value=(list(output_channels_map.keys())[0] if len(list(output_channels_map.keys())) >= 1 else "") if not client_mode else None,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ input_audio_gain = gr.Slider(
+ minimum=0,
+ maximum=2500,
+ label=translations["input_audio_gain_label"],
+ info=translations["input_audio_gain_info"],
+ value=100,
+ step=1,
+ interactive=True
+ )
+ output_audio_gain = gr.Slider(
+ minimum=0,
+ maximum=4000,
+ label=translations["output_audio_gain_label"],
+ info=translations["output_audio_gain_info"],
+ value=100,
+ step=1,
+ interactive=True
+ )
+ monitor_audio_gain = gr.Slider(
+ minimum=0,
+ maximum=4000,
+ label=translations["monitor_audio_gain_label"],
+ info=translations["monitor_audio_gain_info"],
+ value=100,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row(visible=not client_mode):
+ input_audio_sample_rate = gr.Radio(
+ choices=sample_rate_choice,
+ value=48000,
+ label=translations["samplerate"].split("|")[0],
+ info=translations["samplerate_info"],
+ interactive=True
+ )
+ output_audio_sample_rate = gr.Radio(
+ choices=sample_rate_choice,
+ value=48000,
+ label=translations["samplerate"].split("|")[1],
+ info=translations["samplerate_info"],
+ interactive=True
+ )
+ monitor_audio_sample_rate = gr.Radio(
+ choices=sample_rate_choice,
+ value=48000,
+ label=translations["samplerate"].split("|")[2],
+ info=translations["samplerate_info"],
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ asio_output_stereo = gr.Checkbox(
+ label=translations["stereo_device"].split("|")[0],
+ value=True,
+ interactive=True,
+ visible=False
+ )
+ asio_monitor_stereo = gr.Checkbox(
+ label=translations["stereo_device"].split("|")[1],
+ value=True,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row(visible=False) as asio_row:
+ input_asio_channels = gr.Slider(
+ minimum=-1,
+ maximum=128,
+ label=translations["input_asio_channels_label"],
+ info=translations["input_asio_channels_info"],
+ value=0,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ output_asio_channels = gr.Slider(
+ minimum=-1,
+ maximum=128,
+ label=translations["output_asio_channels_label"],
+ info=translations["output_asio_channels_info"],
+ value=0,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ monitor_asio_channels = gr.Slider(
+ minimum=-1,
+ maximum=128,
+ label=translations["monitor_asio_channels_label"],
+ info=translations["monitor_asio_channels_info"],
+ value=0,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ refresh_audio_device = gr.Button(
+ value=translations["refresh_audio_device"],
+ variant="secondary"
+ )
+ with gr.Row():
+ start_realtime = gr.Button(
+ value=translations["start_realtime_button"],
+ variant="primary",
+ interactive=True
+ )
+ stop_realtime = gr.Button(
+ value=translations["stop_realtime_button"],
+ variant="stop",
+ interactive=False
+ )
+ with gr.Row():
+ with gr.Accordion(translations["record"], open=False):
+ with gr.Column():
+ record_audio_path = gr.Textbox(
+ label=translations["output_path"],
+ placeholder="audios/record_audio.wav",
+ info=translations["output_path_info"],
+ value="audios/record_audio.wav",
+ interactive=True,
+ )
+ export_format = gr.Radio(
+ label=translations["export_format"],
+ info=translations["export_info"],
+ choices=export_format_choices,
+ value="wav",
+ interactive=True,
+ )
+ record_audio = gr.Button(
+ translations["start_record"]
+ )
+ record_output = gr.Audio(
+ label=translations["output_audio"],
+ **audio_params
+ )
+ with gr.Row():
+ chunk_size = gr.Slider(
+ minimum=2.7,
+ maximum=2730.7,
+ step=0.1,
+ label=translations["chunk_size"],
+ info=translations["chunk_size_info"],
+ value=256,
+ interactive=True
+ )
+ pitch = gr.Slider(
+ minimum=-24,
+ maximum=24,
+ step=1,
+ info=translations["pitch_info"],
+ label=translations["pitch"],
+ value=0,
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Column():
+ with gr.Accordion(
+ translations["model_accordion"],
+ open=True
+ ):
+ with gr.Row():
+ model_pth = gr.Dropdown(
+ label=translations["model_name"],
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ model_index = gr.Dropdown(
+ label=translations["index_path"],
+ choices=index_path,
+ value=index_path[0] if len(index_path) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ model_refresh = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Row():
+ index_strength = gr.Slider(
+ label=translations["index_strength"],
+ info=translations["index_strength_info"],
+ minimum=0,
+ maximum=1,
+ value=0.0,
+ step=0.01,
+ interactive=True,
+ visible=True
+ )
+ nprobe = gr.Slider(
+ label=translations["nprobe"],
+ info=translations["nprobe_info"],
+ minimum=1,
+ maximum=64,
+ value=9,
+ step=1,
+ interactive=True,
+ visible=True
+ )
+ with gr.Row():
+ sid_dict, architecture_dict = get_speakers_id_and_architecture(model_pth.value)
+ if sid_dict["visible"] == "hidden": sid_dict["visible"] = False
+ if architecture_dict["visible"] == "hidden": architecture_dict["visible"] = False
+
+ sids = gr.Dropdown(
+ label=translations["sids_label"],
+ info=translations["sids_info"],
+ choices=sid_dict["choices"],
+ value=sid_dict["value"],
+ visible=sid_dict["visible"],
+ interactive=True
+ )
+ with gr.Column():
+ with gr.Accordion(
+ translations["f0_method"],
+ open=True
+ ):
+ with gr.Group():
+ with gr.Row():
+ predictor_onnx = gr.Checkbox(
+ label=translations["predictor_onnx"],
+ info=translations["predictor_onnx_info"],
+ value=configs.get("int8", False),
+ interactive=True
+ )
+ unlock_full_method = gr.Checkbox(
+ label=translations["f0_unlock"],
+ info=translations["f0_unlock_info"],
+ value=False,
+ interactive=True
+ )
+ f0_method = gr.Radio(
+ label=translations["f0_method"],
+ info=translations["f0_method_info"],
+ choices=[m for m in method_f0 if m != "hybrid"],
+ value="fcpe",
+ interactive=True
+ )
+ hop_length = gr.Slider(
+ label=translations['hop_length'],
+ info=translations["hop_length_info"],
+ minimum=64,
+ maximum=512,
+ value=160,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Column():
+ with gr.Accordion(
+ translations["hubert_model"],
+ open=True
+ ):
+ embedders_mix = gr.Checkbox(
+ label=translations["embedders_mix"],
+ info=translations["embedders_mix_info"],
+ value=False,
+ interactive=True
+ )
+ embedder_mode = gr.Radio(
+ label=translations["embed_mode"],
+ info=translations["embed_mode_info"],
+ value="onnx" if configs.get("int8", False) else "fairseq",
+ choices=embedders_mode,
+ interactive=True,
+ visible=True)
+ embedders = gr.Radio(
+ label=translations["hubert_model"],
+ info=translations["hubert_info"],
+ choices=embedders_model,
+ value="hubert_base",
+ interactive=True
+ )
+ custom_embedders = gr.Textbox(
+ label=translations["modelname"],
+ info=translations["modelname_info"],
+ value="",
+ placeholder="hubert_base",
+ interactive=True,
+ visible=False
+ )
+ with gr.Column(visible=False) as embedders_mix_column:
+ embedders_mix_layers = gr.Slider(
+ label=translations["embedders_mix_layers"],
+ info=translations["embedders_mix_layers_info"],
+ minimum=1,
+ maximum=12,
+ value=9,
+ step=1,
+ interactive=True
+ )
+ embedders_mix_ratio = gr.Slider(
+ label=translations["embedders_mix_ratio"],
+ info=translations["embedders_mix_ratio_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["setting"],
+ open=True
+ ):
+ with gr.Row():
+ f0_autotune = gr.Checkbox(
+ label=translations["autotune"],
+ value=False,
+ interactive=True
+ )
+ proposal_pitch = gr.Checkbox(
+ label=translations["proposal_pitch"],
+ value=False,
+ interactive=True
+ )
+ with gr.Group():
+ with gr.Row():
+ f0_autotune_strength = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["autotune_rate"],
+ info=translations["autotune_rate_info"],
+ value=1,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ proposal_pitch_threshold = gr.Slider(
+ minimum=50.0,
+ maximum=1200.0,
+ label=translations["proposal_pitch_threshold"],
+ info=translations["proposal_pitch_threshold_info"],
+ value=255.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ rms_mix_rate = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["rms_mix_rate"],
+ info=translations["rms_mix_rate_info"],
+ value=1,
+ step=0.1,
+ interactive=True
+ )
+ protect = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["protect"],
+ info=translations["protect_info"],
+ value=0.33,
+ step=0.01,
+ interactive=True
+ )
+ with gr.Row():
+ clean_strength = gr.Slider(
+ label=translations["clean_strength"],
+ info=translations["clean_strength_info"],
+ minimum=0,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ filter_radius = gr.Slider(
+ minimum=0,
+ maximum=7,
+ label=translations["filter_radius"],
+ info=translations["filter_radius_info"],
+ value=3,
+ step=1,
+ interactive=True
+ )
+ with gr.Column():
+ silent_threshold = gr.Slider(
+ minimum=-90,
+ maximum=-60,
+ label=translations["silent_threshold_label"],
+ info=translations["silent_threshold_info"],
+ value=-60,
+ step=1,
+ interactive=True
+ )
+ extra_convert_size = gr.Slider(
+ minimum=0.1,
+ maximum=5,
+ label=translations["extra_convert_size_label"],
+ info=translations["extra_convert_size_info"],
+ value=0.8,
+ step=0.1,
+ interactive=True
+ )
+ cross_fade_overlap_size = gr.Slider(
+ minimum=0.05,
+ maximum=0.2,
+ label=translations["cross_fade_overlap_size_label"],
+ info=translations["cross_fade_overlap_size_info"],
+ value=0.05,
+ step=0.01,
+ interactive=True
+ )
+ noise_scale = gr.Slider(
+ minimum=0.1,
+ maximum=1.0,
+ label=translations["noise_scale"],
+ info=translations["noise_scale_info"],
+ value=0.4,
+ step=0.01,
+ interactive=True,
+ visible=architecture_dict["visible"]
+ )
+ with gr.Row():
+ vad_sensitivity = gr.Slider(
+ minimum=0,
+ maximum=3,
+ label=translations["vad_sensitivity_label"],
+ info=translations["vad_sensitivity_info"],
+ value=3,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ vad_frame_ms = gr.Slider(
+ minimum=10,
+ maximum=30,
+ label=translations["vad_frame_ms_label"],
+ info=translations["vad_frame_ms_info"],
+ value=30,
+ step=10,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ post_process = gr.Checkbox(
+ label=translations["audio_effects"],
+ value=False,
+ interactive=True
+ )
+ reverb = gr.Checkbox(
+ label=translations["reverb"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ chorus = gr.Checkbox(
+ label=translations["chorus"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ delay = gr.Checkbox(
+ label=translations["delay"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ phaser = gr.Checkbox(
+ label=translations["phaser"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ compressor = gr.Checkbox(
+ label=translations["compressor"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ limiter = gr.Checkbox(
+ label=translations["limiter"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ distortion = gr.Checkbox(
+ label=translations["distortion"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ pitch_shift = gr.Checkbox(
+ label=translations["pitch"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ gain = gr.Checkbox(
+ label=translations["gain"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ bitcrush = gr.Checkbox(
+ label=translations["bitcrush"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ clipping = gr.Checkbox(
+ label=translations["clipping"],
+ value=False,
+ interactive=True,
+ visible=False
+ )
+ with gr.Accordion(
+ translations["reverb"],
+ open=True,
+ visible=False
+ ) as reverb_accordion:
+ reverb_freeze_mode = gr.Checkbox(
+ label=translations["reverb_freeze"],
+ info=translations["reverb_freeze_info"],
+ value=False,
+ interactive=True
+ )
+ reverb_room_size = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.15,
+ label=translations["room_size"],
+ info=translations["room_size_info"],
+ interactive=True
+ )
+ reverb_damping = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.7,
+ label=translations["damping"],
+ info=translations["damping_info"],
+ interactive=True
+ )
+ reverb_wet_gain = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.2,
+ label=translations["wet_level"],
+ info=translations["wet_level_info"],
+ interactive=True
+ )
+ reverb_dry_gain = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.8,
+ label=translations["dry_level"],
+ info=translations["dry_level_info"],
+ interactive=True
+ )
+ reverb_width = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=1,
+ label=translations["width"],
+ info=translations["width_info"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["chorus"],
+ open=True,
+ visible=False
+ ) as chorus_accordion:
+ chorus_depth = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["chorus_depth"],
+ info=translations["chorus_depth_info"],
+ interactive=True
+ )
+ chorus_rate = gr.Slider(
+ minimum=0.1,
+ maximum=10,
+ step=0.1,
+ value=1.5,
+ label=translations["chorus_rate_hz"],
+ info=translations["chorus_rate_hz_info"],
+ interactive=True
+ )
+ chorus_mix = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["chorus_mix"],
+ info=translations["chorus_mix_info"],
+ interactive=True
+ )
+ chorus_center_delay = gr.Slider(
+ minimum=0,
+ maximum=50,
+ step=1,
+ value=10,
+ label=translations["chorus_center_delay_ms"],
+ info=translations["chorus_center_delay_ms_info"],
+ interactive=True
+ )
+ chorus_feedback = gr.Slider(
+ minimum=-1,
+ maximum=1,
+ step=0.01,
+ value=0,
+ label=translations["chorus_feedback"],
+ info=translations["chorus_feedback_info"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["phaser"],
+ open=True,
+ visible=False
+ ) as phaser_accordion:
+ phaser_depth = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["phaser_depth"],
+ info=translations["phaser_depth_info"],
+ interactive=True
+ )
+ phaser_rate_hz = gr.Slider(
+ minimum=0.1,
+ maximum=10,
+ step=0.1,
+ value=1,
+ label=translations["phaser_rate_hz"],
+ info=translations["phaser_rate_hz_info"],
+ interactive=True
+ )
+ phaser_mix = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["phaser_mix"],
+ info=translations["phaser_mix_info"],
+ interactive=True
+ )
+ phaser_centre_frequency_hz = gr.Slider(
+ minimum=50,
+ maximum=5000,
+ step=10,
+ value=1000,
+ label=translations["phaser_centre_frequency_hz"],
+ info=translations["phaser_centre_frequency_hz_info"],
+ interactive=True
+ )
+ phaser_feedback = gr.Slider(
+ minimum=-1,
+ maximum=1,
+ step=0.01,
+ value=0,
+ label=translations["phaser_feedback"],
+ info=translations["phaser_feedback_info"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["delay"],
+ open=True,
+ visible=False
+ ) as delay_accordion:
+ delay_seconds = gr.Slider(
+ minimum=0,
+ maximum=5,
+ step=0.01,
+ value=0.5,
+ label=translations["delay_seconds"],
+ info=translations["delay_seconds_info"],
+ interactive=True
+ )
+ delay_feedback = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["delay_feedback"],
+ info=translations["delay_feedback_info"],
+ interactive=True
+ )
+ delay_mix = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.5,
+ label=translations["delay_mix"],
+ info=translations["delay_mix_info"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["compressor"],
+ open=True,
+ visible=False
+ ) as compressor_accordion:
+ compressor_threshold = gr.Slider(
+ minimum=-60,
+ maximum=0,
+ step=1,
+ value=-20,
+ label=translations["compressor_threshold_db"],
+ info=translations["compressor_threshold_db_info"],
+ interactive=True
+ )
+ compressor_ratio = gr.Slider(
+ minimum=1,
+ maximum=20,
+ step=0.1,
+ value=1,
+ label=translations["compressor_ratio"],
+ info=translations["compressor_ratio_info"],
+ interactive=True
+ )
+ compressor_attack = gr.Slider(
+ minimum=0.1,
+ maximum=100,
+ step=0.1,
+ value=10,
+ label=translations["compressor_attack_ms"],
+ info=translations["compressor_attack_ms_info"],
+ interactive=True
+ )
+ compressor_release = gr.Slider(
+ minimum=10,
+ maximum=1000,
+ step=1,
+ value=100,
+ label=translations["compressor_release_ms"],
+ info=translations["compressor_release_ms_info"],
+ interactive=True
+ )
+ with gr.Accordion(
+ translations["limiter"],
+ open=True,
+ visible=False
+ ) as limiter_accordion:
+ limiter_threshold = gr.Slider(
+ minimum=-60,
+ maximum=0,
+ step=1,
+ value=-1,
+ label=translations["limiter_threshold_db"],
+ info=translations["limiter_threshold_db_info"],
+ interactive=True
+ )
+ limiter_release_time = gr.Slider(
+ minimum=10,
+ maximum=1000,
+ step=1,
+ value=100,
+ label=translations["limiter_release_ms"],
+ info=translations["limiter_release_ms_info"],
+ interactive=True
+ )
+ with gr.Row():
+ distortion_gain = gr.Slider(
+ minimum=0,
+ maximum=50,
+ step=1,
+ value=20,
+ label=translations["distortion"],
+ info=translations["distortion_info"],
+ interactive=True,
+ visible=False
+ )
+ pitch_shift_semitones = gr.Slider(
+ minimum=-24,
+ maximum=24,
+ step=1,
+ value=0,
+ label=translations["pitch"],
+ info=translations["pitch_info"],
+ interactive=True,
+ visible=False
+ )
+ gain_db = gr.Slider(
+ minimum=-60,
+ maximum=60,
+ step=1,
+ value=0,
+ label=translations["gain"],
+ info=translations["gain_info"],
+ interactive=True,
+ visible=False
+ )
+ bitcrush_bit_depth = gr.Slider(
+ minimum=1,
+ maximum=24,
+ step=1,
+ value=16,
+ label=translations["bitcrush_bit_depth"],
+ info=translations["bitcrush_bit_depth_info"],
+ interactive=True,
+ visible=False
+ )
+ clipping_threshold = gr.Slider(
+ minimum=-60,
+ maximum=0,
+ step=1,
+ value=-1,
+ label=translations["clipping_threshold_db"],
+ info=translations["clipping_threshold_db_info"],
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ with gr.Accordion(
+ translations["use_presets"],
+ open=False
+ ):
+ with gr.Row():
+ presets_name = gr.Dropdown(
+ label=translations["file_preset"],
+ choices=realtime_presets_file,
+ value=realtime_presets_file[0] if len(realtime_presets_file) > 0 else '',
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ load_preset_button = gr.Button(
+ translations["load_file"],
+ variant="primary"
+ )
+ refresh_preset_button = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Accordion(
+ translations["export_file"],
+ open=False
+ ):
+ with gr.Row():
+ with gr.Column():
+ name_to_save_file = gr.Textbox(
+ label=translations["filename_to_save"]
+ )
+ save_file_button = gr.Button(
+ translations["export_file"]
+ )
+ with gr.Row():
+ upload_presets = gr.Files(
+ label=translations["upload_presets"],
+ file_types=[".realtime.json"]
+ )
+ with gr.Row():
+ json_audio_hidden = gr.JSON(
+ visible=False
+ )
+ json_button_hidden = gr.JSON(
+ visible=False
+ )
+ json_value_hidden = gr.JSON(
+ visible=False
+ )
+ with gr.Row():
+ post_process.change(
+ fn=lambda a: [
+ visibleFalse(a)
+ for _ in range(11)
+ ],
+ inputs=[
+ post_process
+ ],
+ outputs=[
+ reverb,
+ chorus,
+ delay,
+ phaser,
+ compressor,
+ pitch_shift,
+ limiter,
+ distortion,
+ gain,
+ bitcrush,
+ clipping
+ ]
+ )
+ reverb.change(
+ fn=visible,
+ inputs=[
+ reverb
+ ],
+ outputs=[
+ reverb_accordion
+ ]
+ )
+ chorus.change(
+ fn=visible,
+ inputs=[
+ chorus
+ ],
+ outputs=[
+ chorus_accordion
+ ]
+ )
+ with gr.Row():
+ delay.change(
+ fn=visible,
+ inputs=[
+ delay
+ ],
+ outputs=[
+ delay_accordion
+ ]
+ )
+ phaser.change(
+ fn=visible,
+ inputs=[
+ phaser
+ ],
+ outputs=[
+ phaser_accordion
+ ]
+ )
+ compressor.change(
+ fn=visible,
+ inputs=[
+ compressor
+ ],
+ outputs=[
+ compressor_accordion
+ ]
+ )
+ with gr.Row():
+ limiter.change(
+ fn=visible,
+ inputs=[
+ limiter
+ ],
+ outputs=[
+ limiter_accordion
+ ]
+ )
+ distortion.change(
+ fn=visible,
+ inputs=[
+ distortion
+ ],
+ outputs=[
+ distortion_gain
+ ]
+ )
+ pitch_shift.change(
+ fn=visible,
+ inputs=[
+ pitch_shift
+ ],
+ outputs=[
+ pitch_shift_semitones
+ ]
+ )
+ with gr.Row():
+ gain.change(
+ fn=visible,
+ inputs=[
+ gain
+ ],
+ outputs=[
+ gain_db
+ ]
+ )
+ bitcrush.change(
+ fn=visible,
+ inputs=[
+ bitcrush
+ ],
+ outputs=[
+ bitcrush_bit_depth
+ ]
+ )
+ clipping.change(
+ fn=visible,
+ inputs=[
+ clipping
+ ],
+ outputs=[
+ clipping_threshold
+ ]
+ )
+ with gr.Row():
+ model_pth.change(
+ fn=get_index,
+ inputs=[
+ model_pth
+ ],
+ outputs=[
+ model_index
+ ]
+ )
+ model_index.change(
+ fn=index_strength_show,
+ inputs=[
+ model_index
+ ],
+ outputs=[
+ index_strength,
+ nprobe
+ ]
+ )
+ model_refresh.click(
+ fn=change_models_choices,
+ inputs=[],
+ outputs=[
+ model_pth,
+ model_index
+ ]
+ )
+ with gr.Row():
+ unlock_full_method.change(
+ fn=lambda f0_method: {
+ "choices": [
+ m for m in unlock_f0(f0_method)["choices"]
+ if m != "hybrid"
+ ],
+ "value": "rmvpe",
+ "__type__": "update"
+ },
+ inputs=[
+ unlock_full_method
+ ],
+ outputs=[
+ f0_method
+ ]
+ )
+ f0_method.change(
+ fn=lambda f0_method: hoplength_show(f0_method, None),
+ inputs=[
+ f0_method
+ ],
+ outputs=[
+ hop_length
+ ]
+ )
+ with gr.Row():
+ embedders.change(
+ fn=lambda embedders: visible(embedders == "custom"),
+ inputs=[
+ embedders
+ ],
+ outputs=[
+ custom_embedders
+ ]
+ )
+ f0_autotune.change(
+ fn=visible,
+ inputs=[
+ f0_autotune
+ ],
+ outputs=[
+ f0_autotune_strength
+ ]
+ )
+ clean_audio.change(
+ fn=visible,
+ inputs=[
+ clean_audio
+ ],
+ outputs=[
+ clean_strength
+ ]
+ )
+ with gr.Row():
+ if not client_mode:
+ input_audio_device.change(
+ fn=update_audio_device,
+ inputs=[
+ input_audio_device,
+ output_audio_device,
+ monitor_output_device,
+ monitor
+ ],
+ outputs=[
+ monitor_output_device,
+ monitor_audio_gain,
+ asio_row,
+ input_asio_channels,
+ output_asio_channels,
+ monitor_asio_channels,
+ input_audio_sample_rate,
+ output_audio_sample_rate,
+ monitor_audio_sample_rate,
+ asio_output_stereo,
+ asio_monitor_stereo
+ ]
+ )
+ output_audio_device.change(
+ fn=update_audio_device,
+ inputs=[
+ input_audio_device,
+ output_audio_device,
+ monitor_output_device,
+ monitor
+ ],
+ outputs=[
+ monitor_output_device,
+ monitor_audio_gain,
+ asio_row,
+ input_asio_channels,
+ output_asio_channels,
+ monitor_asio_channels,
+ input_audio_sample_rate,
+ output_audio_sample_rate,
+ monitor_audio_sample_rate,
+ asio_output_stereo,
+ asio_monitor_stereo
+ ]
+ )
+ monitor_output_device.change(
+ fn=update_audio_device,
+ inputs=[
+ input_audio_device,
+ output_audio_device,
+ monitor_output_device,
+ monitor
+ ],
+ outputs=[
+ monitor_output_device,
+ monitor_audio_gain,
+ asio_row,
+ input_asio_channels,
+ output_asio_channels,
+ monitor_asio_channels,
+ input_audio_sample_rate,
+ output_audio_sample_rate,
+ monitor_audio_sample_rate,
+ asio_output_stereo,
+ asio_monitor_stereo
+ ]
+ )
+ with gr.Row():
+ embedders_mix.change(
+ fn=visible,
+ inputs=[
+ embedders_mix
+ ],
+ outputs=[
+ embedders_mix_column
+ ]
+ )
+ if not client_mode:
+ monitor.change(
+ fn=update_audio_device,
+ inputs=[
+ input_audio_device,
+ output_audio_device,
+ monitor_output_device,
+ monitor
+ ],
+ outputs=[
+ monitor_output_device,
+ monitor_audio_gain,
+ asio_row,
+ input_asio_channels,
+ output_asio_channels,
+ monitor_asio_channels,
+ input_audio_sample_rate,
+ output_audio_sample_rate,
+ monitor_audio_sample_rate,
+ asio_output_stereo,
+ asio_monitor_stereo
+ ]
+ )
+ with gr.Row():
+ proposal_pitch.change(
+ fn=visible,
+ inputs=[
+ proposal_pitch
+ ],
+ outputs=[
+ proposal_pitch_threshold
+ ]
+ )
+ vad_enabled.change(
+ fn=lambda a: [
+ visible(a)
+ for _ in range(2)
+ ],
+ inputs=[
+ vad_enabled
+ ],
+ outputs=[
+ vad_sensitivity,
+ vad_frame_ms
+ ]
+ )
+ if not client_mode:
+ refresh_audio_device.click(
+ fn=change_audio_device_choices,
+ inputs=[],
+ outputs=[
+ input_audio_device,
+ output_audio_device,
+ monitor_output_device
+ ]
+ )
+ else:
+ refresh_audio_device.click(
+ fn=None,
+ js="getAudioDevices",
+ inputs=[],
+ outputs=[
+ json_audio_hidden
+ ]
+ )
+ with gr.Row():
+ model_pth.change(
+ fn=get_speakers_id_and_architecture,
+ inputs=[
+ model_pth
+ ],
+ outputs=[
+ sids,
+ noise_scale
+ ]
+ )
+ if client_mode:
+ json_audio_hidden.change(
+ fn=update_dropdowns_from_json,
+ inputs=[
+ json_audio_hidden
+ ],
+ outputs=[
+ input_audio_device,
+ output_audio_device,
+ monitor_output_device
+ ]
+ )
+ json_button_hidden.change(
+ fn=update_button_from_json,
+ inputs=[
+ json_button_hidden
+ ],
+ outputs=[
+ start_realtime,
+ stop_realtime
+ ]
+ )
+ json_value_hidden.change(
+ fn=update_value_from_json,
+ inputs=[
+ json_value_hidden
+ ],
+ outputs=[
+ record_audio,
+ record_output
+ ]
+ )
+ with gr.Row():
+ if not client_mode:
+ start_realtime.click(
+ fn=realtime_start,
+ inputs=[
+ monitor,
+ exclusive_mode,
+ vad_enabled,
+ input_audio_device,
+ output_audio_device,
+ monitor_output_device,
+ input_audio_gain,
+ output_audio_gain,
+ monitor_audio_gain,
+ input_asio_channels,
+ output_asio_channels,
+ monitor_asio_channels,
+ input_audio_sample_rate,
+ output_audio_sample_rate,
+ monitor_audio_sample_rate,
+ chunk_size,
+ pitch,
+ model_pth,
+ model_index,
+ index_strength,
+ predictor_onnx,
+ f0_method,
+ hop_length,
+ embedder_mode,
+ embedders,
+ custom_embedders,
+ f0_autotune,
+ proposal_pitch,
+ f0_autotune_strength,
+ proposal_pitch_threshold,
+ rms_mix_rate,
+ protect,
+ filter_radius,
+ silent_threshold,
+ extra_convert_size,
+ cross_fade_overlap_size,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ post_process,
+ sids,
+ noise_scale,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder,
+ chorus,
+ distortion,
+ reverb,
+ pitch_shift,
+ delay,
+ compressor,
+ limiter,
+ gain,
+ bitcrush,
+ clipping,
+ phaser,
+ chorus_depth,
+ chorus_rate,
+ chorus_mix,
+ chorus_center_delay,
+ chorus_feedback,
+ distortion_gain,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_gain,
+ reverb_dry_gain,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_seconds,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold,
+ compressor_ratio,
+ compressor_attack,
+ compressor_release,
+ limiter_threshold,
+ limiter_release_time,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix,
+ nprobe
+ ],
+ outputs=[
+ status,
+ start_realtime,
+ stop_realtime
+ ]
+ )
+ stop_realtime.click(
+ fn=realtime_stop,
+ inputs=[
+ stop_realtime
+ ],
+ outputs=[
+ status,
+ start_realtime,
+ stop_realtime
+ ]
+ )
+ record_audio.click(
+ fn=soundfile_record_audio,
+ inputs=[
+ record_audio,
+ record_audio_path,
+ export_format
+ ],
+ outputs=[
+ record_audio,
+ record_output
+ ]
+ )
+ else:
+ start_realtime.click(
+ fn=None,
+ js="StreamAudioRealtime",
+ inputs=[
+ monitor,
+ vad_enabled,
+ input_audio_device,
+ output_audio_device,
+ monitor_output_device,
+ input_audio_gain,
+ output_audio_gain,
+ monitor_audio_gain,
+ chunk_size,
+ pitch,
+ model_pth,
+ model_index,
+ index_strength,
+ predictor_onnx,
+ f0_method,
+ hop_length,
+ embedder_mode,
+ embedders,
+ custom_embedders,
+ f0_autotune,
+ proposal_pitch,
+ f0_autotune_strength,
+ proposal_pitch_threshold,
+ rms_mix_rate,
+ protect,
+ filter_radius,
+ silent_threshold,
+ extra_convert_size,
+ cross_fade_overlap_size,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ exclusive_mode,
+ post_process,
+ sids,
+ noise_scale,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder,
+ chorus,
+ distortion,
+ reverb,
+ pitch_shift,
+ delay,
+ compressor,
+ limiter,
+ gain,
+ bitcrush,
+ clipping,
+ phaser,
+ chorus_depth,
+ chorus_rate,
+ chorus_mix,
+ chorus_center_delay,
+ chorus_feedback,
+ distortion_gain,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_gain,
+ reverb_dry_gain,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_seconds,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold,
+ compressor_ratio,
+ compressor_attack,
+ compressor_release,
+ limiter_threshold,
+ limiter_release_time,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix,
+ nprobe
+ ],
+ outputs=[
+ json_button_hidden
+ ]
+ )
+ stop_realtime.click(
+ fn=None,
+ js="StopAudioStream",
+ inputs=[],
+ outputs=[
+ json_button_hidden
+ ]
+ )
+ record_audio.click(
+ fn=None,
+ js="SoundfileRecordAudio",
+ inputs=[
+ record_audio,
+ record_audio_path,
+ export_format
+ ],
+ outputs=[
+ json_value_hidden
+ ]
+ )
+ with gr.Row():
+ cross_fade_overlap_size.change(
+ js="(value) => window.ChangeConfig(value, 'cross_fade_overlap_size')" if client_mode else None,
+ fn=(lambda value: change_config(value, "cross_fade_overlap_size")) if not client_mode else None,
+ inputs=[
+ cross_fade_overlap_size
+ ],
+ outputs=[]
+ )
+ extra_convert_size.change(
+ js="(value) => window.ChangeConfig(value, 'extra_convert_size')" if client_mode else None,
+ fn=(lambda value: change_config(value, "extra_convert_size")) if not client_mode else None,
+ inputs=[
+ extra_convert_size
+ ],
+ outputs=[]
+ )
+ silent_threshold.change(
+ js="(value) => window.ChangeConfig(value, 'silent_threshold')" if client_mode else None,
+ fn=(lambda value: change_config(value, "silent_threshold")) if not client_mode else None,
+ inputs=[
+ silent_threshold
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ vad_enabled.change(
+ js="(value) => window.ChangeConfig(value, 'vad_enabled')" if client_mode else None,
+ fn=(lambda value: change_config(value, "vad_enabled")) if not client_mode else None,
+ inputs=[
+ vad_enabled
+ ],
+ outputs=[]
+ )
+ vad_sensitivity.change(
+ js="(value) => window.ChangeConfig(value, 'vad_sensitivity')" if client_mode else None,
+ fn=(lambda value: change_config(value, "vad_sensitivity")) if not client_mode else None,
+ inputs=[
+ vad_sensitivity
+ ],
+ outputs=[]
+ )
+ vad_frame_ms.change(
+ js="(value) => window.ChangeConfig(value, 'vad_frame_ms')" if client_mode else None,
+ fn=(lambda value: change_config(value, "vad_frame_ms")) if not client_mode else None,
+ inputs=[
+ vad_frame_ms
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ input_audio_gain.change(
+ js="(value) => window.ChangeConfig(value, 'input_audio_gain')" if client_mode else None,
+ fn=(lambda value: change_config(value / 100.0, "input_audio_gain")) if not client_mode else None,
+ inputs=[
+ input_audio_gain
+ ],
+ outputs=[]
+ )
+ output_audio_gain.change(
+ js="(value) => window.ChangeConfig(value, 'output_audio_gain')" if client_mode else None,
+ fn=(lambda value: change_config(value / 100.0, "output_audio_gain")) if not client_mode else None,
+ inputs=[
+ output_audio_gain
+ ],
+ outputs=[]
+ )
+ monitor_audio_gain.change(
+ js="(value) => window.ChangeConfig(value, 'monitor_audio_gain')" if client_mode else None,
+ fn=(lambda value: change_config(value / 100.0, "monitor_audio_gain")) if not client_mode else None,
+ inputs=[
+ monitor_audio_gain
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ index_strength.change(
+ js="(value) => window.ChangeConfig(value, 'index_rate')" if client_mode else None,
+ fn=(lambda value: change_config(value, "index_rate")) if not client_mode else None,
+ inputs=[
+ index_strength
+ ],
+ outputs=[]
+ )
+ nprobe.change(
+ js="(value) => window.ChangeConfig(value, 'nprobe')" if client_mode else None,
+ fn=(lambda value: change_config(value, "nprobe")) if not client_mode else None,
+ inputs=[
+ nprobe
+ ],
+ outputs=[]
+ )
+ rms_mix_rate.change(
+ js="(value) => window.ChangeConfig(value, 'rms_mix_rate')" if client_mode else None,
+ fn=(lambda value: change_config(value, "rms_mix_rate")) if not client_mode else None,
+ inputs=[
+ rms_mix_rate
+ ],
+ outputs=[]
+ )
+ protect.change(
+ js="(value) => window.ChangeConfig(value, 'protect')" if client_mode else None,
+ fn=(lambda value: change_config(value, "protect")) if not client_mode else None,
+ inputs=[
+ protect
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ pitch.change(
+ js="(value) => window.ChangeConfig(value, 'f0_up_key')" if client_mode else None,
+ fn=(lambda value: change_config(value, "f0_up_key")) if not client_mode else None,
+ inputs=[
+ pitch
+ ],
+ outputs=[]
+ )
+ f0_autotune.change(
+ js="(value) => window.ChangeConfig(value, 'f0_autotune')" if client_mode else None,
+ fn=(lambda value: change_config(value, "f0_autotune")) if not client_mode else None,
+ inputs=[
+ f0_autotune
+ ],
+ outputs=[]
+ )
+ f0_autotune_strength.change(
+ js="(value) => window.ChangeConfig(value, 'f0_autotune_strength')" if client_mode else None,
+ fn=(lambda value: change_config(value, "f0_autotune_strength")) if not client_mode else None,
+ inputs=[
+ f0_autotune_strength
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ proposal_pitch.change(
+ js="(value) => window.ChangeConfig(value, 'proposal_pitch')" if client_mode else None,
+ fn=(lambda value: change_config(value, "proposal_pitch")) if not client_mode else None,
+ inputs=[
+ proposal_pitch
+ ],
+ outputs=[]
+ )
+ proposal_pitch_threshold.change(
+ js="(value) => window.ChangeConfig(value, 'proposal_pitch_threshold')" if client_mode else None,
+ fn=(lambda value: change_config(value, "proposal_pitch_threshold")) if not client_mode else None,
+ inputs=[
+ proposal_pitch_threshold
+ ],
+ outputs=[]
+ )
+ clean_audio.change(
+ js="(value) => window.ChangeConfig(value, 'clean_audio')" if client_mode else None,
+ fn=(lambda value: change_config(value, "clean_audio")) if not client_mode else None,
+ inputs=[
+ clean_audio
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ clean_strength.change(
+ js="(value) => window.ChangeConfig(value, 'clean_strength')" if client_mode else None,
+ fn=(lambda value: change_config(value, "clean_strength")) if not client_mode else None,
+ inputs=[
+ clean_strength
+ ],
+ outputs=[]
+ )
+ post_process.change(
+ js="(value) => window.ChangeConfig(value, 'post_process')" if client_mode else None,
+ fn=(lambda value: change_config(value, "post_process")) if not client_mode else None,
+ inputs=[post_process],
+ outputs=[]
+ )
+ reverb.change(
+ js="(value) => window.ChangeConfig(value, 'reverb', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "reverb", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ reverb
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ chorus.change(
+ js="(value) => window.ChangeConfig(value, 'chorus', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "chorus", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ chorus
+ ],
+ outputs=[]
+ )
+ delay.change(
+ js="(value) => window.ChangeConfig(value, 'delay', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "delay", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ delay
+ ],
+ outputs=[]
+ )
+ phaser.change(
+ js="(value) => window.ChangeConfig(value, 'phaser', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "phaser", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ phaser
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ compressor.change(
+ js="(value) => window.ChangeConfig(value, 'compressor', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "compressor", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ compressor
+ ],
+ outputs=[]
+ )
+ limiter.change(
+ js="(value) => window.ChangeConfig(value, 'limiter', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "limiter", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ limiter
+ ],
+ outputs=[]
+ )
+ distortion.change(
+ js="(value) => window.ChangeConfig(value, 'distortion', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "distortion", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ distortion
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ pitch_shift.change(
+ js="(value) => window.ChangeConfig(value, 'pitch_shift', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "pitch_shift", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ pitch_shift
+ ],
+ outputs=[]
+ )
+ gain.change(
+ js="(value) => window.ChangeConfig(value, 'gain', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "gain", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ gain
+ ],
+ outputs=[]
+ )
+ bitcrush.change(
+ js="(value) => window.ChangeConfig(value, 'bitcrush', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "bitcrush", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ bitcrush
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ clipping.change(
+ js="(value) => window.ChangeConfig(value, 'clipping', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "clipping", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ clipping
+ ],
+ outputs=[]
+ )
+ pitch_shift_semitones.change(
+ js="(value) => window.ChangeConfig(value, 'pitch_shift_semitones', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "pitch_shift_semitones", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ pitch_shift_semitones
+ ],
+ outputs=[]
+ )
+ gain_db.change(
+ js="(value) => window.ChangeConfig(value, 'gain_db', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "gain_db", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ gain_db
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ phaser_depth.change(
+ js="(value) => window.ChangeConfig(value, 'phaser_depth', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "phaser_depth", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ phaser_depth
+ ],
+ outputs=[]
+ )
+ phaser_rate_hz.change(
+ js="(value) => window.ChangeConfig(value, 'phaser_rate_hz', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "phaser_rate_hz", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ phaser_rate_hz
+ ],
+ outputs=[]
+ )
+ phaser_mix.change(
+ js="(value) => window.ChangeConfig(value, 'phaser_mix', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "phaser_mix", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ phaser_mix
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ phaser_centre_frequency_hz.change(
+ js="(value) => window.ChangeConfig(value, 'phaser_centre_frequency_hz', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "phaser_centre_frequency_hz", if_kwargs=True)) if not client_mode else None,
+ inputs=[phaser_centre_frequency_hz],
+ outputs=[]
+ )
+ phaser_feedback.change(
+ js="(value) => window.ChangeConfig(value, 'phaser_feedback', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "phaser_feedback", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ phaser_feedback
+ ],
+ outputs=[]
+ )
+ reverb_room_size.change(
+ js="(value) => window.ChangeConfig(value, 'reverb_room_size', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "reverb_room_size", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ reverb_room_size
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ reverb_damping.change(
+ js="(value) => window.ChangeConfig(value, 'reverb_damping', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "reverb_damping", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ reverb_damping
+ ],
+ outputs=[]
+ )
+ reverb_wet_gain.change(
+ js="(value) => window.ChangeConfig(value, 'reverb_wet_level', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "reverb_wet_level", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ reverb_wet_gain
+ ],
+ outputs=[]
+ )
+ reverb_dry_gain.change(
+ js="(value) => window.ChangeConfig(value, 'reverb_dry_level', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "reverb_dry_level", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ reverb_dry_gain
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ reverb_width.change(
+ js="(value) => window.ChangeConfig(value, 'reverb_width', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "reverb_width", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ reverb_width
+ ],
+ outputs=[]
+ )
+ reverb_freeze_mode.change(
+ js="(value) => window.ChangeConfig(value, 'reverb_freeze_mode', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "reverb_freeze_mode", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ reverb_freeze_mode
+ ],
+ outputs=[]
+ )
+ limiter_threshold.change(
+ js="(value) => window.ChangeConfig(value, 'limiter_threshold', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "limiter_threshold", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ limiter_threshold
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ limiter_release_time.change(
+ js="(value) => window.ChangeConfig(value, 'limiter_release', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "limiter_release", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ limiter_release_time
+ ],
+ outputs=[]
+ )
+ chorus_rate.change(
+ js="(value) => window.ChangeConfig(value, 'chorus_rate', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "chorus_rate", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ chorus_rate
+ ],
+ outputs=[]
+ )
+ chorus_depth.change(
+ js="(value) => window.ChangeConfig(value, 'chorus_depth', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "chorus_depth", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ chorus_depth
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ chorus_center_delay.change(
+ js="(value) => window.ChangeConfig(value, 'chorus_delay', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "chorus_delay", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ chorus_center_delay
+ ],
+ outputs=[]
+ )
+ chorus_feedback.change(
+ js="(value) => window.ChangeConfig(value, 'chorus_feedback', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "chorus_feedback", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ chorus_feedback
+ ],
+ outputs=[]
+ )
+ chorus_mix.change(
+ js="(value) => window.ChangeConfig(value, 'chorus_mix', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "chorus_mix", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ chorus_mix
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ bitcrush_bit_depth.change(
+ js="(value) => window.ChangeConfig(value, 'bitcrush_bit_depth', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "bitcrush_bit_depth", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ bitcrush_bit_depth
+ ],
+ outputs=[]
+ )
+ clipping_threshold.change(
+ js="(value) => window.ChangeConfig(value, 'clipping_threshold', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "clipping_threshold", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ clipping_threshold
+ ],
+ outputs=[]
+ )
+ distortion_gain.change(
+ js="(value) => window.ChangeConfig(value, 'distortion_gain', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "distortion_gain", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ distortion_gain
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ compressor_threshold.change(
+ js="(value) => window.ChangeConfig(value, 'compressor_threshold', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "compressor_threshold", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ compressor_threshold
+ ],
+ outputs=[]
+ )
+ compressor_ratio.change(
+ js="(value) => window.ChangeConfig(value, 'compressor_ratio', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "compressor_ratio", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ compressor_ratio
+ ],
+ outputs=[]
+ )
+ compressor_attack.change(
+ js="(value) => window.ChangeConfig(value, 'compressor_attack', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "compressor_attack", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ compressor_attack
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ compressor_release.change(
+ js="(value) => window.ChangeConfig(value, 'compressor_release', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "compressor_release", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ compressor_release
+ ],
+ outputs=[]
+ )
+ delay_seconds.change(
+ js="(value) => window.ChangeConfig(value, 'delay_seconds', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "delay_seconds", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ delay_seconds
+ ],
+ outputs=[]
+ )
+ delay_feedback.change(
+ js="(value) => window.ChangeConfig(value, 'delay_feedback', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "delay_feedback", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ delay_feedback
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ delay_mix.change(
+ js="(value) => window.ChangeConfig(value, 'delay_mix', if_kwargs=true)" if client_mode else None,
+ fn=(lambda value: change_config(value, "delay_mix", if_kwargs=True)) if not client_mode else None,
+ inputs=[
+ delay_mix
+ ],
+ outputs=[]
+ )
+ model_pth.change(
+ js="(value) => window.ChangeConfig(value, 'model_path')" if client_mode else None,
+ fn=(lambda value: change_config(value, "model_path")) if not client_mode else None,
+ inputs=[
+ model_pth
+ ],
+ outputs=[]
+ )
+ model_index.change(
+ js="(value) => window.ChangeConfig(value, 'index_path')" if client_mode else None,
+ fn=(lambda value: change_config(value, "index_path")) if not client_mode else None,
+ inputs=[
+ model_index
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ f0_method.change(
+ js="(value) => window.ChangeConfig(value, 'f0_method')" if client_mode else None,
+ fn=(lambda value: change_config(value, "f0_method")) if not client_mode else None,
+ inputs=[
+ f0_method
+ ],
+ outputs=[]
+ )
+ predictor_onnx.change(
+ js="(value) => window.ChangeConfig(value, 'predictor_onnx')" if client_mode else None,
+ fn=(lambda value: change_config(value, "predictor_onnx")) if not client_mode else None,
+ inputs=[
+ predictor_onnx
+ ],
+ outputs=[]
+ )
+ embedders.change(
+ js="(value) => window.ChangeConfig(value, 'embedder_model')" if client_mode else None,
+ fn=(lambda value: change_config(value, "embedder_model")) if not client_mode else None,
+ inputs=[
+ embedders
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ custom_embedders.change(
+ js="(value) => window.ChangeConfig(value, 'embedder_model_custom')" if client_mode else None,
+ fn=(lambda value: change_config(value, "embedder_model_custom")) if not client_mode else None,
+ inputs=[
+ custom_embedders
+ ],
+ outputs=[]
+ )
+ embedder_mode.change(
+ js="(value) => window.ChangeConfig(value, 'embedders_mode')" if client_mode else None,
+ fn=(lambda value: change_config(value, "embedders_mode")) if not client_mode else None,
+ inputs=[
+ embedder_mode
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ embedders_mix.change(
+ js="(value) => window.ChangeConfig(value, 'embedders_mix')" if client_mode else None,
+ fn=(lambda value: change_config(value, "embedders_mix")) if not client_mode else None,
+ inputs=[
+ embedders_mix
+ ],
+ outputs=[]
+ )
+ embedders_mix_layers.change(
+ js="(value) => window.ChangeConfig(value, 'embedders_mix_layers')" if client_mode else None,
+ fn=(lambda value: change_config(value, "embedders_mix_layers")) if not client_mode else None,
+ inputs=[
+ embedders_mix_layers
+ ],
+ outputs=[]
+ )
+ embedders_mix_ratio.change(
+ js="(value) => window.ChangeConfig(value, 'embedders_mix_ratio')" if client_mode else None,
+ fn=(lambda value: change_config(value, "embedders_mix_ratio")) if not client_mode else None,
+ inputs=[
+ embedders_mix_ratio
+ ],
+ outputs=[]
+ )
+ use_phase_vocoder.change(
+ js="(value) => window.ChangeConfig(value, 'use_phase_vocoder')" if client_mode else None,
+ fn=(lambda value: change_config(value, "use_phase_vocoder")) if not client_mode else None,
+ inputs=[
+ use_phase_vocoder
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ noise_scale.change(
+ js="(value) => window.ChangeConfig(value, 'noise_scale')" if client_mode else None,
+ fn=(lambda value: change_config(value, "noise_scale")) if not client_mode else None,
+ inputs=[
+ noise_scale
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ load_preset_button.click(
+ fn=load_realtime_config,
+ inputs=[
+ presets_name,
+ exclusive_mode,
+ vad_enabled,
+ chunk_size,
+ pitch,
+ index_strength,
+ predictor_onnx,
+ hop_length,
+ embedder_mode,
+ f0_autotune,
+ proposal_pitch,
+ f0_autotune_strength,
+ proposal_pitch_threshold,
+ rms_mix_rate,
+ protect,
+ filter_radius,
+ silent_threshold,
+ extra_convert_size,
+ cross_fade_overlap_size,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ post_process,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder,
+ chorus,
+ distortion,
+ reverb,
+ pitch_shift,
+ delay,
+ compressor,
+ limiter,
+ gain,
+ bitcrush,
+ clipping,
+ phaser,
+ chorus_depth,
+ chorus_rate,
+ chorus_mix,
+ chorus_center_delay,
+ chorus_feedback,
+ distortion_gain,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_gain,
+ reverb_dry_gain,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_seconds,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold,
+ compressor_ratio,
+ compressor_attack,
+ compressor_release,
+ limiter_threshold,
+ limiter_release_time,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix
+ ],
+ outputs=[
+ exclusive_mode,
+ vad_enabled,
+ chunk_size,
+ pitch,
+ index_strength,
+ predictor_onnx,
+ hop_length,
+ embedder_mode,
+ f0_autotune,
+ proposal_pitch,
+ f0_autotune_strength,
+ proposal_pitch_threshold,
+ rms_mix_rate,
+ protect,
+ filter_radius,
+ silent_threshold,
+ extra_convert_size,
+ cross_fade_overlap_size,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ post_process,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder,
+ chorus,
+ distortion,
+ reverb,
+ pitch_shift,
+ delay,
+ compressor,
+ limiter,
+ gain,
+ bitcrush,
+ clipping,
+ phaser,
+ chorus_depth,
+ chorus_rate,
+ chorus_mix,
+ chorus_center_delay,
+ chorus_feedback,
+ distortion_gain,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_gain,
+ reverb_dry_gain,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_seconds,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold,
+ compressor_ratio,
+ compressor_attack,
+ compressor_release,
+ limiter_threshold,
+ limiter_release_time,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix
+ ]
+ )
+ refresh_preset_button.click(
+ fn=change_realtime_preset_choices,
+ inputs=[],
+ outputs=[
+ presets_name
+ ]
+ )
+ save_file_button.click(
+ fn=save_realtime_config,
+ inputs=[
+ name_to_save_file,
+ exclusive_mode,
+ vad_enabled,
+ chunk_size,
+ pitch,
+ index_strength,
+ predictor_onnx,
+ hop_length,
+ embedder_mode,
+ f0_autotune,
+ proposal_pitch,
+ f0_autotune_strength,
+ proposal_pitch_threshold,
+ rms_mix_rate,
+ protect,
+ filter_radius,
+ silent_threshold,
+ extra_convert_size,
+ cross_fade_overlap_size,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ post_process,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder,
+ chorus,
+ distortion,
+ reverb,
+ pitch_shift,
+ delay,
+ compressor,
+ limiter,
+ gain,
+ bitcrush,
+ clipping,
+ phaser,
+ chorus_depth,
+ chorus_rate,
+ chorus_mix,
+ chorus_center_delay,
+ chorus_feedback,
+ distortion_gain,
+ reverb_room_size,
+ reverb_damping,
+ reverb_wet_gain,
+ reverb_dry_gain,
+ reverb_width,
+ reverb_freeze_mode,
+ pitch_shift_semitones,
+ delay_seconds,
+ delay_feedback,
+ delay_mix,
+ compressor_threshold,
+ compressor_ratio,
+ compressor_attack,
+ compressor_release,
+ limiter_threshold,
+ limiter_release_time,
+ gain_db,
+ bitcrush_bit_depth,
+ clipping_threshold,
+ phaser_rate_hz,
+ phaser_depth,
+ phaser_centre_frequency_hz,
+ phaser_feedback,
+ phaser_mix
+ ],
+ outputs=[
+ presets_name
+ ]
+ )
+ with gr.Row():
+ upload_presets.upload(
+ fn=lambda presets_in: [
+ shutil_move(preset.name, configs["presets_path"])
+ for preset in presets_in
+ ][0],
+ inputs=[
+ upload_presets
+ ],
+ outputs=[
+ presets_name
+ ]
+ )
\ No newline at end of file
diff --git a/main/app/tabs/training/child/create_dataset.py b/main/app/tabs/training/child/create_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ff1c225eb4c44c3e7ef3def556b4687e10e732f
--- /dev/null
+++ b/main/app/tabs/training/child/create_dataset.py
@@ -0,0 +1,519 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.training import create_dataset
+
+from main.app.core.ui import (
+ visible,
+ create_dataset_change,
+ valueFalse_interactive
+)
+from main.app.variables import (
+ vr_models,
+ uvr_model,
+ mdx_models,
+ translations,
+ reverb_models,
+ denoise_models,
+ sample_rate_choice
+)
+
+def create_dataset_tab():
+ with gr.Row():
+ gr.Markdown(translations["create_dataset_markdown_2"])
+ with gr.Group():
+ with gr.Row():
+ separate = gr.Checkbox(
+ label=translations["separator_tab"],
+ value=False,
+ interactive=True
+ )
+ clean_dataset = gr.Checkbox(
+ label=translations["clear_audio"],
+ value=False,
+ interactive=True
+ )
+ skip_seconds = gr.Checkbox(
+ label=translations["skip"],
+ value=False,
+ interactive=True
+ )
+ separate_reverb = gr.Checkbox(
+ label=translations["dereveb_audio"],
+ value=False,
+ interactive=False
+ )
+ with gr.Row(visible=False) as row:
+ enable_tta = gr.Checkbox(
+ label=translations["enable_tta"],
+ value=False,
+ interactive=False
+ )
+ high_end_process = gr.Checkbox(
+ label=translations["high_end_process"],
+ value=False,
+ interactive=False
+ )
+ enable_post_process = gr.Checkbox(
+ label=translations["enable_post_process"],
+ value=False,
+ interactive=False
+ )
+ enable_denoise = gr.Checkbox(
+ label=translations["denoise_mdx"],
+ value=False,
+ interactive=False
+ )
+ with gr.Row():
+ dataset_url = gr.Textbox(
+ label=translations["url_audio"],
+ info=translations["create_dataset_url"],
+ value="",
+ placeholder="https://www.youtube.com/...",
+ interactive=True,
+ scale=5
+ )
+ output_dataset = gr.Textbox(
+ label=translations["output_data"],
+ info=translations["output_data_info"],
+ value="dataset",
+ placeholder="dataset",
+ interactive=True
+ )
+ with gr.Row():
+ create_dataset_button = gr.Button(
+ translations["createdataset"],
+ variant="primary",
+ scale=2,
+ min_width=4000
+ )
+ with gr.Row(visible=False) as row_2:
+ model_name = gr.Dropdown(
+ label=translations["separator_model"],
+ value=uvr_model[0],
+ choices=uvr_model,
+ interactive=True
+ )
+ reverb_model = gr.Dropdown(
+ label=translations["dereveb_model"],
+ value=list(reverb_models.keys())[0],
+ choices=list(reverb_models.keys()),
+ interactive=True
+ )
+ denoise_model = gr.Dropdown(
+ label=translations["denoise_model"],
+ value=list(denoise_models.keys())[0],
+ choices=list(denoise_models.keys()),
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ with gr.Column(visible=False) as row_3:
+ with gr.Group():
+ with gr.Row():
+ overlap = gr.Radio(
+ label=translations["overlap"],
+ info=translations["overlap_info"],
+ choices=[
+ "0.25",
+ "0.5",
+ "0.75",
+ "0.99"
+ ],
+ value="0.25",
+ interactive=True)
+ with gr.Row():
+ window_size = gr.Slider(
+ label=translations["window_size"],
+ info=translations["window_size_info"],
+ minimum=320,
+ maximum=1024,
+ value=512,
+ step=32,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ shifts = gr.Slider(
+ label=translations["shift"],
+ info=translations["shift_info"],
+ minimum=1,
+ maximum=20,
+ value=2,
+ step=1,
+ interactive=True
+ )
+ segments_size = gr.Slider(
+ label=translations["segments_size"],
+ info=translations["segments_size_info"],
+ minimum=32,
+ maximum=3072,
+ value=256,
+ step=32,
+ interactive=True
+ )
+ with gr.Row():
+ batch_size = gr.Slider(
+ label=translations["batch_size"],
+ info=translations["mdx_batch_size_info"],
+ minimum=1,
+ maximum=64,
+ value=1,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ hop_length = gr.Slider(
+ label=translations['hop_length'],
+ info=translations["hop_length_info"],
+ minimum=64,
+ maximum=8192,
+ value=1024,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ post_process_threshold = gr.Slider(
+ label=translations['post_process_threshold'],
+ info=translations["post_process_threshold_info"],
+ minimum=0.1,
+ maximum=0.3,
+ value=0.2,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ aggression = gr.Slider(
+ label=translations['aggression'],
+ info=translations["aggression_info"],
+ minimum=1,
+ maximum=50,
+ value=5,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Column():
+ sample_rate = gr.Radio(
+ choices=sample_rate_choice,
+ value=48000,
+ label=translations["sr"],
+ info=translations["sr_info"],
+ interactive=True
+ )
+ clean_strength = gr.Slider(
+ minimum=0,
+ maximum=1,
+ step=0.1,
+ value=0.5,
+ label=translations["clean_strength"],
+ info=translations["clean_strength_info"],
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ skip_start = gr.Textbox(
+ label=translations["skip_start"],
+ info=translations["skip_start_info"],
+ value="",
+ placeholder="0,...",
+ interactive=True,
+ visible=False
+ )
+ skip_end = gr.Textbox(
+ label=translations["skip_end"],
+ info=translations["skip_end_info"],
+ value="",
+ placeholder="0,...",
+ interactive=True,
+ visible=False
+ )
+ create_dataset_info = gr.Textbox(
+ label=translations["create_dataset_info"],
+ value="",
+ interactive=False,
+ lines=2
+ )
+ with gr.Row():
+ separate.change(
+ fn=lambda a: [
+ visible(a)
+ for _ in range(3)
+ ],
+ inputs=[
+ separate
+ ],
+ outputs=[
+ row,
+ row_2,
+ row_3
+ ]
+ )
+ separate.change(
+ fn=valueFalse_interactive,
+ inputs=[
+ separate
+ ],
+ outputs=[
+ separate_reverb
+ ]
+ )
+ separate.change(
+ fn=create_dataset_change,
+ inputs=[
+ model_name,
+ reverb_model,
+ enable_post_process,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ with gr.Row():
+ model_name.change(
+ fn=create_dataset_change,
+ inputs=[
+ model_name,
+ reverb_model,
+ enable_post_process,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ reverb_model.change(
+ fn=create_dataset_change,
+ inputs=[
+ model_name,
+ reverb_model,
+ enable_post_process,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ denoise_model.change(
+ fn=create_dataset_change,
+ inputs=[
+ model_name,
+ reverb_model,
+ enable_post_process,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ with gr.Row():
+ separate_reverb.change(
+ fn=create_dataset_change,
+ inputs=[
+ model_name,
+ reverb_model,
+ enable_post_process,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ enable_denoise.change(
+ fn=create_dataset_change,
+ inputs=[
+ model_name,
+ reverb_model,
+ enable_post_process,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ enable_post_process.change(
+ fn=create_dataset_change,
+ inputs=[
+ model_name,
+ reverb_model,
+ enable_post_process,
+ separate_reverb,
+ enable_denoise
+ ],
+ outputs=[
+ reverb_model,
+ overlap,
+ segments_size,
+ hop_length,
+ batch_size,
+ shifts,
+ window_size,
+ aggression,
+ post_process_threshold,
+ denoise_model,
+ enable_tta,
+ high_end_process,
+ enable_post_process,
+ ]
+ )
+ with gr.Row():
+ skip_seconds.change(
+ fn=lambda a: [
+ visible(a)
+ for _ in range(2)
+ ],
+ inputs=[
+ skip_seconds
+ ],
+ outputs=[
+ skip_start,
+ skip_end
+ ]
+ )
+ clean_dataset.change(
+ fn=visible,
+ inputs=[
+ clean_dataset
+ ],
+ outputs=[
+ clean_strength
+ ]
+ )
+ with gr.Row():
+ model_name.change(
+ fn=lambda a: valueFalse_interactive(a in list(mdx_models.keys()) + list(vr_models.keys())),
+ inputs=[
+ model_name
+ ],
+ outputs=[
+ enable_denoise
+ ]
+ )
+ separate_reverb.change(
+ fn=valueFalse_interactive,
+ inputs=[
+ separate_reverb
+ ],
+ outputs=[
+ enable_denoise
+ ]
+ )
+ with gr.Row():
+ create_dataset_button.click(
+ fn=create_dataset,
+ inputs=[
+ dataset_url,
+ output_dataset,
+ skip_seconds,
+ skip_start,
+ skip_end,
+ separate,
+ model_name,
+ reverb_model,
+ denoise_model,
+ sample_rate,
+ shifts,
+ batch_size,
+ overlap,
+ aggression,
+ hop_length,
+ window_size,
+ segments_size,
+ post_process_threshold,
+ enable_tta,
+ enable_denoise,
+ high_end_process,
+ enable_post_process,
+ separate_reverb,
+ clean_dataset,
+ clean_strength
+ ],
+ outputs=[
+ create_dataset_info
+ ],
+ api_name="create_dataset"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/training/child/create_reference.py b/main/app/tabs/training/child/create_reference.py
new file mode 100644
index 0000000000000000000000000000000000000000..102db58770765ab574c0c3c46a466c6d2a2fcbb6
--- /dev/null
+++ b/main/app/tabs/training/child/create_reference.py
@@ -0,0 +1,374 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.training import create_reference
+
+from main.app.core.ui import (
+ visible,
+ unlock_f0,
+ shutil_move,
+ change_audios_choices
+)
+
+from main.app.variables import (
+ configs,
+ method_f0,
+ file_types,
+ audio_params,
+ translations,
+ embedders_mode,
+ paths_for_files,
+ embedders_model,
+ hybrid_f0_method,
+)
+
+def create_reference_tab():
+ with gr.Row():
+ gr.Markdown(translations["create_reference_markdown_2"])
+ with gr.Row():
+ pitch_guidance = gr.Checkbox(
+ label=translations["training_pitch"],
+ value=True,
+ interactive=True
+ )
+ f0_autotune = gr.Checkbox(
+ label=translations["autotune"],
+ value=False,
+ interactive=True
+ )
+ proposal_pitch = gr.Checkbox(
+ label=translations["proposal_pitch"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ create_reference_button = gr.Button(
+ translations["create_reference"],
+ variant="primary"
+ )
+ with gr.Row():
+ f0_up_key = gr.Slider(
+ minimum=-24,
+ maximum=24,
+ step=1,
+ info=translations["pitch_info"],
+ label=translations["pitch"],
+ value=0,
+ interactive=True
+ )
+ proposal_pitch_threshold = gr.Slider(
+ minimum=50.0,
+ maximum=1200.0,
+ label=translations["proposal_pitch_threshold"],
+ info=translations["proposal_pitch_threshold_info"],
+ value=255.0,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ filter_radius = gr.Slider(
+ minimum=0,
+ maximum=7,
+ label=translations["filter_radius"],
+ info=translations["filter_radius_info"],
+ value=3,
+ step=1,
+ interactive=True
+ )
+ f0_autotune_strength = gr.Slider(
+ minimum=0,
+ maximum=1,
+ label=translations["autotune_rate"],
+ info=translations["autotune_rate_info"],
+ value=1,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Row():
+ with gr.Column():
+ with gr.Accordion(
+ translations["input_output"],
+ open=False
+ ):
+ with gr.Column():
+ input_audio = gr.Dropdown(
+ label=translations["audio_path"],
+ value="",
+ choices=paths_for_files,
+ info=translations["provide_audio"],
+ allow_custom_value=True,
+ interactive=True
+ )
+ reference_name = gr.Textbox(
+ label=translations["reference_name"],
+ value="reference",
+ placeholder="reference",
+ info=translations["reference_name_info"],
+ interactive=True
+ )
+ with gr.Column():
+ refresh_audio = gr.Button(
+ translations["refresh"]
+ )
+ with gr.Column():
+ upload_audio = gr.Files(
+ label=translations["drop_audio"],
+ file_types=file_types
+ )
+ with gr.Column():
+ play_audio = gr.Audio(
+ interactive=False,
+ label=translations["input_audio"],
+ **audio_params
+ )
+ with gr.Column() as f0_method_column:
+ with gr.Accordion(
+ label=translations["f0_method"],
+ open=False
+ ):
+ with gr.Group():
+ with gr.Row():
+ predictor_onnx = gr.Checkbox(
+ label=translations["predictor_onnx"],
+ value=configs.get("int8", False),
+ interactive=True
+ )
+ unlock_full_method = gr.Checkbox(
+ label=translations["f0_unlock"],
+ value=False,
+ interactive=True
+ )
+ f0_method = gr.Radio(
+ label=translations["f0_method"],
+ info=translations["f0_method_info"],
+ choices=method_f0,
+ value="rmvpe",
+ interactive=True
+ )
+ f0_hybrid_method = gr.Dropdown(
+ label=translations["f0_method_hybrid"],
+ info=translations["f0_method_hybrid_info"],
+ choices=hybrid_f0_method,
+ value=hybrid_f0_method[0],
+ interactive=True,
+ allow_custom_value=True,
+ visible=False
+ )
+ with gr.Row():
+ alpha = gr.Slider(
+ label=translations["alpha_label"],
+ info=translations["alpha_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Column():
+ with gr.Accordion(
+ label=translations["hubert_model"],
+ open=False
+ ):
+ embedders_mix = gr.Checkbox(
+ label=translations["embedders_mix"],
+ info=translations["embedders_mix_info"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ version = gr.Radio(
+ label=translations["training_version"],
+ info=translations["training_version_info"],
+ choices=[
+ "v1",
+ "v2"
+ ],
+ value="v2",
+ interactive=True
+ )
+ with gr.Group():
+ embedder_mode = gr.Radio(
+ label=translations["embed_mode"],
+ info=translations["embed_mode_info"],
+ value="onnx" if configs.get("int8", False) else "fairseq",
+ choices=embedders_mode,
+ interactive=True,
+ visible=True
+ )
+ embedders = gr.Radio(
+ label=translations["hubert_model"],
+ info=translations["hubert_info"],
+ choices=embedders_model,
+ value="hubert_base",
+ interactive=True
+ )
+ with gr.Row():
+ embedders_custom = gr.Textbox(
+ label=translations["modelname"],
+ info=translations["modelname_info"],
+ value="",
+ placeholder="hubert_base",
+ interactive=True,
+ visible=False
+ )
+ with gr.Column(visible=False) as embedders_mix_column:
+ embedders_mix_layers = gr.Slider(
+ label=translations["embedders_mix_layers"],
+ info=translations["embedders_mix_layers_info"],
+ minimum=1,
+ maximum=12,
+ value=9,
+ step=1,
+ interactive=True
+ )
+ embedders_mix_ratio = gr.Slider(
+ label=translations["embedders_mix_ratio"],
+ info=translations["embedders_mix_ratio_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True
+ )
+ with gr.Row():
+ create_reference_info = gr.Textbox(
+ label=translations["reference_info"],
+ value="",
+ interactive=False,
+ lines=2
+ )
+ with gr.Row():
+ f0_autotune.change(
+ fn=visible,
+ inputs=[
+ f0_autotune
+ ],
+ outputs=[
+ f0_autotune_strength
+ ]
+ )
+ proposal_pitch.change(
+ fn=visible,
+ inputs=[
+ proposal_pitch
+ ],
+ outputs=[
+ proposal_pitch_threshold
+ ]
+ )
+ unlock_full_method.change(
+ fn=unlock_f0,
+ inputs=[
+ unlock_full_method
+ ],
+ outputs=[
+ f0_method
+ ]
+ )
+ with gr.Row():
+ input_audio.change(
+ fn=lambda audio: audio,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ play_audio
+ ]
+ )
+ refresh_audio.click(
+ fn=change_audios_choices,
+ inputs=[
+ input_audio
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ f0_method.change(
+ fn=lambda method: [
+ visible(method == "hybrid")
+ for _ in range(2)
+ ],
+ inputs=[
+ f0_method
+ ],
+ outputs=[
+ f0_hybrid_method,
+ alpha
+ ]
+ )
+ with gr.Row():
+ upload_audio.upload(
+ fn=lambda audio_in: [
+ shutil_move(audio.name, configs["audios_path"])
+ for audio in audio_in
+ ][0],
+ inputs=[
+ upload_audio
+ ],
+ outputs=[
+ input_audio
+ ]
+ )
+ embedders.change(
+ fn=lambda embedders: visible(embedders == "custom"),
+ inputs=[
+ embedders
+ ],
+ outputs=[
+ embedders_custom
+ ]
+ )
+ with gr.Row():
+ pitch_guidance.change(
+ fn=visible,
+ inputs=[
+ pitch_guidance
+ ],
+ outputs=[
+ f0_method_column
+ ]
+ )
+ embedders_mix.change(
+ fn=visible,
+ inputs=[
+ embedders_mix
+ ],
+ outputs=[
+ embedders_mix_column
+ ]
+ )
+ create_reference_button.click(
+ fn=create_reference,
+ inputs=[
+ input_audio,
+ reference_name,
+ pitch_guidance,
+ version,
+ embedders,
+ embedder_mode,
+ f0_method,
+ predictor_onnx,
+ f0_up_key,
+ filter_radius,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ alpha,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ ],
+ outputs=[
+ create_reference_info
+ ],
+ api_name="create_reference"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/training/child/training.py b/main/app/tabs/training/child/training.py
new file mode 100644
index 0000000000000000000000000000000000000000..99a95237880ffc76cd3eb8abcf53dc7c56fb39fa
--- /dev/null
+++ b/main/app/tabs/training/child/training.py
@@ -0,0 +1,979 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.core.process import zip_file
+
+from main.app.core.training import (
+ extract,
+ training,
+ preprocess,
+ create_index
+)
+
+from main.app.variables import (
+ config,
+ method_f0,
+ file_types,
+ model_name,
+ index_path,
+ pretrainedG,
+ pretrainedD,
+ translations,
+ reference_list,
+ embedders_mode,
+ embedders_model,
+ hybrid_f0_method
+)
+
+from main.app.core.ui import (
+ visible,
+ unlock_f0,
+ gr_warning,
+ shutil_move,
+ get_gpu_info,
+ vocoders_lock,
+ hoplength_show,
+ gpu_number_str,
+ pitch_guidance_lock,
+ change_models_choices,
+ change_reference_choices,
+ change_pretrained_choices
+)
+
+def training_model_tab():
+ with gr.Row():
+ gr.Markdown(translations["training_markdown"])
+ with gr.Row():
+ with gr.Column():
+ with gr.Row():
+ with gr.Column():
+ modelname = gr.Textbox(
+ label=translations["modelname"],
+ info=translations["training_model_name"],
+ value="",
+ placeholder=translations["modelname"],
+ interactive=True
+ )
+ sample_rate = gr.Radio(
+ label=translations["sample_rate"],
+ info=translations["sample_rate_info"],
+ choices=[
+ "24k",
+ "32k",
+ "40k",
+ "44.1k",
+ "48k"
+ ],
+ value="48k",
+ interactive=True
+ )
+ rvc_version = gr.Radio(
+ label=translations["training_version"],
+ info=translations["training_version_info"],
+ choices=[
+ "v1",
+ "v2"
+ ],
+ value="v2",
+ interactive=True
+ )
+ with gr.Group():
+ with gr.Row():
+ clean_dataset = gr.Checkbox(
+ label=translations["clear_dataset"],
+ value=False,
+ interactive=True
+ )
+ process_effects = gr.Checkbox(
+ label=translations["preprocess_effect"],
+ value=False,
+ interactive=True
+ )
+ pitch_guidance = gr.Checkbox(
+ label=translations["training_pitch"],
+ value=True,
+ interactive=True
+ )
+ with gr.Row():
+ custom_reference = gr.Checkbox(
+ label=translations["custom_reference"],
+ value=False,
+ interactive=True
+ )
+ checkpointing = gr.Checkbox(
+ label=translations["memory_efficient_training"],
+ value=False,
+ interactive=True
+ )
+ dataset_upload = gr.Checkbox(
+ label=translations["upload_dataset"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ split_audio_mode = gr.Radio(
+ label=translations["split_audio_mode"],
+ info=translations["split_audio_mode_info"],
+ value="Automatic",
+ choices=[
+ "Automatic",
+ "Simple",
+ "Skip"
+ ],
+ interactive=True
+ )
+ normalization_mode = gr.Radio(
+ label=translations["normalization_mode"],
+ info=translations["normalization_mode_info"],
+ value="post",
+ choices=[
+ "none",
+ "pre",
+ "post"
+ ],
+ interactive=True
+ )
+ with gr.Row(visible=False) as custom_reference_row:
+ with gr.Accordion(
+ translations["custom_reference"],
+ open=True
+ ):
+ reference_name = gr.Dropdown(
+ label=translations["reference_name"],
+ info=translations["reference_name_info"],
+ choices=reference_list,
+ value=reference_list[0] if len(reference_list) >= 1 else "",
+ allow_custom_value=True,
+ interactive=True
+ )
+ reference_refresh = gr.Button(
+ translations["refresh"],
+ scale=2
+ )
+ with gr.Row(visible=False) as clean_dataset_row:
+ clean_dataset_strength = gr.Slider(
+ label=translations["clean_strength"],
+ info=translations["clean_strength_info"],
+ minimum=0,
+ maximum=1,
+ value=0.7,
+ step=0.1,
+ interactive=True
+ )
+ with gr.Column():
+ preprocess_button = gr.Button(
+ translations["preprocess_button"],
+ scale=2
+ )
+ upload_dataset = gr.Files(
+ label=translations["drop_audio"],
+ file_types=file_types,
+ visible=False
+ )
+ preprocess_info = gr.Textbox(
+ label=translations["preprocess_info"],
+ value="",
+ interactive=False,
+ container=True,
+ lines=2
+ )
+ with gr.Column():
+ with gr.Row():
+ with gr.Column():
+ with gr.Accordion(
+ label=translations["f0_method"],
+ open=False
+ ):
+ with gr.Group():
+ with gr.Row():
+ predictor_onnx = gr.Checkbox(
+ label=translations["predictor_onnx"],
+ value=config.int8,
+ interactive=True
+ )
+ unlock_full_method = gr.Checkbox(
+ label=translations["f0_unlock"],
+ value=False,
+ interactive=True
+ )
+ autotune = gr.Checkbox(
+ label=translations["autotune"],
+ value=False,
+ interactive=True
+ )
+ f0_method = gr.Radio(
+ label=translations["f0_method"],
+ info=translations["f0_method_info"],
+ choices=method_f0,
+ value="rmvpe",
+ interactive=True
+ )
+ hybrid_f0method = gr.Dropdown(
+ label=translations["f0_method_hybrid"],
+ info=translations["f0_method_hybrid_info"],
+ choices=hybrid_f0_method,
+ value=hybrid_f0_method[0],
+ interactive=True,
+ allow_custom_value=True,
+ visible=False
+ )
+ hop_length = gr.Slider(
+ label=translations['hop_length'],
+ info=translations["hop_length_info"],
+ minimum=64,
+ maximum=512,
+ value=160,
+ step=1,
+ interactive=True,
+ visible=False
+ )
+ f0_autotune_strength = gr.Slider(
+ label=translations["autotune_rate"],
+ info=translations["autotune_rate_info"],
+ minimum=0,
+ maximum=1,
+ value=1,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ alpha = gr.Slider(
+ label=translations["alpha_label"],
+ info=translations["alpha_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True,
+ visible=False
+ )
+ with gr.Accordion(
+ label=translations["hubert_model"],
+ open=False
+ ):
+ embedders_mix = gr.Checkbox(
+ label=translations["embedders_mix"],
+ info=translations["embedders_mix_info"],
+ value=False,
+ interactive=True
+ )
+ with gr.Group():
+ embedder_mode = gr.Radio(
+ label=translations["embed_mode"],
+ info=translations["embed_mode_info"],
+ value="onnx" if config.int8 else "fairseq",
+ choices=embedders_mode,
+ interactive=True,
+ visible=True
+ )
+ embedders = gr.Radio(
+ label=translations["hubert_model"],
+ info=translations["hubert_info"],
+ choices=embedders_model,
+ value="hubert_base",
+ interactive=True
+ )
+ with gr.Row():
+ embedders_custom = gr.Textbox(
+ label=translations["modelname"],
+ info=translations["modelname_info"],
+ value="",
+ placeholder="hubert_base",
+ interactive=True,
+ visible=False
+ )
+ with gr.Column(visible=False) as embedders_mix_column:
+ embedders_mix_layers = gr.Slider(
+ label=translations["embedders_mix_layers"],
+ info=translations["embedders_mix_layers_info"],
+ minimum=1,
+ maximum=12,
+ value=9,
+ step=1,
+ interactive=True
+ )
+ embedders_mix_ratio = gr.Slider(
+ label=translations["embedders_mix_ratio"],
+ info=translations["embedders_mix_ratio_info"],
+ minimum=0.1,
+ maximum=1,
+ value=0.5,
+ step=0.1,
+ interactive=True
+ )
+ with gr.Column():
+ extract_button = gr.Button(
+ translations["extract_button"],
+ scale=2
+ )
+ extract_info = gr.Textbox(
+ label=translations["extract_info"],
+ value="",
+ interactive=False,
+ lines=2
+ )
+ with gr.Column():
+ with gr.Row():
+ with gr.Column():
+ total_epochs = gr.Slider(
+ label=translations["total_epoch"],
+ info=translations["total_epoch_info"],
+ minimum=1,
+ maximum=10000,
+ value=300,
+ step=1,
+ interactive=True
+ )
+ save_epochs = gr.Slider(
+ label=translations["save_epoch"],
+ info=translations["save_epoch_info"],
+ minimum=1,
+ maximum=10000,
+ value=50,
+ step=1,
+ interactive=True
+ )
+ with gr.Column():
+ create_index_button = gr.Button(
+ f"3. {translations['create_index']}",
+ variant="primary",
+ scale=2
+ )
+ training_button = gr.Button(
+ f"4. {translations['training_model']}",
+ variant="primary",
+ scale=2
+ )
+ with gr.Row():
+ with gr.Accordion(
+ label=translations["setting"],
+ open=False
+ ):
+ with gr.Row():
+ index_algorithm = gr.Radio(
+ label=translations["index_algorithm"],
+ info=translations["index_algorithm_info"],
+ choices=[
+ "Auto",
+ "Faiss",
+ "KMeans"
+ ],
+ value="Auto",
+ interactive=True
+ )
+ with gr.Row():
+ nprobe = gr.Slider(
+ label=translations["nprobe"],
+ info=translations["nprobe_info"],
+ minimum=1,
+ maximum=64,
+ value=9,
+ step=1,
+ interactive=True
+ )
+ with gr.Row():
+ cache_in_gpu = gr.Checkbox(
+ label=translations["cache_in_gpu"],
+ info=translations["cache_in_gpu_info"],
+ value=True,
+ interactive=True
+ )
+ overtraining_detector = gr.Checkbox(
+ label=translations["overtraining_detector"],
+ info=translations["overtraining_detector_info"],
+ value=False,
+ interactive=True
+ )
+ with gr.Row():
+ custom_dataset = gr.Checkbox(
+ label=translations["custom_dataset"],
+ info=translations["custom_dataset_info"],
+ value=False,
+ interactive=True
+ )
+ save_only_latest = gr.Checkbox(
+ label=translations["save_only_latest"],
+ info=translations["save_only_latest_info"],
+ value=True,
+ interactive=True
+ )
+ save_every_weights = gr.Checkbox(
+ label=translations["save_every_weights"],
+ info=translations["save_every_weights_info"],
+ value=True,
+ interactive=True
+ )
+ with gr.Row():
+ cleanup_training = gr.Checkbox(
+ label=translations["cleanup_training"],
+ info=translations["cleanup_training_info"],
+ value=False,
+ interactive=True
+ )
+ not_use_pretrain = gr.Checkbox(
+ label=translations["not_use_pretrain_2"],
+ info=translations["not_use_pretrain_info"],
+ value=False,
+ interactive=True
+ )
+ custom_pretrain = gr.Checkbox(
+ label=translations["custom_pretrain"],
+ info=translations["custom_pretrain_info"],
+ value=False,
+ interactive=True
+ )
+ with gr.Column():
+ dataset_path = gr.Textbox(
+ label=translations["dataset_folder"],
+ value="dataset",
+ interactive=True,
+ visible=False
+ )
+ with gr.Column():
+ include_mutes = gr.Slider(
+ minimum=0,
+ maximum=10,
+ value=2,
+ step=1,
+ label=translations["include_mutes"],
+ info=translations["include_mutes_info"],
+ interactive=True
+ )
+ with gr.Row(visible=False) as simple_option:
+ chunk_len = gr.Slider(
+ minimum=0.5,
+ maximum=5.0,
+ value=3.0,
+ step=0.1,
+ label=translations["chunk_length"],
+ info=translations["chunk_length_info"],
+ interactive=True
+ )
+ overlap_len = gr.Slider(
+ minimum=0.0,
+ maximum=0.4,
+ value=0.3,
+ step=0.1,
+ label=translations["overlap_length"],
+ info=translations["overlap_length_info"],
+ interactive=True
+ )
+ threshold = gr.Slider(
+ minimum=1,
+ maximum=100,
+ value=50,
+ step=1,
+ label=translations["threshold"],
+ info=translations["overtraining_threshold"],
+ interactive=True,
+ visible=False
+ )
+ with gr.Accordion(
+ translations["setting_cpu_gpu"],
+ open=False
+ ):
+ with gr.Row():
+ architecture = gr.Radio(
+ label=translations["architecture"],
+ info=translations["architecture_info"],
+ choices=[
+ "RVC",
+ "SVC"
+ ],
+ value="RVC",
+ interactive=True
+ )
+ with gr.Column():
+ gpu_number = gr.Textbox(
+ label=translations["gpu_number"],
+ value=gpu_number_str(),
+ info=translations["gpu_number_info"],
+ interactive=True
+ )
+ gpu_str, gpu_len = get_gpu_info()
+ gr.Textbox(
+ label=translations["gpu_info"],
+ value=gpu_str,
+ info=translations["gpu_info_2"],
+ interactive=False,
+ lines=gpu_len
+ )
+ cpu_core = gr.Slider(
+ label=translations["cpu_core"],
+ info=translations["cpu_core_info"],
+ minimum=1,
+ maximum=os.cpu_count(),
+ value=min(os.cpu_count(), 4),
+ step=1,
+ interactive=True
+ )
+ batch_size = gr.Slider(
+ label=translations["batch_size"],
+ info=translations["batch_size_info"],
+ minimum=1,
+ maximum=64,
+ value=8,
+ step=1,
+ interactive=True
+ )
+ with gr.Group():
+ multiscale_mel_loss = gr.Checkbox(
+ label=translations["multiscale_mel_loss"],
+ info=translations["multiscale_mel_loss_info"],
+ value=False,
+ interactive=True
+ )
+ cosine_annealing_lr = gr.Checkbox(
+ label=translations["cosine_annealing_lr"],
+ info=translations["cosine_annealing_lr_info"],
+ value=False,
+ interactive=True
+ )
+ vocoders = gr.Radio(
+ label=translations["vocoder"],
+ info=translations["vocoder_info"],
+ choices=[
+ "Default",
+ "MRF-HiFi-GAN",
+ "RefineGAN",
+ "BigVGAN"
+ ],
+ value="Default",
+ interactive=True
+ )
+ with gr.Row():
+ deterministic = gr.Checkbox(
+ label=translations["deterministic"],
+ info=translations["deterministic_info"],
+ value=False,
+ interactive=config.device.startswith("cuda") and not config.is_zluda
+ )
+ benchmark = gr.Checkbox(
+ label=translations["benchmark"],
+ info=translations["benchmark_info"],
+ value=False,
+ interactive=config.device.startswith("cuda") and not config.is_zluda
+ )
+ with gr.Row():
+ optimizer = gr.Radio(
+ label=translations["optimizer"],
+ info=translations["optimizer_info"],
+ value="AdamW",
+ choices=[
+ "AdamW",
+ "RAdam",
+ "AnyPrecisionAdamW",
+ "AdaBelief"
+ ],
+ interactive=True
+ )
+ with gr.Row():
+ model_author = gr.Textbox(
+ label=translations["training_author"],
+ info=translations["training_author_info"],
+ value="",
+ placeholder=translations["training_author"],
+ interactive=True
+ )
+ with gr.Row():
+ with gr.Column():
+ with gr.Accordion(
+ translations["custom_pretrain_info"],
+ open=False,
+ visible=False
+ ) as pretrain_setting:
+ pretrained_D = gr.Dropdown(
+ label=translations["pretrain_file"].format(dg="D"),
+ choices=pretrainedD,
+ value=pretrainedD[0] if len(pretrainedD) > 0 else '',
+ interactive=True,
+ allow_custom_value=True
+ )
+ pretrained_G = gr.Dropdown(
+ label=translations["pretrain_file"].format(dg="G"),
+ choices=pretrainedG,
+ value=pretrainedG[0] if len(pretrainedG) > 0 else '',
+ interactive=True,
+ allow_custom_value=True
+ )
+ pretrained_refresh = gr.Button(
+ translations["refresh"],
+ scale=2
+ )
+ with gr.Row():
+ training_info = gr.Textbox(
+ label=translations["train_info"],
+ value="",
+ interactive=False,
+ lines=3
+ )
+ with gr.Row():
+ with gr.Column():
+ with gr.Accordion(
+ translations["export_model"],
+ open=False
+ ):
+ with gr.Row():
+ model_file = gr.Dropdown(
+ label=translations["model_name"],
+ choices=model_name,
+ value=model_name[0] if len(model_name) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ index_file = gr.Dropdown(
+ label=translations["index_path"],
+ choices=index_path,
+ value=index_path[0] if len(index_path) >= 1 else "",
+ interactive=True,
+ allow_custom_value=True
+ )
+ with gr.Row():
+ refresh_file = gr.Button(
+ f"1. {translations['refresh']}",
+ scale=2
+ )
+ zip_model = gr.Button(
+ translations["zip_model"],
+ variant="primary",
+ scale=2
+ )
+ with gr.Row():
+ zip_output = gr.File(
+ label=translations["output_zip"],
+ file_types=[".zip"],
+ interactive=False,
+ visible=False
+ )
+ with gr.Row():
+ vocoders.change(
+ fn=pitch_guidance_lock,
+ inputs=[
+ vocoders
+ ],
+ outputs=[
+ pitch_guidance
+ ]
+ )
+ pitch_guidance.change(
+ fn=vocoders_lock,
+ inputs=[
+ pitch_guidance
+ ],
+ outputs=[
+ vocoders
+ ]
+ )
+ unlock_full_method.change(
+ fn=unlock_f0,
+ inputs=[
+ unlock_full_method
+ ],
+ outputs=[
+ f0_method
+ ]
+ )
+ with gr.Row():
+ refresh_file.click(
+ fn=change_models_choices,
+ inputs=[],
+ outputs=[
+ model_file,
+ index_file
+ ]
+ )
+ zip_model.click(
+ fn=zip_file,
+ inputs=[
+ modelname,
+ model_file,
+ index_file
+ ],
+ outputs=[
+ zip_output
+ ]
+ )
+ dataset_path.change(
+ fn=lambda folder: os.makedirs(folder, exist_ok=True) if folder else None,
+ inputs=[
+ dataset_path
+ ],
+ outputs=[]
+ )
+ with gr.Row():
+ dataset_upload.change(
+ fn=visible,
+ inputs=[
+ dataset_upload
+ ],
+ outputs=[
+ upload_dataset
+ ]
+ )
+ overtraining_detector.change(
+ fn=visible,
+ inputs=[
+ overtraining_detector
+ ],
+ outputs=[
+ threshold
+ ]
+ )
+ clean_dataset.change(
+ fn=visible,
+ inputs=[
+ clean_dataset
+ ],
+ outputs=[
+ clean_dataset_row
+ ]
+ )
+ with gr.Row():
+ custom_dataset.change(
+ fn=lambda custom_dataset: [
+ visible(custom_dataset),
+ "dataset"
+ ],
+ inputs=[
+ custom_dataset
+ ],
+ outputs=[
+ dataset_path,
+ dataset_path
+ ]
+ )
+ with gr.Row():
+ custom_reference.change(
+ fn=visible,
+ inputs=[
+ custom_reference
+ ],
+ outputs=[
+ custom_reference_row
+ ]
+ )
+ f0_method.change(
+ fn=lambda method, hybrid: [
+ visible(method == "hybrid"),
+ visible(method == "hybrid"),
+ hoplength_show(method, hybrid)
+ ],
+ inputs=[
+ f0_method,
+ hybrid_f0method
+ ],
+ outputs=[
+ hybrid_f0method,
+ alpha,
+ hop_length
+ ]
+ )
+ hybrid_f0method.change(
+ fn=hoplength_show,
+ inputs=[
+ f0_method,
+ hybrid_f0method
+ ],
+ outputs=[
+ hop_length
+ ]
+ )
+ with gr.Row():
+ autotune.change(
+ fn=visible,
+ inputs=[
+ autotune
+ ],
+ outputs=[
+ f0_autotune_strength
+ ]
+ )
+ split_audio_mode.change(
+ fn=lambda a: visible(a == "Simple"),
+ inputs=[
+ split_audio_mode
+ ],
+ outputs=[
+ simple_option
+ ]
+ )
+ upload_dataset.upload(
+ fn=lambda files, folder: [
+ shutil_move(f.name, os.path.join(folder, os.path.split(f.name)[1]))
+ for f in files
+ ] if folder != "" else gr_warning(translations["dataset_folder1"]),
+ inputs=[
+ upload_dataset,
+ dataset_path
+ ],
+ outputs=[],
+ api_name="upload_dataset"
+ )
+ with gr.Row():
+ not_use_pretrain.change(
+ fn=lambda a, b: visible(a and not b),
+ inputs=[
+ custom_pretrain,
+ not_use_pretrain
+ ],
+ outputs=[
+ pretrain_setting
+ ]
+ )
+ custom_pretrain.change(
+ fn=lambda a, b: visible(a and not b),
+ inputs=[
+ custom_pretrain,
+ not_use_pretrain
+ ],
+ outputs=[
+ pretrain_setting
+ ]
+ )
+ pretrained_refresh.click(
+ fn=change_pretrained_choices,
+ inputs=[],
+ outputs=[
+ pretrained_D,
+ pretrained_G
+ ]
+ )
+ with gr.Row():
+ embedders.change(
+ fn=lambda embedders: visible(embedders == "custom"),
+ inputs=[
+ embedders
+ ],
+ outputs=[
+ embedders_custom
+ ]
+ )
+ reference_refresh.click(
+ fn=change_reference_choices,
+ inputs=[],
+ outputs=[
+ reference_name
+ ]
+ )
+ with gr.Row():
+ embedders_mix.change(
+ fn=visible,
+ inputs=[
+ embedders_mix
+ ],
+ outputs=[
+ embedders_mix_column
+ ]
+ )
+ with gr.Row():
+ preprocess_button.click(
+ fn=preprocess,
+ inputs=[
+ modelname,
+ sample_rate,
+ cpu_core,
+ split_audio_mode,
+ process_effects,
+ dataset_path,
+ clean_dataset,
+ clean_dataset_strength,
+ chunk_len,
+ overlap_len,
+ normalization_mode,
+ architecture
+ ],
+ outputs=[
+ preprocess_info
+ ],
+ api_name="preprocess"
+ )
+ with gr.Row():
+ extract_button.click(
+ fn=extract,
+ inputs=[
+ modelname,
+ rvc_version,
+ f0_method,
+ pitch_guidance,
+ hop_length,
+ cpu_core,
+ gpu_number,
+ sample_rate,
+ embedders,
+ embedders_custom,
+ predictor_onnx,
+ embedder_mode,
+ autotune,
+ f0_autotune_strength,
+ hybrid_f0method,
+ alpha,
+ include_mutes,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ architecture
+ ],
+ outputs=[
+ extract_info
+ ],
+ api_name="extract"
+ )
+ with gr.Row():
+ create_index_button.click(
+ fn=create_index,
+ inputs=[
+ modelname,
+ rvc_version,
+ index_algorithm,
+ nprobe
+ ],
+ outputs=[
+ training_info
+ ],
+ api_name="create_index"
+ )
+ with gr.Row():
+ training_button.click(
+ fn=training,
+ inputs=[
+ modelname,
+ rvc_version,
+ save_epochs,
+ save_only_latest,
+ save_every_weights,
+ total_epochs,
+ sample_rate,
+ batch_size,
+ gpu_number,
+ pitch_guidance,
+ not_use_pretrain,
+ custom_pretrain,
+ pretrained_G,
+ pretrained_D,
+ overtraining_detector,
+ threshold,
+ cleanup_training,
+ cache_in_gpu,
+ model_author,
+ vocoders,
+ checkpointing,
+ deterministic,
+ benchmark,
+ optimizer,
+ custom_reference,
+ reference_name,
+ multiscale_mel_loss,
+ embedders,
+ embedders_custom,
+ cosine_annealing_lr,
+ architecture
+ ],
+ outputs=[
+ training_info
+ ],
+ api_name="training_model"
+ )
\ No newline at end of file
diff --git a/main/app/tabs/training/training.py b/main/app/tabs/training/training.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f7714bb874e8d41d0df86a3aef3a8c5835ee676
--- /dev/null
+++ b/main/app/tabs/training/training.py
@@ -0,0 +1,34 @@
+import os
+import sys
+
+import gradio as gr
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import (
+ configs,
+ translations
+)
+
+def training_tab():
+ with gr.TabItem(translations["training_model"]):
+ if configs.get("training_tab", True):
+ from main.app.tabs.training.child.training import training_model_tab
+
+ with gr.TabItem(translations["training_model"]):
+ gr.Markdown(f"## {translations['training_model']}")
+ training_model_tab()
+
+ if configs.get("create_dataset_tab", True):
+ from main.app.tabs.training.child.create_dataset import create_dataset_tab
+
+ with gr.TabItem(translations["createdataset"]):
+ gr.Markdown(translations["create_dataset_markdown"])
+ create_dataset_tab()
+
+ if configs.get("create_reference_tab", True):
+ from main.app.tabs.training.child.create_reference import create_reference_tab
+
+ with gr.TabItem(translations["create_reference"]):
+ gr.Markdown(translations["create_reference_markdown"])
+ create_reference_tab()
\ No newline at end of file
diff --git a/main/app/variables.py b/main/app/variables.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d560bf0a119deff085bdb551271fba89cf06aa4
--- /dev/null
+++ b/main/app/variables.py
@@ -0,0 +1,427 @@
+import os
+import re
+import sys
+import csv
+import json
+import logging
+import logging.handlers
+
+import gradio as gr
+
+from packaging import version
+
+sys.path.append(os.getcwd())
+
+from main.configs.config import Config
+
+# Load core system environment settings
+config = Config()
+python = sys.executable
+configs = config.configs
+translations = config.translations
+configs_json = config.configs_path
+
+log_file = os.path.join(configs["logs_path"], "app.log")
+
+# Automatically cleans up the log file if it exceeds the limit.
+if os.path.exists(log_file):
+ file_size = os.path.getsize(log_file)
+ try:
+ if file_size >= (10 * 1024 * 1024):
+ f = open(log_file, "w", encoding="utf-8")
+ f.close()
+ except:
+ pass
+
+# Logger Initialization & Hierarchy Isolation
+logger = logging.getLogger(__name__)
+logger.propagate = False # Prevent logs from bubbling up to the root logger duplicate entries
+
+# Detect Gradio version to backward-stabilize structural parameter compatibility
+gradio_version = version.parse(gr.__version__) <= version.parse("6.0.0")
+
+# Set up operational audio parameters based on the parsed Gradio version spec
+audio_params = {
+ "show_download_button": True,
+ "show_share_button": True
+} if gradio_version else {
+ "buttons": ["download", "share"]
+}
+
+# When the application restarts, the web interface does not reload; this code is used to reload the interface.
+reload_js = """
+() => {
+ function Checking() {
+ fetch(window.location.origin)
+ .then(response => {
+ if (response.ok) {
+ location.reload();
+ } else {
+ setTimeout(Checking, 3000);
+ }
+ })
+ .catch(err => {
+ setTimeout(Checking, 3000);
+ });
+ }
+ setTimeout(Checking, 10000);
+}
+"""
+
+# Configure File & Console Logging Handlers
+if not logger.hasHandlers():
+ # Console stream logging configuration
+ console_handler = logging.StreamHandler()
+ console_formatter = logging.Formatter(fmt="\n%(asctime)s.%(msecs)03d | %(levelname)s | %(module)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+ console_handler.setFormatter(console_formatter)
+ console_handler.setLevel(logging.DEBUG if config.debug_mode else logging.INFO)
+ # Rotating file storage logging configuration (Persists debug logs to app.log)
+ file_handler = logging.handlers.RotatingFileHandler(log_file, maxBytes=0, backupCount=3, encoding='utf-8')
+ file_formatter = logging.Formatter(fmt="\n%(asctime)s.%(msecs)03d | %(levelname)s | %(module)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+ file_handler.setFormatter(file_formatter)
+ file_handler.setLevel(logging.DEBUG)
+ # Attach pipelines to active system interface logger
+ logger.addHandler(console_handler)
+ logger.addHandler(file_handler)
+ logger.setLevel(logging.DEBUG)
+
+# Guard 1: Verify FP16 half-precision computation compatibility on low-tier or non-CUDA compute backends
+if config.device.startswith(("cpu", "mps", "ocl", "privateuseone")) and configs.get("fp16", False) and not configs.get("allow_fp16_all_backend", False):
+ logger.warning(translations["fp16_not_support"])
+ configs["fp16"] = config.is_half = False
+
+# Guard 2: Handle fallback scenarios for invalid/non-standard GPU requests by forcing host defaults
+if config.invalid_gpu and not config.device.startswith("cpu"):
+ logger.warning(translations["invalid_gpu"])
+ config.onnx_device_idx = {"device_id": 0}
+ config.pytorch_device_idx = 0
+ configs["gpu_idx"] = 0
+
+# Guard 3: Warn against incompatible combinations of INT8 quantization inside TensorRT architectures
+if config.int8 and config.providers[0][0].startswith("Tensor"):
+ logger.warning(translations["int8_warn"])
+ configs["int8"] = config.int8 = False
+
+# Commit synchronized setup back to state JSON configuration files
+with open(configs_json, "w") as f:
+ json.dump(configs, f, indent=4)
+
+# Optional: Enable Audio Stream Input/Output (ASIO) environment flag flags if explicitly targeted
+if configs.get("asio_enabled", False):
+ logger.info(translations["enabled_asio"])
+ os.environ["SD_ENABLE_ASIO"] = "1"
+
+# Static Data Structures & Choices Mappings
+models = {}
+model_options = {}
+
+file_types = [
+ ".wav",
+ ".mp3",
+ ".flac",
+ ".ogg",
+ ".opus",
+ ".m4a",
+ ".mp4",
+ ".aac",
+ ".alac",
+ ".wma",
+ ".aiff",
+ ".webm",
+ ".ac3"
+]
+
+export_format_choices = [
+ "wav",
+ "mp3",
+ "flac",
+ "ogg",
+ "opus",
+ "m4a",
+ "mp4",
+ "aac",
+ "alac",
+ "wma",
+ "aiff",
+ "webm",
+ "ac3"
+]
+
+# Fundamental base variants for F0 pitch extraction algorithms
+method_f0 = [
+ "mangio-crepe-full",
+ "crepe-full",
+ "fcpe",
+ "rmvpe",
+ "harvest-stonemask",
+ "pyin",
+ "hybrid"
+]
+# Comprehensive enumeration list covering all supported F0 model configurations
+method_f0_full = [
+ "rmvpe-mix",
+ "rmvpe-mix-medfilt",
+ "hpa-rmvpe",
+ "hpa-rmvpe-medfilt",
+ "hpa-rmvpe-previous",
+ "hpa-rmvpe-previous-medfilt",
+ "hpa-rmvpe-v4",
+ "hpa-rmvpe-v4-medfilt",
+ "rmvpe",
+ "rmvpe-medfilt",
+ "djcm",
+ "djcm-medfilt",
+ "djcm-svs",
+ "djcm-svs-medfilt",
+ "swift",
+ "fcpe",
+ "fcpe-previous",
+ "fcpe-legacy",
+ "mangio-crepe-full",
+ "crepe-full",
+ "mangio-crepe-large",
+ "crepe-large",
+ "mangio-crepe-medium",
+ "crepe-medium",
+ "mangio-crepe-small",
+ "crepe-small",
+ "mangio-crepe-tiny",
+ "crepe-tiny",
+ "harvest-stonemask",
+ "harvest",
+ "pesto",
+ "swipe-stonemask",
+ "swipe",
+ "mangio-penn",
+ "penn",
+ "dio-stonemask",
+ "dio",
+ "pm-ac",
+ "pm-cc",
+ "pm-shs",
+ "pyin",
+ "yin",
+ "piptrack",
+ "hybrid"
+]
+# Hybrid execution combinations for combining pitch models
+hybrid_f0_method = [
+ "hybrid[pm+dio]",
+ "hybrid[pm+crepe-tiny]",
+ "hybrid[pm+crepe]",
+ "hybrid[pm+fcpe]",
+ "hybrid[pm+rmvpe]",
+ "hybrid[pm+harvest]",
+ "hybrid[pm+yin]",
+ "hybrid[dio+crepe-tiny]",
+ "hybrid[dio+crepe]",
+ "hybrid[dio+fcpe]",
+ "hybrid[dio+rmvpe]",
+ "hybrid[dio+harvest]",
+ "hybrid[dio+yin]",
+ "hybrid[crepe-tiny+crepe]",
+ "hybrid[crepe-tiny+fcpe]",
+ "hybrid[crepe-tiny+rmvpe]",
+ "hybrid[crepe-tiny+harvest]",
+ "hybrid[crepe+fcpe]",
+ "hybrid[crepe+rmvpe]",
+ "hybrid[crepe+harvest]",
+ "hybrid[crepe+yin]",
+ "hybrid[fcpe+rmvpe]",
+ "hybrid[fcpe+harvest]",
+ "hybrid[fcpe+yin]",
+ "hybrid[rmvpe+harvest]",
+ "hybrid[rmvpe+yin]",
+ "hybrid[harvest+yin]"
+]
+
+embedders_mode = [
+ "fairseq",
+ "onnx",
+ "transformers"
+]
+
+embedders_model = [
+ "hubert_base",
+ "contentvec_base",
+ "vietnamese_hubert_base",
+ "japanese_hubert_base",
+ "korean_hubert_base",
+ "chinese_hubert_base",
+ "portuguese_hubert_base",
+ "spin-v1",
+ "spin-v2",
+ "custom"
+]
+
+whisper_model = [
+ "tiny",
+ "tiny.en",
+ "base",
+ "base.en",
+ "small",
+ "small.en",
+ "medium",
+ "medium.en",
+ "large-v1",
+ "large-v2",
+ "large-v3",
+ "large-v3-turbo"
+]
+
+whisper_languages = configs.get("whisper_languages", ["vi", "en"])
+
+sample_rate_choice = [
+ 8000,
+ 11025,
+ 12000,
+ 16000,
+ 22050,
+ 24000,
+ 32000,
+ 44100,
+ 48000,
+ 88200,
+ 96000,
+ 176400,
+ 192000,
+ 352800,
+ 384000
+]
+
+# Discover and sort available input source audio files
+paths_for_files = sorted([
+ os.path.abspath(os.path.join(root, f))
+ for root, _, files in os.walk(configs["audios_path"])
+ for f in files
+ if os.path.splitext(f)[1].lower() in file_types and not f.startswith(("Convert", "output"))
+])
+
+# Scan and discover references dataset folders
+reference_list = sorted([
+ re.sub(r'_v\d+_(?:[A-Za-z0-9_]+?)_(True|False)$', '', name)
+ for name in os.listdir(configs["reference_path"])
+ if (
+ os.path.exists(os.path.join(configs["reference_path"], name)) and
+ os.path.isdir(os.path.join(configs["reference_path"], name))
+ )
+])
+
+# Scan available deployment weights (.pth / .onnx inference configurations)
+model_name = sorted([
+ model for model in os.listdir(configs["weights_path"])
+ if model.endswith((".pth", ".onnx")) and not model.startswith("G_") and not model.startswith("D_")
+])
+
+# Map existing feature index alignment vectors (.index clusters)
+index_path = sorted([
+ os.path.join(root, name)
+ for root, _, files in os.walk(configs["logs_path"], topdown=False)
+ for name in files
+ if name.endswith(".index") and "trained" not in name
+])
+
+# Load custom model components from paths
+pretraineds = os.listdir(configs["pretrained_custom_path"])
+
+pretrainedD = [
+ model for model in pretraineds
+ if model.endswith(".pth") and "D" in model
+]
+
+pretrainedG = [
+ model for model in pretraineds
+ if model.endswith(".pth") and "G" in model
+]
+
+# Parse operational and design presets across UI modules
+all_presets = os.listdir(configs["presets_path"])
+
+presets_file = sorted([
+ f for f in all_presets
+ if f.endswith(".conversion.json")
+])
+
+audio_effect_presets_file = sorted([
+ f for f in all_presets
+ if f.endswith(".effect.json")
+])
+
+realtime_presets_file = sorted([
+ f for f in all_presets
+ if f.endswith(".realtime.json")
+])
+
+# Scan pre-extracted pitch cache metrics maps (.txt pitch data files)
+f0_file = sorted([
+ os.path.abspath(os.path.join(root, f))
+ for root, _, files in os.walk(configs["f0_path"])
+ for f in files if f.endswith(".txt")
+])
+
+# Localization and aesthetic assets setup
+language = configs.get("language", "vi-VN")
+theme = configs.get("theme", "NoCrypt/miku")
+# TTS configurations structures loading
+edgetts = configs.get("edge_tts", ["vi-VN-HoaiMyNeural", "vi-VN-NamMinhNeural"])
+google_tts_voice = configs.get("google_tts_voice", ["vi", "en"])
+# UVR background processing neural architectures dictionary map
+vr_models = configs.get("vr_models", "")
+demucs_models = configs.get("demucs_models", "")
+mdx_models = configs.get("mdx_models", "")
+karaoke_models = configs.get("karaoke_models", "")
+reverb_models = configs.get("reverb_models", "")
+denoise_models = configs.get("denoise_models", "")
+uvr_model = list(demucs_models.keys()) + list(vr_models.keys()) + list(mdx_models.keys())
+
+font = configs.get("font", "https://fonts.googleapis.com/css2?family=Saira&display=swap")
+csv_path = configs["csv_path"]
+
+# OS Specific Extended Storage Privileges Isolation (Grading full system access paths)
+try:
+ if "--allow_all_disk" in sys.argv:
+ import ctypes
+
+ allow_disk = []
+
+ if sys.platform == "win32": # Retrieve all available logical drive letters on Windows platforms
+ bitmask = ctypes.windll.kernel32.GetLogicalDrives()
+ for letter in "ABCDEFGHIJKLMNOPQRSTUVWXYZ":
+ if bitmask & 1: allow_disk.append(f"{letter}:\\")
+ bitmask >>= 1
+ elif sys.platform == "linux": # Traverse systemic mounts lists via standard library functions on Linux systems
+ class Mntent(ctypes.Structure):
+ _fields_ = [("mnt_fsname", ctypes.c_char_p), ("mnt_dir", ctypes.c_char_p), ("mnt_type", ctypes.c_char_p), ("mnt_opts", ctypes.c_char_p), ("mnt_freq", ctypes.c_int), ("mnt_passno", ctypes.c_int)]
+
+ libc = ctypes.CDLL(None)
+ libc.setmntent.restype = ctypes.c_void_p
+ libc.getmntent.restype = ctypes.POINTER(Mntent)
+ libc.endmntent.restype = ctypes.c_int
+
+ fp = libc.setmntent(b"/proc/mounts", b"r")
+
+ if not fp: allow_disk = []
+ else:
+ while 1:
+ entry_ptr = libc.getmntent(fp)
+ if not entry_ptr: break
+ allow_disk.append(entry_ptr.contents.mnt_fsname.decode('utf-8', errors='ignore'))
+ else: allow_disk = []
+ else: allow_disk = []
+except:
+ allow_disk = []
+
+# Index Hub Content Aggregation (Parsing HuggingFace mirrors links from CSV trackers)
+try:
+ for row in list(csv.DictReader(open(csv_path, newline='', encoding='utf-8'))):
+ filename = row['Filename']
+ url = None
+
+ for value in row.values():
+ if isinstance(value, str) and "huggingface" in value:
+ url = value
+ break
+
+ if url: models[filename] = url
+except:
+ pass
\ No newline at end of file
diff --git a/main/configs/config.json b/main/configs/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..43b4576ca138c8ec370e4e4c5d48af24ceb0ccb1
--- /dev/null
+++ b/main/configs/config.json
@@ -0,0 +1,744 @@
+{
+ "language": "vi-VN",
+ "support_language": [
+ "en-US",
+ "vi-VN"
+ ],
+ "theme": "NoCrypt/miku",
+ "themes": [
+ "NoCrypt/miku",
+ "gstaff/xkcd",
+ "JohnSmith9982/small_and_pretty",
+ "ParityError/Interstellar",
+ "earneleh/paris",
+ "shivi/calm_seafoam",
+ "Hev832/Applio",
+ "YTheme/Minecraft",
+ "gstaff/sketch",
+ "SebastianBravo/simci_css",
+ "allenai/gradio-theme",
+ "Nymbo/Nymbo_Theme_5",
+ "lone17/kotaemon",
+ "Zarkel/IBM_Carbon_Theme",
+ "SherlockRamos/Feliz",
+ "freddyaboulton/dracula_revamped",
+ "freddyaboulton/bad-theme-space",
+ "gradio/dracula_revamped",
+ "abidlabs/dracula_revamped",
+ "gradio/dracula_test",
+ "gradio/seafoam",
+ "gradio/glass",
+ "gradio/monochrome",
+ "gradio/soft",
+ "gradio/default",
+ "gradio/base",
+ "abidlabs/pakistan",
+ "dawood/microsoft_windows",
+ "ysharma/steampunk",
+ "ysharma/huggingface",
+ "abidlabs/Lime",
+ "freddyaboulton/this-theme-does-not-exist-2",
+ "aliabid94/new-theme",
+ "aliabid94/test2",
+ "aliabid94/test3",
+ "aliabid94/test4",
+ "abidlabs/banana",
+ "freddyaboulton/test-blue",
+ "gstaff/whiteboard",
+ "ysharma/llamas",
+ "abidlabs/font-test",
+ "YenLai/Superhuman",
+ "bethecloud/storj_theme",
+ "sudeepshouche/minimalist",
+ "knotdgaf/gradiotest",
+ "ParityError/Anime",
+ "Ajaxon6255/Emerald_Isle",
+ "ParityError/LimeFace",
+ "finlaymacklon/smooth_slate",
+ "finlaymacklon/boxy_violet",
+ "derekzen/stardust",
+ "EveryPizza/Cartoony-Gradio-Theme",
+ "Ifeanyi/Cyanister",
+ "Tshackelton/IBMPlex-DenseReadable",
+ "snehilsanyal/scikit-learn",
+ "Himhimhim/xkcd",
+ "nota-ai/theme",
+ "rawrsor1/Everforest",
+ "rottenlittlecreature/Moon_Goblin",
+ "abidlabs/test-yellow",
+ "abidlabs/test-yellow3",
+ "idspicQstitho/dracula_revamped",
+ "kfahn/AnimalPose",
+ "HaleyCH/HaleyCH_Theme",
+ "simulKitke/dracula_test",
+ "braintacles/CrimsonNight",
+ "wentaohe/whiteboardv2",
+ "reilnuud/polite",
+ "remilia/Ghostly",
+ "Franklisi/darkmode",
+ "coding-alt/soft",
+ "xiaobaiyuan/theme_land",
+ "step-3-profit/Midnight-Deep",
+ "xiaobaiyuan/theme_demo",
+ "Taithrah/Minimal",
+ "Insuz/SimpleIndigo",
+ "zkunn/Alipay_Gradio_theme",
+ "Insuz/Mocha",
+ "xiaobaiyuan/theme_brief",
+ "Ama434/434-base-Barlow",
+ "Ama434/def_barlow",
+ "Ama434/neutral-barlow",
+ "dawood/dracula_test",
+ "nuttea/Softblue",
+ "BlueDancer/Alien_Diffusion",
+ "naughtondale/monochrome",
+ "Dagfinn1962/standard",
+ "default"
+ ],
+ "mdx_models": {
+ "Main_340": "UVR-MDX-NET_Main_340.onnx",
+ "Main_390": "UVR-MDX-NET_Main_390.onnx",
+ "Main_406": "UVR-MDX-NET_Main_406.onnx",
+ "Main_427": "UVR-MDX-NET_Main_427.onnx",
+ "Main_438": "UVR-MDX-NET_Main_438.onnx",
+ "Inst_full_292": "UVR-MDX-NET-Inst_full_292.onnx",
+ "Inst_HQ_1": "UVR-MDX-NET-Inst_HQ_1.onnx",
+ "Inst_HQ_2": "UVR-MDX-NET-Inst_HQ_2.onnx",
+ "Inst_HQ_3": "UVR-MDX-NET-Inst_HQ_3.onnx",
+ "Inst_HQ_4": "UVR-MDX-NET-Inst_HQ_4.onnx",
+ "Inst_HQ_5": "UVR-MDX-NET-Inst_HQ_5.onnx",
+ "Kim_Vocal_1": "Kim_Vocal_1.onnx",
+ "Kim_Vocal_2": "Kim_Vocal_2.onnx",
+ "Kim_Inst": "Kim_Inst.onnx",
+ "Inst_187_beta": "UVR-MDX-NET_Inst_187_beta.onnx",
+ "Inst_82_beta": "UVR-MDX-NET_Inst_82_beta.onnx",
+ "Inst_90_beta": "UVR-MDX-NET_Inst_90_beta.onnx",
+ "Voc_FT": "UVR-MDX-NET-Voc_FT.onnx",
+ "Crowd_HQ": "UVR-MDX-NET_Crowd_HQ_1.onnx",
+ "MDXNET_9482": "UVR_MDXNET_9482.onnx",
+ "Inst_1": "UVR-MDX-NET-Inst_1.onnx",
+ "Inst_2": "UVR-MDX-NET-Inst_2.onnx",
+ "Inst_3": "UVR-MDX-NET-Inst_3.onnx",
+ "MDXNET_1_9703": "UVR_MDXNET_1_9703.onnx",
+ "MDXNET_2_9682": "UVR_MDXNET_2_9682.onnx",
+ "MDXNET_3_9662": "UVR_MDXNET_3_9662.onnx",
+ "Inst_Main": "UVR-MDX-NET-Inst_Main.onnx",
+ "MDXNET_Main": "UVR_MDXNET_Main.onnx"
+ },
+ "demucs_models": {
+ "HT-Tuned": "htdemucs_ft.pth",
+ "HT-Normal": "htdemucs.pth",
+ "HD_MMI": "hdemucs_mmi.pth",
+ "HT_6S": "htdemucs_6s.pth"
+ },
+ "vr_models": {
+ "HP-1": "1_HP-UVR.pth",
+ "HP-2": "2_HP-UVR.pth",
+ "HP-Vocal-1": "3_HP-Vocal-UVR.pth",
+ "HP-Vocal-2": "4_HP-Vocal-UVR.pth",
+ "HP2-1": "7_HP2-UVR.pth",
+ "HP2-2": "8_HP2-UVR.pth",
+ "HP2-3": "9_HP2-UVR.pth",
+ "SP-2B-1": "10_SP-UVR-2B-32000-1.pth",
+ "SP-2B-2": "11_SP-UVR-2B-32000-2.pth",
+ "SP-3B-1": "12_SP-UVR-3B-44100.pth",
+ "SP-4B-1": "13_SP-UVR-4B-44100-1.pth",
+ "SP-4B-2": "14_SP-UVR-4B-44100-2.pth",
+ "SP-MID-1": "15_SP-UVR-MID-44100-1.pth",
+ "SP-MID-2": "16_SP-UVR-MID-44100-2.pth"
+ },
+ "karaoke_models": {
+ "MDX-Version-1": "UVR_MDXNET_KARA.onnx",
+ "MDX-Version-2": "UVR_MDXNET_KARA_2.onnx",
+ "VR-Version-1": "5_HP-Karaoke-UVR.pth",
+ "VR-Version-2": "6_HP-Karaoke-UVR.pth"
+ },
+ "reverb_models": {
+ "MDX-Reverb": "Reverb_HQ_By_FoxJoy.onnx",
+ "VR-Reverb": "UVR-DeEcho-DeReverb.pth",
+ "Echo-Aggressive": "UVR-De-Echo-Aggressive.pth",
+ "Echo-Normal": "UVR-De-Echo-Normal.pth"
+ },
+ "denoise_models": {
+ "Lite": "UVR-DeNoise-Lite.pth",
+ "Normal": "UVR-DeNoise.pth"
+ },
+ "edge_tts": [
+ "af-ZA-AdriNeural",
+ "af-ZA-WillemNeural",
+ "sq-AL-AnilaNeural",
+ "sq-AL-IlirNeural",
+ "am-ET-AmehaNeural",
+ "am-ET-MekdesNeural",
+ "ar-DZ-AminaNeural",
+ "ar-DZ-IsmaelNeural",
+ "ar-BH-AliNeural",
+ "ar-BH-LailaNeural",
+ "ar-EG-SalmaNeural",
+ "ar-EG-ShakirNeural",
+ "ar-IQ-BasselNeural",
+ "ar-IQ-RanaNeural",
+ "ar-JO-SanaNeural",
+ "ar-JO-TaimNeural",
+ "ar-KW-FahedNeural",
+ "ar-KW-NouraNeural",
+ "ar-LB-LaylaNeural",
+ "ar-LB-RamiNeural",
+ "ar-LY-ImanNeural",
+ "ar-LY-OmarNeural",
+ "ar-MA-JamalNeural",
+ "ar-MA-MounaNeural",
+ "ar-OM-AbdullahNeural",
+ "ar-OM-AyshaNeural",
+ "ar-QA-AmalNeural",
+ "ar-QA-MoazNeural",
+ "ar-SA-HamedNeural",
+ "ar-SA-ZariyahNeural",
+ "ar-SY-AmanyNeural",
+ "ar-SY-LaithNeural",
+ "ar-TN-HediNeural",
+ "ar-TN-ReemNeural",
+ "ar-AE-FatimaNeural",
+ "ar-AE-HamdanNeural",
+ "ar-YE-MaryamNeural",
+ "ar-YE-SalehNeural",
+ "az-AZ-BabekNeural",
+ "az-AZ-BanuNeural",
+ "bn-BD-NabanitaNeural",
+ "bn-BD-PradeepNeural",
+ "bn-IN-BashkarNeural",
+ "bn-IN-TanishaaNeural",
+ "bs-BA-GoranNeural",
+ "bs-BA-VesnaNeural",
+ "bg-BG-BorislavNeural",
+ "bg-BG-KalinaNeural",
+ "my-MM-NilarNeural",
+ "my-MM-ThihaNeural",
+ "ca-ES-EnricNeural",
+ "ca-ES-JoanaNeural",
+ "zh-HK-HiuGaaiNeural",
+ "zh-HK-HiuMaanNeural",
+ "zh-HK-WanLungNeural",
+ "zh-CN-XiaoxiaoNeural",
+ "zh-CN-XiaoyiNeural",
+ "zh-CN-YunjianNeural",
+ "zh-CN-YunxiNeural",
+ "zh-CN-YunxiaNeural",
+ "zh-CN-YunyangNeural",
+ "zh-CN-liaoning-XiaobeiNeural",
+ "zh-TW-HsiaoChenNeural",
+ "zh-TW-YunJheNeural",
+ "zh-TW-HsiaoYuNeural",
+ "zh-CN-shaanxi-XiaoniNeural",
+ "hr-HR-GabrijelaNeural",
+ "hr-HR-SreckoNeural",
+ "cs-CZ-AntoninNeural",
+ "cs-CZ-VlastaNeural",
+ "da-DK-ChristelNeural",
+ "da-DK-JeppeNeural",
+ "nl-BE-ArnaudNeural",
+ "nl-BE-DenaNeural",
+ "nl-NL-ColetteNeural",
+ "nl-NL-FennaNeural",
+ "nl-NL-MaartenNeural",
+ "en-AU-NatashaNeural",
+ "en-AU-WilliamNeural",
+ "en-CA-ClaraNeural",
+ "en-CA-LiamNeural",
+ "en-HK-SamNeural",
+ "en-HK-YanNeural",
+ "en-IN-NeerjaExpressiveNeural",
+ "en-IN-NeerjaNeural",
+ "en-IN-PrabhatNeural",
+ "en-IE-ConnorNeural",
+ "en-IE-EmilyNeural",
+ "en-KE-AsiliaNeural",
+ "en-KE-ChilembaNeural",
+ "en-NZ-MitchellNeural",
+ "en-NZ-MollyNeural",
+ "en-NG-AbeoNeural",
+ "en-NG-EzinneNeural",
+ "en-PH-JamesNeural",
+ "en-PH-RosaNeural",
+ "en-SG-LunaNeural",
+ "en-SG-WayneNeural",
+ "en-ZA-LeahNeural",
+ "en-ZA-LukeNeural",
+ "en-TZ-ElimuNeural",
+ "en-TZ-ImaniNeural",
+ "en-GB-LibbyNeural",
+ "en-GB-MaisieNeural",
+ "en-GB-RyanNeural",
+ "en-GB-SoniaNeural",
+ "en-GB-ThomasNeural",
+ "en-US-AvaMultilingualNeural",
+ "en-US-AndrewMultilingualNeural",
+ "en-US-EmmaMultilingualNeural",
+ "en-US-BrianMultilingualNeural",
+ "en-US-AvaNeural",
+ "en-US-AndrewNeural",
+ "en-US-EmmaNeural",
+ "en-US-BrianNeural",
+ "en-US-AnaNeural",
+ "en-US-AriaNeural",
+ "en-US-ChristopherNeural",
+ "en-US-EricNeural",
+ "en-US-GuyNeural",
+ "en-US-JennyNeural",
+ "en-US-MichelleNeural",
+ "en-US-RogerNeural",
+ "en-US-SteffanNeural",
+ "et-EE-AnuNeural",
+ "et-EE-KertNeural",
+ "fil-PH-AngeloNeural",
+ "fil-PH-BlessicaNeural",
+ "fi-FI-HarriNeural",
+ "fi-FI-NooraNeural",
+ "fr-BE-CharlineNeural",
+ "fr-BE-GerardNeural",
+ "fr-CA-ThierryNeural",
+ "fr-CA-AntoineNeural",
+ "fr-CA-JeanNeural",
+ "fr-CA-SylvieNeural",
+ "fr-FR-VivienneMultilingualNeural",
+ "fr-FR-RemyMultilingualNeural",
+ "fr-FR-DeniseNeural",
+ "fr-FR-EloiseNeural",
+ "fr-FR-HenriNeural",
+ "fr-CH-ArianeNeural",
+ "fr-CH-FabriceNeural",
+ "gl-ES-RoiNeural",
+ "gl-ES-SabelaNeural",
+ "ka-GE-EkaNeural",
+ "ka-GE-GiorgiNeural",
+ "de-AT-IngridNeural",
+ "de-AT-JonasNeural",
+ "de-DE-SeraphinaMultilingualNeural",
+ "de-DE-FlorianMultilingualNeural",
+ "de-DE-AmalaNeural",
+ "de-DE-ConradNeural",
+ "de-DE-KatjaNeural",
+ "de-DE-KillianNeural",
+ "de-CH-JanNeural",
+ "de-CH-LeniNeural",
+ "el-GR-AthinaNeural",
+ "el-GR-NestorasNeural",
+ "gu-IN-DhwaniNeural",
+ "gu-IN-NiranjanNeural",
+ "he-IL-AvriNeural",
+ "he-IL-HilaNeural",
+ "hi-IN-MadhurNeural",
+ "hi-IN-SwaraNeural",
+ "hu-HU-NoemiNeural",
+ "hu-HU-TamasNeural",
+ "is-IS-GudrunNeural",
+ "is-IS-GunnarNeural",
+ "id-ID-ArdiNeural",
+ "id-ID-GadisNeural",
+ "ga-IE-ColmNeural",
+ "ga-IE-OrlaNeural",
+ "it-IT-GiuseppeNeural",
+ "it-IT-DiegoNeural",
+ "it-IT-ElsaNeural",
+ "it-IT-IsabellaNeural",
+ "ja-JP-KeitaNeural",
+ "ja-JP-NanamiNeural",
+ "jv-ID-DimasNeural",
+ "jv-ID-SitiNeural",
+ "kn-IN-GaganNeural",
+ "kn-IN-SapnaNeural",
+ "kk-KZ-AigulNeural",
+ "kk-KZ-DauletNeural",
+ "km-KH-PisethNeural",
+ "km-KH-SreymomNeural",
+ "ko-KR-HyunsuNeural",
+ "ko-KR-InJoonNeural",
+ "ko-KR-SunHiNeural",
+ "lo-LA-ChanthavongNeural",
+ "lo-LA-KeomanyNeural",
+ "lv-LV-EveritaNeural",
+ "lv-LV-NilsNeural",
+ "lt-LT-LeonasNeural",
+ "lt-LT-OnaNeural",
+ "mk-MK-AleksandarNeural",
+ "mk-MK-MarijaNeural",
+ "ms-MY-OsmanNeural",
+ "ms-MY-YasminNeural",
+ "ml-IN-MidhunNeural",
+ "ml-IN-SobhanaNeural",
+ "mt-MT-GraceNeural",
+ "mt-MT-JosephNeural",
+ "mr-IN-AarohiNeural",
+ "mr-IN-ManoharNeural",
+ "mn-MN-BataaNeural",
+ "mn-MN-YesuiNeural",
+ "ne-NP-HemkalaNeural",
+ "ne-NP-SagarNeural",
+ "nb-NO-FinnNeural",
+ "nb-NO-PernilleNeural",
+ "ps-AF-GulNawazNeural",
+ "ps-AF-LatifaNeural",
+ "fa-IR-DilaraNeural",
+ "fa-IR-FaridNeural",
+ "pl-PL-MarekNeural",
+ "pl-PL-ZofiaNeural",
+ "pt-BR-ThalitaNeural",
+ "pt-BR-AntonioNeural",
+ "pt-BR-FranciscaNeural",
+ "pt-PT-DuarteNeural",
+ "pt-PT-RaquelNeural",
+ "ro-RO-AlinaNeural",
+ "ro-RO-EmilNeural",
+ "ru-RU-DmitryNeural",
+ "ru-RU-SvetlanaNeural",
+ "sr-RS-NicholasNeural",
+ "sr-RS-SophieNeural",
+ "si-LK-SameeraNeural",
+ "si-LK-ThiliniNeural",
+ "sk-SK-LukasNeural",
+ "sk-SK-ViktoriaNeural",
+ "sl-SI-PetraNeural",
+ "sl-SI-RokNeural",
+ "so-SO-MuuseNeural",
+ "so-SO-UbaxNeural",
+ "es-AR-ElenaNeural",
+ "es-AR-TomasNeural",
+ "es-BO-MarceloNeural",
+ "es-BO-SofiaNeural",
+ "es-CL-CatalinaNeural",
+ "es-CL-LorenzoNeural",
+ "es-ES-XimenaNeural",
+ "es-CO-GonzaloNeural",
+ "es-CO-SalomeNeural",
+ "es-CR-JuanNeural",
+ "es-CR-MariaNeural",
+ "es-CU-BelkysNeural",
+ "es-CU-ManuelNeural",
+ "es-DO-EmilioNeural",
+ "es-DO-RamonaNeural",
+ "es-EC-AndreaNeural",
+ "es-EC-LuisNeural",
+ "es-SV-LorenaNeural",
+ "es-SV-RodrigoNeural",
+ "es-GQ-JavierNeural",
+ "es-GQ-TeresaNeural",
+ "es-GT-AndresNeural",
+ "es-GT-MartaNeural",
+ "es-HN-CarlosNeural",
+ "es-HN-KarlaNeural",
+ "es-MX-DaliaNeural",
+ "es-MX-JorgeNeural",
+ "es-NI-FedericoNeural",
+ "es-NI-YolandaNeural",
+ "es-PA-MargaritaNeural",
+ "es-PA-RobertoNeural",
+ "es-PY-MarioNeural",
+ "es-PY-TaniaNeural",
+ "es-PE-AlexNeural",
+ "es-PE-CamilaNeural",
+ "es-PR-KarinaNeural",
+ "es-PR-VictorNeural",
+ "es-ES-AlvaroNeural",
+ "es-ES-ElviraNeural",
+ "es-US-AlonsoNeural",
+ "es-US-PalomaNeural",
+ "es-UY-MateoNeural",
+ "es-UY-ValentinaNeural",
+ "es-VE-PaolaNeural",
+ "es-VE-SebastianNeural",
+ "su-ID-JajangNeural",
+ "su-ID-TutiNeural",
+ "sw-KE-RafikiNeural",
+ "sw-KE-ZuriNeural",
+ "sw-TZ-DaudiNeural",
+ "sw-TZ-RehemaNeural",
+ "sv-SE-MattiasNeural",
+ "sv-SE-SofieNeural",
+ "ta-IN-PallaviNeural",
+ "ta-IN-ValluvarNeural",
+ "ta-MY-KaniNeural",
+ "ta-MY-SuryaNeural",
+ "ta-SG-AnbuNeural",
+ "ta-SG-VenbaNeural",
+ "ta-LK-KumarNeural",
+ "ta-LK-SaranyaNeural",
+ "te-IN-MohanNeural",
+ "te-IN-ShrutiNeural",
+ "th-TH-NiwatNeural",
+ "th-TH-PremwadeeNeural",
+ "tr-TR-AhmetNeural",
+ "tr-TR-EmelNeural",
+ "uk-UA-OstapNeural",
+ "uk-UA-PolinaNeural",
+ "ur-IN-GulNeural",
+ "ur-IN-SalmanNeural",
+ "ur-PK-AsadNeural",
+ "ur-PK-UzmaNeural",
+ "uz-UZ-MadinaNeural",
+ "uz-UZ-SardorNeural",
+ "vi-VN-HoaiMyNeural",
+ "vi-VN-NamMinhNeural",
+ "cy-GB-AledNeural",
+ "cy-GB-NiaNeural",
+ "zu-ZA-ThandoNeural",
+ "zu-ZA-ThembaNeural"
+ ],
+ "google_tts_voice": [
+ "af",
+ "am",
+ "ar",
+ "bg",
+ "bn",
+ "bs",
+ "ca",
+ "cs",
+ "cy",
+ "da",
+ "de",
+ "el",
+ "en",
+ "es",
+ "et",
+ "eu",
+ "fi",
+ "fr",
+ "fr-CA",
+ "gl",
+ "gu",
+ "ha",
+ "hi",
+ "hr",
+ "hu",
+ "id",
+ "is",
+ "it",
+ "iw",
+ "ja",
+ "jw",
+ "km",
+ "kn",
+ "ko",
+ "la",
+ "lt",
+ "lv",
+ "ml",
+ "mr",
+ "ms",
+ "my",
+ "ne",
+ "nl",
+ "no",
+ "pa",
+ "pl",
+ "pt",
+ "pt-PT",
+ "ro",
+ "ru",
+ "si",
+ "sk",
+ "sq",
+ "sr",
+ "su",
+ "sv",
+ "sw",
+ "ta",
+ "te",
+ "th",
+ "tl",
+ "tr",
+ "uk",
+ "ur",
+ "vi",
+ "yue",
+ "zh-CN",
+ "zh-TW",
+ "zh"
+ ],
+ "whisper_languages": [
+ "en",
+ "zh",
+ "de",
+ "es",
+ "ru",
+ "ko",
+ "fr",
+ "ja",
+ "pt",
+ "tr",
+ "pl",
+ "ca",
+ "nl",
+ "ar",
+ "sv",
+ "it",
+ "id",
+ "hi",
+ "fi",
+ "vi",
+ "he",
+ "uk",
+ "el",
+ "ms",
+ "cs",
+ "ro",
+ "da",
+ "hu",
+ "ta",
+ "no",
+ "th",
+ "ur",
+ "hr",
+ "bg",
+ "lt",
+ "la",
+ "mi",
+ "ml",
+ "cy",
+ "sk",
+ "te",
+ "fa",
+ "lv",
+ "bn",
+ "sr",
+ "az",
+ "sl",
+ "kn",
+ "et",
+ "mk",
+ "br",
+ "eu",
+ "is",
+ "hy",
+ "ne",
+ "mn",
+ "bs",
+ "kk",
+ "sq",
+ "sw",
+ "gl",
+ "mr",
+ "pa",
+ "si",
+ "km",
+ "sn",
+ "yo",
+ "so",
+ "af",
+ "oc",
+ "ka",
+ "be",
+ "tg",
+ "sd",
+ "gu",
+ "am",
+ "yi",
+ "lo",
+ "uz",
+ "fo",
+ "ht",
+ "ps",
+ "tk",
+ "nn",
+ "mt",
+ "sa",
+ "lb",
+ "my",
+ "bo",
+ "tl",
+ "mg",
+ "as",
+ "tt",
+ "haw",
+ "ln",
+ "ha",
+ "ba",
+ "jw",
+ "su",
+ "yue"
+ ],
+ "fp16": false,
+ "editing_tab": true,
+ "inference_tab": true,
+ "create_and_training_tab": true,
+ "extra_tab": true,
+ "separator_tab": true,
+ "convert_tab": true,
+ "convert_with_vad_tab": true,
+ "tts_tab": true,
+ "effects_tab": true,
+ "quirk_tab": true,
+ "create_dataset_tab": true,
+ "training_tab": true,
+ "fushion_tab": true,
+ "read_tab": true,
+ "onnx_tab": true,
+ "svc_tab": true,
+ "downloads_tab": true,
+ "f0_extractor_tab": true,
+ "settings_tab": true,
+ "create_srt_tab": true,
+ "realtime_tab": true,
+ "create_reference_tab": true,
+ "analyzer_tab": true,
+ "font": "https://fonts.googleapis.com/css2?family=Saira&display=swap",
+ "app_port": 7860,
+ "tensorboard_port": 6870,
+ "num_of_restart": 5,
+ "server_name": "0.0.0.0",
+ "app_show_error": true,
+ "delete_exists_file": false,
+ "audio_effects_path": "main/inference/audio_effects.py",
+ "convert_path": "main/inference/conversion/convert.py",
+ "separate_path": "main/inference/separate_music.py",
+ "create_dataset_path": "main/inference/create_dataset.py",
+ "preprocess_path": "main/inference/preprocess/preprocess.py",
+ "extract_path": "main/inference/extracting/extract.py",
+ "create_index_path": "main/inference/create_index.py",
+ "train_path": "main/inference/training/train.py",
+ "create_reference_path": "main/inference/create_reference.py",
+ "ico_path": "assets/ico.png",
+ "csv_path": "assets/spreadsheet.csv",
+ "weights_path": "assets/weights",
+ "logs_path": "assets/logs",
+ "binary_path": "assets/binary",
+ "f0_path": "assets/f0",
+ "language_path": "assets/languages",
+ "presets_path": "assets/presets",
+ "embedders_path": "assets/models/embedders",
+ "predictors_path": "assets/models/predictors",
+ "pretrained_custom_path": "assets/models/pretrained_custom",
+ "pretrained_v1_path": "assets/models/pretrained_v1",
+ "pretrained_v2_path": "assets/models/pretrained_v2",
+ "speaker_diarization_path": "assets/models/speaker_diarization",
+ "uvr5_path": "assets/models/uvr5",
+ "audios_path": "audios",
+ "reference_path": "assets/logs/reference",
+ "demucs_segments_enable": true,
+ "demucs_cpu_mode": false,
+ "limit_f0": 12,
+ "debug_mode": false,
+ "pretrain_strict": true,
+ "cpu_mode": false,
+ "brain": false,
+ "tf32": false,
+ "discord_presence": true,
+ "f0_min": 50,
+ "f0_max": 1100,
+ "turn_on_resize_f0_for_all": false,
+ "allow_fp16_all_backend": false,
+ "asio_enabled": false,
+ "ffmpeg_download": true,
+ "compile_all": false,
+ "compile_mode": null,
+ "compile_cache_dir": "assets/cache",
+ "check_data": false,
+ "tensorrt": false,
+ "faiss_cpu": false,
+ "allow_unsafe_fairseq": false,
+ "del4stem": true,
+ "enable_rmvpe_chunk": false,
+ "rmvpe_chunk_size": 32000,
+ "int8": false,
+ "invert_using_spec": false,
+ "gpu_idx": 0
+}
\ No newline at end of file
diff --git a/main/configs/config.py b/main/configs/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..1acfd51bc1f25ab773f33165f8c2c1c3a3c583d6
--- /dev/null
+++ b/main/configs/config.py
@@ -0,0 +1,354 @@
+import os
+import sys
+import json
+import torch
+import onnxruntime
+
+sys.path.append(os.getcwd())
+
+from main.library.backends import directml, opencl, xpu, zluda
+
+def singleton(cls):
+ """
+ A decorator to implement the Singleton pattern for a class.
+ Ensures only one instance of the class is created across the application.
+ """
+
+ instances = {}
+
+ def get_instance(*args, **kwargs):
+ if cls not in instances: instances[cls] = cls(*args, **kwargs)
+ return instances[cls]
+
+ return get_instance
+
+@singleton
+class Config:
+ """
+ Configuration manager class that loads, parses, and validates settings
+ from a JSON file, and automatically configures hardware backends
+ (CUDA, XPU, DirectML, OpenCL, MPS, CPU) for PyTorch and ONNX Runtime.
+ """
+
+ def __init__(self):
+ """Initializes configuration paths, hardware status, and optimization flags."""
+
+ # Define paths and load JSON configuration file
+ self.configs_path = os.path.join("main", "configs", "config.json")
+ self.configs = json.load(open(self.configs_path, "r"))
+ # Base execution flags
+ self.cpu_mode = self.configs.get("cpu_mode", False)
+ self.cuda_available = torch.cuda.is_available()
+ # Check for AMD ZLUDA environment mimicking CUDA
+ self.is_zluda = self.cuda_available and torch.cuda.get_device_name().endswith("[ZLUDA]")
+ self.hip_version = torch.version.hip if hasattr(torch.version, "hip") else None
+ if self.is_zluda: zluda.init_zluda()
+ # Debug and localization setups
+ self.debug_mode = self.configs.get("debug_mode", False)
+ self.translations = self.multi_language()
+ # Device capability trackers
+ self.invalid_gpu = False
+ self.gpu_mem = None
+ self.major = None # Compute capability major version
+ # Map target device indexes for PyTorch and ONNX
+ self.pytorch_device_idx = self.device_idx(self.configs.get("gpu_idx", 0))
+ self.onnx_device_idx = {"device_id": self.pytorch_device_idx}
+ # Default scaling and device auto-detection
+ self.per_preprocess = 3.7
+ self.device = self.get_default_device()
+ self.tensorrt = self.configs.get("tensorrt", False)
+ self.providers = self.get_providers()
+ # Check precision acceleration capabilities (TF32 & BF16)
+ self.cuda_tf32 = self.cuda_available and hasattr(torch.cuda, "is_tf32_supported") and self.major is not None and self.major >= 8 and torch.cuda.is_tf32_supported()
+ self.cuda_bf16 = self.cuda_available and hasattr(torch.cuda, "is_bf16_supported") and self.major is not None and self.major >= 8 and torch.cuda.is_bf16_supported()
+ self.xpu_fp16 = hasattr(torch, "xpu") and torch.xpu.is_available() and hasattr(torch.xpu, "is_bf16_supported") and torch.xpu.is_bf16_supported()
+ # Determine structural support based on current active device
+ self.tf32_support = self.device.startswith("cuda") and self.cuda_tf32
+ self.bf16_support = self.device.startswith(("cuda", "xpu")) and (self.cuda_bf16 or self.xpu_fp16)
+ # Apply optimal math precision modes (Bfloat16 / TensorFloat32)
+ self.brain = self.configs.get("brain", False) and self.bf16_support and not self.cpu_mode
+ self.tf32 = self.configs.get("tf32", False) and self.tf32_support and not self.is_zluda and not self.cpu_mode
+ # Configure model quantization and compiler variables
+ self.is_half = self.is_fp16()
+ self.int8 = self.configs.get("int8", False)
+ self.compile_all = self.device.startswith("cuda") and self.configs.get("compile_all", False)
+ self.compile_mode = self.configs.get("compile_mode", None)
+ self.allow_is_half = not self.device.startswith(("cpu", "mps", "ocl", "privateuseone")) or self.configs.get("allow_fp16_all_backend", False)
+ # Pull pad/query/center/max threshold properties based on the device configurations
+ self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
+ if self.compile_all: self.setup_compile()
+
+ def device_idx(self, device_id):
+ """
+ Validates whether the provided device ID falls within the available range of hardware backends.
+
+ Args:
+ device_id (int): Target hardware device index.
+
+ Returns:
+ int: Validated device index (defaults to 0 if out of range).
+ """
+
+ total_device = torch.cuda.device_count() + directml.device_count() + opencl.device_count()
+ if hasattr(torch, "xpu"): total_device += torch.xpu.device_count()
+
+ if device_id >= 0 and device_id < total_device: return device_id
+ else:
+ self.invalid_gpu = True
+ return 0
+
+ def multi_language(self):
+ """
+ Loads localization strings from JSON translation files based on the config settings.
+
+ Returns:
+ dict: Translated key-value pairs or None if an error occurs.
+ """
+
+ translations = None
+
+ try:
+ lang = self.configs.get("language", "vi-VN")
+ # Check if directory contains any valid localization files
+ if len([l for l in os.listdir(self.configs["language_path"]) if l.endswith(".json")]) < 1:
+ raise FileNotFoundError("Không tìm thấy bất cứ gói ngôn ngữ nào(No package languages found)")
+
+ if not lang: lang = "vi-VN"
+ # Validate language compatibility
+ if lang not in self.configs["support_language"]:
+ raise ValueError("Ngôn ngữ không được hỗ trợ (Language not supported)")
+
+ # Resolve paths, fall back to Vietnamese if target language file is missing
+ lang_path = os.path.join(self.configs["language_path"], f"{lang}.json")
+ if not os.path.exists(lang_path):
+ lang_path = os.path.join(self.configs["language_path"], "vi-VN.json")
+
+ with open(lang_path, encoding="utf-8") as f:
+ translations = json.load(f)
+ except json.JSONDecodeError:
+ # Output error using pre-existing translations structure if possible
+ print(self.translations["empty_json"].format(file=lang))
+ pass
+
+ return translations
+
+ def is_fp16(self):
+ """
+ Determines if FP16 precision is enabled and turns it off if the current hardware doesn't natively support it.
+
+ Returns:
+ bool: FP16 activation state.
+ """
+
+ fp16 = self.configs.get("fp16", False)
+ # Force disable FP16 for basic or unoptimized backends to avoid runtime arithmetic crashes
+ if self.device in ["cpu", "mps", "ocl", "privateuseone"] and fp16:
+ self.configs["fp16"] = False
+ fp16 = False
+
+ # Update the configuration file with modified parameters
+ with open(self.configs_path, "w") as f:
+ json.dump(self.configs, f, indent=4)
+
+ if not fp16: self.per_preprocess = 3.0
+ return fp16
+
+ def setup_compile(self):
+ """
+ Validates requirements and prepares environment configurations for PyTorch 2.0+ graph compilation (torch.compile).
+ """
+
+ import importlib
+
+ # Check for Triton availability required by TorchInductor
+ try:
+ importlib.import_module("triton")
+ except ModuleNotFoundError:
+ self.compile_all = False
+ self.configs["compile_all"] = False
+
+ with open(self.configs_path, "w") as f:
+ json.dump(self.configs, f, indent=4)
+
+ if self.compile_all:
+ dirs = self.configs.get("compile_cache_dir", "none")
+ # Setup persistent compilation cache paths
+ if dirs != "none":
+ if not os.path.exists(dirs): os.makedirs(dirs)
+ os.environ["TORCHINDUCTOR_CACHE_DIR"] = dirs
+
+ import torch._inductor.config as config
+ # Optimize graph captures and model freezing behaviors
+ torch._dynamo.config.capture_scalar_outputs = True
+ config.freezing = True
+
+ def device_config(self):
+ """
+ Retrieves baseline model matrix padding and processing thresholds based on target device VRAM or precision.
+
+ Returns:
+ tuple: Contains (x_pad, x_query, x_center, x_max) parameter thresholds.
+ """
+
+ # Lower processing parameters for low-end VRAM profiles (<= 4GB)
+ if self.gpu_mem is not None and self.gpu_mem <= 4:
+ self.per_preprocess = 3.0
+ return 1, 5, 30, 32
+
+ # High parameters for standard FP16 execution, versus normal FP32 processing
+ return (3, 10, 60, 65) if self.is_half else (1, 6, 38, 41)
+
+ def setup_cpu_mode(self):
+ """
+ Configures fallback CPU multithreading environment, turns off gradients,
+ and stubs GPU availabilities to optimize pure CPU tracking pipelines.
+ """
+
+ # Allocate logical processor thread constraints
+ cores = max(os.cpu_count() - 2, 2)
+ torch.set_num_threads(cores)
+ torch.set_num_interop_threads(cores)
+
+ # General inference speed enhancements for CPU
+ torch.set_grad_enabled(False)
+ torch.backends.mkldnn.enabled = True
+ # Monkey-patch availability hooks to force runtime modules onto the CPU
+ torch.cuda.is_available = lambda : False
+ directml.is_available = lambda : False
+ opencl.is_available = lambda : False
+ torch.backends.mps.is_available = lambda : False
+ if hasattr(torch, "xpu"): torch.xpu.is_available = lambda: False
+
+ def get_default_device(self):
+ """
+ Scans all available hardware backends across the operating system
+ and extracts total memory details to determine the default target device string.
+
+ Returns:
+ str: Selected Torch device identifier string.
+ """
+
+ if not self.cpu_mode:
+ if torch.cuda.is_available(): # 1. NVIDIA CUDA Execution Track
+ device = f"cuda:{self.pytorch_device_idx}"
+ device_properties = torch.cuda.get_device_properties(self.pytorch_device_idx)
+
+ self.major = device_properties.major
+ self.gpu_mem = device_properties.total_memory // (1024**3)
+ elif hasattr(torch, "xpu") and torch.xpu.is_available(): # 2. Intel XPU Execution Track
+ device = f"xpu:{self.pytorch_device_idx}"
+ device_properties = torch.xpu.get_device_properties(self.pytorch_device_idx)
+
+ self.gpu_mem = device_properties.total_memory // (1024**3)
+ self.onnx_device_idx["device_type"] = "GPU"
+
+ xpu.setup_onnxruntime_xpu()
+ xpu.setup_gradscaler()
+ elif directml.is_available(): # 3. Windows DirectML Execution Track
+ device = f"privateuseone:{self.pytorch_device_idx}"
+ device_properties = directml.get_device_properties(self.pytorch_device_idx)
+
+ self.gpu_mem = device_properties.total_memory // (1024**3)
+ self.onnx_device_idx = {"device_id": directml.mapping_directml(self.pytorch_device_idx)}
+ elif opencl.is_available(): # 4. Cross-platform OpenCL Track
+ device = f"ocl:{self.pytorch_device_idx}"
+ device_properties = opencl.get_device_properties(self.pytorch_device_idx)
+
+ self.gpu_mem = device_properties.total_memory // (1024**3)
+ elif torch.backends.mps.is_available(): # 5. Apple Silicon Metal Performance Shaders (MPS) Track
+ # Not entirely certain; it's just inherited from the original RVC.
+ device = "mps"
+ else: # 6. Fallback path if no accelerators are configured or detected
+ device = "cpu"
+ self.setup_cpu_mode()
+ else:
+ device = "cpu"
+ self.setup_cpu_mode()
+
+ return device
+
+ def get_providers(self):
+ """
+ Generates an ordered priority list of execution provider mappings for ONNX Runtime sessions.
+
+ Returns:
+ list: List of execution provider tuples containing backend names and configuration dicts.
+ """
+
+ ort_providers = onnxruntime.get_available_providers() if self.cpu_mode is False else []
+ providers = []
+
+ if not self.cpu_mode and not self.device.startswith("cpu"):
+ # Check and append Nvidia hardware acceleration stacks
+ if "TensorrtExecutionProvider" in ort_providers and self.tensorrt: providers.append(("TensorrtExecutionProvider", self.onnx_device_idx))
+ if "CUDAExecutionProvider" in ort_providers: providers.append(("CUDAExecutionProvider", self.onnx_device_idx))
+ # Check and append AMD ROCm hardware acceleration stacks
+ if "ROCMExecutionProvider" in ort_providers: providers.append(("ROCMExecutionProvider", self.onnx_device_idx))
+ if "MIGraphXExecutionProvider" in ort_providers: providers.append(("MIGraphXExecutionProvider", self.onnx_device_idx))
+ # Check and append DirectX/DirectML or Intel OpenVINO stacks
+ if "DmlExecutionProvider" in ort_providers: providers.append(("DmlExecutionProvider", self.onnx_device_idx))
+ if "OpenVINOExecutionProvider" in ort_providers: providers.append(("OpenVINOExecutionProvider", self.onnx_device_idx))
+ # Intel OneDNN or Apple CoreML structures
+ if "DnnlExecutionProvider" in ort_providers: providers.append(("DnnlExecutionProvider", {}))
+ if "CoreMLExecutionProvider" in ort_providers: providers.append(("CoreMLExecutionProvider", {}))
+
+ # Ensure baseline CPU fallback option always sits at the bottom of the stack
+ providers.append(("CPUExecutionProvider", {}))
+ return providers
+
+ def get_gpu_list(self):
+ """
+ Retrieves a collection listing names and storage capacities of every usable graphics card
+ matching the current application context.
+
+ Returns:
+ tuple: (int: total_gpu_count, list: list_of_gpu_specification_strings)
+ """
+
+ if self.device.startswith("cuda"):
+ ngpu = torch.cuda.device_count()
+ gpu_infos = [
+ (
+ f"{i}: {torch.cuda.get_device_name(i)} " +
+ f"({int(torch.cuda.get_device_properties(i).total_memory / (1024**3) + 0.4)} GB)"
+ )
+ for i in range(ngpu)
+ if self.device.startswith("cuda") or ngpu != 0
+ ]
+ elif self.device.startswith("xpu"):
+ ngpu = torch.xpu.device_count()
+ gpu_infos = [
+ (
+ f"{i}: {torch.xpu.get_device_name(i)} " +
+ f"({int(torch.xpu.get_device_properties(i).total_memory / (1024**3) + 0.4)} GB)"
+ )
+ for i in range(ngpu)
+ if self.device.startswith("xpu") or ngpu != 0
+ ]
+ elif self.device.startswith("privateuseone"):
+ ngpu = directml.device_count()
+
+ gpu_infos = [
+ (
+ f"{i}: {directml.get_device_name(i)} " +
+ f"({int(directml.get_device_properties(i).total_memory / (1024**3) + 0.4)} GB)"
+ )
+ for i in range(ngpu)
+ if self.device.startswith("privateuseone") or ngpu != 0
+ ]
+ elif self.device.startswith("ocl"):
+ ngpu = opencl.device_count()
+
+ gpu_infos = [
+ (
+ f"{i}: {opencl.get_device_name(i)} " +
+ f"({int(opencl.get_device_properties(i).total_memory / (1024**3) + 0.4)} GB)"
+ )
+ for i in range(ngpu)
+ if self.device.startswith("ocl") or ngpu != 0
+ ]
+ else:
+ ngpu = 0
+ gpu_infos = []
+
+ return ngpu, gpu_infos
\ No newline at end of file
diff --git a/main/configs/rpc.py b/main/configs/rpc.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b86c71dc43720859782e581ff198aae71805ee2
--- /dev/null
+++ b/main/configs/rpc.py
@@ -0,0 +1,125 @@
+import os
+import sys
+import json
+import time
+import struct
+import codecs
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations
+
+CLIENT_ID = "1392816674159202396"
+
+def create_payload(opcode, payload):
+ """
+ Encodes a payload dictionary into JSON and packs it into a binary frame
+ following the Discord IPC protocol specification.
+
+ Args:
+ opcode (int): The operation code (e.g., 0 for Handshake, 1 for Frame).
+ payload (dict): The dictionary containing data/command arguments.
+
+ Returns:
+ bytes: The fully structured binary payload ready to be sent over the pipe.
+ """
+
+ # Serialize the payload dictionary into a UTF-8 encoded bytes sequence
+ data = json.dumps(payload).encode("utf-8")
+
+ # Structure:
+ # - "= 1:
+ # Design a 4th-order low-pass Butterworth filter
+ b, a = butter(4, frequency / (0.5 * sample_rate), btype='low')
+ boosted = _filtfilt(b, a, audio)
+ # Apply linear scale gain conversion to boosted components
+ return boosted * (10 ** (gain_db / 20))
+ return audio
+
+ def treble_boost(audio, gain_db, frequency, sample_rate):
+ """Applies a high-pass Butterworth filter to boost high frequencies (treble)."""
+
+ if gain_db >= 1:
+ # Design a 4th-order high-pass Butterworth filter
+ b, a = butter(4, frequency / (0.5 * sample_rate), btype='high')
+ boosted = _filtfilt(b, a, audio)
+ # Apply linear scale gain conversion to boosted components
+ return boosted * (10 ** (gain_db / 20))
+ return audio
+
+ def fade_out_effect(audio, sr, duration=3.0):
+ """Applies a linear fade-out attenuation envelope to the end of the track."""
+
+ length = int(duration * sr)
+ end = audio.shape[0]
+ if length > end: length = end
+ start = end - length
+ # Multiply audio signal with a linear vector dropping from 1.0 down to 0.0
+ audio[start:end] = audio[start:end] * np.linspace(1.0, 0.0, length)
+ return audio
+
+ def fade_in_effect(audio, sr, duration=3.0):
+ """Applies a linear fade-in attenuation envelope to the beginning of the track."""
+
+ length = int(duration * sr)
+ start = 0
+ if length > audio.shape[0]: length = audio.shape[0]
+ end = length
+ # Multiply audio signal with a linear vector growing from 0.0 up to 1.0
+ audio[start:end] = audio[start:end] * np.linspace(0.0, 1.0, length)
+ return audio
+
+ if not input_path or not os.path.exists(input_path):
+ logger.warning(translations["input_not_valid"])
+ sys.exit(1)
+
+ if not output_path:
+ logger.warning(translations["output_not_valid"])
+ sys.exit(1)
+
+ # Overwrite protection: Clear existing target file before rewriting
+ if os.path.exists(output_path): os.remove(output_path)
+
+ try:
+ # Sanitize path formatting strings
+ input_path = input_path.strip().strip('"').strip("\n").strip('"').strip()
+
+ try:
+ # Primary choice: Use Soundfile for fast high-precision reading
+ audio, sample_rate = sf.read(input_path, dtype=np.float64)
+ except:
+ # Fallback choice: Use Librosa if format is unsupported by Soundfile
+ audio, sample_rate = librosa.load(input_path, sr=None)
+ except Exception as e:
+ logger.debug(e)
+ raise RuntimeError(translations['errors_loading_audio'])
+
+ try:
+ # Initialize Pedalboard structure starting with a clean highpass filter baseline
+ board = Pedalboard([HighpassFilter()])
+
+ if chorus:
+ board.append(
+ Chorus(
+ depth=chorus_depth,
+ rate_hz=chorus_rate,
+ mix=chorus_mix,
+ centre_delay_ms=chorus_delay,
+ feedback=chorus_feedback
+ )
+ )
+
+ if distortion:
+ board.append(
+ Distortion(
+ drive_db=distortion_drive
+ )
+ )
+
+ if reverb:
+ board.append(
+ Reverb(
+ room_size=reverb_room_size,
+ damping=reverb_damping,
+ wet_level=reverb_wet_level,
+ dry_level=reverb_dry_level,
+ width=reverb_width,
+ freeze_mode=int(reverb_freeze_mode)
+ )
+ )
+
+ if pitchshift:
+ board.append(
+ PitchShift(
+ semitones=pitch_shift
+ )
+ )
+
+ if delay:
+ board.append(
+ Delay(
+ delay_seconds=delay_seconds,
+ feedback=delay_feedback,
+ mix=delay_mix
+ )
+ )
+
+ if compressor:
+ board.append(
+ Compressor(
+ threshold_db=compressor_threshold,
+ ratio=compressor_ratio,
+ attack_ms=compressor_attack_ms,
+ release_ms=compressor_release_ms
+ )
+ )
+
+ if limiter:
+ board.append(
+ Limiter(
+ threshold_db=limiter_threshold,
+ release_ms=limiter_release
+ )
+ )
+
+ if gain:
+ board.append(
+ Gain(
+ gain_db=gain_db
+ )
+ )
+
+ if bitcrush:
+ board.append(
+ Bitcrush(
+ bit_depth=bitcrush_bit_depth
+ )
+ )
+
+ if clipping:
+ board.append(
+ Clipping(
+ threshold_db=clipping_threshold
+ )
+ )
+
+ if phaser: board.append(
+ Phaser(
+ rate_hz=phaser_rate_hz,
+ depth=phaser_depth,
+ centre_frequency_hz=phaser_centre_frequency_hz,
+ feedback=phaser_feedback,
+ mix=phaser_mix
+ )
+ )
+
+ # Process the raw waveform through our pedalboard chain configuration
+ processed_audio = board(audio, sample_rate)
+
+ # Apply manual EQ Filters if enabled
+ if treble_bass_boost:
+ processed_audio = bass_boost(
+ processed_audio,
+ bass_boost_db,
+ bass_boost_frequency,
+ sample_rate
+ )
+
+ processed_audio = treble_boost(
+ processed_audio,
+ treble_boost_db,
+ treble_boost_frequency,
+ sample_rate
+ )
+
+ # Apply millisecond fade transitions if enabled
+ if fade_in_out:
+ # Convert millisecond parameter durations to relative time ratios
+ processed_audio = fade_in_effect(
+ processed_audio,
+ sample_rate,
+ fade_in_duration / 1000
+ )
+
+ processed_audio = fade_out_effect(
+ processed_audio,
+ sample_rate,
+ fade_out_duration / 1000
+ )
+
+ # Handle audio resampling adjustments
+ if resample and resample_sr != sample_rate and resample_sr > 0:
+ processed_audio = librosa.resample(
+ processed_audio,
+ orig_sr=sample_rate,
+ target_sr=resample_sr,
+ res_type="soxr_vhq"
+ )
+
+ sample_rate = resample_sr
+
+ # Save primary outputs out to system file systems
+ sf.write(
+ replace_export_format(output_path, export_format),
+ processed_audio,
+ sample_rate,
+ format=export_format
+ )
+
+ if audio_combination:
+ # Load overlay tracks, apply volume changes, blend them together, and re-export
+ pydub_load(
+ audio_combination_input,
+ combination_volume
+ ).overlay(
+ pydub_load(
+ replace_export_format(output_path, export_format),
+ main_volume
+ )
+ ).export(
+ replace_export_format(output_path, export_format),
+ format=export_format
+ )
+ except Exception as e:
+ import traceback
+ logger.debug(traceback.format_exc())
+ raise RuntimeError(translations["apply_error"].format(e=e))
+ return output_path
+
+def main():
+ """
+ Main execution entry point. Parses runtime parameters and passes them
+ directly into the audio processing suite.
+ """
+
+ args = parse_arguments()
+
+ process_audio(
+ input_path=args.input_path,
+ output_path=args.output_path,
+ resample=args.resample,
+ resample_sr=args.resample_sr,
+ chorus_depth=args.chorus_depth,
+ chorus_rate=args.chorus_rate,
+ chorus_mix=args.chorus_mix,
+ chorus_delay=args.chorus_delay,
+ chorus_feedback=args.chorus_feedback,
+ distortion_drive=args.drive_db,
+ reverb_room_size=args.reverb_room_size,
+ reverb_damping=args.reverb_damping,
+ reverb_wet_level=args.reverb_wet_level,
+ reverb_dry_level=args.reverb_dry_level,
+ reverb_width=args.reverb_width,
+ reverb_freeze_mode=args.reverb_freeze_mode,
+ pitch_shift=args.pitch_shift,
+ delay_seconds=args.delay_seconds,
+ delay_feedback=args.delay_feedback,
+ delay_mix=args.delay_mix,
+ compressor_threshold=args.compressor_threshold,
+ compressor_ratio=args.compressor_ratio,
+ compressor_attack_ms=args.compressor_attack_ms,
+ compressor_release_ms=args.compressor_release_ms,
+ limiter_threshold=args.limiter_threshold,
+ limiter_release=args.limiter_release,
+ gain_db=args.gain_db,
+ bitcrush_bit_depth=args.bitcrush_bit_depth,
+ clipping_threshold=args.clipping_threshold,
+ phaser_rate_hz=args.phaser_rate_hz,
+ phaser_depth=args.phaser_depth,
+ phaser_centre_frequency_hz=args.phaser_centre_frequency_hz,
+ phaser_feedback=args.phaser_feedback,
+ phaser_mix=args.phaser_mix,
+ bass_boost_db=args.bass_boost_db,
+ bass_boost_frequency=args.bass_boost_frequency,
+ treble_boost_db=args.treble_boost_db,
+ treble_boost_frequency=args.treble_boost_frequency,
+ fade_in_duration=args.fade_in_duration,
+ fade_out_duration=args.fade_out_duration,
+ export_format=args.export_format,
+ chorus=args.chorus,
+ distortion=args.distortion,
+ reverb=args.reverb,
+ pitchshift=args.pitchshift,
+ delay=args.delay,
+ compressor=args.compressor,
+ limiter=args.limiter,
+ gain=args.gain,
+ bitcrush=args.bitcrush,
+ clipping=args.clipping,
+ phaser=args.phaser,
+ treble_bass_boost=args.treble_bass_boost,
+ fade_in_out=args.fade_in_out,
+ audio_combination=args.audio_combination,
+ audio_combination_input=args.audio_combination_input,
+ main_volume=args.main_volume,
+ combination_volume=args.combination_volume
+ )
+
+if __name__ == "__main__": main()
\ No newline at end of file
diff --git a/main/inference/conversion/convert.py b/main/inference/conversion/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..78c676afdd4d95855df316fcd4b11cc4f8dab2a5
--- /dev/null
+++ b/main/inference/conversion/convert.py
@@ -0,0 +1,768 @@
+import os
+import sys
+import time
+import torch
+import librosa
+import logging
+import argparse
+import warnings
+
+import numpy as np
+import soundfile as sf
+
+from tqdm import tqdm
+from scipy import signal
+
+sys.path.append(os.getcwd())
+
+from main.library.audio.upscaler import FlashSR
+from main.app.core.ui import replace_export_format
+from main.library.audio.noisereduce import TorchGate
+from main.inference.conversion.pipeline import Pipeline
+from main.library.audio.audio import load_audio, cut, restore
+from main.app.variables import config, logger, translations, file_types
+from main.library.audio.audio_processing import preprocess, postprocess
+from main.library.utils import check_assets, check_upscaler, load_embedders_model, load_model, strtobool, load_faiss_index
+
+if not config.debug_mode:
+ warnings.filterwarnings("ignore")
+
+ for l in [
+ "torch",
+ "faiss",
+ "omegaconf",
+ "httpx",
+ "httpcore",
+ "faiss.loader",
+ "numba.core",
+ "urllib3",
+ "transformers",
+ "matplotlib"
+ ]:
+ logging.getLogger(l).setLevel(logging.ERROR)
+
+def parse_arguments():
+ """
+ Parses command-line arguments for the voice conversion pipeline.
+
+ Returns:
+ argparse.Namespace: The parsed command-line arguments.
+ """
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--convert", action='store_true')
+ parser.add_argument("--pitch", type=int, default=0)
+ parser.add_argument("--filter_radius", type=int, default=3)
+ parser.add_argument("--index_rate", type=float, default=0.5)
+ parser.add_argument("--rms_mix_rate", type=float, default=1)
+ parser.add_argument("--protect", type=float, default=0.33)
+ parser.add_argument("--hop_length", type=int, default=64)
+ parser.add_argument("--f0_method", type=str, default="rmvpe")
+ parser.add_argument("--embedder_model", type=str, default="hubert_base")
+ parser.add_argument("--input_path", type=str, required=True)
+ parser.add_argument("--output_path", type=str, default="./audios/output.wav")
+ parser.add_argument("--export_format", type=str, default="wav")
+ parser.add_argument("--pth_path", type=str, required=True)
+ parser.add_argument("--index_path", type=str, default="")
+ parser.add_argument("--f0_autotune", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--f0_autotune_strength", type=float, default=1)
+ parser.add_argument("--clean_audio", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--clean_strength", type=float, default=0.7)
+ parser.add_argument("--resample_sr", type=int, default=0)
+ parser.add_argument("--split_audio", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--checkpointing", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--f0_file", type=str, default="")
+ parser.add_argument("--predictor_onnx", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--embedders_mode", type=str, default="fairseq")
+ parser.add_argument("--formant_shifting", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--formant_qfrency", type=float, default=0.8)
+ parser.add_argument("--formant_timbre", type=float, default=0.8)
+ parser.add_argument("--proposal_pitch", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--proposal_pitch_threshold", type=float, default=255.0)
+ parser.add_argument("--audio_processing", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--alpha", type=float, default=0.5)
+ parser.add_argument("--sid", type=int, default=0, required=False)
+ parser.add_argument("--embedders_mix", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--embedders_mix_layers", type=int, default=9, required=False)
+ parser.add_argument("--embedders_mix_ratio", type=float, default=0.5)
+ parser.add_argument("--noise_scale", type=float, default=0.35)
+ parser.add_argument("--nprobe", type=int, default=1)
+ parser.add_argument("--audio_upscaler", type=lambda x: bool(strtobool(x)), default=False)
+
+ return parser.parse_args()
+
+def main():
+ """
+ Main entry point function. Unpacks command line arguments and hands over execution
+ to the conversion controller.
+ """
+
+ args = parse_arguments()
+
+ # Unpack parsed arguments safely into a local tuple structure
+ (
+ pitch,
+ filter_radius,
+ index_rate,
+ rms_mix_rate,
+ protect,
+ hop_length,
+ f0_method,
+ input_path,
+ output_path,
+ pth_path,
+ index_path,
+ f0_autotune,
+ f0_autotune_strength,
+ clean_audio,
+ clean_strength,
+ export_format,
+ embedder_model,
+ resample_sr,
+ split_audio,
+ checkpointing,
+ f0_file,
+ predictor_onnx,
+ embedders_mode,
+ formant_shifting,
+ formant_qfrency,
+ formant_timbre,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ audio_processing,
+ alpha,
+ sid,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ noise_scale,
+ nprobe,
+ audio_upscaler
+ ) = (
+ args.pitch,
+ args.filter_radius,
+ args.index_rate,
+ args.rms_mix_rate,
+ args.protect,
+ args.hop_length,
+ args.f0_method,
+ args.input_path,
+ args.output_path,
+ args.pth_path,
+ args.index_path,
+ args.f0_autotune,
+ args.f0_autotune_strength,
+ args.clean_audio,
+ args.clean_strength,
+ args.export_format,
+ args.embedder_model,
+ args.resample_sr,
+ args.split_audio,
+ args.checkpointing,
+ args.f0_file,
+ args.predictor_onnx,
+ args.embedders_mode,
+ args.formant_shifting,
+ args.formant_qfrency,
+ args.formant_timbre,
+ args.proposal_pitch,
+ args.proposal_pitch_threshold,
+ args.audio_processing,
+ args.alpha,
+ args.sid,
+ args.embedders_mix,
+ args.embedders_mix_layers,
+ args.embedders_mix_ratio,
+ args.noise_scale,
+ args.nprobe,
+ args.audio_upscaler
+ )
+
+ # Run the compiled configurations in the pipeline controller
+ run_convert_script(
+ pitch=pitch,
+ filter_radius=filter_radius,
+ index_rate=index_rate,
+ rms_mix_rate=rms_mix_rate,
+ protect=protect,
+ hop_length=hop_length,
+ f0_method=f0_method,
+ input_path=input_path,
+ output_path=output_path,
+ pth_path=pth_path,
+ index_path=index_path,
+ f0_autotune=f0_autotune,
+ f0_autotune_strength=f0_autotune_strength,
+ clean_audio=clean_audio,
+ clean_strength=clean_strength,
+ export_format=export_format,
+ embedder_model=embedder_model,
+ resample_sr=resample_sr,
+ split_audio=split_audio,
+ checkpointing=checkpointing,
+ f0_file=f0_file,
+ predictor_onnx=predictor_onnx,
+ embedders_mode=embedders_mode,
+ formant_shifting=formant_shifting,
+ formant_qfrency=formant_qfrency,
+ formant_timbre=formant_timbre,
+ proposal_pitch=proposal_pitch,
+ proposal_pitch_threshold=proposal_pitch_threshold,
+ audio_processing=audio_processing,
+ alpha=alpha,
+ sid=sid,
+ embedders_mix=embedders_mix,
+ embedders_mix_layers=embedders_mix_layers,
+ embedders_mix_ratio=embedders_mix_ratio,
+ noise_scale=noise_scale,
+ nprobe=nprobe,
+ audio_upscaler=audio_upscaler
+ )
+
+def run_convert_script(
+ pitch=0,
+ filter_radius=3,
+ index_rate=0.5,
+ rms_mix_rate=1,
+ protect=0.5,
+ hop_length=64,
+ f0_method="rmvpe",
+ input_path=None,
+ output_path="./output.wav",
+ pth_path=None,
+ index_path=None,
+ f0_autotune=False,
+ f0_autotune_strength=1,
+ clean_audio=False,
+ clean_strength=0.7,
+ export_format="wav",
+ embedder_model="hubert_base",
+ resample_sr=0,
+ split_audio=False,
+ checkpointing=False,
+ f0_file=None,
+ predictor_onnx=False,
+ embedders_mode="fairseq",
+ formant_shifting=False,
+ formant_qfrency=0.8,
+ formant_timbre=0.8,
+ proposal_pitch=False,
+ proposal_pitch_threshold=255.0,
+ audio_processing=False,
+ alpha=0.5,
+ sid=0,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5,
+ noise_scale = 0.35,
+ nprobe = 1,
+ audio_upscaler = False
+):
+ """
+ Validates components, coordinates IO batching/single files, and maps runtime configurations
+ to execute the conversion pipeline.
+
+ Args:
+ pitch (int): Semitone pitch shift value. Defaults to 0.
+ filter_radius (int): Median filter radius for pitch smoothing. Defaults to 3.
+ index_rate (float): FAISS index feature blend ratio. Defaults to 0.5.
+ rms_mix_rate (float): Volume envelope blend ratio. Defaults to 1.
+ protect (float): Consonant protection factor threshold. Defaults to 0.5.
+ hop_length (int): Frame step size for pitch tracking components. Defaults to 64.
+ f0_method (str): Core pitch extraction methodology name. Defaults to "rmvpe".
+ input_path (str): Target file system directory or single audio file location. Defaults to None.
+ output_path (str): Path where processed audio will be saved. Defaults to "./output.wav".
+ pth_path (str): Path to weights file (.pth or .onnx formats). Defaults to None.
+ index_path (str): Path to matching .index file tracker. Defaults to None.
+ f0_autotune (bool): Enable snapping the F0 pitch sequence to the nearest musical notes.
+ f0_autotune_strength (float): Blend factor for autotune (0.0 = raw pitch, 1.0 = fully snapped).
+ clean_audio (bool): Applies background noise gating blocks. Defaults to False.
+ clean_strength (float): Suppression ratio for noise reduction gate. Defaults to 0.7.
+ export_format (str): Outbound audio encoding identifier (e.g., 'wav', 'mp3'). Defaults to "wav".
+ embedder_model (str): Acoustic feature model identification. Defaults to "hubert_base".
+ resample_sr (int): Forced target resample sampling rate. Defaults to 0.
+ split_audio (bool): Segments long files by silence thresholds. Defaults to False.
+ checkpointing (bool): Toggles PyTorch gradient checkpointing blocks. Defaults to False.
+ f0_file (str): Input external manual pitch data matrix track. Defaults to None.
+ predictor_onnx (bool): Switches F0 tracking to accelerated ONNX backend. Defaults to False.
+ embedders_mode (str): Framework source indicator for speech embedding models. Defaults to "fairseq".
+ formant_shifting (bool): Modulates vocal format frequencies early on. Defaults to False.
+ formant_qfrency (float): Formant queuing factor filter width. Defaults to 0.8.
+ formant_timbre (float): Formant frequency transposition scalar ratio. Defaults to 0.8.
+ proposal_pitch (bool): Enable automatic pitch key shifting calculation based on median F0 alignment.
+ proposal_pitch_threshold (float): The maximum allowed semitone boundary limit (floor/ceiling) for the proposed pitch shift calculation.
+ audio_processing (bool): Evaluates early/late acoustic stage equalization treatments. Defaults to False.
+ alpha (float): Feature blending weighting for underlying components. Defaults to 0.5.
+ sid (int): Target speaker index for multi-speaker networks. Defaults to 0.
+ embedders_mix (bool): Combines multiple internal layer hidden states. Defaults to False.
+ embedders_mix_layers (int): Number of embedding layers to slice. Defaults to 9.
+ embedders_mix_ratio (float): Proportional weighting for embedding layers blending. Defaults to 0.5.
+ noise_scale (float): Control scalar scaling flow variance. Defaults to 0.35.
+ nprobe (int): IVF FAISS space search accuracy parameter. Defaults to 1.
+ audio_upscaler (bool): Enforces super-resolution upscaling pipelines. Defaults to False.
+ """
+
+ # Verify presence of dependent external assets and upscaler weights
+ if audio_upscaler: check_upscaler()
+ check_assets(f0_method, embedder_model, predictor_onnx=predictor_onnx, embedders_mode=embedders_mode)
+
+ # Dictionary construction for debugging logs containing human-readable localizations
+ log_data = {
+ translations["pitch"]: pitch,
+ translations["filter_radius"]: filter_radius,
+ translations["index_strength"]: index_rate,
+ translations["rms_mix_rate"]: rms_mix_rate,
+ translations["protect"]: protect,
+ translations["hop_length"]: hop_length,
+ translations["f0_method"]: f0_method,
+ translations["audio_path"]: input_path,
+ translations["output_path"]: replace_export_format(output_path, export_format),
+ translations["model_path"]: pth_path,
+ translations["indexpath"]: index_path,
+ translations["autotune"]: f0_autotune,
+ translations["autotune_rate_info"]: f0_autotune_strength,
+ translations["clear_audio"]: clean_audio,
+ translations["clean_strength"]: clean_strength,
+ translations["sample_rate"]: resample_sr,
+ translations["export_format"]: export_format,
+ translations["hubert_model"]: embedder_model,
+ translations["split_audio"]: split_audio,
+ translations["memory_efficient_training"]: checkpointing,
+ translations["predictor_onnx"]: predictor_onnx,
+ translations["f0_file"]: f0_file,
+ translations["embed_mode"]: embedders_mode,
+ translations["proposal_pitch"]: proposal_pitch,
+ translations["proposal_pitch_threshold"]: proposal_pitch_threshold,
+ translations["audio_processing"]: audio_processing,
+ translations["alpha_label"]: alpha,
+ translations["embedders_mix"]: embedders_mix,
+ translations["embedders_mix_layers"]: embedders_mix_layers,
+ translations["embedders_mix_ratio"]: embedders_mix_ratio,
+ translations["formant_qfrency"]: formant_qfrency,
+ translations["formant_timbre"]: formant_timbre,
+ translations["noise_scale"]: noise_scale,
+ translations["nprobe"]: nprobe,
+ translations["audio_upscaler"]: audio_upscaler
+ }
+
+ for key, value in log_data.items():
+ logger.debug(f"{key}: {value}")
+
+ # Model sanity validation check
+ if not pth_path or not os.path.exists(pth_path) or os.path.isdir(pth_path) or not pth_path.endswith((".pth", ".onnx")):
+ logger.warning(translations["provide_model"])
+ sys.exit(1)
+
+ start_time = time.time()
+ # Instantiate the wrapper converter architecture
+ cvt = VoiceConverter(pth_path, embedder_model, embedders_mode, sid, noise_scale, checkpointing, hop_length, alpha, predictor_onnx, clean_audio, clean_strength, audio_upscaler)
+
+ # Write current Process ID (PID) to disk so other sub-processes or GUIs can track/cancel this task
+ pid_path = os.path.join("assets", "convert_pid.txt")
+ with open(pid_path, "w") as pid_file:
+ pid_file.write(str(os.getpid()))
+
+ def convert_audio(audio_path, output_audio):
+ """Helper inner function wrapping the conversion pipeline parameters invocation."""
+
+ cvt.convert_audio(
+ pitch=pitch,
+ filter_radius=filter_radius,
+ index_rate=index_rate,
+ rms_mix_rate=rms_mix_rate,
+ protect=protect,
+ f0_method=f0_method,
+ audio_input_path=audio_path,
+ audio_output_path=output_audio,
+ index_path=index_path,
+ f0_autotune=f0_autotune,
+ f0_autotune_strength=f0_autotune_strength,
+ export_format=export_format,
+ resample_sr=resample_sr,
+ f0_file=f0_file,
+ formant_shifting=formant_shifting,
+ formant_qfrency=formant_qfrency,
+ formant_timbre=formant_timbre,
+ split_audio=split_audio,
+ proposal_pitch=proposal_pitch,
+ proposal_pitch_threshold=proposal_pitch_threshold,
+ audio_processing=audio_processing,
+ embedders_mix=embedders_mix,
+ embedders_mix_layers=embedders_mix_layers,
+ embedders_mix_ratio=embedders_mix_ratio,
+ nprobe=nprobe
+ )
+
+ if os.path.isdir(input_path):
+ # Case A: Batch Processing if the input path points to a Directory
+
+ logger.info(translations["convert_batch"])
+ # Filter and extract compatible audio items from target path
+ audio_files = [
+ f
+ for f in os.listdir(input_path)
+ if f.lower().endswith(tuple(file_types))
+ ]
+
+ if not audio_files:
+ logger.warning(translations["not_found_audio"])
+ sys.exit(1)
+
+ logger.info(translations["found_audio"].format(audio_files=len(audio_files)))
+ # Loop through valid audio filenames and initiate conversions
+ for audio in audio_files:
+ audio_path = os.path.join(input_path, audio)
+ output_audio = os.path.join(input_path, os.path.splitext(audio)[0] + f"_output.{export_format}")
+
+ logger.info(f"{translations['convert_audio']} '{audio_path}'...")
+ if os.path.exists(output_audio): os.remove(output_audio)
+
+ convert_audio(audio_path, output_audio)
+
+ logger.info(
+ translations["convert_batch_success"].format(
+ elapsed_time=f"{(time.time() - start_time):.2f}",
+ output_path=replace_export_format(output_path, export_format)
+ )
+ )
+ else:
+ # Case B: Standard single audio file processing
+
+ if not os.path.exists(input_path):
+ logger.warning(translations["not_found_audio"])
+ sys.exit(1)
+
+ logger.info(f"{translations['convert_audio']} '{input_path}'...")
+ if os.path.exists(output_path): os.remove(output_path)
+
+ convert_audio(input_path, output_path)
+
+ logger.info(
+ translations["convert_audio_success"].format(
+ input_path=input_path,
+ elapsed_time=f"{(time.time() - start_time):.2f}",
+ output_path=replace_export_format(output_path, export_format)
+ )
+ )
+
+ # Cleanup the PID file after execution finishes successfully
+ if os.path.exists(pid_path): os.remove(pid_path)
+
+class VoiceConverter:
+ """
+ Handles initialization of neural networks (Vocoders, Embedders, Synthesisers)
+ and drives the overall inference sequence.
+ """
+
+ def __init__(
+ self,
+ model_path,
+ embedder_model,
+ embedders_mode,
+ sid = 0,
+ noise_scale = 0.35,
+ checkpointing = False,
+ hop_length = 160,
+ alpha = 0.5,
+ predictor_onnx = False,
+ clean_audio = False,
+ clean_strength = 0.5,
+ audio_upscaler = False
+ ):
+ """
+ Initializes sub-components, handles tensor dimensions, and configures hardware mapping.
+
+ Args:
+ model_path (str): File system path to the generator target weights file (.pth or .onnx).
+ embedder_model (str): Name string identifier for the target speech representation embedder model.
+ embedders_mode (str): Backend selection framework flag (e.g., 'fairseq').
+ sid (int): Source/Target speaker unique identifier integer index. Defaults to 0.
+ noise_scale (float): Ground truth flow-matching distribution scale variance. Defaults to 0.35.
+ checkpointing (bool): Reduces peak memory consumption footprints at the cost of processing speed. Defaults to False.
+ hop_length (int): Resolution frame configuration window stride length. Defaults to 160.
+ alpha (float): Balancing parameter ratio across underlying modules. Defaults to 0.5.
+ predictor_onnx (bool): Routes frequency extraction modules into ONNX runtime engine. Defaults to False.
+ clean_audio (bool): Activates noise reduction threshold filter profiles. Defaults to False.
+ clean_strength (float): Suppression gain level factor for the acoustic noise gate. Defaults to 0.5.
+ audio_upscaler (bool): Flags ultra-high super-resolution networks execution. Defaults to False.
+ """
+
+ self.vc = None
+ self.index = None
+ self.net_g = None
+ self.tgt_sr = None
+ self.big_tsr = None
+ self.hubert_model = None
+ self.f0_generator = None
+
+ self.alpha = alpha
+ self.sample_rate = 16000
+ self.hop_length = hop_length
+ self.predictor_onnx = predictor_onnx
+ # Decide floating point mode based on configuration settings (FP16 or FP32)
+ self.dtype = torch.float16 if config.is_half else torch.float32
+
+ # Step 1: Initialize the acoustic embedder model (HuBERT/ContentVec)
+ self.setup_hubert(embedder_model, embedders_mode)
+ # Step 2: Initialize generator weights (RVC/SVC Synthesizer backbone)
+ self.setup_vc(model_path, sid, checkpointing, noise_scale)
+ # Step 3: Conditional setup for noise gating modules
+ self.tg = TorchGate(self.tgt_sr, prop_decrease=clean_strength).to(config.device) if clean_audio else None
+ # Step 4: Conditional setup for audio super-resolution models
+ self.flash_sr = FlashSR(os.path.join("assets", "models", "upscalers", "upscalers.pth"), device=config.device, is_half=config.is_half) if audio_upscaler else None
+
+ def convert_audio(
+ self,
+ audio_input_path,
+ audio_output_path,
+ index_path,
+ pitch,
+ f0_method,
+ index_rate,
+ rms_mix_rate,
+ protect,
+ f0_autotune,
+ f0_autotune_strength,
+ filter_radius,
+ export_format,
+ resample_sr = 0,
+ f0_file = None,
+ formant_shifting = False,
+ formant_qfrency = 0.8,
+ formant_timbre = 0.8,
+ split_audio = False,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 0,
+ audio_processing = False,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5,
+ nprobe = 1
+ ):
+ """
+ Executes internal pipeline logic on loaded numpy waveforms to yield the
+ timbre-converted audio output.
+
+ Args:
+ audio_input_path (str): File path pointing to the original source audio sample.
+ audio_output_path (str): Path indicating where the final synthetic file will sit.
+ index_path (str): Matching feature indexing database vector path file (.index).
+ pitch (int): Transposition factor interval shifts in semitone quantities.
+ f0_method (str): Algorithm method name chosen to construct pitch tracks.
+ index_rate (float): Distance blending contribution ratio scale from FAISS query vectors.
+ rms_mix_rate (float): Volume dynamic envelope mixing factor scaling coefficient.
+ protect (float): Structural unvoiced phonemes and breath protect boundary threshold.
+ f0_autotune (bool): Enable snapping the F0 pitch sequence to the nearest musical notes.
+ f0_autotune_strength (float): Blend factor for autotune (0.0 = raw pitch, 1.0 = fully snapped).
+ filter_radius (int): Window width used to compute median filter smoothing on tracking logs.
+ export_format (str): Audio output wrapper container metadata encoding format type.
+ resample_sr (int): Secondary forced outbound sample rate adjustment stage. Defaults to 0.
+ f0_file (str): Overriding custom timeline array values text file source. Defaults to None.
+ formant_shifting (bool): Pre-adjusts speech format structure early in the loading block. Defaults to False.
+ formant_qfrency (float): Shape resonance quality bandwidth adjustments mapping. Defaults to 0.8.
+ formant_timbre (float): Vowel structure timbre placement shift multiplier values. Defaults to 0.8.
+ split_audio (bool): Cuts dense sound files at low silence nodes into smaller batches. Defaults to False.
+ proposal_pitch (bool): Enable automatic pitch key shifting calculation based on median F0 alignment.
+ proposal_pitch_threshold (float): The maximum allowed semitone boundary limit (floor/ceiling) for the proposed pitch shift calculation.
+ audio_processing (bool): Activates signal equalization enhancement steps. Defaults to False.
+ embedders_mix (bool): Accumulates discrete deep layered representation layers together. Defaults to False.
+ embedders_mix_layers (int): Upper layer range constraint target slicing amount. Defaults to 9.
+ embedders_mix_ratio (float): Blending step coefficient ratio for the mixed representation values. Defaults to 0.5.
+ nprobe (int): Search depth parameter applied inside indexed space buckets. Defaults to 1.
+ """
+
+ def inference(audio, index, big_tsr, f0_file, pbar):
+ """Executes inner pipeline process flow call."""
+
+ return self.vc.pipeline(
+ audio=audio,
+ f0_up_key=pitch,
+ f0_method=f0_method,
+ index=index,
+ big_tsr=big_tsr,
+ index_rate=index_rate,
+ filter_radius=filter_radius,
+ rms_mix_rate=rms_mix_rate,
+ protect=protect,
+ f0_autotune=f0_autotune,
+ f0_autotune_strength=f0_autotune_strength,
+ f0_file=f0_file,
+ pbar=pbar,
+ proposal_pitch=proposal_pitch,
+ proposal_pitch_threshold=proposal_pitch_threshold,
+ embedders_mix=embedders_mix,
+ embedders_mix_layers=embedders_mix_layers,
+ embedders_mix_ratio=embedders_mix_ratio,
+ )
+
+ try:
+ with tqdm(total=10, desc=translations["convert_audio"], ncols=100, unit="a", leave=not split_audio) as pbar:
+ # Load input audio with optional early formant-shifting augmentations
+ audio = load_audio(audio_input_path, sample_rate=self.sample_rate, formant_shifting=formant_shifting, formant_qfrency=formant_qfrency, formant_timbre=formant_timbre)
+ if audio_processing: audio = preprocess(audio, self.sample_rate)
+
+ # Peak volume normalization safeguard block
+ try:
+ audio_max = np.abs(audio).max() / 0.95
+ if audio_max > 1: audio /= audio_max
+ except:
+ # In case of numeric failures, copy source file directly as a fallback measure
+ import shutil
+
+ shutil.copy(audio_input_path, audio_output_path)
+ return
+
+ # Dynamically load FAISS indices if vectors are required but absent
+ if index_rate != 0 and (self.index is None or self.big_tsr is None):
+ self.index, self.big_tsr = load_faiss_index(index_path.strip().strip('"').strip("\n").strip('"').strip().replace("trained", "added"), nprobe, cpu_mode=True)
+
+ pbar.update(1)
+ # Chunk splitting strategy for long or intensive files
+ if split_audio:
+ pbar.close()
+ # Segment audio files based on -60dB silence thresholds
+ chunks = cut(audio, self.sample_rate, db_thresh=-60, min_interval=500)
+
+ logger.info(f"{translations['split_total']}: {len(chunks)}")
+ # Instantiate recalculated chunk-length progress tracker bars
+ pbar = tqdm(total=len(chunks) * 5 + 4, desc=translations["convert_audio"], ncols=100, unit="a", leave=True)
+
+ pbar.update(1)
+
+ # Perform the underlying voice inference operations
+ if split_audio:
+ converted_chunks = [(start, end, inference(waveform, index=self.index, big_tsr=self.big_tsr, f0_file=None, pbar=pbar)) for waveform, start, end in chunks]
+ # Stitch converted audio pieces back together seamlessly
+ audio_output = restore(converted_chunks, total_len=len(audio), dtype=converted_chunks[0][2].dtype)
+ else: audio_output = inference(audio, index=self.index, big_tsr=self.big_tsr, f0_file=f0_file, pbar=pbar)
+
+ pbar.update(1)
+
+ # Acoustic post-processing filtering & Noise reduction gate evaluation
+ if audio_processing: audio_output = postprocess(audio_output, self.tgt_sr)
+ if self.tg is not None: audio_output = self.tg(torch.from_numpy(audio_output).unsqueeze(0).to(config.device).float()).squeeze(0).cpu().detach().numpy()
+
+ audio_output_resample = None
+ target_len = int(np.round(len(audio) / self.sample_rate * self.tgt_sr))
+ # Polyphase resampling filter alignment if length mismatches are observed
+ if len(audio_output) != target_len: audio_output = signal.resample_poly(audio_output, target_len, len(audio_output))
+
+ # Handle upscaling super-resolution routines or standard resampling
+ if self.flash_sr is not None:
+ audio_output_resample = self.flash_sr.upscaler(audio_output, sample_rate=self.tgt_sr, pbar=pbar)
+ self.tgt_sr = 192000 # FlashSR outputs ultra-high fidelity audio
+ elif self.tgt_sr != resample_sr and resample_sr > 0:
+ audio_output_resample = librosa.resample(audio_output, orig_sr=self.tgt_sr, target_sr=resample_sr, res_type="soxr_vhq")
+ self.tgt_sr = resample_sr
+
+ pbar.update(1)
+
+ # Export audio array block back into a physical file on disk
+ try:
+ sf.write(
+ audio_output_path,
+ audio_output if audio_output_resample is None else audio_output_resample,
+ self.tgt_sr,
+ format=export_format
+ )
+ except:
+ # Secondary fallback to highly compatible standard 48kHz sampling rate if hardware/drivers object
+ logger.info(translations["sr_not_support"])
+
+ sf.write(
+ audio_output_path,
+ librosa.resample(audio_output, orig_sr=self.tgt_sr, target_sr=48000, res_type="soxr_vhq"),
+ 48000,
+ format=export_format
+ )
+
+ pbar.update(1)
+ except Exception as e:
+ import traceback
+
+ logger.debug(traceback.format_exc())
+ logger.error(translations["error_convert"].format(e=e))
+
+ def setup_hubert(self, embedder_model, embedders_mode):
+ """Loads and optimizes the discrete feature representation model."""
+
+ models = load_embedders_model(embedder_model, embedders_mode)
+
+ if isinstance(models, torch.nn.Module):
+ models = models.to(config.device).to(self.dtype).eval()
+ # Compile model via TorchInductor if requested for accelerated graph execution
+ if config.compile_all: models = torch.compile(models, mode=config.compile_mode)
+
+ self.hubert_model = models
+
+ def setup_predictor(self):
+ """Prepares the fundamental frequency (F0) tracking generator module."""
+
+ from main.library.predictors.Generator import Generator
+
+ self.f0_generator = Generator(
+ self.sample_rate,
+ self.hop_length,
+ config.configs.get("f0_min", 50),
+ config.configs.get("f0_max", 1100),
+ self.alpha,
+ config.is_half,
+ config.device,
+ self.predictor_onnx
+ )
+
+ def setup_vc(self, weight_root, sid, checkpointing, noise_scale):
+ """Loads and configures the target voice synthesizer architectures."""
+
+ model = load_model(weight_root)
+
+ if weight_root.endswith(".pth"):
+ # Standard .pth PyTorch model pipeline
+ from main.library.algorithm.synthesizers import Synthesizer, SynthesizerSVC
+
+ self.tgt_sr = model["config"][-1]
+ model["config"][-3] = model["weight"]["emb_g.weight"].shape[0]
+
+ use_f0 = model.get("f0", 1)
+ version = model.get("version", "v1")
+ vocoder = model.get("vocoder", "Default")
+ hidden_dim = 768 if version == "v2" else 256
+
+ # Differentiate RVC vs SVC model architectures
+ if model.get("architecture", "RVC"):
+ self.net_g = Synthesizer(
+ *model["config"],
+ use_f0=use_f0,
+ text_enc_hidden_dim=hidden_dim,
+ vocoder=vocoder,
+ checkpointing=checkpointing
+ )
+ else:
+ self.net_g = SynthesizerSVC(
+ *model["config"],
+ text_enc_hidden_dim=hidden_dim,
+ vocoder=vocoder,
+ checkpointing=checkpointing,
+ noise_scale=noise_scale
+ )
+
+ # Inject weights into neural network state dictionary architectures
+ self.net_g.load_state_dict(model["weight"], strict=False)
+ self.net_g.eval().to(config.device).to(self.dtype)
+ self.net_g.remove_weight_norm()
+ del self.net_g.enc_q
+
+ if config.compile_all: self.net_g = torch.compile(self.net_g, mode=config.compile_mode)
+ else: # ONNX model fallback graph parsing routes
+ self.net_g = model.to(config.device)
+ self.tgt_sr = model.cpt.get("tgt_sr", 32000)
+ use_f0 = model.cpt.get("f0", 1)
+ version = model.cpt.get("version", "v1")
+
+ # Initialize tracking predictors if pitch computation is flagged
+ if use_f0: self.setup_predictor()
+ # Wrap configurations directly into runtime Pipeline components
+ sid = torch.tensor(sid, device=config.device).unsqueeze(0).long()
+ self.vc = Pipeline(self.tgt_sr, config, self.net_g, self.hubert_model, self.f0_generator, version, sid, self.dtype)
+
+if __name__ == "__main__": main()
\ No newline at end of file
diff --git a/main/inference/conversion/pipeline.py b/main/inference/conversion/pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf30226fbed7240ec13e011a502fb4ed9e9979a2
--- /dev/null
+++ b/main/inference/conversion/pipeline.py
@@ -0,0 +1,359 @@
+import os
+import sys
+import torch
+
+import numpy as np
+import torch.nn.functional as F
+
+from scipy import signal
+
+sys.path.append(os.getcwd())
+
+from main.library.audio.features import change_rms
+from main.app.variables import translations, logger
+from main.library.utils import extract_features, clear_gpu_cache
+
+# Initialize a 5th-order Butterworth high-pass filter to remove sub-bass noise below 48Hz
+bh, ah = signal.butter(
+ N=5,
+ Wn=48,
+ btype="high",
+ fs=16000
+)
+
+class Pipeline:
+ """
+ Handles the structured step-by-step processing for voice conversion.
+ Manages audio padding, chunking/slicing for long audio signals to optimize VRAM,
+ and coordinates the feature extraction and generator inference.
+ """
+
+ def __init__(
+ self,
+ tgt_sr,
+ config,
+ net_g,
+ hubert_model,
+ f0_generator,
+ version,
+ sid,
+ dtype
+ ):
+ """
+ Initializes pipeline hyperparameters, padding configurations, and core models.
+
+ Args:
+ tgt_sr (int): Target sampling rate for the output audio (e.g., 40000, 48000).
+ config (object): Configuration object containing hardware devices and padding thresholds.
+ net_g (torch.nn.Module): The generator network used for synthesizing the converted audio.
+ hubert_model (torch.nn.Module): Semantic speech feature extraction model (e.g., HuBERT/ContentVec).
+ f0_generator (object): Pitch extraction utility (e.g., Harvest, Crepe, PM). Can be None for f0-less models.
+ version (str): Model framework version flag ('v1' or 'v2').
+ sid (torch.Tensor): Speaker ID target index for multi-speaker synthesis.
+ dtype (torch.dtype): PyTorch tensor precision type (e.g., torch.float32, torch.float16).
+ """
+ self.window = 160
+ self.sample_rate = 16000
+ # Padding configurations for boundary artifact prevention
+ self.x_pad = config.x_pad
+ self.t_pad_tgt = tgt_sr * self.x_pad
+ self.t_pad = self.sample_rate * self.x_pad
+ self.t_max = self.sample_rate * config.x_max
+ self.t_query = self.sample_rate * config.x_query
+ self.t_center = self.sample_rate * config.x_center
+ self.t_pad2 = self.t_pad * 2
+ # Model and runtime environment settings
+ self.sid = sid
+ self.dtype = dtype
+ self.net_g = net_g
+ self.tgt_sr = tgt_sr
+ self.version = version
+ self.device = config.device
+ self.f0_generator = f0_generator
+ self.hubert_model = hubert_model
+ self.pitch_guidance = f0_generator is not None
+
+ def voice_conversion(
+ self,
+ audio,
+ pitch,
+ pitchf,
+ index,
+ big_tsr,
+ index_rate,
+ protect,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5
+ ):
+ """
+ Executes raw model-level inference for a single chunk of audio.
+
+ Args:
+ audio (np.ndarray): Input audio slice array.
+ pitch (torch.Tensor): Discretized pitch values.
+ pitchf (torch.Tensor): Continuous pitch float values.
+ index (object): The Index Wrapper class handles the retrieval process.
+ big_tsr (torch.Tensor): Saved base feature vectors corresponding to FAISS index.
+ index_rate (float): Blending ratio for index retrieval features.
+ protect (float): Consonant and breath sound protection scale factor.
+ embedders_mix (bool): Flag to mix multiple hidden layers of the embedder.
+ embedders_mix_layers (int): Number of layers to mix.
+ embedders_mix_ratio (float): Ratio weighting for layer mixing.
+
+ Returns:
+ np.ndarray: Converted raw float32 audio waveform chunk.
+ """
+
+ # Convert raw numpy audio chunk into a PyTorch tensor on target device
+ feats = torch.from_numpy(audio).to(self.device).to(self.dtype)
+ feats = feats.mean(-1) if feats.dim() == 2 else feats # Convert stereo to mono if needed
+ assert feats.dim() == 1, feats.dim()
+
+ with torch.inference_mode():
+ # Step 1: Extract semantic speech embedding features using the HuBERT/ContentVec model
+ feats = extract_features(
+ self.hubert_model,
+ feats.view(1, -1),
+ self.version,
+ mix=embedders_mix,
+ mix_layers=embedders_mix_layers,
+ mix_ratio=embedders_mix_ratio
+ )
+
+ # Save a clean copy of unblended features if the consonant protection mechanism is triggered
+ feats0 = feats.clone() if protect < 0.5 and self.pitch_guidance else None
+
+ # Step 2: Apply FAISS index feature mapping if index file is provided and index_rate > 0
+ if index is not None and big_tsr is not None and index_rate != 0:
+ # Perform K-Nearest Neighbors search (k=8) on the feature tensor
+ score, ix = index.search(feats[0], k=8)
+ weight = (1 / score).square() # Inverse distance squared weighting
+ # Compute weighted average of retrieved index vectors
+ query = (big_tsr[ix] * (weight / weight.sum(dim=1, keepdim=True)).unsqueeze(2)).sum(dim=1)
+ # Blend the retrieved query features with raw extracted features based on index_rate
+ feats = query.unsqueeze(0) * index_rate + (1.0 - index_rate) * feats
+
+ # Step 3: Up-sample the features by a factor of 2 to match the frame size expectations of the generator
+ feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+ p_len = min(audio.shape[0] // self.window, feats.shape[1])
+
+ # Align pitch trajectory dimensions with feature sequence length
+ if self.pitch_guidance: pitch, pitchf = pitch[:, :p_len], pitchf[:, :p_len]
+
+ # Step 4: Execute consonant and breath protection blending
+ if feats0 is not None:
+ pitchff = pitchf.clone()
+ pitchff[pitchf > 0] = 1 # Voiced frames take full conversion features
+ pitchff[pitchf < 1] = protect # Unvoiced frames blend original features based on 'protect'
+ pitchff = pitchff.unsqueeze(-1)
+
+ # Up-sample the copy of original features to match current sequence length
+ feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+ # Blend the features linearly using the protection mask
+ feats = (feats * pitchff + feats0 * (1 - pitchff)).to(feats0.dtype)
+
+ # Step 5: Synthesize final output audio waveform using the generator model
+ p_len = torch.tensor([p_len], device=self.device).long()
+ if self.pitch_guidance: pitchf = pitchf.to(self.dtype)
+ feats = feats.to(self.dtype)
+
+ audio1 = self.net_g.infer(
+ feats,
+ p_len,
+ pitch,
+ pitchf,
+ self.sid
+ )[0, 0].cpu().float().numpy()
+
+ # Explicitly delete temporary tensors and clean memory cache
+ del feats, feats0, p_len
+ clear_gpu_cache()
+ return audio1
+
+ def pipeline(
+ self,
+ audio,
+ f0_up_key,
+ f0_method,
+ index,
+ big_tsr,
+ index_rate,
+ filter_radius,
+ rms_mix_rate,
+ protect,
+ f0_autotune,
+ f0_autotune_strength,
+ f0_file=None,
+ pbar=None,
+ proposal_pitch=False,
+ proposal_pitch_threshold=255.0,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5,
+ ):
+ """
+ The orchestrator method. Filters, segments long audios at silence points,
+ extracts pitch trajectories, loops over chunks, and concatenates final outputs.
+
+ Args:
+ audio (np.ndarray): Target 1D input audio array sampled at 16kHz.
+ f0_up_key (int): Pitch shift key value in semitones (positive for higher pitch, negative for lower).
+ f0_method (str): Pitch extraction algorithm method (e.g., 'harvest', 'crepe', 'rmvpe').
+ index (object): The Index Wrapper class handles the retrieval process.
+ big_tsr (torch.Tensor): Saved base feature vectors corresponding to FAISS index.
+ index_rate (float): Blending ratio for index retrieval features (0.0 to 1.0).
+ filter_radius (int): Median filtering radius applied to smooth out the extracted F0 curve.
+ rms_mix_rate (float): Volume envelope adjustment rate (1.0 keeps target volume, lower values blend source volume).
+ protect (float): Consonant and breath sound protection factor (0.0 to 0.5).
+ f0_autotune (bool): Enable snapping the F0 pitch sequence to the nearest musical notes.
+ f0_autotune_strength (float): Blend factor for autotune (0.0 = raw pitch, 1.0 = fully snapped).
+ f0_file (str, optional): Path to a raw text file containing external comma-separated pitch values. Defaults to None.
+ pbar (object): Tqdm tracker object for state tracking.
+ proposal_pitch (bool): Enable automatic pitch key shifting calculation based on median F0 alignment.
+ proposal_pitch_threshold (float): The maximum allowed semitone boundary limit (floor/ceiling) for the proposed pitch shift calculation.
+ embedders_mix (bool): Flag to mix multiple hidden layers of the embedder. Defaults to False.
+ embedders_mix_layers (int): Number of layers to mix. Defaults to 9.
+ embedders_mix_ratio (float): Ratio weighting for layer mixing. Defaults to 0.5.
+
+ Returns:
+ np.ndarray: Final concatenated, normalized, and converted full length audio waveform matrix.
+ """
+
+ s = 0
+ t, inp_f0 = None, None
+ opt_ts, audio_opt = [], []
+ # Apply the high-pass filter to eliminate DC offsets and low-frequency rumble
+ audio = signal.filtfilt(bh, ah, audio)
+ # Apply a minor boundary padding to secure stable audio edge processing
+ audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
+
+ pbar.update(1)
+ # Step 1: Detect optimal splitting locations for long audios to prevent VRAM spikes
+ if audio_pad.shape[0] > self.t_max:
+ audio_sum = np.zeros_like(audio)
+ # Compute short-term moving energy window across the waveform
+ for i in range(self.window):
+ audio_sum += audio_pad[i : i - self.window]
+
+ # Find the absolute minimum volume index within query bounds to act as a split joint
+ for t in range(self.t_center, audio.shape[0], self.t_center):
+ opt_ts.append(
+ t - self.t_query + np.where(np.abs(audio_sum[t - self.t_query : t + self.t_query]) == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min())[0][0]
+ )
+
+ # Re-pad audio fully using pipeline configuration dimensions
+ audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
+ p_len = audio_pad.shape[0] // self.window
+ pbar.update(1)
+
+ # Step 2: Read manual external F0 curve inputs if provided via text format
+ if f0_file and os.path.exists(f0_file) and f0_file.endswith(".txt"):
+ try:
+ with open(f0_file, "r") as f:
+ raw_lines = f.read()
+
+ if len(raw_lines) > 0:
+ inp_f0 = []
+
+ for line in raw_lines.strip("\n").split("\n"):
+ inp_f0.append([float(i) for i in line.split(",")])
+
+ inp_f0 = np.array(inp_f0, dtype=np.float32)
+ except:
+ logger.error(translations["error_readfile"])
+ inp_f0 = None
+
+ pbar.update(1)
+
+ # Step 3: Extract fundamental pitch (F0) tracking arrays
+ if self.pitch_guidance:
+ pitch, pitchf = self.f0_generator.calculator(
+ self.x_pad,
+ f0_method,
+ audio_pad,
+ f0_up_key,
+ p_len,
+ filter_radius,
+ f0_autotune,
+ f0_autotune_strength,
+ manual_f0=inp_f0,
+ proposal_pitch=proposal_pitch,
+ proposal_pitch_threshold=proposal_pitch_threshold
+ )
+
+ if self.device == "mps": pitchf = pitchf.astype(np.float32)
+ # Move pitch arrays into PyTorch Tensors on target hardware
+ pitch, pitchf = (
+ torch.tensor(pitch[:p_len], device=self.device).unsqueeze(0).long(),
+ torch.tensor(pitchf[:p_len], device=self.device).unsqueeze(0).float()
+ )
+
+ pbar.update(1)
+ pbar.total = pbar.total + len(opt_ts)
+ pbar.refresh()
+
+ # Step 4: Sequentially process each audio slice identified in step 1
+ for t in opt_ts:
+ t = t // self.window * self.window
+ start = s // self.window
+ end = (t + self.t_pad2) // self.window
+
+ # Convert chunk and crop padding fields out from the output block
+ audio_opt.append(
+ self.voice_conversion(
+ audio_pad[s : t + self.t_pad2 + self.window],
+ pitch[:, start:end] if self.pitch_guidance else None,
+ pitchf[:, start:end] if self.pitch_guidance else None,
+ index,
+ big_tsr,
+ index_rate,
+ protect,
+ embedders_mix=embedders_mix,
+ embedders_mix_layers=embedders_mix_layers,
+ embedders_mix_ratio=embedders_mix_ratio
+ )[self.t_pad_tgt : -self.t_pad_tgt]
+ )
+
+ s = t
+ pbar.update(1)
+
+ # Step 5: Convert the remaining trailing segment of the audio track
+ start_opt = (t // self.window) if t is not None else 0
+ audio_opt.append(
+ self.voice_conversion(
+ audio_pad[t:],
+ pitch[:, start_opt:] if self.pitch_guidance else None,
+ pitchf[:, start_opt:] if self.pitch_guidance else None,
+ index,
+ big_tsr,
+ index_rate,
+ protect,
+ embedders_mix=embedders_mix,
+ embedders_mix_layers=embedders_mix_layers,
+ embedders_mix_ratio=embedders_mix_ratio
+ )[self.t_pad_tgt : -self.t_pad_tgt]
+ )
+
+ pbar.update(1)
+ # Concatenate all generated slices back into a continuous signal matrix
+ audio_opt = np.concatenate(audio_opt)
+
+ # Step 6: Blend source volume amplitude envelope back if requested (rms_mix_rate != 1)
+ if rms_mix_rate != 1:
+ audio_opt = change_rms(
+ audio,
+ self.sample_rate,
+ audio_opt,
+ self.tgt_sr,
+ rms_mix_rate,
+ device=self.device
+ ).cpu().numpy()
+
+ # Final peak normalization control block (0.99 threshold boundary scale)
+ audio_max = np.abs(audio_opt).max() / 0.99
+ if audio_max > 1: audio_opt /= audio_max
+ if self.pitch_guidance: del pitch, pitchf
+
+ clear_gpu_cache()
+ return audio_opt
\ No newline at end of file
diff --git a/main/inference/create_dataset.py b/main/inference/create_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..4042e4d1a2efa57058bd0d5a1ca2365e17508440
--- /dev/null
+++ b/main/inference/create_dataset.py
@@ -0,0 +1,550 @@
+import os
+import sys
+import time
+import torch
+import yt_dlp
+import shutil
+import librosa
+import argparse
+import warnings
+
+import numpy as np
+import soundfile as sf
+
+from urllib.parse import urlparse
+
+sys.path.append(os.getcwd())
+
+from main.library.utils import strtobool
+from main.inference.separate_music import _separate
+from main.app.variables import config, logger, translations
+
+dataset_temp = "dataset_temp"
+
+def parse_arguments():
+ """
+ Parses command-line arguments configuring the dataset pipeline properties.
+
+ Returns:
+ argparse.Namespace: Object containing validated operational configurations.
+ """
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--create_dataset", action='store_true')
+ parser.add_argument("--input_data", type=str, required=True)
+ parser.add_argument("--output_dirs", type=str, default="./dataset")
+ parser.add_argument("--skip_seconds", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--skip_start_audios", type=str, default="0")
+ parser.add_argument("--skip_end_audios", type=str, default="0")
+ parser.add_argument("--separate", type=lambda x: bool(strtobool(x)), default=True)
+ parser.add_argument("--model_name", type=str, default="MDXNET_Main")
+ parser.add_argument("--reverb_model", type=str, default="MDX-Reverb")
+ parser.add_argument("--denoise_model", type=str, default="Normal")
+ parser.add_argument("--sample_rate", type=int, default=48000)
+ parser.add_argument("--shifts", type=int, default=2)
+ parser.add_argument("--batch_size", type=int, default=1)
+ parser.add_argument("--overlap", type=float, default=0.25)
+ parser.add_argument("--aggression", type=int, default=5)
+ parser.add_argument("--hop_length", type=int, default=1024)
+ parser.add_argument("--window_size", type=int, default=512)
+ parser.add_argument("--segments_size", type=int, default=256)
+ parser.add_argument("--post_process_threshold", type=float, default=0.2)
+ parser.add_argument("--enable_tta", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--enable_denoise", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--high_end_process", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--enable_post_process", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--separate_reverb", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--clean_dataset", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--clean_strength", type=float, default=0.7)
+
+ return parser.parse_args()
+
+def main():
+ """Main execution orchestrator routing runtime command-line arguments."""
+
+ args = parse_arguments()
+
+ (
+ input_data,
+ output_dirs,
+ skip_seconds,
+ skip_start_audios,
+ skip_end_audios,
+ separate,
+ model_name,
+ reverb_model,
+ denoise_model,
+ sample_rate,
+ shifts,
+ batch_size,
+ overlap,
+ aggression,
+ hop_length,
+ window_size,
+ segments_size,
+ post_process_threshold,
+ enable_tta,
+ enable_denoise,
+ high_end_process,
+ enable_post_process,
+ separate_reverb,
+ clean_dataset,
+ clean_strength
+ ) = (
+ args.input_data,
+ args.output_dirs,
+ args.skip_seconds,
+ args.skip_start_audios,
+ args.skip_end_audios,
+ args.separate,
+ args.model_name,
+ args.reverb_model,
+ args.denoise_model,
+ args.sample_rate,
+ args.shifts,
+ args.batch_size,
+ args.overlap,
+ args.aggression,
+ args.hop_length,
+ args.window_size,
+ args.segments_size,
+ args.post_process_threshold,
+ args.enable_tta,
+ args.enable_denoise,
+ args.high_end_process,
+ args.enable_post_process,
+ args.separate_reverb,
+ args.clean_dataset,
+ args.clean_strength
+ )
+
+ create_dataset(
+ input_data,
+ output_dirs,
+ skip_seconds,
+ skip_start_audios,
+ skip_end_audios,
+ separate,
+ model_name,
+ reverb_model,
+ denoise_model,
+ sample_rate,
+ shifts,
+ batch_size,
+ overlap,
+ aggression,
+ hop_length,
+ window_size,
+ segments_size,
+ post_process_threshold,
+ enable_tta,
+ enable_denoise,
+ high_end_process,
+ enable_post_process,
+ separate_reverb,
+ clean_dataset,
+ clean_strength
+ )
+
+def create_dataset(
+ input_data,
+ output_dirs,
+ skip_seconds,
+ skip_start_audios,
+ skip_end_audios,
+ separate,
+ model_name,
+ reverb_model="MDX-Reverb",
+ denoise_model="Normal",
+ sample_rate=48000,
+ shifts=2,
+ batch_size=1,
+ overlap=0.25,
+ aggression=5,
+ hop_length=1024,
+ window_size=512,
+ segments_size=256,
+ post_process_threshold=0.2,
+ enable_tta=False,
+ enable_denoise=False,
+ high_end_process=False,
+ enable_post_process=False,
+ separate_reverb=False,
+ clean_dataset=False,
+ clean_strength=0.7
+):
+ """
+ Executes the comprehensive processing loop to build a normalized dataset.
+ Downloads, cuts, merges, separates, filters, and relocates final audio assets.
+
+ Returns:
+ str: Finalized target directory containing formatted assets.
+ """
+
+ # Map parameters to dictionary layout for localized console visibility debugging
+ log_data = {
+ translations["audio_path"]: input_data,
+ translations["output_path"]: output_dirs,
+ translations["skip"]: skip_seconds,
+ translations["separator_tab"]: separate,
+ translations["modelname"]: model_name,
+ translations["dereveb_audio"]: separate_reverb,
+ translations["sr"]: sample_rate,
+ translations["shift"]: shifts,
+ translations["batch_size"]: batch_size,
+ translations["overlap"]: overlap,
+ translations["aggression"]: aggression,
+ translations["hop_length"]: hop_length,
+ translations["window_size"]: window_size,
+ translations["segments_size"]: segments_size,
+ translations["post_process_threshold"]: post_process_threshold,
+ translations["enable_tta"]: enable_tta,
+ translations["denoise_mdx"]: enable_denoise,
+ translations["high_end_process"]: high_end_process,
+ translations["enable_post_process"]: enable_post_process,
+ translations["clear_dataset"]: clean_dataset,
+ translations["clean_strength"]: clean_strength,
+ translations["dereveb_model"]: reverb_model,
+ translations["denoise_model"]: denoise_model,
+ translations["skip_start"]: skip_start_audios,
+ translations["skip_end"]: skip_end_audios
+ }
+
+ for key, value in log_data.items():
+ logger.debug(f"{key}: {value}")
+
+ start_time = time.time()
+ inputs_data = input_data.replace(", ", ",").split(",")
+
+ # Log operational Process ID (PID) to track script instances
+ pid_path = os.path.join("assets", "create_dataset_pid.txt")
+ with open(pid_path, "w") as pid_file:
+ pid_file.write(str(os.getpid()))
+
+ try:
+ # Re-initialize target workspace folder environment
+ if os.path.exists(dataset_temp): shutil.rmtree(dataset_temp, ignore_errors=True)
+ else: os.makedirs(dataset_temp, exist_ok=True)
+
+ # Resolve remote URLs down to local audio path files
+ audio_path = [
+ downloader(
+ url,
+ f"audio_{str(inputs_data.index(url))}"
+ ) if is_url(url) else url
+ for url in inputs_data
+ ]
+
+ # Trim explicit temporal durations out from sample inputs if required
+ if skip_seconds:
+ skip_start_audios, skip_end_audios = (
+ skip_start_audios.replace(", ", ",").split(","),
+ skip_end_audios.replace(", ", ",").split(",")
+ )
+
+ # Guard against mismatched trimming arguments relative to target array lengths
+ if len(skip_start_audios) < len(audio_path) or len(skip_end_audios) < len(audio_path):
+ logger.warning(translations["skip len(audio_path) or len(skip_end_audios) > len(audio_path):
+ logger.warning(translations["skip>audio"])
+ sys.exit(1)
+ else:
+ audio_path = [
+ skip_duration(
+ audio,
+ skip_start_audio,
+ skip_end_audio
+ )
+ for audio, skip_start_audio, skip_end_audio in zip(
+ audio_path,
+ skip_start_audios,
+ skip_end_audios
+ )
+ ]
+
+ # Group and unify short samples up into maximum chunks
+ audio_path = merge_audios(audio_path, sample_rate)
+
+ # Apply Demixing isolation using specialized extraction rules
+ if separate:
+ audio_path = [
+ separate_main(
+ audio,
+ audio_path.index(audio),
+ model_name,
+ sample_rate,
+ reverb_model,
+ denoise_model,
+ shifts,
+ batch_size,
+ overlap,
+ aggression,
+ hop_length,
+ window_size,
+ segments_size,
+ post_process_threshold,
+ enable_tta,
+ enable_denoise,
+ high_end_process,
+ enable_post_process,
+ separate_reverb
+ )
+ for audio in audio_path
+ ]
+
+ # Instatiate TorchGate Neural Mask filter instance if noise gating is enabled
+ if clean_dataset:
+ from main.library.audio.noisereduce import TorchGate
+
+ tg = TorchGate(
+ sr,
+ prop_decrease=clean_strength
+ ).to(config.device)
+
+ # Uniform signal transformation and format export loop
+ for audio in audio_path:
+ data, sr = read_file(audio)
+ # Enforce strict 1D mono layout configuration profile
+ if len(data.shape) > 1:
+ data = librosa.to_mono(data.T)
+
+ # Resample file if current properties violate target sample definitions
+ if sr != sample_rate:
+ data = librosa.resample(
+ data,
+ orig_sr=sr,
+ target_sr=sample_rate,
+ res_type="soxr_vhq"
+ )
+
+ # Run dynamic neural gate masking algorithm
+ if clean_dataset:
+ data = tg(
+ torch.from_numpy(data).unsqueeze(0).to(config.device).float()
+ ).squeeze(0).cpu().detach().numpy()
+
+ # Write temporary normalized waves out to their finalized directory target endpoints
+ sf.write(audio, data, sr)
+ os.makedirs(output_dirs, exist_ok=True)
+ output_path = os.path.join(output_dirs, os.path.basename(audio))
+
+ if os.path.exists(output_path): os.remove(output_path)
+ shutil.move(audio, output_path)
+
+ # Clear working caches
+ if os.path.exists(dataset_temp): shutil.rmtree(dataset_temp, ignore_errors=True)
+ except Exception as e:
+ logger.error(f"{translations['create_dataset_error']}: {e}")
+ import traceback
+ logger.error(traceback.format_exc())
+
+ elapsed_time = time.time() - start_time
+ if os.path.exists(pid_path): os.remove(pid_path)
+
+ logger.info(translations["create_dataset_success"].format(elapsed_time=f"{elapsed_time:.2f}"))
+ return output_dirs
+
+def separate_main(
+ input_path,
+ index,
+ model_name,
+ sample_rate,
+ reverb_model="MDX-Reverb",
+ denoise_model="Normal",
+ shifts=2,
+ batch_size=1,
+ overlap=0.25,
+ aggression=5,
+ hop_length=1024,
+ window_size=512,
+ segments_size=256,
+ post_process_threshold=0.2,
+ enable_tta=False,
+ enable_denoise=False,
+ high_end_process=False,
+ enable_post_process=False,
+ separate_reverb=False
+):
+ """
+ Acts as an isolation proxy wrapper tracking output tracks handled
+ by model inference routines.
+ """
+
+ original_vocals, _, _, _ = _separate(
+ input_path,
+ dataset_temp,
+ model_name,
+ reverb_model=reverb_model,
+ denoise_model=denoise_model,
+ sample_rate=sample_rate,
+ shifts=shifts,
+ batch_size=batch_size,
+ overlap=overlap,
+ aggression=aggression,
+ hop_length=hop_length,
+ window_size=window_size,
+ segments_size=segments_size,
+ post_process_threshold=post_process_threshold,
+ enable_tta=enable_tta,
+ enable_denoise=enable_denoise,
+ high_end_process=high_end_process,
+ enable_post_process=enable_post_process,
+ separate_reverb=separate_reverb
+ )
+
+ # Rename isolated vocal waveform files to match simple incremental indices
+ vocals = os.path.join(dataset_temp, f"dataset_{index}.wav")
+ os.rename(original_vocals, vocals)
+
+ return vocals
+
+def is_url(path):
+ """Validates if a target input string fits internet URI structures."""
+
+ try:
+ result = urlparse(path)
+ return all([result.scheme, result.netloc])
+ except ValueError:
+ return False
+
+def downloader(
+ url,
+ name
+):
+ """Fetches streaming audio distributions via yt_dlp using high-grade WAV encoding extraction."""
+
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
+
+ ydl_opts = {
+ "format": "bestaudio/best",
+ "outtmpl": os.path.join(dataset_temp, f"{name}"),
+ "postprocessors": [{
+ "key": "FFmpegExtractAudio",
+ "preferredcodec": "wav",
+ "preferredquality": "192"
+ }],
+ "no_warnings": True,
+ "noplaylist": True,
+ "noplaylist": True,
+ "verbose": False
+ }
+
+ logger.info(f"{translations['starting_download']}: {url}...")
+
+ with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+ ydl.extract_info(url)
+ logger.info(f"{translations['download_success']}: {url}")
+
+ return os.path.join(dataset_temp, f"{name}" + ".wav")
+
+def read_file(file):
+ """Robust multi-backend audio reader falling back to Librosa if Soundfile fails."""
+
+ try:
+ data, sr = sf.read(file, dtype=np.float32)
+ except:
+ data, sr = librosa.load(file, sr=None)
+
+ return data, sr
+
+def skip_duration(
+ audio,
+ skip_start_audio,
+ skip_end_audio
+):
+ """Applies consecutive linear temporal trims to both ends of a file path location."""
+
+ skip_start(audio, int(skip_start_audio))
+ skip_end(audio, int(skip_end_audio))
+
+ return audio
+
+def skip_start(
+ input_file,
+ seconds
+):
+ """Slices duration segments off the beginning of an audio asset."""
+
+ data, sr = read_file(input_file)
+ total_duration = len(data) / sr
+
+ if seconds <= 0:
+ logger.warning(translations["=<0"])
+ elif seconds >= total_duration:
+ logger.warning(translations["skip_warning"].format(seconds=seconds, total_duration=f"{total_duration:.2f}"))
+ else:
+ logger.info(f"{translations['skip_start']}: {input_file}...")
+ sf.write(input_file, data[int(seconds * sr):], sr)
+
+ logger.info(translations["skip_start_audio"].format(input_file=input_file))
+
+def skip_end(
+ input_file,
+ seconds
+):
+ """Slices duration segments off the trailing tail end of an audio asset."""
+
+ data, sr = read_file(input_file)
+ total_duration = len(data) / sr
+
+ if seconds <= 0:
+ logger.warning(translations["=<0"])
+ elif seconds > total_duration:
+ logger.warning(translations["skip_warning"].format(seconds=seconds, total_duration=f"{total_duration:.2f}"))
+ else:
+ logger.info(f"{translations['skip_end']}: {input_file}...")
+ sf.write(input_file, data[:-int(seconds * sr)], sr)
+
+ logger.info(translations["skip_end_audio"].format(input_file=input_file))
+
+def merge_audios(audio_paths, sample_rate=48000):
+ """
+ Concatenates collections of separate audio components into sequential master chunks,
+ guaranteeing that no individual master block exceeds a total duration limit of 600 seconds (10 minutes).
+ """
+
+ current_duration = 0
+ chunks, current_audio = [], []
+
+ for audio in audio_paths:
+ data, sr = read_file(audio)
+
+ if len(data.shape) > 1:
+ data = librosa.to_mono(data.T)
+
+ if sr != sample_rate:
+ data = librosa.resample(
+ data,
+ orig_sr=sr,
+ target_sr=sample_rate,
+ res_type="soxr_vhq"
+ )
+
+ duration = len(data) / sample_rate
+ # Enforce maximum single file duration chunk allocation limits
+ if current_duration + duration > 600:
+ merged = np.concatenate(current_audio)
+ output = os.path.join(dataset_temp, f"merged_{len(chunks)}.wav")
+
+ sf.write(output, merged, sample_rate)
+ chunks.append(output)
+
+ current_audio = []
+ current_duration = 0
+
+ current_audio.append(data)
+ current_duration += duration
+
+ # Flush out remaining remaining tracking buffer queues
+ if current_audio:
+ merged = np.concatenate(current_audio)
+ output = os.path.join(dataset_temp, f"merged_{len(chunks)}.wav")
+
+ sf.write(output, merged, sample_rate)
+ chunks.append(output)
+
+ return chunks
+
+if __name__ == "__main__": main()
\ No newline at end of file
diff --git a/main/inference/create_index.py b/main/inference/create_index.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb82e31c1e05503eb4b331d6fcb5b250f5fc0164
--- /dev/null
+++ b/main/inference/create_index.py
@@ -0,0 +1,141 @@
+import os
+import sys
+import faiss
+import argparse
+
+import numpy as np
+
+from multiprocessing import cpu_count
+from sklearn.cluster import MiniBatchKMeans
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import logger, translations, configs
+
+def parse_arguments():
+ """
+ Parses command-line arguments configuring the FAISS index pipeline.
+
+ Returns:
+ argparse.Namespace: Object containing validated operational configurations.
+ """
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--create_index", action='store_true')
+ parser.add_argument("--model_name", type=str, required=True)
+ parser.add_argument("--rvc_version", type=str, default="v2")
+ parser.add_argument("--index_algorithm", type=str, default="Auto")
+ parser.add_argument("--nprobe", type=int, default=1)
+
+ return parser.parse_args()
+
+def main():
+ """
+ Executes core vector gathering, clustering optimization, and FAISS index model export pipeline.
+ """
+
+ args = parse_arguments()
+ # Resolve target asset directories and variables
+ exp_dir = os.path.join(configs["logs_path"], args.model_name)
+ version, index_algorithm, nprobe = args.rvc_version, args.index_algorithm, args.nprobe
+ # Construct execution configuration parameters map for logging
+ log_data = {
+ translations['modelname']: args.model_name,
+ translations['model_path']: exp_dir,
+ translations['training_version']: version,
+ translations['index_algorithm_info']: index_algorithm,
+ translations['nprobe']: nprobe
+ }
+
+ for key, value in log_data.items():
+ logger.debug(f"{key}: {value}")
+
+ try:
+ npys = []
+ feature_dir = os.path.join(exp_dir, f"{version}_extracted")
+ model_name = os.path.basename(exp_dir)
+
+ # Scan and aggregate extracted feature vector arrays
+ for name in sorted(os.listdir(feature_dir)):
+ npys.append(np.load(os.path.join(feature_dir, name)))
+
+ # Unify collection lists into one continuous multi-dimensional matrix stack
+ big_npy = np.concatenate(npys, axis=0)
+ big_npy_idx = np.arange(big_npy.shape[0])
+ # Randomize vector index arrangements to eliminate localized sample ordering bias
+ np.random.shuffle(big_npy_idx)
+ big_npy = big_npy[big_npy_idx]
+
+ # Downsample extremely massive collections through k-means centroid quantization
+ if big_npy.shape[0] > 2e5 and (index_algorithm == "Auto" or index_algorithm == "KMeans"):
+ big_npy = (
+ MiniBatchKMeans(
+ n_clusters=10000,
+ verbose=True,
+ batch_size=256 * cpu_count(),
+ compute_labels=False,
+ init="random"
+ ).fit(big_npy).cluster_centers_
+ )
+
+ # Export aggregated features baseline matrix
+ np.save(
+ os.path.join(exp_dir, "total_fea.npy"),
+ big_npy
+ )
+ # Dynamically compute optimal Inverted File (IVF) cell partitioning scale counts
+ n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
+
+ # Phase 1: Build and Train structural baseline Index
+ index_trained = faiss.index_factory(
+ 256 if version == "v1" else 768, # Resolve target dimension widths (v1 features use 256, v2 architectures use 768)
+ f"IVF{n_ivf},Flat"
+ )
+
+ index_ivf_trained = faiss.extract_index_ivf(index_trained)
+ index_ivf_trained.nprobe = nprobe
+ # Fit vector quantization spaces
+ index_trained.train(big_npy)
+
+ faiss.write_index(
+ index_trained,
+ os.path.join(
+ exp_dir,
+ f"trained_IVF{n_ivf}_Flat_nprobe_{index_ivf_trained.nprobe}_{model_name}_{version}.index"
+ )
+ )
+
+ # Phase 2: Build, Train, and Populate Added-Vectors Index
+ index_added = faiss.index_factory(
+ 256 if version == "v1" else 768,
+ f"IVF{n_ivf},Flat"
+ )
+
+ index_ivf_added = faiss.extract_index_ivf(index_added)
+ index_ivf_added.nprobe = nprobe
+ index_added.train(big_npy)
+
+ # Incrementally stream clustered matrices sequentially into index vector tables
+ batch_size_add = 8192
+ for i in range(0, big_npy.shape[0], batch_size_add):
+ index_added.add(
+ big_npy[i : i + batch_size_add]
+ )
+
+ index_filepath_added = os.path.join(
+ exp_dir,
+ f"added_IVF{n_ivf}_Flat_nprobe_{index_ivf_added.nprobe}_{model_name}_{version}.index"
+ )
+
+ faiss.write_index(
+ index_added,
+ index_filepath_added
+ )
+
+ logger.info(f"{translations['save_index']} '{index_filepath_added}'")
+ except Exception as e:
+ logger.error(f"{translations['create_index_error']}: {e}")
+ import traceback
+ logger.debug(traceback.format_exc())
+
+if __name__ == "__main__": main()
\ No newline at end of file
diff --git a/main/inference/create_reference.py b/main/inference/create_reference.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4527a9ba939e26998409e94c5724894b34c2209
--- /dev/null
+++ b/main/inference/create_reference.py
@@ -0,0 +1,295 @@
+import os
+import sys
+import time
+import torch
+import shutil
+import warnings
+import argparse
+
+import numpy as np
+
+from tqdm import tqdm
+
+sys.path.append(os.getcwd())
+
+from main.library.audio.audio import load_audio
+from main.app.variables import config, logger, translations, configs
+from main.library.utils import load_embedders_model, extract_features, check_assets, strtobool
+
+warnings.filterwarnings("ignore")
+
+# Signal processing global parameters initialized from config specifications
+F0_MIN, F0_MAX, HOP_SIZE, SAMPLE_RATE, FRAME_LENGTH = configs.get("f0_min", 50), configs.get("f0_max", 1100), 160, 16000, 2048
+
+def parse_arguments():
+ """
+ Parses command-line arguments configuring the audio reference creation parameters.
+
+ Returns:
+ argparse.Namespace: Object containing validated operational configurations.
+ """
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--create_reference", action='store_true')
+ parser.add_argument("--audio_path", type=str, required=True)
+ parser.add_argument("--reference_name", type=str, default="reference")
+ parser.add_argument("--pitch_guidance", type=lambda x: bool(strtobool(x)), default=True)
+ parser.add_argument("--version", type=str, default="v2")
+ parser.add_argument("--embedder_model", type=str, default="hubert_base")
+ parser.add_argument("--embedders_mode", type=str, default="fairseq")
+ parser.add_argument("--f0_method", type=str, default="rmvpe")
+ parser.add_argument("--predictor_onnx", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--f0_up_key", type=int, default=0)
+ parser.add_argument("--filter_radius", type=int, default=3)
+ parser.add_argument("--f0_autotune", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--f0_autotune_strength", type=float, default=1)
+ parser.add_argument("--proposal_pitch", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--proposal_pitch_threshold", type=float, default=255.0)
+ parser.add_argument("--alpha", type=float, default=0.5)
+ parser.add_argument("--embedders_mix", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--embedders_mix_layers", type=int, default=9, required=False)
+ parser.add_argument("--embedders_mix_ratio", type=float, default=0.5)
+
+ return parser.parse_args()
+
+def main():
+ """Main execution orchestrator routing runtime command-line arguments."""
+
+ args = parse_arguments()
+
+ (
+ audio_path,
+ reference_name,
+ pitch_guidance,
+ version,
+ embedder_model,
+ embedders_mode,
+ f0_method,
+ predictor_onnx,
+ f0_up_key,
+ filter_radius,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ alpha,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ ) = (
+ args.audio_path,
+ args.reference_name,
+ args.pitch_guidance,
+ args.version,
+ args.embedder_model,
+ args.embedders_mode,
+ args.f0_method,
+ args.predictor_onnx,
+ args.f0_up_key,
+ args.filter_radius,
+ args.f0_autotune,
+ args.f0_autotune_strength,
+ args.proposal_pitch,
+ args.proposal_pitch_threshold,
+ args.alpha,
+ args.embedders_mix,
+ args.embedders_mix_layers,
+ args.embedders_mix_ratio
+ )
+
+ # Configure structural log data map mapping parameter values to localization files
+ log_data = {
+ translations["audio_path"]: audio_path,
+ translations["reference_name"]: reference_name,
+ translations['extract_f0']: pitch_guidance,
+ translations['training_version']: version,
+ translations['hubert_model']: embedder_model,
+ translations["embed_mode"]: embedders_mode,
+ translations['f0_method']: f0_method,
+ translations["predictor_onnx"]: predictor_onnx,
+ translations["pitch"]: f0_up_key,
+ translations["filter_radius"]: filter_radius,
+ translations["autotune"]: f0_autotune,
+ translations["autotune_rate_info"]: f0_autotune_strength,
+ translations["proposal_pitch"]: proposal_pitch,
+ translations["proposal_pitch_threshold"]: proposal_pitch_threshold,
+ translations["alpha_label"]: alpha,
+ translations["embedders_mix"]: embedders_mix,
+ translations["embedders_mix_layers"]: embedders_mix_layers,
+ translations["embedders_mix_ratio"]: embedders_mix_ratio,
+ }
+
+ for key, value in log_data.items():
+ logger.debug(f"{key}: {value}")
+
+ # Validate the availability of target network files before entering computing phases
+ check_assets(
+ f0_method,
+ embedder_model,
+ predictor_onnx,
+ embedders_mode
+ )
+
+ create_reference(
+ audio_path,
+ reference_name,
+ pitch_guidance,
+ version,
+ embedder_model,
+ embedders_mode,
+ f0_method,
+ predictor_onnx,
+ f0_up_key,
+ filter_radius,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ alpha,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ )
+
+def create_reference(
+ audio_path,
+ reference_name,
+ pitch_guidance = True,
+ version = "v2",
+ embedder_model = "hubert_base",
+ embedders_mode = "fairseq",
+ f0_method = "rmvpe",
+ predictor_onnx = False,
+ f0_up_key = 0,
+ filter_radius = 3,
+ f0_autotune = False,
+ f0_autotune_strength = 1,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 255.0,
+ alpha = 0.5,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5
+):
+ """
+ Loads target waveforms, extracts voice encoder hidden units,
+ tracks explicit frequency lines, and saves representations to a structured cache directory.
+ """
+
+ device = config.device
+ is_half = config.is_half
+
+ # Enforce standard early escape boundary if the requested source media path remains null
+ if not audio_path:
+ logger.warning(translations["not_found_audio"])
+ sys.exit(1)
+
+ # Establish complete workspace string path mapping target variables
+ output_reference = os.path.join(
+ configs["reference_path"],
+ f"{reference_name}_{version}_{embedder_model}_{pitch_guidance}"
+ )
+
+ # Purge legacy target assets directory locations matching current profiles
+ if os.path.exists(output_reference): shutil.rmtree(reference_name, ignore_errors=True)
+
+ os.makedirs(output_reference, exist_ok=True)
+ logger.info(translations["start_create_reference"])
+ start_time = time.time()
+
+ with tqdm(total=4, desc=translations["create_reference"], ncols=100, unit="a") as pbar:
+ # Step 1: Decode source waveform tracks mapping uniform resampling constraints
+ audio = load_audio(audio_path, sample_rate=SAMPLE_RATE)
+ pbar.update(1)
+
+ # Apply strict peak normalization bounding constraints to prevent digital clipping
+ audio_max = np.abs(audio).max() / 0.95
+ if audio_max > 1: audio /= audio_max
+
+ # Trim residual tail frames out to match uniform encoder segment structures (320 hop sizing ratios)
+ trimmed_len = (len(audio) // 320) * 320
+ audio = audio[:trimmed_len]
+
+ # Pad boundaries sequentially to compensate for receptive field edge alignment compression losses
+ audio_pad = torch.nn.functional.pad(
+ torch.from_numpy(audio).to(
+ torch.float16 if is_half else torch.float32
+ ).to(device).unsqueeze(0),
+ (40, 40),
+ mode="reflect"
+ )
+ pbar.update(1)
+
+ # Step 2: Initialize embedding extractor model assets via designated system devices
+ embedder = load_embedders_model(embedder_model, embedders_mode)
+ if isinstance(embedder, torch.nn.Module):
+ embedder = embedder.to(torch.float16 if is_half else torch.float32).eval().to(device)
+ if config.compile_all: embedder = torch.compile(embedder, mode=config.compile_mode)
+
+ # Step 3: Extract feature tokens without tracking gradient graphs
+ with torch.no_grad():
+ feats = extract_features(
+ embedder,
+ audio_pad.view(1, -1),
+ version,
+ mix=embedders_mix,
+ mix_layers=embedders_mix_layers,
+ mix_ratio=embedders_mix_ratio
+ )
+
+ # Save voice linguistic identity vectors to output folder
+ np.save(
+ os.path.join(output_reference, "feats.npy"),
+ feats.squeeze(0).float().cpu().numpy(),
+ allow_pickle=False
+ )
+
+ pbar.update(1)
+ # Step 4: Extract and compute Pitch (F0) profiles if requested
+ if pitch_guidance:
+ from main.library.predictors.Generator import Generator
+
+ generator = Generator(
+ sample_rate=SAMPLE_RATE,
+ hop_length=HOP_SIZE,
+ f0_min=F0_MIN,
+ f0_max=F0_MAX,
+ alpha=alpha,
+ is_half=is_half,
+ device=device,
+ predictor_onnx=predictor_onnx
+ )
+
+ # Compute fine-grained pitch curves alongside coarsened quantization assignments
+ pitch, pitchf = generator.calculator(
+ x_pad=config.x_pad,
+ f0_method=f0_method,
+ x=audio,
+ f0_up_key=f0_up_key,
+ p_len=audio.shape[0] // 160 + 1,
+ filter_radius=filter_radius,
+ f0_autotune=f0_autotune,
+ f0_autotune_strength=f0_autotune_strength,
+ manual_f0=None,
+ proposal_pitch=proposal_pitch,
+ proposal_pitch_threshold=proposal_pitch_threshold
+ )
+
+ # Write individual binary arrays out to workspace destination points
+ np.save(
+ os.path.join(output_reference, "pitch_coarse.npy"),
+ pitch,
+ allow_pickle=False
+ )
+
+ np.save(
+ os.path.join(output_reference, "pitch_fine.npy"),
+ pitchf,
+ allow_pickle=False
+ )
+
+ pbar.update(1)
+
+ logger.info(translations["create_reference_success"].format(elapsed_time=f"{(time.time() - start_time):.2f}"))
+
+if __name__ == "__main__": main()
\ No newline at end of file
diff --git a/main/inference/extracting/embedding.py b/main/inference/extracting/embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..80a10f9fd654a3515f0684e0a6702e3ffa1ecef0
--- /dev/null
+++ b/main/inference/extracting/embedding.py
@@ -0,0 +1,208 @@
+import os
+import sys
+import time
+import tqdm
+import torch
+import traceback
+import concurrent.futures
+
+import numpy as np
+
+sys.path.append(os.getcwd())
+
+from main.library.audio.audio import load_audio
+from main.app.variables import logger, translations, config
+from main.inference.extracting.setup_path import setup_paths
+from main.library.utils import load_embedders_model, extract_features
+
+def process_file_embedding(
+ files,
+ embedder_model,
+ embedders_mode,
+ device,
+ version,
+ is_half,
+ threads,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5
+):
+ """
+ Processes a slice of audio files on a designated device to extract content embeddings.
+
+ Uses a thread pool executor internally to parallelize audio reading, model
+ inference, and `.npy` matrix writes to disk.
+
+ Args:
+ files (list): A list of tuples containing (source_wav_path, target_output_directory).
+ embedder_model (str): Name or path identifier of the embedder model.
+ embedders_mode (str): Backend framework format (e.g., 'fairseq', 'onnx').
+ device (str/torch.device): Target device used for model evaluations (e.g., 'cuda:0', 'cpu').
+ version (str): RVC/SVC framework version ('v1' or 'v2').
+ is_half (bool): If True, loads and processes via half-precision float16 tensor weights.
+ threads (int): Maximum number of concurrent worker threads inside this worker process.
+ embedders_mix (bool, optional): Enables multi-layer blending features. Defaults to False.
+ embedders_mix_layers (int, optional): The target layer boundary index to mix. Defaults to 9.
+ embedders_mix_ratio (float, optional): Alpha/mixing factor for layers. Defaults to 0.5.
+ """
+
+ # Select working data precision type
+ dtype = torch.float16 if is_half else torch.float32
+ # Load and initialize the selected acoustic embedding model architecture
+ model = load_embedders_model(
+ embedder_model,
+ embedders_mode
+ )
+ # If the loaded instance is a PyTorch native module, move it to target device and evaluate mode
+ if isinstance(model, torch.nn.Module):
+ model = model.to(device).to(dtype).eval()
+ if config.compile_all: model = torch.compile(model, mode=config.compile_mode)
+
+ def worker(file_info):
+ """Inner worker thread executing individual audio file processing safely."""
+
+ try:
+ file, out_path = file_info
+ # Build full destination path; handles both unified dir structures or direct targets
+ out_file_path = os.path.join(
+ out_path,
+ os.path.basename(file.replace("wav", "npy"))
+ ) if os.path.isdir(out_path) else out_path
+
+ # Skip execution if features were computed and saved previously
+ if os.path.exists(out_file_path): return
+
+ feats = torch.from_numpy(
+ load_audio(file, sample_rate=16000)
+ ).to(device).to(dtype)
+
+ # Pass features to model without gradient historical graphs tracking
+ with torch.no_grad():
+ feats = extract_features(
+ model,
+ feats.view(1, -1),
+ version,
+ mix=embedders_mix,
+ mix_layers=embedders_mix_layers,
+ mix_ratio=embedders_mix_ratio
+ )
+
+ # Squeeze batch dimensions, revert back to system CPU memory space as floating type numpy matrix
+ feats = feats.squeeze(0).float().cpu().numpy()
+ # Confirm integrity of computed array matrix (Check for corruption/NaNs)
+ if not np.isnan(feats).any():
+ np.save(
+ out_file_path,
+ feats,
+ allow_pickle=False
+ )
+ else: logger.warning(f"{file} {translations['NaN']}")
+ except:
+ # Safely capture operational anomalies without blocking sister parallel execution threads
+ logger.debug(traceback.format_exc())
+
+ # Dispatch tasks to thread-pool and update terminal interactive progress bar status metrics
+ with tqdm.tqdm(total=len(files), ncols=100, unit="p", leave=True) as pbar:
+ with concurrent.futures.ThreadPoolExecutor(max_workers=threads) as executor:
+ for _ in concurrent.futures.as_completed([executor.submit(worker, f) for f in files]):
+ pbar.update(1)
+
+def run_embedding_extraction(
+ exp_dir,
+ version,
+ num_processes,
+ devices,
+ embedder_model,
+ embedders_mode,
+ is_half,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5
+):
+ """
+ Orchestrates embedding extraction across the entire dataset using multi-processing.
+
+ Splits the workload seamlessly across available compute instances (GPUs/CPUs/XPUs).
+
+ Args:
+ exp_dir (str): Main logging/experiment home directory path.
+ version (str): Target framework execution variant.
+ num_processes (int): Upper bound limits on allocated thread distributions.
+ devices (list): String IDs of target logical device pools to schedule jobs on.
+ embedder_model (str): Name or path identifier of the embedder model.
+ embedders_mode (str): Model backend platform (e.g., 'fairseq', 'onnx').
+ is_half (bool): Flag indicating half-precision application.
+ embedders_mix (bool, optional): Layer mixing feature status flag. Defaults to False.
+ embedders_mix_layers (int, optional): Boundary index for blending. Defaults to 9.
+ embedders_mix_ratio (float, optional): Alpha ratio blend configurations. Defaults to 0.5.
+ """
+
+ # Setup working folders according to the standard project tree configuration structure
+ wav_path, out_path = setup_paths(exp_dir, version)
+ logger.info(translations["start_extract_hubert"])
+
+ if ( # Fallback to single process execution if specific hardware/ONNX constraints exist
+ (config.device.startswith("ocl") and embedders_mode == "onnx") or
+ config.device.startswith("privateuseone")
+ ):
+ num_processes = 1
+
+ # Collect and sort all valid source wave file paths within dataset
+ paths = sorted([
+ (os.path.join(wav_path, file), out_path)
+ for file in os.listdir(wav_path) if file.endswith(".wav")
+ ])
+
+ start_time = time.time()
+ # Spawn a ProcessPoolExecutor containing one dedicated process per computing device
+ with concurrent.futures.ProcessPoolExecutor(max_workers=len(devices)) as executor:
+ concurrent.futures.wait([
+ executor.submit(
+ process_file_embedding,
+ paths[i::len(devices)], # Slice paths to distribute subset evenly among devices
+ embedder_model, embedders_mode,
+ devices[i],
+ version,
+ is_half,
+ num_processes // len(devices), # Split thread counts proportionally per device process
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ )
+ for i in range(len(devices))
+ ])
+
+ logger.info(translations["extract_hubert_success"].format(elapsed_time=f"{(time.time() - start_time):.2f}"))
+
+def create_mute_file(version, embedder_model, embedders_mode, is_half):
+ """
+ Generates a reference embedding matrix for a standard silent/mute audio file.
+
+ This silent file is essential for padding or stabilizing RVC/SVC context sequences.
+
+ Args:
+ version (str): RVC/SVC version ('v1' or 'v2').
+ embedder_model (str): Name or path identifier of the embedder model.
+ embedders_mode (str): Model format (e.g., 'fairseq', 'onnx').
+ is_half (bool): Use half precision if supported.
+ """
+
+ start_time = time.time()
+ logger.info(translations["start_extract_hubert"])
+
+ # Extract embedding for the static dummy mute file path mapping
+ process_file_embedding(
+ [(
+ os.path.join(config.configs["logs_path"], "mute", "sliced_audios_16k", "mute.wav"),
+ os.path.join(config.configs["logs_path"], "mute", f"{version}_extracted", f"mute_{embedder_model}.npy")
+ )],
+ embedder_model,
+ embedders_mode,
+ config.device,
+ version,
+ is_half,
+ 1 # Single thread limit is sufficient for a single small item
+ )
+
+ # Force cleanup after background operations wrap up
+ logger.info(translations["extract_hubert_success"].format(elapsed_time=f"{(time.time() - start_time):.2f}"))
\ No newline at end of file
diff --git a/main/inference/extracting/extract.py b/main/inference/extracting/extract.py
new file mode 100644
index 0000000000000000000000000000000000000000..0311994f70dc3495fca2c90e3a91e816fe5fb79e
--- /dev/null
+++ b/main/inference/extracting/extract.py
@@ -0,0 +1,204 @@
+import os
+import sys
+import logging
+import argparse
+import warnings
+
+import torch.multiprocessing as mp
+
+sys.path.append(os.getcwd())
+
+from main.library.utils import check_assets, strtobool
+from main.inference.extracting.feature import run_pitch_extraction
+from main.app.variables import config, logger, translations, configs
+from main.inference.extracting.embedding import run_embedding_extraction
+from main.inference.extracting.preparing_files import generate_config, generate_filelist
+
+if not config.debug_mode:
+ warnings.filterwarnings("ignore")
+ for l in ["torch", "faiss", "httpx", "httpcore", "faiss.loader", "numba.core", "urllib3", "matplotlib"]:
+ logging.getLogger(l).setLevel(logging.ERROR)
+
+def parse_arguments():
+ """
+ Parses command-line arguments for the extraction process.
+
+ Returns:
+ argparse.Namespace: Object containing all parsed command-line arguments.
+ """
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--extract", action='store_true')
+ parser.add_argument("--model_name", type=str, required=True)
+ parser.add_argument("--rvc_version", type=str, default="v2")
+ parser.add_argument("--f0_method", type=str, default="rmvpe")
+ parser.add_argument("--pitch_guidance", type=lambda x: bool(strtobool(x)), default=True)
+ parser.add_argument("--hop_length", type=int, default=128)
+ parser.add_argument("--cpu_cores", type=int, default=2)
+ parser.add_argument("--gpu", type=str, default="-")
+ parser.add_argument("--sample_rate", type=int, required=True)
+ parser.add_argument("--embedder_model", type=str, default="hubert_base")
+ parser.add_argument("--predictor_onnx", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--embedders_mode", type=str, default="fairseq")
+ parser.add_argument("--f0_autotune", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--f0_autotune_strength", type=float, default=1)
+ parser.add_argument("--alpha", type=float, default=0.5)
+ parser.add_argument("--include_mutes", type=int, default=2)
+ parser.add_argument("--embedders_mix", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--embedders_mix_layers", type=int, default=9, required=False)
+ parser.add_argument("--embedders_mix_ratio", type=float, default=0.5)
+ parser.add_argument("--architecture", type=str, default="RVC")
+
+ return parser.parse_args()
+
+def main():
+ """
+ Main orchestrator function for the feature extraction pipeline.
+ Validates assets, maps execution devices, handles logging, and runs
+ the pitch/embedding extractions sequentially.
+ """
+
+ args = parse_arguments()
+ # Unpack and map command-line arguments to descriptive local variables
+ (
+ f0_method,
+ hop_length,
+ num_processes,
+ gpus, version,
+ pitch_guidance,
+ sample_rate,
+ embedder_model,
+ predictor_onnx,
+ embedders_mode,
+ f0_autotune,
+ f0_autotune_strength,
+ alpha,
+ include_mutes,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ architecture
+ ) = (
+ args.f0_method,
+ args.hop_length,
+ args.cpu_cores,
+ args.gpu,
+ args.rvc_version,
+ args.pitch_guidance,
+ args.sample_rate,
+ args.embedder_model,
+ args.predictor_onnx,
+ args.embedders_mode,
+ args.f0_autotune,
+ args.f0_autotune_strength,
+ args.alpha,
+ args.include_mutes,
+ args.embedders_mix,
+ args.embedders_mix_layers,
+ args.embedders_mix_ratio,
+ args.architecture
+ )
+
+ # Step 1: Pre-execution validations (Ensure dependency models/assets exist)
+ check_assets(f0_method, embedder_model, predictor_onnx=predictor_onnx, embedders_mode=embedders_mode)
+ # Step 2: Establish paths and resource allocation limits
+ exp_dir = os.path.join(configs["logs_path"], args.model_name)
+ num_processes = max(1, num_processes) # Ensure at least 1 process runs
+
+ # Step 3: Hardware device mapping based on the system backends (CUDA, Intel XPU, OpenCL, DirectML)
+ devices = ["cpu"] if gpus == "-" else [
+ (
+ f"cuda:{idx}"
+ ) if config.device.startswith("cuda") else (
+ f"xpu:{idx}" if config.device.startswith("xpu") else f"{'ocl' if config.device.startswith('ocl') else 'privateuseone'}:{idx}"
+ )
+ for idx in gpus.split("-")
+ ]
+
+ # Step 4: Compile configuration parameters for debug logging
+ log_data = {
+ translations['modelname']: args.model_name,
+ translations['export_process']: exp_dir,
+ translations['f0_method']: f0_method,
+ translations['pretrain_sr']: sample_rate,
+ translations['cpu_core']: num_processes,
+ "Gpu": gpus,
+ translations['hop_length']: hop_length,
+ translations['training_version']: version,
+ translations['extract_f0']: pitch_guidance,
+ translations['hubert_model']: embedder_model,
+ translations["predictor_onnx"]: predictor_onnx,
+ translations["embed_mode"]: embedders_mode,
+ translations["alpha_label"]: alpha,
+ translations["include_mutes"]: include_mutes,
+ translations["embedders_mix"]: embedders_mix,
+ translations["embedders_mix_layers"]: embedders_mix_layers,
+ translations["embedders_mix_ratio"]: embedders_mix_ratio,
+ translations["architecture"]: architecture
+ }
+
+ # Print the operational parameters to the log file/terminal for tracking
+ for key, value in log_data.items():
+ logger.debug(f"{key}: {value}")
+
+ # Step 5: Save current Process ID (PID) to track/kill execution if needed
+ pid_path = os.path.join(exp_dir, "extract_pid.txt")
+ with open(pid_path, "w") as pid_file:
+ pid_file.write(str(os.getpid()))
+
+ # Step 6: Sequential Execution of Core Feature Extractions
+ try:
+ # Extract fundamental frequencies (Pitch tracking)
+ run_pitch_extraction(
+ exp_dir,
+ f0_method,
+ hop_length,
+ num_processes,
+ devices,
+ predictor_onnx,
+ config.is_half,
+ f0_autotune,
+ f0_autotune_strength,
+ alpha
+ )
+ # Extract content embeddings
+ run_embedding_extraction(
+ exp_dir,
+ version,
+ num_processes,
+ devices,
+ embedder_model,
+ embedders_mode,
+ config.is_half,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ )
+ # Create a training configuration file
+ generate_config(
+ version,
+ sample_rate,
+ exp_dir
+ )
+ # Generate the formatted dataset path filelist required by the trainer
+ generate_filelist(
+ pitch_guidance,
+ exp_dir,
+ version,
+ sample_rate,
+ embedders_mode,
+ embedder_model,
+ include_mutes
+ )
+ except Exception as e:
+ # Log localized error if any processing step crashes
+ logger.error(f"{translations['extract_error']}: {e}")
+
+ # Step 7: Post-execution cleanup (Remove PID tracking file)
+ if os.path.exists(pid_path): os.remove(pid_path)
+ logger.info(f"{translations['extract_success']} {args.model_name}.")
+
+if __name__ == "__main__":
+ # Force 'spawn' multiprocessing method to guarantee cross-platform stability
+ mp.set_start_method("spawn", force=True)
+ main()
\ No newline at end of file
diff --git a/main/inference/extracting/feature.py b/main/inference/extracting/feature.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba2de47df5ad3c09f10996dd3419fb2f9eb7b87f
--- /dev/null
+++ b/main/inference/extracting/feature.py
@@ -0,0 +1,251 @@
+import os
+import sys
+import tqdm
+import time
+import traceback
+import concurrent.futures
+
+import numpy as np
+
+sys.path.append(os.getcwd())
+
+from main.library.audio.audio import load_audio
+from main.inference.extracting.setup_path import setup_paths
+from main.app.variables import config, logger, translations, configs
+
+class FeatureInput:
+ """
+ Handles initialization and state management of pitch calculation pipelines.
+
+ Encapsulates target pitch constraints, hardware state allocations, and lazy
+ initialization of the fundamental frequency generator instance.
+ """
+
+ def __init__(
+ self,
+ is_half=False,
+ device="cuda"
+ ):
+ """
+ Initializes the FeatureInput processor with default or specified runtime variables.
+
+ Args:
+ is_half (bool, optional): Runs calculation under half-precision FP16 if True.
+ device (str, optional): Target compute context instance (e.g., 'cuda:0').
+ """
+
+ self.sample_rate = 16000
+ # Retrieve target frequency boundaries with standard defaults if unassigned
+ self.f0_min = configs.get("f0_min", 50)
+ self.f0_max = configs.get("f0_max", 1100)
+ self.is_half = is_half
+ self.device = device
+ self.f0_gen = None # Lazy initializer placeholder for the Generator core module
+
+ def process_file(
+ self,
+ file_info,
+ f0_method,
+ hop_length,
+ predictor_onnx,
+ f0_autotune,
+ f0_autotune_strength,
+ alpha
+ ):
+ """
+ Calculates and exports coarse and fine fundamental frequency arrays for a single file.
+
+ Args:
+ file_info (tuple): Structure containing (input_path, output_coarse, output_fine, file_input_source).
+ f0_method (str): Key of the selected algorithm (e.g., 'rmvpe', 'harvest').
+ hop_length (int): Hop step frame sizes utilized during extraction.
+ predictor_onnx (bool): Toggles ONNX runtime environment execution.
+ f0_autotune (bool): Flag indicating autotuning feature toggles.
+ f0_autotune_strength (float): Scalar strength multiplier for autotuning modifications.
+ alpha (float): Blending factor configuration scalar.
+ """
+
+ # Lazy load the Generator engine within the worker process boundaries to avoid serialization traps
+ if self.f0_gen is None:
+ from main.library.predictors.Generator import Generator
+
+ self.f0_gen = Generator(
+ self.sample_rate,
+ hop_length,
+ self.f0_min,
+ self.f0_max,
+ alpha,
+ self.is_half,
+ self.device,
+ predictor_onnx
+ )
+
+ inp_path, opt_path1, opt_path2, file_inp = file_info
+ # Avoid redundant operations if the output coarse/fine files already exist on disk
+ if os.path.exists(opt_path1 + ".npy") and os.path.exists(opt_path2 + ".npy"): return
+
+ try:
+ # Perform numerical pitch tracking calculation
+ pitch, pitchf = self.f0_gen.calculator(
+ x_pad=config.x_pad,
+ f0_method=f0_method,
+ x=load_audio(file_inp, sample_rate=self.sample_rate), # Force downsample to 16kHz
+ f0_up_key=0,
+ p_len=None,
+ filter_radius=3,
+ f0_autotune=f0_autotune,
+ f0_autotune_strength=f0_autotune_strength,
+ manual_f0=None,
+ proposal_pitch=False,
+ proposal_pitch_threshold=0.0
+ )
+
+ # Export extracted matrices safely without saving pickle metadata headers
+ np.save(
+ opt_path2,
+ pitchf,
+ allow_pickle=False
+ )
+
+ np.save(
+ opt_path1,
+ pitch,
+ allow_pickle=False
+ )
+ except Exception as e:
+ # Log clear contextual localized target failures without crashing global loops
+ logger.info(f"{translations['extract_file_error']} {inp_path}: {e}")
+ logger.debug(traceback.format_exc())
+
+ def process_files(
+ self,
+ files,
+ f0_method,
+ hop_length,
+ predictor_onnx,
+ device,
+ is_half,
+ threads,
+ f0_autotune,
+ f0_autotune_strength,
+ alpha
+ ):
+ """
+ Processes a block slice of files allocated to a process via internal I/O threadpools.
+
+ Args:
+ files (list): Aggregation of file information tuples.
+ f0_method (str): Name of target computation algorithm.
+ hop_length (int): Frame stride constraints.
+ predictor_onnx (bool): ONNX application setting toggles.
+ device (str): Process-specific target hardware architecture.
+ is_half (bool): Precision mapping controller.
+ threads (int): Maximum thread configurations allowed within this process block.
+ f0_autotune (bool): Autotune toggle activation state.
+ f0_autotune_strength (float): Power modifier for internal autotune operations.
+ alpha (float): Scaling modifier configuration.
+ """
+
+ # Assign current worker process scope states
+ self.device = device
+ self.is_half = is_half
+
+ def worker(file_info):
+ """Inner scope runner executing thread calculations securely."""
+
+ self.process_file(
+ file_info,
+ f0_method,
+ hop_length,
+ predictor_onnx,
+ f0_autotune,
+ f0_autotune_strength,
+ alpha
+ )
+
+ # Spin up a ThreadPoolExecutor to maximize CPU utilization during file loading and saving
+ with tqdm.tqdm(total=len(files), ncols=100, unit="p", leave=True) as pbar:
+ with concurrent.futures.ThreadPoolExecutor(max_workers=threads) as executor:
+ for _ in concurrent.futures.as_completed([executor.submit(worker, f) for f in files]):
+ pbar.update(1)
+
+def run_pitch_extraction(
+ exp_dir,
+ f0_method,
+ hop_length,
+ num_processes,
+ devices,
+ predictor_onnx,
+ is_half,
+ f0_autotune,
+ f0_autotune_strength,
+ alpha
+):
+ """
+ Orchestrates fundamental frequency (F0) extraction across the dataset using multi-processing.
+ Splits file batches evenly based on the total number of hardware execution contexts.
+
+ Args:
+ exp_dir (str): Main experiment folder location on disk.
+ f0_method (str): Core target tracking algorithm moniker.
+ hop_length (int): Time-domain step windows.
+ num_processes (int): Max CPU thread allocation scale.
+ devices (list): Sequence list of operational compute strings.
+ predictor_onnx (bool): Flag indicating ONNX model state operations.
+ is_half (bool): Half precision enablement controller.
+ f0_autotune (bool): Enable snapping the F0 pitch sequence to the nearest musical notes.
+ f0_autotune_strength (float): Blend factor for autotune (0.0 = raw pitch, 1.0 = fully snapped).
+ alpha (float): Blending factor configuration variable.
+ """
+
+ # Unpack output directory pathways mapped out by structural workspace parameters
+ input_root, *output_roots = setup_paths(exp_dir)
+ output_root1, output_root2 = output_roots if len(output_roots) == 2 else (output_roots[0], None)
+
+ logger.info(translations["extract_f0_method"].format(num_processes=num_processes, f0_method=f0_method))
+ # Throttle process limits to 1 to guarantee stability on non-native platform backends when using complex neural models
+ if config.device.startswith(("ocl", "privateuseone")) and (
+ "crepe" in f0_method or
+ "fcpe" in f0_method or
+ "rmvpe" in f0_method or
+ "penn" in f0_method or
+ "swift" in f0_method or
+ "pesto" in f0_method
+ ):
+ num_processes = 1
+
+ # Map complete source-to-destination path tuples while filtering out spectator file structures
+ paths = [
+ (
+ os.path.join(input_root, name),
+ os.path.join(output_root1, name) if output_root1 else None,
+ os.path.join(output_root2, name) if output_root2 else None,
+ os.path.join(input_root, name)
+ )
+ for name in sorted(os.listdir(input_root))
+ if "spec" not in name
+ ]
+
+ start_time = time.time()
+ feature_input = FeatureInput(is_half=config.is_half, device=config.device)
+
+ # Distribute the dataset slices via a ProcessPoolExecutor (one process mapped per logical computing device)
+ with concurrent.futures.ProcessPoolExecutor(max_workers=len(devices)) as executor:
+ concurrent.futures.wait([
+ executor.submit(
+ feature_input.process_files,
+ paths[i::len(devices)], # Stride slicing maps data to devices evenly
+ f0_method,
+ hop_length,
+ predictor_onnx,
+ devices[i],
+ is_half,
+ num_processes // len(devices), # Scale down thread distribution per process proportionally
+ f0_autotune,
+ f0_autotune_strength,
+ alpha
+ )
+ for i in range(len(devices))
+ ])
+
+ logger.info(translations["extract_f0_success"].format(elapsed_time=f"{(time.time() - start_time):.2f}"))
\ No newline at end of file
diff --git a/main/inference/extracting/preparing_files.py b/main/inference/extracting/preparing_files.py
new file mode 100644
index 0000000000000000000000000000000000000000..9dba71721c2d5f706b7033bd691abbfcf8e8e4a6
--- /dev/null
+++ b/main/inference/extracting/preparing_files.py
@@ -0,0 +1,175 @@
+import os
+import sys
+import json
+import shutil
+
+from random import shuffle
+
+sys.path.append(os.getcwd())
+
+from main.app.core.ui import configs, config
+from main.inference.extracting.embedding import create_mute_file
+
+def mute_file(
+ embedders_mode,
+ embedders_model,
+ mute_base_path,
+ rvc_version
+):
+ """
+ Locates or generates the correct silent (.npy) reference embedding file.
+
+ Selects pre-computed embedding assets based on the specific language model variation,
+ or dynamically creates one using the corresponding extraction backend if missing.
+
+ Args:
+ embedders_mode (str): Backend framework configuration type (e.g., 'fairseq', 'onnx').
+ embedders_model (str): Name or path identifier of the embedder model.
+ mute_base_path (str): Root workspace folder containing the reference mute assets.
+ rvc_version (str): Target framework execution variant ('v1' or 'v2').
+
+ Returns:
+ str: Absolute system filepath pointing to the verified mute array file.
+ """
+
+ # Map explicit pre-built file names based on language-specific embedder variants
+ mute_file = {
+ "contentvec_base": "mute.npy",
+ "hubert_base": "mute.npy",
+ "vietnamese_hubert_base": "mute_vietnamese.npy",
+ "japanese_hubert_base": "mute_japanese.npy",
+ "korean_hubert_base": "mute_korean.npy",
+ "chinese_hubert_base": "mute_chinese.npy",
+ "portuguese_hubert_base": "mute_portuguese.npy",
+ "spin-v1": "mute_spin-v1.npy",
+ "spin-v2": "mute_spin-v2.npy"
+ }.get(embedders_model, None)
+
+ # Dynamic fallback: trigger extraction if file mapping is unregistered or missing from disk
+ if mute_file is None or not os.path.exists(os.path.join(mute_base_path, f"{rvc_version}_extracted", mute_file)):
+ create_mute_file(
+ rvc_version,
+ embedders_model,
+ embedders_mode,
+ config.is_half
+ )
+ # Assign newly extracted file naming syntax convention
+ mute_file = f"mute_{embedders_model}.npy"
+
+ return os.path.join(mute_base_path, f"{rvc_version}_extracted", mute_file)
+
+def generate_config(rvc_version, sample_rate, model_path):
+ """
+ Clones the global architecture JSON template into the local model experiment folder.
+
+ Args:
+ rvc_version (str): RVC version variant used to select the config subfolder.
+ sample_rate (int): Target audio sample rate used to match the JSON template.
+ model_path (str): Target directory where config.json will be saved.
+ """
+
+ config_save_path = os.path.join(model_path, "config.json")
+ # Only clone template if no localized instance currently populates the experiment workspace
+ if not os.path.exists(config_save_path):
+ shutil.copy(
+ os.path.join("main", "configs", rvc_version, f"{sample_rate}.json"),
+ config_save_path
+ )
+
+def generate_filelist(
+ pitch_guidance,
+ model_path,
+ rvc_version,
+ sample_rate,
+ embedders_mode = "fairseq",
+ embedder_model = "hubert_base",
+ include_mutes = 2
+):
+ """
+ Validates completed extraction artifacts and builds the dataset filelist index.
+
+ Cross-references file outputs, appends silent placeholder entries allocated per
+ identified speaker ID (SID), updates training state configuration details, and writes
+ the completed `filelist.txt`.
+
+ Args:
+ pitch_guidance (bool): If True, requires and writes paths for coarse and fine F0 components.
+ model_path (str): Local path pointing to active training workspace directories.
+ rvc_version (str): Active pipeline variation ('v1' or 'v2').
+ sample_rate (int): Sample rate used to locate the correct silent placeholder file.
+ embedders_mode (str, optional): Target extraction library format. Defaults to "fairseq".
+ embedder_model (str, optional): Identity key name of the embedder. Defaults to "hubert_base".
+ include_mutes (int, optional): Number of silent references to generate per Speaker ID. Defaults to 2.
+ """
+
+ f0_dir, f0nsf_dir = None, None
+ # Map core workspace paths for chopped audio slices and content embeddings
+ gt_wavs_dir, feature_dir = (
+ os.path.join(model_path, "sliced_audios"),
+ os.path.join(model_path, f"{rvc_version}_extracted")
+ )
+
+ # Conditionally include pitch-tracking folders based on model needs
+ if pitch_guidance:
+ f0_dir, f0nsf_dir = (
+ os.path.join(model_path, "f0"),
+ os.path.join(model_path, "f0_voiced")
+ )
+
+ # Extract base filenames (excluding extensions) to align dataset features
+ gt_wavs_files, feature_files = (
+ set(name.split(".")[0] for name in os.listdir(gt_wavs_dir)),
+ set(name.split(".")[0] for name in os.listdir(feature_dir))
+ )
+
+ # Use set intersections to find files that successfully completed all extraction steps
+ names = gt_wavs_files & feature_files
+
+ if pitch_guidance:
+ names = (
+ names &
+ set(name.split(".")[0] for name in os.listdir(f0_dir)) &
+ set(name.split(".")[0] for name in os.listdir(f0nsf_dir))
+ )
+
+ options, sids = [], []
+ mute_base_path = os.path.join(configs["logs_path"], "mute")
+ # Step 1: Parse verified names, extract speaker IDs, and format dataset rows
+ for name in names:
+ sid = name.split("_")[0]
+ if sid not in sids: sids.append(sid)
+
+ # Build paths separated by pipe characters matching structural requirements expected by RVC datasets
+ option = f"{gt_wavs_dir}/{name}.wav|{feature_dir}/{name}.npy|{f0_dir}/{name}.wav.npy|{f0nsf_dir}/{name}.wav.npy|{sid}" if pitch_guidance else f"{gt_wavs_dir}/{name}.wav|{feature_dir}/{name}.npy|{sid}"
+ options.append(option)
+
+ # Step 2: Format and inject silent placeholder rows to prevent voice artifacting
+ if include_mutes > 0:
+ mute_audio_path, mute_feature_path = (
+ os.path.join(mute_base_path, "sliced_audios", f"mute{sample_rate}.wav"),
+ mute_file(embedders_mode, embedder_model, mute_base_path, rvc_version)
+ )
+
+ mute_f0_path, mute_f0nsf_path = (
+ os.path.join(mute_base_path, 'f0', 'mute.wav.npy'),
+ os.path.join(mute_base_path, 'f0_voiced', 'mute.wav.npy')
+ )
+
+ # Distribute silent entries proportionally across all discovered unique speaker categories
+ for sid in sids * include_mutes:
+ option = f"{mute_audio_path}|{mute_feature_path}|{mute_f0_path}|{mute_f0nsf_path}|{sid}" if pitch_guidance else f"{mute_audio_path}|{mute_feature_path}|{sid}"
+ options.append(option)
+
+ # Step 3: Shuffle execution lines randomly to minimize batch bias during training loops
+ shuffle(options)
+ with open(os.path.join(model_path, "filelist.txt"), "w") as f:
+ f.write("\n".join(options))
+
+ # Step 4: Dynamically update global dictionary dimensions ('sid' count parameter) within config JSON file
+ configs_path = os.path.join(model_path, "config.json")
+ with open(configs_path, "r", encoding="utf-8") as f:
+ data = json.load(f)
+ data["sid"] = len(sids)
+
+ with open(configs_path, "w", encoding="utf-8") as f:
+ json.dump(data, f, indent=4)
\ No newline at end of file
diff --git a/main/inference/extracting/setup_path.py b/main/inference/extracting/setup_path.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d50a069013c035e4f21415e5991064bea5eab73
--- /dev/null
+++ b/main/inference/extracting/setup_path.py
@@ -0,0 +1,39 @@
+import os
+
+def setup_paths(exp_dir, version = None):
+ """
+ Sets up and creates the necessary workspace directories for feature extraction.
+
+ Depending on whether an RVC/SVC `version` is provided, this function dynamically
+ routes path building for either structural feature embeddings or fundamental
+ frequency (F0) outputs.
+
+ Args:
+ exp_dir (str): Main experiment directory path on disk.
+ version (str, optional): Target framework variation version (e.g., 'v1' or 'v2'). If provided, configures embedding paths. Defaults to None.
+
+ Returns:
+ tuple: Contains (wav_path, out_path) if version is provided, otherwise (wav_path, output_root1, output_root2).
+ """
+
+ # Define the common absolute folder holding standard 16kHz downsampled audio chunks
+ wav_path = os.path.join(exp_dir, "sliced_audios_16k")
+
+ if version:
+ # Branch A: Setup workspace infrastructure for embedding extraction (e.g., v2_extracted)
+ out_path = os.path.join(exp_dir, f"{version}_extracted")
+ os.makedirs(out_path, exist_ok=True)
+
+ return wav_path, out_path
+ else:
+ # Branch B: Setup workspace infrastructure for pitch estimation (coarse vs fine voiced pitch arrays)
+ output_root1, output_root2 = (
+ os.path.join(exp_dir, "f0"),
+ os.path.join(exp_dir, "f0_voiced")
+ )
+
+ # Ensure directories exist safely without throwing errors if they were previously created
+ os.makedirs(output_root1, exist_ok=True)
+ os.makedirs(output_root2, exist_ok=True)
+
+ return wav_path, output_root1, output_root2
\ No newline at end of file
diff --git a/main/inference/preprocess/preprocess.py b/main/inference/preprocess/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..360ea3f4b0f986297cd8f28c834697b9e722a086
--- /dev/null
+++ b/main/inference/preprocess/preprocess.py
@@ -0,0 +1,542 @@
+import os
+import sys
+import time
+import torch
+import logging
+import librosa
+import argparse
+import warnings
+
+import numpy as np
+import torch.multiprocessing as mp
+
+from tqdm import tqdm
+from scipy import signal
+from scipy.io import wavfile
+from concurrent.futures import ProcessPoolExecutor, as_completed
+
+sys.path.append(os.getcwd())
+
+from main.library.utils import strtobool
+from main.library.audio.audio import load_audio
+from main.inference.preprocess.slicer2 import Slicer
+from main.app.variables import config, logger, translations, configs, file_types
+
+if not config.debug_mode:
+ warnings.filterwarnings("ignore")
+ for l in ["numba.core.byteflow", "numba.core.ssa", "numba.core.interpreter"]:
+ logging.getLogger(l).setLevel(logging.ERROR)
+
+OVERLAP, MAX_AMPLITUDE, ALPHA, HIGH_PASS_CUTOFF, SAMPLE_RATE_16K = 0.3, 0.9, 0.75, 48, 16000
+
+def parse_arguments():
+ """
+ Parses command-line arguments for the dataset preprocessing script.
+
+ Returns:
+ argparse.Namespace: A namespace object populated with parsed arguments.
+ """
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--preprocess", action='store_true')
+ parser.add_argument("--model_name", type=str, required=True)
+ parser.add_argument("--dataset_path", type=str, default="./dataset")
+ parser.add_argument("--sample_rate", type=int, required=True)
+ parser.add_argument("--cpu_cores", type=int, default=2)
+ parser.add_argument("--cut_preprocess", type=str, default="Automatic")
+ parser.add_argument("--process_effects", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--clean_dataset", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--clean_strength", type=float, default=0.7)
+ parser.add_argument("--chunk_len", type=float, default=3.0, required=False)
+ parser.add_argument("--overlap_len", type=float, default=0.3, required=False)
+ parser.add_argument("--normalization_mode", type=str, default="none", required=False)
+ parser.add_argument("--architecture", type=str, default="RVC")
+
+ return parser.parse_args()
+
+class PreProcess:
+ """
+ Handles audio file parsing, normalization, slicing, and resampling
+ to prepare clean datasets for RVC model training.
+ """
+
+ def __init__(
+ self,
+ sr,
+ exp_dir,
+ per,
+ architecture = "RVC"
+ ):
+ """
+ Initializes the PreProcess class instance with pipeline hyper-parameters.
+
+ Args:
+ sr (int): Target sample rate for the processing output.
+ exp_dir (str): Root directory where sliced output files will be written.
+ per (float): Duration per window block used during automatic splitting.
+ architecture (str, optional): Target architecture style. Defaults to "RVC".
+ """
+
+ # Configure the voice activity detection slicer depending on the model architecture type
+ if architecture == "RVC": slicer_params = {"sr": sr, "threshold": -42, "min_length": 1500, "min_interval": 400, "hop_size": 15, "max_sil_kept": 500}
+ else: slicer_params = {"sr": sr, "threshold": -40, "min_length": 7500, "min_interval": 100, "hop_size": 10, "max_sil_kept": 800}
+
+ self.slicer = Slicer(
+ **slicer_params
+ )
+ # Design a 5th-order digital High-Pass Butterworth filter to strip low-end rumbling
+ self.b_high, self.a_high = signal.butter(
+ N=5,
+ Wn=HIGH_PASS_CUTOFF,
+ btype="high",
+ fs=sr
+ )
+ self.sr = sr
+ self.tg = None
+ self.per = per
+ self.exp_dir = exp_dir
+ # Fall back to CPU processing if non-standard OpenCL/private hardware backends are specified
+ self.device = config.device if not config.device.startswith(("ocl", "privateuseone")) else "cpu"
+ # Set up processing output locations for source audio rate and downsampled 16kHz tracks
+ self.gt_wavs_dir = os.path.join(exp_dir, "sliced_audios")
+ self.wavs16k_dir = os.path.join(exp_dir, "sliced_audios_16k")
+ os.makedirs(self.gt_wavs_dir, exist_ok=True)
+ os.makedirs(self.wavs16k_dir, exist_ok=True)
+
+ def _normalize_audio(self, audio):
+ """
+ Normalizes the amplitude profile of a given numpy array track block.
+
+ Args:
+ audio (np.ndarray): Single-channel floating-point audio time series.
+
+ Returns:
+ np.ndarray: Rescaled audio data, or None if signal clip threshold is breached.
+ """
+
+ tmp_max = np.abs(audio).max()
+ # Discard corrupted segments or samples with massive distortion artifacts
+ if tmp_max > 2.5: return None
+ # Dynamic range compression using blending alpha coefficients
+ return (audio / tmp_max * (MAX_AMPLITUDE * ALPHA)) + (1 - ALPHA) * audio
+
+ def process_audio_segment(self, normalized_audio, sid, idx0, idx1, normalization_mode):
+ """
+ Saves a chunked waveform segment into target folders at both full scale and 16kHz sample rates.
+
+ Args:
+ normalized_audio (Optional[np.ndarray]): Fragment wave data to preserve.
+ sid (int): Speaker identification token label.
+ idx0 (int): Base audio index identifier.
+ idx1 (int): Sub-segment counter within the current stream partition.
+ normalization_mode (str): String flag indicating whether to apply late normalization.
+ """
+
+ if normalized_audio is None:
+ logger.debug(f"{sid}-{idx0}-{idx1}-filtered")
+ return
+
+ if normalization_mode == "post": normalized_audio = self._normalize_audio(normalized_audio)
+
+ # Output standard target sample rate audio snippet
+ wavfile.write(
+ os.path.join(self.gt_wavs_dir, f"{sid}_{idx0}_{idx1}.wav"),
+ self.sr,
+ normalized_audio.astype(np.float32)
+ )
+ # Resample utilizing SoX high-quality algorithm and save down to a standard 16kHz structure
+ wavfile.write(
+ os.path.join(self.wavs16k_dir, f"{sid}_{idx0}_{idx1}.wav"),
+ SAMPLE_RATE_16K,
+ librosa.resample(
+ normalized_audio,
+ orig_sr=self.sr,
+ target_sr=SAMPLE_RATE_16K,
+ res_type="soxr_vhq"
+ ).astype(np.float32)
+ )
+
+ def simple_cut(
+ self,
+ audio,
+ sid,
+ idx0,
+ chunk_len,
+ overlap_len,
+ normalization_mode
+ ):
+ """
+ Splits a single continuous audio vector via uniform chunks and fixed overlaps.
+
+ Args:
+ audio (np.ndarray): The raw sound array dataset.
+ sid (int): Speaker tracking identification index.
+ idx0 (int): Master catalog index referencing global tracks.
+ chunk_len (float): Desired block parsing length in seconds.
+ overlap_len (float): Overlapping offset margin boundary calculated in seconds.
+ normalization_mode (str): Parameter selecting late audio normalizer tracking.
+ """
+
+ chunk_length = int(self.sr * chunk_len)
+ overlap_length = int(self.sr * overlap_len)
+ i = 0
+
+ while i < len(audio):
+ chunk = audio[i : i + chunk_length]
+ if normalization_mode == "post": chunk = self._normalize_audio(chunk)
+ # Ensure that partial trailing tracks matching less than window boundaries are omitted
+ if len(chunk) == chunk_length:
+ wavfile.write(
+ os.path.join(self.gt_wavs_dir, f"{sid}_{idx0}_{i // (chunk_length - overlap_length)}.wav"),
+ self.sr,
+ chunk.astype(np.float32)
+ )
+
+ wavfile.write(
+ os.path.join(self.wavs16k_dir, f"{sid}_{idx0}_{i // (chunk_length - overlap_length)}.wav"),
+ SAMPLE_RATE_16K,
+ librosa.resample(
+ chunk,
+ orig_sr=self.sr,
+ target_sr=SAMPLE_RATE_16K,
+ res_type="soxr_vhq"
+ ).astype(np.float32)
+ )
+
+ i += chunk_length - overlap_length
+
+ def process_audio(
+ self,
+ path,
+ idx0,
+ sid,
+ cut_preprocess,
+ process_effects,
+ clean_dataset,
+ clean_strength,
+ chunk_len,
+ overlap_len,
+ normalization_mode
+ ):
+ """
+ Primary execution node for a file unit processing tracking sequence.
+
+ Args:
+ path (str): Relative or absolute target path pointing to disk source files.
+ idx0 (int): Base reference catalog identifier.
+ sid (int): Associated speaker identification marker tag.
+ cut_preprocess (str): Target slice configuration directive ("Skip", "Simple", "Automatic").
+ process_effects (bool): Enables or disables high pass frequency scrubbing routines.
+ clean_dataset (bool): Toggles deep gating modules on input sound tracks.
+ clean_strength (float): Dynamic dampening strength targeting signal noises.
+ chunk_len (float): Chunk sizing metrics in seconds.
+ overlap_len (float): Tracking overlap intervals in seconds.
+ normalization_mode (str): Normalization window context flag selection.
+
+ Returns:
+ float: Evaluated length of processed signal duration parameter block in seconds.
+ """
+
+ dataset_length = 0
+
+ try:
+ # Load input sample tracking metrics from memory maps
+ audio = load_audio(path, sample_rate=self.sr)
+ dataset_length = librosa.get_duration(y=audio, sr=self.sr)
+
+ # Apply high-pass linear filtering routines if requested
+ if process_effects: audio = signal.lfilter(self.b_high, self.a_high, audio)
+ if normalization_mode == "pre": audio = self._normalize_audio(audio)
+
+ # Implement deep learning noise-gate architectures via PyTorch operations
+ if clean_dataset:
+ if self.tg is None:
+ from main.library.audio.noisereduce import TorchGate
+
+ self.tg = TorchGate(
+ self.sr,
+ prop_decrease=clean_strength
+ ).to(self.device)
+
+ # Format data vectors to match expected Torch tensor shapes before gating
+ audio = self.tg(
+ torch.from_numpy(audio).unsqueeze(0).to(self.device).float()
+ ).squeeze(0).cpu().detach().numpy()
+
+ # Route execution logic branches based on slicing type configuration
+ if cut_preprocess == "Skip":
+ self.process_audio_segment(
+ audio,
+ sid,
+ idx0,
+ 0,
+ normalization_mode,
+ )
+ elif cut_preprocess == "Simple":
+ self.simple_cut(
+ audio,
+ sid,
+ idx0,
+ chunk_len,
+ overlap_len,
+ normalization_mode,
+ )
+ elif cut_preprocess == "Automatic":
+ idx1 = 0
+ # Use voice detection boundaries to extract audio components cleanly
+ for audio_segment in self.slicer.slice(audio):
+ i = 0
+
+ while 1:
+ start = int(self.sr * (self.per - OVERLAP) * i)
+ i += 1
+
+ if len(audio_segment[start:]) > (self.per + OVERLAP) * self.sr:
+ self.process_audio_segment(
+ audio_segment[start : start + int(self.per * self.sr)],
+ sid,
+ idx0,
+ idx1,
+ normalization_mode
+ )
+
+ idx1 += 1
+ else:
+ self.process_audio_segment(
+ audio_segment[start:],
+ sid,
+ idx0,
+ idx1,
+ normalization_mode
+ )
+
+ idx1 += 1
+ break
+ except Exception as e:
+ raise RuntimeError(f"{translations['process_audio_error']}: {e}")
+ return dataset_length
+
+def format_duration(seconds):
+ """
+ Converts raw floating-point second arrays into readable timestamps.
+
+ Args:
+ seconds (float): Raw metric count denoting total tracked timeline seconds.
+
+ Returns:
+ str: Timestamp string formatted cleanly as HH:MM:SS.
+ """
+
+ return f"{int(seconds // 3600):02}:{int((seconds % 3600) // 60):02}:{int(seconds % 60):02}"
+
+def process_file(args):
+ """
+ Unpacks multi-processing pipeline arguments to invoke the core audio preprocessing method.
+
+ Args:
+ args (Tuple): Complex zipped container holding configuration metrics, references, and paths.
+
+ Returns:
+ float: Calculated temporal duration length values back to tracking monitors.
+ """
+
+ (
+ pp,
+ file,
+ cut_preprocess,
+ process_effects,
+ clean_dataset,
+ clean_strength,
+ chunk_len,
+ overlap_len,
+ normalization_mode
+ ) = args
+
+ file_path, idx0, sid = file
+
+ return pp.process_audio(
+ file_path,
+ idx0,
+ sid,
+ cut_preprocess,
+ process_effects,
+ clean_dataset,
+ clean_strength,
+ chunk_len,
+ overlap_len,
+ normalization_mode
+ )
+
+def preprocess_training_set(
+ input_root,
+ sr,
+ num_processes,
+ exp_dir,
+ per,
+ cut_preprocess,
+ process_effects,
+ clean_dataset,
+ clean_strength,
+ chunk_len,
+ overlap_len,
+ normalization_mode,
+ architecture = "RVC"
+):
+ """
+ Crawls raw audio file locations and parallelizes data-preprocessing tasks using concurrent workers.
+
+ Args:
+ input_root (str): Source dataset tree traversal path.
+ sr (int): Target frequency sample tracking size.
+ num_processes (int): Worker pool scale dimensions.
+ exp_dir (str): Logging target output path.
+ per (float): Sliding partition tracker segment index metrics.
+ cut_preprocess (str): Target pipeline operational slicing mode parameter.
+ process_effects (bool): High pass filter enabling state flag.
+ clean_dataset (bool): Gating routine implementation variable.
+ clean_strength (float): Scaling factor for background noise floor attenuation.
+ chunk_len (float): Fragment duration scale.
+ overlap_len (float): Splitting block margin overlap offsets.
+ normalization_mode (str): System sound volume modifier parameters.
+ architecture (str): Target baseline neural infrastructure configuration. Defaults to "RVC".
+ """
+
+ start_time = time.time()
+ logger.info(translations["start_preprocess"].format(num_processes=num_processes))
+ pp = PreProcess(sr, exp_dir, per, architecture)
+ dataset_length, idx = 0, 0
+ files = []
+
+ # Walk through the dataset file directory structure
+ for root, _, filenames in os.walk(input_root):
+ try:
+ # Map sub-folder names to speaker IDs (root folder equals default speaker ID 0)
+ sid = 0 if root == input_root else int(os.path.basename(root))
+ for f in filenames:
+ if f.lower().endswith(tuple(file_types)):
+ files.append((os.path.join(root, f), idx, sid))
+ idx += 1
+ except ValueError:
+ raise ValueError(f"{translations['not_integer']} '{os.path.basename(root)}'.")
+
+ # Launch multi-core parallel process execution pools
+ with tqdm(total=len(files), ncols=100, unit="f") as pbar:
+ with ProcessPoolExecutor(max_workers=num_processes) as executor:
+ futures = [
+ executor.submit(
+ process_file,
+ (
+ pp,
+ file,
+ cut_preprocess,
+ process_effects,
+ clean_dataset,
+ clean_strength,
+ chunk_len,
+ overlap_len,
+ normalization_mode
+ )
+ )
+ for file in files
+ ]
+
+ # Aggregate process completions safely and compute aggregate dataset timeline lengths
+ for future in as_completed(futures):
+ try:
+ dataset_length += future.result()
+ except Exception as e:
+ raise RuntimeError(f"{translations['process_error']}: {e}")
+ pbar.update(1)
+
+ elapsed_time = time.time() - start_time
+ logger.info(translations["dataset_duration"].format(duration=format_duration(dataset_length)))
+ logger.info(translations["preprocess_success"].format(elapsed_time=f"{elapsed_time:.2f}"))
+
+def main():
+ """
+ Orchestrates the program execution lifecycle, logs runtime parameters,
+ and secures cross-process execution tracking using PID locks.
+ """
+
+ args = parse_arguments()
+ experiment_directory = os.path.join(configs["logs_path"], args.model_name)
+
+ num_processes = args.cpu_cores
+ num_processes = 2 if num_processes is None else int(num_processes)
+
+ (
+ dataset,
+ sample_rate,
+ cut_preprocess,
+ preprocess_effects,
+ clean_dataset,
+ clean_strength,
+ chunk_len,
+ overlap_len,
+ normalization_mode,
+ architecture
+ ) = (
+ args.dataset_path,
+ args.sample_rate,
+ args.cut_preprocess,
+ args.process_effects,
+ args.clean_dataset,
+ args.clean_strength,
+ args.chunk_len,
+ args.overlap_len,
+ args.normalization_mode,
+ args.architecture
+ )
+
+ os.makedirs(experiment_directory, exist_ok=True)
+ # Dictionary containing configurations to display for verification
+ log_data = {
+ translations['modelname']: args.model_name,
+ translations['export_process']: experiment_directory,
+ translations['dataset_folder']: dataset,
+ translations['pretrain_sr']: sample_rate,
+ translations['cpu_core']: num_processes,
+ translations['split_audio']: cut_preprocess,
+ translations['preprocess_effect']: preprocess_effects,
+ translations['clear_audio']: clean_dataset,
+ translations['clean_strength']: clean_strength,
+ translations["architecture"]: architecture
+ }
+
+ for key, value in log_data.items():
+ logger.debug(f"{key}: {value}")
+
+ # Write a temporary PID tracking file to register the current process instance locks
+ pid_path = os.path.join(experiment_directory, "preprocess_pid.txt")
+ with open(pid_path, "w") as pid_file:
+ pid_file.write(str(os.getpid()))
+
+ try:
+ preprocess_training_set(
+ dataset,
+ sample_rate,
+ num_processes,
+ experiment_directory,
+ config.per_preprocess,
+ cut_preprocess,
+ preprocess_effects,
+ clean_dataset,
+ clean_strength,
+ chunk_len,
+ overlap_len,
+ normalization_mode,
+ architecture
+ )
+ except Exception as e:
+ logger.error(f"{translations['process_audio_error']} {e}")
+ import traceback
+ logger.debug(traceback.format_exc())
+
+ # Clean up the runtime PID file upon normal script termination or error handling routines
+ if os.path.exists(pid_path): os.remove(pid_path)
+ logger.info(f"{translations['preprocess_model_success']} {args.model_name}")
+
+if __name__ == "__main__":
+ # Force 'spawn' multiprocessing method to guarantee cross-platform stability
+ mp.set_start_method("spawn", force=True)
+ main()
\ No newline at end of file
diff --git a/main/inference/preprocess/slicer2.py b/main/inference/preprocess/slicer2.py
new file mode 100644
index 0000000000000000000000000000000000000000..91860ad992a655ebc9f9af0045fa7c56f4d40301
--- /dev/null
+++ b/main/inference/preprocess/slicer2.py
@@ -0,0 +1,343 @@
+import numpy as np
+
+class Slicer:
+ """
+ Slices audio waveforms based on Root Mean Square (RMS) energy thresholds to separate
+ voiced segments from silences.
+ """
+
+ def __init__(
+ self,
+ sr,
+ threshold = -40.0,
+ min_length = 5000,
+ min_interval = 300,
+ hop_size = 20,
+ max_sil_kept = 5000
+ ):
+ """
+ Initializes the Slicer with explicit temporal and amplitude configurations.
+
+ Args:
+ sr (int): Sampling rate of the audio signals.
+ threshold (float): Threshold in dB below which audio is treated as silence. Defaults to -40.0.
+ min_length (int): Minimum length of a valid audio chunk in milliseconds. Defaults to 5000.
+ min_interval (int): Minimum duration of silence needed to trigger a cut in milliseconds. Defaults to 300.
+ hop_size (int): Step size for frame feature extraction in milliseconds. Defaults to 20.
+ max_sil_kept (int): Maximum silence buffer retained at chunk boundaries in milliseconds. Defaults to 5000.
+ """
+
+ # Convert absolute milliseconds to sample counts and frame numbers
+ min_interval = sr * min_interval / 1000
+ self.threshold = 10 ** (threshold / 20.0) # Convert dB to linear amplitude scale
+ self.hop_size = round(sr * hop_size / 1000)
+ # Window size is bounded by the minimum requested interval or 4x the hop size to preserve local context
+ self.win_size = min(round(min_interval), 4 * self.hop_size)
+ # Scale time parameters into unit values matching the frame tracking array (divided by hop_size)
+ self.min_length = round(sr * min_length / 1000 / self.hop_size)
+ self.min_interval = round(min_interval / self.hop_size)
+ self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
+
+ def _apply_slice(self, waveform, begin, end):
+ """
+ Extracts a segment from the waveform mapping frame boundaries back to time-domain sample indexes.
+
+ Args:
+ waveform (np.ndarray): Target audio array of shape (samples,) or (channels, samples).
+ begin (int): Starting frame index.
+ end (int): Terminating frame index.
+
+ Returns:
+ np.ndarray: Sliced subsection of the original audio waveform.
+ """
+
+ start_idx = begin * self.hop_size
+ # Check dimensionality to handle mono arrays vs multichannel matrices correctly
+ return waveform[
+ :, start_idx:min(waveform.shape[1], end * self.hop_size)
+ ] if len(waveform.shape) > 1 else waveform[
+ start_idx:min(waveform.shape[0], end * self.hop_size)
+ ]
+
+ def slice(self, waveform):
+ """
+ Slices a waveform into a collection of variable-length audio chunks without returning indices.
+
+ Args:
+ waveform (np.ndarray): Audio track array profile to parse.
+
+ Returns:
+ List[np.ndarray]: List of split non-silent audio array fragments.
+ """
+
+ # Downmix audio to single channel mean representations if multidimensional input is given
+ samples = waveform.mean(axis=0) if len(waveform.shape) > 1 else waveform
+ # Immediately return the complete original file if its frame depth falls short of minimum split thresholds
+ if samples.shape[0] <= self.min_length: return [waveform]
+
+ # Extract continuous frame root-mean-square amplitude envelopes
+ rms_list = get_rms(
+ y=samples,
+ frame_length=self.win_size,
+ hop_length=self.hop_size
+ ).squeeze(0)
+
+ sil_tags = []
+ silence_start, clip_start = None, 0
+ # Scan across RMS frame elements to track noise gates
+ for i, rms in enumerate(rms_list):
+ if rms < self.threshold:
+ if silence_start is None: silence_start = i
+ continue
+
+ if silence_start is None: continue
+ # Identify edge cases such as long trailing silence buffers or leading gaps
+ is_leading_silence = silence_start == 0 and i > self.max_sil_kept
+
+ need_slice_middle = (
+ i - silence_start >= self.min_interval and i - clip_start >= self.min_length
+ )
+
+ if not is_leading_silence and not need_slice_middle:
+ silence_start = None
+ continue
+
+ if i - silence_start <= self.max_sil_kept: # Case A: Silence sequence fits nicely inside the max retained padding limits
+ pos = rms_list[silence_start : i + 1].argmin() + silence_start
+
+ sil_tags.append(
+ (0, pos) if silence_start == 0 else (pos, pos)
+ )
+
+ clip_start = pos
+ elif i - silence_start <= self.max_sil_kept * 2: # Case B: Silence sequence extends past safety margins but stays below double padding limits
+ pos = rms_list[
+ i - self.max_sil_kept : silence_start + self.max_sil_kept + 1
+ ].argmin()
+
+ pos += i - self.max_sil_kept
+ pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
+
+ if silence_start == 0:
+ sil_tags.append((0, pos_r))
+ clip_start = pos_r
+ else:
+ sil_tags.append((
+ min((rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start), pos),
+ max(pos_r, pos)
+ ))
+
+ clip_start = max(pos_r, pos)
+ else: # Case C: Extended deep silence block spanning beyond standard tracking capacities
+ pos_r = (rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept)
+
+ sil_tags.append(
+ (
+ 0,
+ pos_r
+ ) if silence_start == 0 else (
+ (rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start),
+ pos_r
+ )
+ )
+
+ clip_start = pos_r
+
+ silence_start = None
+
+ total_frames = rms_list.shape[0]
+ # Handle hanging fragments residing at the end of tracking timelines
+ if (
+ silence_start is not None and
+ total_frames - silence_start >= self.min_interval
+ ):
+ sil_tags.append((
+ rms_list[silence_start : min(total_frames, silence_start + self.max_sil_kept) + 1].argmin() + silence_start,
+ total_frames + 1
+ ))
+
+ # Return original track wrapper if zero slicing configurations matched
+ if not sil_tags: return [waveform]
+ else:
+ # Assemble extracted audio snippets based on tag timestamps
+ chunks = []
+ if sil_tags[0][0] > 0: chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0]))
+
+ for i in range(len(sil_tags) - 1):
+ chunks.append(self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]))
+
+ if sil_tags[-1][1] < total_frames: chunks.append(self._apply_slice(waveform, sil_tags[-1][1], total_frames))
+ return chunks
+
+class Slicer2(Slicer):
+ """
+ An extended implementation of Slicer that returns both the audio chunks
+ and their exact sample boundaries (start and end indexes).
+ """
+
+ def slice2(self, waveform):
+ """
+ Slices a waveform into chunks, returning each segment along with its sample timestamps.
+
+ Args:
+ waveform (np.ndarray): Audio track array profile to parse.
+
+ Returns:
+ List[Tuple[np.ndarray, int, int]]: List of tuples containing (sliced_waveform, start_sample, end_sample).
+ """
+
+ samples = waveform.mean(axis=0) if len(waveform.shape) > 1 else waveform
+
+ if samples.shape[0] <= self.min_length:
+ return [(
+ waveform,
+ 0,
+ samples.shape[0]
+ )]
+
+ rms_list = get_rms(
+ y=samples,
+ frame_length=self.win_size,
+ hop_length=self.hop_size
+ ).squeeze(0)
+
+ sil_tags = []
+ silence_start, clip_start = None, 0
+ # Scan across RMS frame elements to track noise gates
+ for i, rms in enumerate(rms_list):
+ if rms < self.threshold:
+ if silence_start is None: silence_start = i
+ continue
+
+ if silence_start is None: continue
+
+ is_leading_silence = silence_start == 0 and i > self.max_sil_kept
+
+ need_slice_middle = (
+ i - silence_start >= self.min_interval and i - clip_start >= self.min_length
+ )
+
+ if not is_leading_silence and not need_slice_middle:
+ silence_start = None
+ continue
+
+ if i - silence_start <= self.max_sil_kept:
+ pos = rms_list[silence_start : i + 1].argmin() + silence_start
+ sil_tags.append((0, pos) if silence_start == 0 else (pos, pos))
+
+ clip_start = pos
+ elif i - silence_start <= self.max_sil_kept * 2:
+ pos = rms_list[
+ i - self.max_sil_kept : silence_start + self.max_sil_kept + 1
+ ].argmin()
+
+ pos += i - self.max_sil_kept
+ pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
+
+ if silence_start == 0:
+ sil_tags.append((0, pos_r))
+ clip_start = pos_r
+ else:
+ sil_tags.append((
+ min((rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start), pos),
+ max(pos_r, pos)
+ ))
+
+ clip_start = max(pos_r, pos)
+ else:
+ pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
+
+ sil_tags.append(
+ (
+ 0,
+ pos_r
+ ) if silence_start == 0 else (
+ (rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start),
+ pos_r
+ )
+ )
+
+ clip_start = pos_r
+
+ silence_start = None
+
+ total_frames = rms_list.shape[0]
+ if (silence_start is not None and total_frames - silence_start >= self.min_interval):
+ sil_tags.append((
+ rms_list[silence_start : min(total_frames, silence_start + self.max_sil_kept) + 1].argmin() + silence_start,
+ total_frames + 1
+ ))
+
+ if not sil_tags:
+ return [(
+ waveform,
+ 0,
+ samples.shape[-1]
+ )]
+ else:
+ chunks = []
+ # Package snippets with absolute sample-index indicators (frame index multiplied by hop_size)
+ if sil_tags[0][0] > 0:
+ chunks.append((
+ self._apply_slice(waveform, 0, sil_tags[0][0]),
+ 0,
+ sil_tags[0][0] * self.hop_size
+ ))
+
+ for i in range(len(sil_tags) - 1):
+ chunks.append((
+ self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]),
+ sil_tags[i][1] * self.hop_size,
+ sil_tags[i + 1][0] * self.hop_size
+ ))
+
+ if sil_tags[-1][1] < total_frames:
+ chunks.append((
+ self._apply_slice(waveform, sil_tags[-1][1], total_frames),
+ sil_tags[-1][1] * self.hop_size,
+ samples.shape[-1]
+ ))
+
+ return chunks
+
+def get_rms(y, frame_length=2048, hop_length=512, pad_mode="constant"):
+ """
+ Calculates Root Mean Square (RMS) energy envelopes using high-speed NumPy sliding strides.
+
+ Args:
+ y (np.ndarray): 1D single-channel floating-point audio data.
+ frame_length (int): Length of each window frame loop. Defaults to 2048.
+ hop_length (int): Distance in samples between adjacent windows. Defaults to 512.
+ pad_mode (str): Padding strategy for array margin safety. Defaults to "constant".
+
+ Returns:
+ np.ndarray: Computed RMS energy contours array.
+ """
+
+ # Symmetric margin padding guarantees tracking snapshots remain centered across target metrics
+ y = np.pad(y, (int(frame_length // 2), int(frame_length // 2)), mode=pad_mode)
+ axis = -1
+
+ x_shape_trimmed = list(y.shape)
+ x_shape_trimmed[axis] -= frame_length - 1
+ # Extract high-efficiency internal pointers via sliding strides to prevent large data re-allocations
+ xw = np.moveaxis(
+ np.lib.stride_tricks.as_strided(
+ y,
+ shape=tuple(x_shape_trimmed) + tuple([frame_length]),
+ strides=y.strides + tuple([y.strides[axis]])
+ ),
+ -1,
+ axis - 1 if axis < 0 else axis + 1
+ )
+
+ slices = [slice(None)] * xw.ndim
+ slices[axis] = slice(0, None, hop_length)
+ # Square values, compute local mean cross-sections, and extract root profiles
+ return np.sqrt(
+ np.mean(
+ np.abs(xw[tuple(slices)]) ** 2,
+ axis=-2,
+ keepdims=True
+ )
+ )
\ No newline at end of file
diff --git a/main/inference/realtime/audio.py b/main/inference/realtime/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d4e3e20be11d074966ab1568d68cc860cee5def
--- /dev/null
+++ b/main/inference/realtime/audio.py
@@ -0,0 +1,597 @@
+import os
+import sys
+import soxr
+import traceback
+
+import numpy as np
+
+from queue import Queue
+
+sys.path.append(os.getcwd())
+
+import main.library.audio.sounddevice as sd
+
+from main.app.variables import logger, translations
+
+class ServerAudioDevice:
+ """
+ A data class representation holding hardware parameters and structural descriptions
+ for an audio peripheral queried from the host system configuration.
+ """
+
+ def __init__(
+ self,
+ index,
+ name,
+ host_api,
+ max_input_channels,
+ max_output_channels,
+ default_samplerate
+ ):
+ """
+ Initializes hardware structural properties for a device entity descriptor.
+
+ Args:
+ index (int): Numeric identifier assigned by the sound backend driver.
+ name (str): Transmitted device hardware identification name.
+ host_api (str): Underlying audio system host architecture API string (e.g., MME, ASIO, WASAPI).
+ max_input_channels (int): Total count of physical concurrent capturing paths available.
+ max_output_channels (int): Total count of physical concurrent playback paths available.
+ default_samplerate (int): Standard baseline native configuration operating sample rate.
+ """
+
+ self.index = index
+ self.name = name
+ self.host_api = host_api
+ self.max_input_channels = max_input_channels
+ self.max_output_channels = max_output_channels
+ self.default_samplerate = default_samplerate
+
+def check_the_device(device, type = "input"):
+ """
+ Verifies the operational viability of a selected sound device by attempting
+ to temporarily open a test context stream.
+
+ Args:
+ device (dict): Properties dictionary of the targeted device returned via sounddevice query.
+ type (str): Operational verification target direction; either "input" or "output".
+
+ Returns:
+ bool: True if the test hardware context opens successfully, False otherwise.
+ """
+
+ # Strict fallback boundary check for Linux systems to limit selections only to direct ALSA hardware interfaces
+ if sys.platform == "linux" and "hw:" not in device["name"]:
+ return False
+
+ # Bind the verification stream class depending on the intended target mode
+ stream_device = (
+ sd.InputStream if type == "input" else sd.OutputStream
+ )
+
+ try:
+ # Context block deployment to guarantee proper resource clean-up and closure after querying
+ with stream_device(
+ device=device["index"],
+ dtype=np.float32,
+ samplerate=device["default_samplerate"]
+ ):
+ return True
+ except Exception:
+ return False
+
+def list_audio_device():
+ """
+ Scans the system hardware APIs to filter out, identify, and categorize valid, active
+ audio acquisition and rendering devices.
+
+ Returns:
+ tuple: Contains two lists of ServerAudioDevice objects: (input_device_list, output_device_list).
+ """
+
+ try:
+ audio_device_list = sd.query_devices()
+ except Exception as e:
+ logger.error(translations["error_occurred"].format(e=e))
+ audio_device_list = []
+ except OSError as e:
+ logger.debug(translations["error_occurred"].format(e=e))
+ audio_device_list = []
+
+ # Comprehend list entries matching active properties and successful context loop generation
+ input_audio_device_list = [
+ d for d in audio_device_list
+ if d["max_input_channels"] > 0 and check_the_device(d, "input")
+ ]
+
+ output_audio_device_list = [
+ d for d in audio_device_list
+ if d["max_output_channels"] > 0 and check_the_device(d, "output")
+ ]
+
+ try:
+ hostapis = sd.query_hostapis()
+ except Exception as e:
+ logger.error(translations["error_occurred"].format(e=e))
+ hostapis = []
+ except OSError as e:
+ logger.debug(translations["error_occurred"].format(e=e))
+ hostapis = []
+
+ audio_input_device, audio_output_device = [], []
+
+ # Map raw system payload objects to formalized class wrappers for input channels
+ for d in input_audio_device_list:
+ input_audio_device = ServerAudioDevice(
+ index=d["index"],
+ name=d["name"],
+ host_api=hostapis[d["hostapi"]]["name"],
+ max_input_channels=d["max_input_channels"],
+ max_output_channels=d["max_output_channels"],
+ default_samplerate=d["default_samplerate"],
+ )
+ audio_input_device.append(input_audio_device)
+
+ # Map raw system payload objects to formalized class wrappers for output channels
+ for d in output_audio_device_list:
+ output_audio_device = ServerAudioDevice(
+ index=d["index"],
+ name=d["name"],
+ host_api=hostapis[d["hostapi"]]["name"],
+ max_input_channels=d["max_input_channels"],
+ max_output_channels=d["max_output_channels"],
+ default_samplerate=d["default_samplerate"],
+ )
+ audio_output_device.append(output_audio_device)
+
+ return audio_input_device, audio_output_device
+
+def audio_device():
+ """
+ Queries valid peripherals, prioritizes them based on software identifier tags,
+ and returns localized dictionary mappings suited for runtime UI lookups.
+
+ Returns:
+ tuple: (input_device_dict, output_device_dict) containing name keys matched to [hardware_index, channels].
+ """
+
+ try:
+ input_devices, output_devices = list_audio_device()
+
+ def priority(name):
+ """Assigns sorting weights to prioritize clean hardware loops over standard virtual loops."""
+
+ n = name.lower()
+
+ if "virtual" in n:
+ return 0
+ if "vb" in n:
+ return 1
+
+ return 2
+
+ # Sort layouts asynchronously: physical devices bubble up first; virtual structures trail down
+ output_sorted = sorted(
+ output_devices,
+ key=lambda d: priority(d.name)
+ )
+ input_sorted = sorted(
+ input_devices, key=lambda d: priority(d.name), reverse=True
+ )
+
+ # Build dictionary indexing profiles with user-facing readable label keys
+ input_device_list = {
+ f"{input_sorted.index(d)+1}: {d.name} ({d.host_api})": [d.index, d.max_input_channels] for d in input_sorted
+ }
+ output_device_list = {
+ f"{output_sorted.index(d)+1}: {d.name} ({d.host_api})": [d.index, d.max_output_channels] for d in output_sorted
+ }
+
+ return input_device_list, output_device_list
+ except Exception:
+ return {}, {}
+
+class Audio:
+ """
+ Core runtime environment manager orchestrating parallel real-time asynchronous streaming context loops,
+ volume gain controls, inter-callback queueing systems, and multi-channel ASIO/WASAPI configuration bindings.
+ """
+
+ def __init__(
+ self,
+ callbacks,
+ f0_up_key = 0,
+ index_rate = 0.5,
+ protect = 0.5,
+ filter_radius = 3,
+ rms_mix_rate = 1,
+ f0_autotune = False,
+ f0_autotune_strength = 1,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 255.0,
+ input_audio_gain = 1.0,
+ output_audio_gain = 1.0,
+ monitor_audio_gain = 1.0,
+ monitor = False,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5,
+ use_phase_vocoder = True
+ ):
+ """Initializes internal tracking buffers, status flags, and algorithmic execution options."""
+
+ self.callbacks = callbacks
+ self.mon_queue = Queue() # Thread-safe pipeline interface feeding the monitoring channel loop asynchronously
+ self.performance = [0, 0, 0]
+ self.stream = None
+ self.input_stream = None
+ self.output_stream = None
+ self.monitor = None
+ self.running = False
+ # Level controls
+ self.input_audio_gain = input_audio_gain
+ self.output_audio_gain = output_audio_gain
+ self.monitor_audio_gain = monitor_audio_gain
+ self.use_monitor = monitor
+ # Algorithmic configurations
+ self.f0_up_key = f0_up_key
+ self.index_rate = index_rate
+ self.protect = protect
+ self.filter_radius = filter_radius
+ self.rms_mix_rate = rms_mix_rate
+ self.f0_autotune = f0_autotune
+ self.f0_autotune_strength = f0_autotune_strength
+ self.proposal_pitch = proposal_pitch
+ self.proposal_pitch_threshold = proposal_pitch_threshold
+ self.embedders_mix = embedders_mix
+ self.embedders_mix_layers = embedders_mix_layers
+ self.embedders_mix_ratio = embedders_mix_ratio
+ self.use_phase_vocoder = use_phase_vocoder
+
+ def get_audio_device(self, input_index = None, output_index = None):
+ """Looks up specific input and output devices by index from the verified available device list."""
+
+ audioinput, audiooutput = list_audio_device()
+
+ inputs, outputs = (
+ [
+ x for x in audioinput
+ if x.index == input_index
+ ] if input_index is not None else [],
+ [
+ x for x in audiooutput
+ if x.index == output_index
+ ] if output_index is not None else []
+ )
+
+ return (inputs[0] if len(inputs) > 0 else None), (outputs[0] if len(outputs) > 0 else None)
+
+ def process_data_with_time(self, indata):
+ """
+ Applies input channel amplification, downmixes stereo/multi-channel data to mono,
+ and invokes the acoustic framework conversion pipeline.
+
+ Args:
+ indata (np.ndarray): Interleaved input frame chunk from the audio capture device.
+
+ Returns:
+ np.ndarray: Modified single-channel floating point synthesized audio sequence.
+ """
+
+ # Apply pre-inference input volume scaling
+ indata = indata * self.input_audio_gain
+ # Execute downmixing to single-channel (mono) via channel-wise averaging if data is multi-channel
+ unpacked_data = np.mean(indata, axis=1) if indata.shape[1] > 1 else indata.flatten()
+
+ # Forward formatted segment to the underlying RVC conversion layer
+ out_wav, vol, perf = self.callbacks.change_voice(
+ unpacked_data,
+ self.f0_up_key,
+ self.index_rate,
+ self.protect,
+ self.filter_radius,
+ self.rms_mix_rate,
+ self.f0_autotune,
+ self.f0_autotune_strength,
+ self.proposal_pitch,
+ self.proposal_pitch_threshold,
+ self.embedders_mix,
+ self.embedders_mix_layers,
+ self.embedders_mix_ratio,
+ self.use_phase_vocoder
+ )
+
+ # Cache runtime tracking parameters for diagnostics
+ self.performance = perf
+ self.volume = vol
+ return out_wav
+
+ def audio_stream_no_output_callback(self, indata, frames, times, status):
+ """Asynchronous stream acquisition callback deployed when input and output devices are split."""
+
+ try:
+ out_wav = self.process_data_with_time(indata)
+ # Push processed sample data into thread-safe queue to pass to the matching output context thread
+ self.mon_queue.put(out_wav)
+ except Exception as e:
+ logger.error(translations["error_occurred"].format(e=e))
+ logger.debug(traceback.format_exc())
+
+ def audio_stream_callback(self, indata, outdata, frames, times, status):
+ """Asynchronous stream callback deployed when a unified duplex I/O sound interface configuration is used."""
+
+ try:
+ # Clear target array slice bounds and drop frames if processing state is turned off
+ if not self.running:
+ outdata_fill = np.zeros_like(outdata)
+ outdata[:] = outdata_fill
+ return
+
+ out_wav = self.process_data_with_time(indata)
+ output_channels = outdata.shape[1]
+ # If monitoring is active, duplicate the chunk into the sidecar queue
+ if self.use_monitor: self.mon_queue.put(out_wav)
+
+ # Re-tile mono vector slices to match structural multi-channel system playback requirements
+ outdata[:] = (
+ np.repeat(out_wav, output_channels).reshape(-1, output_channels)
+ * self.output_audio_gain
+ )
+ except Exception as e:
+ logger.error(translations["error_occurred"].format(e=e))
+ logger.debug(traceback.format_exc())
+
+ def audio_queue(self, outdata, gain, sample_rate, sample_rate_out = None):
+ """
+ Pulls processed audio chunks from the thread queue, handles sample-rate adjustments,
+ and plays back audio via the asynchronous streaming output context loop.
+ """
+
+ try:
+ if not self.running:
+ outdata_fill = np.zeros_like(outdata)
+ outdata[:] = outdata_fill
+ return
+
+ # Extract the next sequential frame block from the tracking queue context
+ mon_wav = self.mon_queue.get()
+ # Real-time frame dropping: purge older pending chunks to minimize monitoring latency if processing queues back up
+ while self.mon_queue.qsize() > 0:
+ self.mon_queue.get()
+
+ # Apply dynamic high-quality resampling via Soxr if hardware and stream sample rates do not match
+ if sample_rate != sample_rate_out: mon_wav = soxr.resample(mon_wav, sample_rate, sample_rate_out)
+ output_channels = outdata.shape[1]
+
+ # Broadcast resampled output block to multi-channel playback configuration
+ outdata[:] = (
+ np.repeat(mon_wav, output_channels).reshape(-1, output_channels) * gain
+ )
+ except Exception as e:
+ logger.error(translations["error_occurred"].format(e=e))
+ logger.debug(traceback.format_exc())
+
+ def run_audio_stream(
+ self,
+ block_frame,
+ input_device_id,
+ output_device_id,
+ output_monitor_id,
+ input_audio_sample_rate,
+ output_audio_sample_rate,
+ output_monitor_sample_rate,
+ input_max_channel,
+ output_max_channel,
+ output_monitor_max_channel,
+ input_extra_setting,
+ output_extra_setting,
+ output_monitor_extra_setting,
+ use_asio = False
+ ):
+ """Configures and activates the asynchronous PortAudio stream loops based on selected devices."""
+
+ # Safe initialization sequence reset tailored for structural ASIO channel profile switching
+ if use_asio:
+ try:
+ sd.terminate()
+ sd.initialize()
+ except:
+ pass
+
+ # Split device path deployment: configured when using separate devices or disparate sample rates
+ if input_device_id != output_device_id or input_audio_sample_rate != output_audio_sample_rate:
+ self.input_stream = sd.InputStream(
+ callback=self.audio_stream_no_output_callback,
+ latency="low",
+ dtype=np.float32,
+ device=input_device_id,
+ blocksize=block_frame,
+ samplerate=input_audio_sample_rate,
+ channels=input_max_channel,
+ extra_settings=input_extra_setting
+ )
+ self.output_stream = sd.OutputStream(
+ callback=lambda outdata, frames, times, status: self.audio_queue(outdata, self.output_audio_gain, input_audio_sample_rate, output_audio_sample_rate),
+ latency="low",
+ dtype=np.float32,
+ device=output_device_id,
+ # Dynamically scale buffer block sizes to match samplerate transformations
+ blocksize=int(block_frame / input_audio_sample_rate * output_audio_sample_rate) if input_audio_sample_rate != output_audio_sample_rate else block_frame,
+ samplerate=output_audio_sample_rate,
+ channels=output_max_channel,
+ extra_settings=output_extra_setting
+ )
+
+ self.input_stream.start()
+ self.output_stream.start()
+ else: # Unified duplex deployment path: used when both capture and rendering are handled on a shared single device identity
+ self.stream = sd.Stream(
+ callback=self.audio_stream_callback,
+ latency="low",
+ dtype=np.float32,
+ device=(input_device_id, output_device_id),
+ blocksize=block_frame,
+ samplerate=input_audio_sample_rate,
+ channels=(input_max_channel, output_max_channel),
+ extra_settings=(input_extra_setting, output_extra_setting),
+ )
+
+ self.stream.start()
+
+ # Sidecar independent output stream setup dedicated for real-time local target audio monitoring
+ if self.use_monitor:
+ self.monitor = sd.OutputStream(
+ callback=lambda outdata, frames, times, status: self.audio_queue(outdata, self.monitor_audio_gain, input_audio_sample_rate, output_monitor_sample_rate),
+ latency="low",
+ dtype=np.float32,
+ device=output_monitor_id,
+ blocksize=int(block_frame / input_audio_sample_rate * output_monitor_sample_rate) if input_audio_sample_rate != output_monitor_sample_rate else block_frame,
+ samplerate=output_monitor_sample_rate,
+ channels=output_monitor_max_channel,
+ extra_settings=output_monitor_extra_setting
+ )
+ self.monitor.start()
+
+ def stop(self):
+ """Gracefully halts and disposes of all active input, output, and monitoring stream context instances."""
+
+ self.running = False
+
+ if self.stream is not None:
+ self.stream.stop()
+ self.stream.close()
+ self.stream = None
+
+ if self.input_stream is not None:
+ self.input_stream.stop()
+ self.input_stream.close()
+ self.input_stream = None
+
+ if self.output_stream is not None:
+ self.output_stream.stop()
+ self.output_stream.close()
+ self.output_stream = None
+
+ if self.monitor is not None:
+ self.monitor.stop()
+ self.monitor.close()
+ self.monitor = None
+
+ def start(
+ self,
+ input_device_id,
+ output_device_id,
+ output_monitor_id,
+ exclusive_mode,
+ asio_input_channel,
+ asio_output_channel,
+ asio_output_monitor_channel,
+ read_chunk_size,
+ input_audio_sample_rate,
+ output_audio_sample_rate,
+ output_monitor_sample_rate,
+ asio_output_stereo = True,
+ asio_monitor_stereo = True
+ ):
+ """Prepares API backend parameters and begins streaming conversion on the chosen audio devices."""
+
+ # Stop any active sessions before initializing a new one
+ self.stop()
+ # Look up explicit target device descriptors
+ input_audio_device, output_audio_device = self.get_audio_device(input_device_id, output_device_id)
+
+ input_channels, output_channels = (
+ input_audio_device.max_input_channels,
+ output_audio_device.max_output_channels
+ )
+
+ use_asio = False
+ input_extra_setting, output_extra_setting = None, None
+ output_monitor_extra_setting, monitor_channels = None, None
+
+ # Build WASAPI Exclusive/Shared parameters or configure specific ASIO hardware channel maps
+ if (
+ input_audio_device and
+ "WASAPI" in input_audio_device.host_api
+ ):
+ input_extra_setting = sd.WasapiSettings(
+ exclusive=exclusive_mode,
+ auto_convert=not exclusive_mode
+ )
+ elif (
+ input_audio_device and
+ "ASIO" in input_audio_device.host_api and
+ asio_input_channel != -1
+ ):
+ input_extra_setting = sd.AsioSettings(
+ channel_selectors=[asio_input_channel]
+ )
+ input_channels = 1
+ use_asio = True
+
+ if (
+ output_audio_device and
+ "WASAPI" in output_audio_device.host_api
+ ):
+ output_extra_setting = sd.WasapiSettings(
+ exclusive=exclusive_mode,
+ auto_convert=not exclusive_mode
+ )
+ elif (
+ input_audio_device and
+ "ASIO" in input_audio_device.host_api and
+ asio_output_channel != -1
+ ):
+ output_selectors = [asio_output_channel, asio_output_channel + 1] if asio_output_stereo else [asio_output_channel]
+ output_extra_setting = sd.AsioSettings(channel_selectors=output_selectors)
+ output_channels = len(output_selectors)
+ use_asio = True
+
+ # Process sidecar configuration if monitoring feature is requested
+ if self.use_monitor:
+ _, output_monitor_device = self.get_audio_device(output_index=output_monitor_id)
+ monitor_channels = output_monitor_device.max_output_channels
+
+ if (
+ output_monitor_device and
+ "WASAPI" in output_monitor_device.host_api
+ ):
+ output_monitor_extra_setting = sd.WasapiSettings(
+ exclusive=exclusive_mode,
+ auto_convert=not exclusive_mode
+ )
+ elif (
+ output_monitor_device and
+ "ASIO" in output_monitor_device.host_api and
+ asio_output_monitor_channel != -1
+ ):
+ monitor_selectors = [output_monitor_device, output_monitor_device + 1] if asio_monitor_stereo else [output_monitor_device]
+ output_monitor_extra_setting = sd.AsioSettings(channel_selectors=monitor_selectors)
+ monitor_channels = len(monitor_selectors)
+ use_asio = True
+
+ # Base block frame conversion calculation (maps chunk factors directly to continuous binary buffers)
+ block_frame = read_chunk_size * 128
+
+ try:
+ # Deploy and execute the configured streaming loops
+ self.run_audio_stream(
+ block_frame,
+ input_device_id,
+ output_device_id,
+ output_monitor_id,
+ input_audio_sample_rate,
+ output_audio_sample_rate,
+ output_monitor_sample_rate,
+ input_channels,
+ output_channels,
+ monitor_channels,
+ input_extra_setting,
+ output_extra_setting,
+ output_monitor_extra_setting,
+ use_asio=use_asio
+ )
+ self.running = True
+ except Exception as e:
+ logger.error(translations["error_occurred"].format(e=e))
+ logger.debug(traceback.format_exc())
\ No newline at end of file
diff --git a/main/inference/realtime/callbacks.py b/main/inference/realtime/callbacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..79e56c76c0be23a36fb73d69ca52c12c4c6291a6
--- /dev/null
+++ b/main/inference/realtime/callbacks.py
@@ -0,0 +1,260 @@
+import os
+import sys
+import threading
+
+import numpy as np
+
+sys.path.append(os.getcwd())
+
+from main.inference.realtime.audio import Audio
+from main.app.variables import logger, translations
+from main.inference.realtime.realtime import VoiceChanger
+
+class AudioCallbacks:
+ """
+ Manages audio processing callbacks for real-time voice changing.
+
+ This class bridges the low-level audio stream handling (via Audio class)
+ and the core voice conversion inference engine (via VoiceChanger class).
+ It supports multi-threaded processing with thread safety using locks.
+ """
+
+ def __init__(
+ self,
+ pass_through = False,
+ read_chunk_size = 192,
+ cross_fade_overlap_size = 0.1,
+ input_sample_rate = 48000,
+ output_sample_rate = 48000,
+ extra_convert_size = 0.5,
+ model_path = None,
+ index_path = None,
+ f0_method = "rmvpe",
+ predictor_onnx = False,
+ embedder_model = "hubert_base",
+ embedders_mode = "fairseq",
+ sample_rate = 16000,
+ hop_length = 160,
+ silent_threshold = -90,
+ f0_up_key = 0,
+ index_rate = 0.5,
+ protect = 0.5,
+ filter_radius = 3,
+ rms_mix_rate = 1,
+ f0_autotune = False,
+ f0_autotune_strength = 1,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 255.0,
+ input_audio_gain = 1.0,
+ output_audio_gain = 1.0,
+ monitor_audio_gain = 1.0,
+ monitor = False,
+ vad_enabled = False,
+ vad_sensitivity = 3,
+ vad_frame_ms = 30,
+ clean_audio = False,
+ clean_strength = 0.7,
+ post_process = False,
+ sid = 0,
+ noise_scale = 0.35,
+ nprobe = 1,
+ record_audio = False,
+ record_audio_path = None,
+ export_format = "WAV",
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5,
+ use_phase_vocoder = True,
+ **kwargs
+ ):
+ """
+ Initializes the AudioCallbacks handler with voice changer configurations.
+
+ Args:
+ pass_through: If True, bypasses conversion and returns raw audio.
+ read_chunk_size: Number of audio samples to process per chunk.
+ cross_fade_overlap_size: Fraction of overlap used for cross-fading chunks.
+ input_sample_rate: Sampling rate of the incoming audio stream.
+ output_sample_rate: Sampling rate of the outgoing audio stream.
+ extra_convert_size: Extra padding buffer size for context.
+ model_path: Path to the target model checkpoint file.
+ index_path: Path to the retrieval index file (.index).
+ f0_method: Pitch extraction algorithm (e.g., 'rmvpe', 'harvest').
+ predictor_onnx: Whether to use ONNX runtime for pitch estimation.
+ embedder_model: Name or path of the feature extractor model.
+ embedders_mode: Framework mode for embedding (e.g., 'fairseq').
+ sample_rate: Internal processing sample rate.
+ hop_length: Frame hop size used in analysis.
+ silent_threshold: Decibel threshold below which audio is treated as silence.
+ f0_up_key: Pitch shift key in semitones (positive or negative).
+ index_rate: Influence ratio of the feature index retrieval.
+ protect: Protection level for vowels/consonants against artifacts.
+ filter_radius: Median filter radius applied to extracted pitch.
+ rms_mix_rate: Mix ratio of original audio envelope to converted.
+ f0_autotune: Enable snapping the F0 pitch sequence to the nearest musical notes.
+ f0_autotune_strength: Blend factor for autotune (0.0 = raw pitch, 1.0 = fully snapped).
+ proposal_pitch: Enable automatic pitch key shifting calculation based on median F0 alignment.
+ proposal_pitch_threshold: The maximum allowed semitone boundary limit (floor/ceiling) for the proposed pitch shift calculation.
+ input_audio_gain: Digital volume multiplier for input stream.
+ output_audio_gain: Digital volume multiplier for output stream.
+ monitor_audio_gain: Volume multiplier for the dedicated monitoring output device.
+ monitor: Enable real-time audio routing to a 3rd output device for monitoring.
+ vad_enabled: Enable Voice Activity Detection.
+ vad_sensitivity: Aggressiveness level for filtering non-speech (1-3).
+ vad_frame_ms: Frame duration in milliseconds for VAD analysis.
+ clean_audio: Enable noise reduction/denoising on the output audio stream.
+ clean_strength: Intensity parameter for the output noise reduction.
+ post_process: Enable additional acoustic post-filtering effects.
+ sid: Speaker ID index (for multi-speaker checkpoints).
+ noise_scale: Scale factor for exploratory noise during generation.
+ nprobe: Number of clusters to query in FAISS index retrieval.
+ record_audio: Enable background recording of the session.
+ record_audio_path: Directory path where recordings are saved.
+ export_format: Audio format for saved file (e.g., 'WAV', 'MP3').
+ embedders_mix: Enable blending multiple feature extraction models.
+ embedders_mix_layers: Target layers from embedder to extract features from.
+ embedders_mix_ratio: Weight ratio when mixing embedding features.
+ use_phase_vocoder: Enable phase vocoder.
+ **kwargs: Dynamic configurations passed directly to the post-processing engine.
+ """
+
+ # Save routing flag and stream sampling properties
+ self.pass_through = pass_through
+ self.input_sample_rate = input_sample_rate
+ self.output_sample_rate = output_sample_rate
+ # Initialize thread lock to prevent concurrent modifications during inference
+ self.lock = threading.Lock()
+ # Instantiate the backend inference handler (kwargs holds post-process configs)
+ self.vc = VoiceChanger(
+ read_chunk_size,
+ cross_fade_overlap_size,
+ input_sample_rate,
+ output_sample_rate,
+ extra_convert_size,
+ model_path,
+ index_path,
+ f0_method,
+ predictor_onnx,
+ embedder_model,
+ embedders_mode,
+ sample_rate,
+ hop_length,
+ silent_threshold,
+ vad_enabled,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ post_process,
+ sid,
+ noise_scale,
+ nprobe,
+ record_audio,
+ record_audio_path,
+ export_format,
+ **kwargs
+ )
+ # Initialize the audio hardware / stream IO wrapper (including monitor device routing)
+ self.audio = Audio(
+ self,
+ f0_up_key,
+ index_rate,
+ protect,
+ filter_radius,
+ rms_mix_rate,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ input_audio_gain,
+ output_audio_gain,
+ monitor_audio_gain,
+ monitor,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder
+ )
+
+ def change_voice(
+ self,
+ received_data,
+ f0_up_key = 0,
+ index_rate = 0.5,
+ protect = 0.5,
+ filter_radius = 3,
+ rms_mix_rate = 1,
+ f0_autotune = False,
+ f0_autotune_strength = 1,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 255.0,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5,
+ use_phase_vocoder = True
+ ):
+ """
+ Processes an incoming raw audio chunk and applies the voice transformation.
+
+ Args:
+ received_data: Input audio waveform array (float32).
+ f0_up_key: Pitch shift key in semitones.
+ index_rate: Feature retrieval influence scale.
+ protect: Protection metric for speech structural preservation.
+ filter_radius: Pitch smoothing filter window radius.
+ rms_mix_rate: Volume envelope blending parameter.
+ f0_autotune: Snaps fundamental frequency (F0) to musical notes.
+ f0_autotune_strength: Correction intensity factor for note snapping.
+ proposal_pitch: Enable automated optimal semitone key estimation via F0 median alignment.
+ proposal_pitch_threshold: Cap limit boundary for calculation of the calculated key offset.
+ embedders_mix: Flag to apply composite multiple embeddings.
+ embedders_mix_layers: Total specific model layers to combine.
+ embedders_mix_ratio: Mix blending balance factor.
+ use_phase_vocoder: Enable phase vocoder.
+
+ Returns:
+ A tuple containing:
+ - np.ndarray: The modified audio array (or original if bypassed).
+ - float: Root Mean Square (RMS) volume metrics of the block.
+ - float: Performance tracking timings or metrics from inference.
+ """
+
+ # If pass-through mode is active, skip voice morphing entirely
+ if self.pass_through:
+ # Calculate the Root Mean Square (RMS) volume of raw input data safely
+ vol = float(np.sqrt(np.square(received_data).mean(dtype=np.float32)))
+ # Return raw data, volume metric, empty performance metrics block, and None placeholder
+ return received_data, vol, 0
+
+ try:
+ # Acquire lock to ensure only one audio chunk is processed by the model at a time
+ with self.lock:
+ # Dispatch audio payload to the main Voice Changer inference engine
+ audio, vol, perf = self.vc.on_request(
+ received_data,
+ f0_up_key,
+ index_rate,
+ protect,
+ filter_radius,
+ rms_mix_rate,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder
+ )
+
+ return audio, vol, perf
+ except RuntimeError as e:
+ import traceback
+
+ # Log detailed fallback traceback information for debugging purposes
+ logger.debug(traceback.format_exc())
+ # Emit localized user-friendly error interface notifications
+ logger.error(translations["error_occurred"].format(e=e))
+
+ # Return a muted fallback buffer to prevent stream audio corruption or crash spikes
+ return np.zeros(1, dtype=np.float32), 0, 0
\ No newline at end of file
diff --git a/main/inference/realtime/client.py b/main/inference/realtime/client.py
new file mode 100644
index 0000000000000000000000000000000000000000..d94e690488fa4dd73fa7bcdc826a34cd591b726d
--- /dev/null
+++ b/main/inference/realtime/client.py
@@ -0,0 +1,420 @@
+import os
+import gc
+import sys
+import json
+
+import numpy as np
+
+from fastapi import FastAPI, WebSocketDisconnect, WebSocket, Request
+
+sys.path.append(os.getcwd())
+
+from main.library.utils import clear_gpu_cache
+from main.inference.realtime.realtime import VoiceChanger
+from main.app.variables import configs, translations, logger, config
+
+app = FastAPI()
+vc_instance = None
+params = {}
+
+PIPELINE_SAMPLE_RATE = 16000
+DEVICE_SAMPLE_RATE = 48000
+
+@app.websocket("/change-config")
+async def change_config(ws: WebSocket):
+ """
+ Handles hot-reloading and runtime reconfiguration of the voice conversion model parameters
+ such as crossfade boundaries, VAD modes, audio cleaners, effects, and index paths via a persistent WebSocket.
+
+ Args:
+ ws (WebSocket): The incoming communication handshake instance from the client layer.
+ """
+
+ global vc_instance, params
+
+ await ws.accept()
+ if vc_instance is None: return
+
+ text = await ws.receive_text()
+ jsons = json.loads(text)
+
+ # Distinguish standard execution settings from extra dictionary keyword arguments (kwargs)
+ if jsons["if_kwargs"]:
+ params["kwargs"][jsons["key"]] = jsons["value"]
+ else:
+ params[jsons["key"]] = jsons["value"]
+
+ # Calculate overlapping structure size windows mapped against the target system sample rate
+ crossfade_frame = int(params.get("cross_fade_overlap_size", 0.1) * DEVICE_SAMPLE_RATE)
+ extra_frame = int(params.get("extra_convert_size", 0.5) * DEVICE_SAMPLE_RATE)
+
+ # Trigger memory reallocation on the underlying core if latency window sizes are altered
+ if (
+ vc_instance.crossfade_frame != crossfade_frame or
+ vc_instance.extra_frame != extra_frame
+ ):
+ # Force-release old arrays to prevent internal calculation leaks before reconfiguration
+ del (
+ vc_instance.fade_in_window,
+ vc_instance.fade_out_window,
+ vc_instance.sola_buffer
+ )
+
+ vc_instance.vc_model.realloc(
+ vc_instance.block_frame,
+ vc_instance.extra_frame,
+ vc_instance.crossfade_frame,
+ vc_instance.sola_search_frame,
+ )
+ vc_instance.generate_strength()
+
+ # Convert the dB floor threshold configuration parameter to standard linear amplitude values
+ vc_instance.vc_model.input_sensitivity = 10 ** (params.get("silent_threshold", -90) / 20)
+
+ # Voice Activity Detection (VAD) Engine Configuration Block
+ vad_enabled = params.get("vad_enabled", True)
+ sensitivity_mode = params.get("vad_sensitivity", 3)
+ vad_frame_ms = params.get("vad_frame_ms", 30)
+
+ if vad_enabled is False:
+ vc_instance.vc_model.vad = None
+ elif vad_enabled and vc_instance.vc_model.vad is None:
+ from main.inference.realtime.vad_utils import VADProcessor
+
+ vc_instance.vc_model.vad = VADProcessor(
+ sensitivity_mode=sensitivity_mode,
+ sample_rate=vc_instance.vc_model.sample_rate,
+ frame_duration_ms=vad_frame_ms
+ )
+
+ if vc_instance.vc_model.vad is not None:
+ vc_instance.vc_model.vad.vad.set_mode(sensitivity_mode)
+ vc_instance.vc_model.vad.frame_length = int(vc_instance.vc_model.sample_rate * (vad_frame_ms / 1000.0))
+
+ # Audio Clean Gate (TorchGate Noise Reduction) Block
+ clean_audio = params.get("clean_audio", False)
+ clean_strength = params.get("clean_strength", 0.5)
+
+ if clean_audio is False:
+ vc_instance.vc_model.tg = None
+ elif clean_audio and vc_instance.vc_model.tg is None:
+ from main.library.audio.noisereduce import TorchGate
+
+ vc_instance.vc_model.tg = (
+ TorchGate(
+ vc_instance.vc_model.pipeline.tgt_sr,
+ prop_decrease=clean_strength,
+ ).to(config.device)
+ )
+
+ if vc_instance.vc_model.tg is not None:
+ vc_instance.vc_model.tg.prop_decrease = clean_strength
+
+ # Pedalboard Effects Post-Processing Block
+ post_process = params.get("post_process", False)
+ kwargs = params.get("kwargs", {})
+
+ if post_process is False:
+ vc_instance.vc_model.board = None
+ vc_instance.vc_model.kwargs = None
+ elif post_process and vc_instance.vc_model.kwargs != kwargs:
+ new_board = vc_instance.vc_model.setup_pedalboard(**kwargs)
+ vc_instance.vc_model.board = new_board
+ vc_instance.vc_model.kwargs = kwargs.copy()
+
+ # Model Weight Reload / Architecture Swap Block
+ noise_scale = params.get("noise_scale", 0.35)
+ model_pth = params.get("model_path", vc_instance.vc_model.model_path)
+ model_pth = os.path.join(configs["weights_path"], model_pth) if not os.path.exists(model_pth) else model_pth
+
+ if model_pth and vc_instance.vc_model.model_path != model_pth:
+ import torch
+ import torchaudio.transforms as tat
+
+ vc_instance.vc_model.model_path = model_pth
+ vc_instance.vc_model.pipeline.vc.setup(model_pth, noise_scale)
+ vc_instance.vc_model.pipeline.use_f0 = vc_instance.vc_model.pipeline.vc.use_f0
+ vc_instance.vc_model.pipeline.tgt_sr = vc_instance.vc_model.pipeline.vc.tgt_sr
+ vc_instance.vc_model.pipeline.version = vc_instance.vc_model.pipeline.vc.version
+
+ # Re-initialize the resampling layer since the target sample rate might have changed
+ vc_instance.vc_model.resample_out = tat.Resample(
+ orig_freq=vc_instance.vc_model.pipeline.tgt_sr,
+ new_freq=vc_instance.vc_model.output_sample_rate,
+ dtype=torch.float32
+ ).to(config.device)
+
+ if clean_audio:
+ from main.library.audio.noisereduce import TorchGate
+
+ vc_instance.vc_model.tg = (
+ TorchGate(
+ vc_instance.vc_model.pipeline.tgt_sr,
+ prop_decrease=clean_strength,
+ ).to(config.device)
+ )
+
+ # Speaker Identity ID Configuration
+ sid = params.get("sid", vc_instance.vc_model.pipeline.sid)
+ if vc_instance.vc_model.pipeline.sid != sid:
+ import torch
+ vc_instance.vc_model.pipeline.torch_sid = torch.tensor(
+ [sid], device=vc_instance.vc_model.pipeline.device, dtype=torch.int64
+ )
+
+ # FAISS Index Tracking Path Search Block
+ index_path = params.get("index_path", None)
+ if index_path:
+ if vc_instance.vc_model.index_path != index_path:
+ from main.library.utils import load_faiss_index
+
+ nprobe = params.get("nprobe", 1)
+ # Sanitize paths to handle literal quote configurations securely
+ index, index_reconstruct = load_faiss_index(
+ index_path.strip().strip('"').strip("\n").strip('"').strip().replace("trained", "added"),
+ nprobe=nprobe
+ )
+
+ vc_instance.vc_model.pipeline.index = index
+ if vc_instance.vc_model.pipeline.index.index is not None: vc_instance.vc_model.pipeline.index.index.nprobe = nprobe
+ vc_instance.vc_model.pipeline.big_tsr = index_reconstruct
+ vc_instance.vc_model.index_path = index_path
+ else:
+ vc_instance.vc_model.pipeline.index = None
+ vc_instance.vc_model.pipeline.big_tsr = None
+ vc_instance.vc_model.index_path = None
+
+ # Pitch Predictor and Feature Embedder Extraction Block
+ f0_method = params.get("f0_method", vc_instance.vc_model.pipeline.f0_method)
+ predictor_onnx = params.get("predictor_onnx", vc_instance.vc_model.pipeline.predictor.predictor_onnx)
+ embedders = params.get("embedder_model", vc_instance.vc_model.embedder_model)
+ embedders_mode = params.get("embedders_mode", vc_instance.vc_model.embedders_mode)
+ custom_embedders = params.get("embedder_model_custom", None)
+ embedder_model = (embedders if embedders != "custom" else custom_embedders)
+
+ # Ensure files and parameters match required asset types
+ from main.library.utils import check_assets
+ check_assets(f0_method, embedders, predictor_onnx, embedders_mode)
+
+ # Hot-swap the pitch predictor backend structures if options are updated
+ if (
+ vc_instance.vc_model.pipeline.vc.use_f0 and (
+ vc_instance.vc_model.pipeline.f0_method != f0_method or
+ vc_instance.vc_model.pipeline.predictor.predictor_onnx != predictor_onnx
+ )
+ ):
+ old_predictor = vc_instance.vc_model.pipeline.predictor
+ del old_predictor.pw, old_predictor.fcpe, old_predictor.djcm, old_predictor.penn, old_predictor.pesto, old_predictor.swift, old_predictor.rmvpe, old_predictor.crepe, old_predictor.mangio_penn, old_predictor.mangio_crepe
+ del old_predictor
+
+ vc_instance.vc_model.pipeline.predictor = vc_instance.vc_model.pipeline.setup_predictor(PIPELINE_SAMPLE_RATE, params.get("hop_length", vc_instance.vc_model.pipeline.predictor.hop_length), predictor_onnx)
+ vc_instance.vc_model.pipeline.f0_method = f0_method
+ vc_instance.vc_model.pipeline.predictor.predictor_onnx = predictor_onnx
+
+ # Hot-swap Content Vec or Hubert feature extractors if modified
+ if embedder_model:
+ if (
+ vc_instance.vc_model.embedder_model != embedder_model or
+ vc_instance.vc_model.embedders_mode != embedders_mode
+ ):
+ old_embedder = vc_instance.vc_model.pipeline.embedder
+ del old_embedder
+
+ vc_instance.vc_model.pipeline.embedder = vc_instance.vc_model.pipeline.setup_embedder(embedder_model, embedders_mode)
+ vc_instance.vc_model.embedder_model = embedder_model
+ vc_instance.vc_model.embedders_mode = embedders_mode
+
+ if (
+ vc_instance.vc_model.pipeline.vc.architecture == "SVC" and
+ vc_instance.vc_model.pipeline.vc.net_g.noise_scale != noise_scale
+ ):
+ vc_instance.vc_model.pipeline.vc.net_g.noise_scale = noise_scale
+
+@app.post("/record")
+async def record(request: Request):
+ """
+ HTTP POST route acting as a remote trigger toggle to handle runtime sound recording sequences
+ of the converted audio streams.
+
+ Args:
+ request (Request): The incoming request payload containing parameters for target path and export format.
+
+ Returns:
+ dict: Operational response packet describing status modifications, info alerts, or warning messages.
+ """
+
+ global vc_instance
+
+ data = await request.json()
+ record_button = data.get("record_button", translations["stop_record"])
+ record_audio_path = data.get("record_audio_path", None)
+ export_format = data.get("export_format", "wav")
+
+ if vc_instance is None:
+ return {
+ "type": "warnings",
+ "value": translations["realtime_not_found"],
+ "button": translations["start_record"],
+ "path": None
+ }
+
+ # Evaluate execution parameters depending on toggle switch intent labels
+ if record_button == translations["start_record"]:
+ if not record_audio_path:
+ record_audio_path = os.path.join(configs["audios_path"], "record_audio.wav")
+
+ vc_instance.record_audio = True
+ vc_instance.record_audio_path = record_audio_path
+ vc_instance.export_format = export_format
+ vc_instance.setup_soundfile_record()
+
+ return {
+ "type": "info",
+ "value": translations["starting_record"],
+ "button": translations["stop_record"],
+ "path": None
+ }
+ else:
+ vc_instance.record_audio = False
+ vc_instance.record_audio_path = None
+ vc_instance.soundfile = None
+
+ return {
+ "type": "info",
+ "value": translations["stopping_record"],
+ "button": translations["start_record"],
+ "path": record_audio_path
+ }
+
+@app.websocket("/ws-audio")
+async def websocket_audio(ws: WebSocket):
+ """
+ Main real-time audio pipeline hub handling binary frame ingestion, inference callback triggers,
+ latency logging metrics, and synthesized sample extraction.
+
+ Args:
+ ws (WebSocket): Continuous binary connection context loop.
+ """
+
+ global vc_instance, params
+ await ws.accept()
+
+ logger.info(translations["ws_connected"])
+
+ try:
+ text = await ws.receive_text()
+ params = json.loads(text)
+
+ read_chunk_size = int(params["chunk_size"])
+ block_frame = read_chunk_size * 128
+ embedders = params["embedders"]
+
+ model_pth = params["model_pth"]
+ model_pth = os.path.join(configs["weights_path"], model_pth) if not os.path.exists(model_pth) else model_pth
+
+ # Structural validity check boundary to reject faulty setups
+ if (
+ not model_pth or
+ not os.path.exists(model_pth) or
+ os.path.isdir(model_pth) or
+ not model_pth.endswith((".pth", ".onnx"))
+ ):
+ logger.warning(translations["provide_model"])
+ await ws.send_text(json.dumps({"type": "warnings", "value": translations["provide_model"]}))
+ return
+
+ logger.info(translations["start_realtime"])
+ # Create the continuous VoiceChanger backend pipeline worker instance if not initialized
+ if vc_instance is None:
+ vc_instance = VoiceChanger(
+ read_chunk_size=read_chunk_size,
+ cross_fade_overlap_size=params["cross_fade_overlap_size"],
+ input_sample_rate=DEVICE_SAMPLE_RATE,
+ output_sample_rate=DEVICE_SAMPLE_RATE,
+ extra_convert_size=params["extra_convert_size"],
+ model_path=model_pth,
+ index_path=params["model_index"],
+ f0_method=params["f0_method"],
+ predictor_onnx=params["predictor_onnx"],
+ embedder_model=(embedders if embedders != "custom" else params["custom_embedders"]),
+ embedders_mode=params["embedders_mode"],
+ sample_rate=PIPELINE_SAMPLE_RATE,
+ hop_length=params["hop_length"],
+ silent_threshold=params["silent_threshold"],
+ vad_enabled=params["vad_enabled"],
+ vad_sensitivity=params["vad_sensitivity"],
+ vad_frame_ms=params["vad_frame_ms"],
+ clean_audio=params["clean_audio"],
+ clean_strength=params["clean_strength"],
+ post_process=params["post_process"],
+ sid=params["sid"],
+ noise_scale=params["noise_scale"],
+ nprobe=params["nprobe"],
+ record_audio=False,
+ record_audio_path=None,
+ export_format="wav",
+ **params["kwargs"]
+ )
+
+ logger.info(translations["realtime_is_ready"])
+ # Continuous listening loop tracking incoming raw binary floating-point stream blocks
+ while 1:
+ audio = await ws.receive_bytes()
+ arr = np.frombuffer(audio, dtype=np.float32)
+
+ # Enforce strict buffer alignment by padding or slicing anomalous incoming frames
+ if arr.size != block_frame:
+ arr = (
+ np.pad(arr, (0, block_frame - arr.size)).astype(np.float32)
+ if arr.size < block_frame else
+ arr[:block_frame].astype(np.float32)
+ )
+
+ if vc_instance is None: return
+
+ # Apply gain amplification and forward the block into the pitch shifting & inference pipeline
+ audio_output, vol, perf = vc_instance.on_request(
+ arr * (params["input_audio_gain"] / 100.0),
+ f0_up_key=params["f0_up_key"],
+ index_rate=params["index_rate"],
+ protect=params["protect"],
+ filter_radius=params["filter_radius"],
+ rms_mix_rate=params["rms_mix_rate"],
+ f0_autotune=params["f0_autotune"],
+ f0_autotune_strength=params["f0_autotune_strength"],
+ proposal_pitch=params["proposal_pitch"],
+ proposal_pitch_threshold=params["proposal_pitch_threshold"],
+ embedders_mix=params["embedders_mix"],
+ embedders_mix_layers=params["embedders_mix_layers"],
+ embedders_mix_ratio=params["embedders_mix_ratio"],
+ use_phase_vocoder=params["use_phase_vocoder"]
+ )
+
+ # Asynchronously dispatch telemetry diagnostic parameters along with the processed binary chunk
+ await ws.send_text(json.dumps({"type": "latency", "value": perf, "volume": vol}))
+ await ws.send_bytes(audio_output.tobytes())
+ except WebSocketDisconnect:
+ logger.info(translations["ws_disconnected"])
+ except Exception as e:
+ import traceback
+
+ logger.debug(traceback.format_exc())
+ logger.info(translations["error_occurred"].format(e=e))
+ finally:
+ # Strict Memory Deallocation and GPU VRAM Cleanup Block
+ if vc_instance is not None:
+ del vc_instance.vc_model.pipeline, vc_instance.vc_model
+ del vc_instance
+ vc_instance = None
+
+ # Force Python's garbage collector to release detached tensors
+ gc.collect()
+ clear_gpu_cache()
+
+ # Safely shut down the socket handshake context loop
+ try:
+ await ws.close()
+ except:
+ pass
+
+ logger.info(translations["ws_closed"])
\ No newline at end of file
diff --git a/main/inference/realtime/pipeline.py b/main/inference/realtime/pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..b986ed9e2f9f3fe257e68f4b16b0acd38cc7b8d0
--- /dev/null
+++ b/main/inference/realtime/pipeline.py
@@ -0,0 +1,391 @@
+import os
+import sys
+import torch
+
+import numpy as np
+import torch.nn.functional as F
+
+sys.path.append(os.getcwd())
+
+from main.library.audio.features import change_rms
+from main.app.variables import config, logger, translations, configs
+from main.library.utils import load_embedders_model, extract_features, load_faiss_index, load_model
+
+class RealtimeVoiceConverter:
+ """
+ Handles loading, setting up, and running inference for Retrieval-based Voice Conversion (RVC)
+ and SVC acoustic models in real-time environments.
+ """
+
+ def __init__(self, weight_root, noise_scale = 0.35):
+ """
+ Initializes the RealtimeVoiceConverter instance.
+
+ Args:
+ weight_root (str): Path to the model weight file (.pth or checkpoint object).
+ noise_scale (float): Noise scale factor for the SynthesizerSVC model architecture.
+ """
+
+ self.model = None
+ self.net_g = None
+ self.tgt_sr = None
+ self.use_f0 = None
+ self.version = None
+ self.architecture = None
+ # Trigger model configuration and device deployment
+ self.setup(weight_root, noise_scale)
+
+ def setup(self, weight_root, noise_scale = 0.35):
+ """
+ Loads the model weights and constructs the target generator architecture (Synthesizer/SynthesizerSVC).
+
+ Args:
+ weight_root (str): Path to the model weight file.
+ noise_scale (float): Scaling factor for noise standard deviation in SVC models.
+
+ Returns:
+ RealtimeVoiceConverter: The configured instance itself.
+ """
+
+ # Load raw dictionary weights or pre-instantiated checkpoints
+ model = load_model(weight_root)
+
+ if weight_root.endswith(".pth"):
+ from main.library.algorithm.synthesizers import Synthesizer, SynthesizerSVC
+
+ # Extract target sample rate from the last position of the config list
+ self.tgt_sr = model["config"][-1]
+ # Dynamic adjustment of the embedding layer weight dimension based on checkpoint data
+ model["config"][-3] = model["weight"]["emb_g.weight"].shape[0]
+
+ # Fetch model metadata attributes with safe default values
+ self.use_f0 = model.get("f0", 1)
+ self.version = model.get("version", "v1")
+ self.vocoder = model.get("vocoder", "Default")
+ self.architecture = model.get("architecture", "RVC")
+
+ # Instantiate appropriate generator based on the specified model architecture
+ if self.architecture == "RVC":
+ net_g = Synthesizer(
+ *model["config"],
+ use_f0=self.use_f0,
+ text_enc_hidden_dim=768 if self.version == "v2" else 256,
+ vocoder=self.vocoder,
+ checkpointing=False
+ )
+ else:
+ net_g = SynthesizerSVC(
+ *model["config"],
+ text_enc_hidden_dim=768 if self.version == "v2" else 256,
+ vocoder=self.vocoder,
+ noise_scale=noise_scale
+ )
+
+ # Load weights into generator, send to target device, and set to evaluation mode
+ net_g.load_state_dict(model["weight"], strict=False)
+ net_g.eval().to(config.device).to(torch.float16 if config.is_half else torch.float32)
+ # Remove weight normalization for faster inference execution
+ net_g.remove_weight_norm()
+ # Delete encoder queues or variables not utilized during inference stage to free VRAM
+ del net_g.enc_q
+
+ # Apply torch.compile if general optimization flags are enabled
+ if config.compile_all: net_g = torch.compile(net_g, mode=config.compile_mode)
+
+ self.net_g = net_g
+ self.model = model
+ else:
+ # For ONNX models
+ self.model = model
+ self.net_g = self.model.to(config.device)
+ self.tgt_sr = self.model.cpt.get("tgt_sr", 32000)
+ self.use_f0 = self.model.cpt.get("f0", 1)
+ self.version = self.model.cpt.get("version", "v1")
+ self.architecture = self.model.cpt.get("architecture", "RVC")
+
+ return self
+
+ def inference(
+ self,
+ feats,
+ p_len,
+ sid,
+ pitch,
+ pitchf,
+ rate
+ ):
+ """
+ Executes raw core neural network forward generation.
+
+ Args:
+ feats (torch.Tensor): Extracted audio content features.
+ p_len (torch.Tensor): Length of the pitch sequence/frames.
+ sid (torch.Tensor): Speaker ID tensor.
+ pitch (torch.Tensor): Coarse pitch sequence (integer indices).
+ pitchf (torch.Tensor): Continuous fundamental frequency (F0) float sequence.
+ rate (torch.Tensor): rate tensor.
+
+ Returns:
+ torch.Tensor: Normalized raw synthesized audio tensor.
+ """
+
+ # Execute generator inference and extract the first element batch/channel
+ output = self.net_g.infer(
+ feats,
+ p_len,
+ pitch,
+ pitchf,
+ sid,
+ rate
+ )[0, 0]
+
+ # Hard clamp output signal to avoid digital clipping artifacts
+ return output.clamp_(-1.0, 1.0)
+
+class Pipeline:
+ """
+ Coordinates the full conversion routine pipeline: including pitch prediction,
+ content embedding extraction, index searching/retrieval, and audio synthesis.
+ """
+
+ def __init__(
+ self,
+ weight_root,
+ index_path=None,
+ f0_method = "rmvpe",
+ predictor_onnx=False,
+ embedders_mode="fairseq",
+ embedder_model="hubert_base",
+ noise_scale = 0.35,
+ sample_rate=16000,
+ hop_length=160,
+ nprobe=1,
+ sid = 0
+ ):
+ """
+ Initializes the Pipeline setup with models, parameters, index indexes, and execution settings.
+ """
+
+ # Instantiate internal voice converter generator unit
+ self.vc = RealtimeVoiceConverter(weight_root, noise_scale)
+
+ self.f0_method = f0_method
+ self.use_f0 = self.vc.use_f0
+ self.tgt_sr = self.vc.tgt_sr
+ self.version = self.vc.version
+ # Initialize core modular blocks: Feature Extractors, Pitch Predictors, and FAISS Index
+ self.embedder = self.setup_embedder(embedder_model, embedders_mode)
+ self.predictor = self.setup_predictor(sample_rate, hop_length, predictor_onnx)
+ # Clean up path layout string formatting and load matching FAISS cluster index vectors
+ self.index, self.big_tsr = load_faiss_index(index_path.strip().strip('"').strip("\n").strip('"').strip().replace("trained", "added"), nprobe=nprobe, cpu_mode=False)
+
+ # Configure system tensor variables cache to reduce memory initialization latency during stream loops
+ self.sid = sid
+ self.device = config.device
+ self.is_half = config.is_half
+ self.dtype = torch.float16 if self.is_half else torch.float32
+
+ self.p_len = torch.zeros(1, device=self.device, dtype=torch.int64)
+ self.rate = torch.zeros(1, device=self.device, dtype=self.dtype)
+ self.torch_sid = torch.tensor([sid], device=self.device, dtype=torch.int64)
+
+ def setup_predictor(self, sample_rate, hop_length, predictor_onnx = False):
+ """
+ Sets up the fundamental frequency (F0) estimator model if pitch tracking is enabled.
+ """
+
+ if self.use_f0:
+ from main.library.predictors.Generator import Generator
+
+ # Initialize pitch tracking generator context
+ predictor = Generator(
+ sample_rate=sample_rate,
+ hop_length=hop_length,
+ f0_min=configs.get("f0_min", 50),
+ f0_max=configs.get("f0_max", 1100),
+ alpha=0,
+ is_half=config.is_half,
+ device=config.device,
+ predictor_onnx=predictor_onnx,
+ return_tensor=True
+ )
+ else: predictor = None
+
+ return predictor
+
+ def setup_embedder(self, embedder_model, embedders_mode):
+ """
+ Loads and prepares content extraction embedder models (e.g., HuBERT).
+ """
+
+ embedder = load_embedders_model(
+ embedder_model,
+ embedders_mode=embedders_mode
+ )
+
+ # Optimize and match tensor types for torch modules
+ if isinstance(embedder, torch.nn.Module):
+ dtype = torch.float16 if config.is_half else torch.float32
+ embedder = embedder.to(config.device).to(dtype).eval()
+ if config.compile_all: embedder = torch.compile(embedder, mode=config.compile_mode)
+
+ return embedder
+
+ def inference(
+ self,
+ audio,
+ pitch = None,
+ pitchf = None,
+ f0_up_key = 0,
+ index_rate = 0.5,
+ p_len = 0,
+ skip_head = None,
+ return_length = None,
+ protect = 0.5,
+ filter_radius = 3,
+ rms_mix_rate = 1,
+ f0_autotune = False,
+ f0_autotune_strength = 1,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 255.0,
+ torchgate = None,
+ board = None,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5,
+ block_size_16k = None,
+ ):
+ """
+ Runs complete conversion inference on an audio chunk.
+ """
+
+ with torch.inference_mode():
+ assert audio.dim() == 1, audio.dim()
+ formant_length = int(np.ceil(return_length * 1.0))
+ # Calculate rolling audio slice boundaries and window sizes for real-time streams
+ shift = (block_size_16k or skip_head * self.predictor.window) // self.predictor.window
+ f0_frame = (block_size_16k + 800 if block_size_16k else skip_head * self.predictor.window + 800)
+ # Adjust RMVPE frame block dimensions dynamically to match algorithm alignment constraints
+ if "rmvpe" in self.f0_method: f0_frame = 5120 * ((f0_frame - 1) // 5120 + 1) - self.predictor.window
+
+ if self.use_f0:
+ # Evaluate pitch tracking using the calculated optimal audio slice window length
+ pitch_new, pitchf_new = self.predictor.realtime_calculator(
+ audio[-min(f0_frame, audio.shape[0]):],
+ self.f0_method,
+ None,
+ None,
+ f0_up_key,
+ filter_radius,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold
+ )
+
+ pitch_new, pitchf_new = pitch_new.squeeze(0), pitchf_new.squeeze(0)
+ # Shift pitch history frames backwards to maintain overlapping buffer structures
+ if shift > 0:
+ pitch[:-shift] = pitch[shift:].clone()
+ pitchf[:-shift] = pitchf[shift:].clone()
+
+ # Trim edge crossfade artifacts from raw real-time pitch estimations
+ interior_pitch = pitch_new[3:-1] if pitch_new.shape[0] > 4 else pitch_new
+ interior_pitchf = pitchf_new[3:-1] if pitchf_new.shape[0] > 4 else pitchf_new
+ # Overwrite tail ends of buffers with the clean interior estimates
+ pitch[-interior_pitch.shape[0]:] = interior_pitch
+ pitchf[-interior_pitchf.shape[0]:] = interior_pitchf
+ else: pitch, pitchf = None, None
+
+ # Extract phonetic content embeddings from raw waveform tensor inputs
+ feats = extract_features(
+ self.embedder,
+ audio.view(1, -1),
+ self.version,
+ mix=embedders_mix,
+ mix_layers=embedders_mix_layers,
+ mix_ratio=embedders_mix_ratio
+ )
+
+ # Pad features sequence via trailing edge replication
+ feats = torch.cat((feats, feats[:, -1:, :]), 1)
+ feats0 = feats.detach().clone() if protect < 0.5 and self.use_f0 else None
+
+ # Retrieve target vector traits from index to enhance accent/timbre fidelity
+ if self.index is not None and self.big_tsr is not None and index_rate != 0:
+ try:
+ skip_offset = skip_head // 2
+ # Perform FAISS KNN query lookup on feature representations
+ score, ix = self.index.search(feats[0][skip_offset :], k=8)
+ weight = (1 / score).square()
+ query = (self.big_tsr[ix] * (weight / weight.sum(dim=1, keepdim=True)).unsqueeze(2)).sum(dim=1)
+ # Linearly blend original raw content embeddings with target vector index matches
+ feats[0][skip_offset :] = query.unsqueeze(0) * index_rate + (1.0 - index_rate) * feats[0][skip_offset :]
+ except AssertionError:
+ logger.warning(translations["index_assertion"])
+ self.index = self.big_tsr = None
+
+ # Resample content embedding dimensions to match the generator timing scale
+ feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)[:, :p_len, :]
+ # Align pitch timelines and scale by the formant length ratio
+ if self.use_f0: pitch, pitchf = pitch[-p_len:].unsqueeze(0), pitchf[-p_len:].unsqueeze(0) * (formant_length / return_length)
+
+ # Apply consonant protection mechanisms to preserve unvoiced pronunciation dynamics
+ if feats0 is not None:
+ pitchff = pitchf.detach().clone()
+ pitchff[pitchf > 0] = 1
+ pitchff[pitchf < 1] = protect
+ pitchff = pitchff.unsqueeze(-1)
+
+ feats0 = F.interpolate(
+ feats0.permute(0, 2, 1),
+ scale_factor=2
+ ).permute(0, 2, 1)[:, :p_len, :]
+
+ feats = (feats * pitchff + feats0 * (1 - pitchff)).to(feats0.dtype)
+
+ if self.use_f0: pitchf = pitchf.to(self.dtype)
+ feats = feats.to(self.dtype)
+
+ # Re-fill memory-cached structural metadata tensors prior to model forwarding
+ self.rate.fill_(return_length / p_len)
+ self.p_len.fill_(p_len)
+
+ # Synthesize structural representations into target audio waveform
+ out_audio = self.vc.inference(
+ feats,
+ self.p_len,
+ self.torch_sid,
+ pitch,
+ pitchf,
+ self.rate
+ )
+
+ # Apply volume/RMS mixing to restore the dynamics of the source audio
+ if rms_mix_rate != 1:
+ out_audio = change_rms(
+ audio[-(return_length * self.predictor.window):],
+ self.predictor.sample_rate,
+ out_audio,
+ self.tgt_sr,
+ rms_mix_rate,
+ device=self.device
+ )
+
+ # Pass signal output through noise gate if available
+ if torchgate is not None:
+ out_audio = torchgate(
+ out_audio.unsqueeze(0)
+ ).squeeze(0)
+
+ # Apply custom internal audio master board effects if present
+ if board is not None:
+ out_audio = torch.as_tensor(
+ board(
+ out_audio.cpu().numpy(),
+ self.tgt_sr
+ ),
+ device=config.device
+ )
+
+ return out_audio.float()
\ No newline at end of file
diff --git a/main/inference/realtime/realtime.py b/main/inference/realtime/realtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..df51cec68f1ca8ea2e8571eb2468bff45541555c
--- /dev/null
+++ b/main/inference/realtime/realtime.py
@@ -0,0 +1,781 @@
+import os
+import sys
+import time
+import torch
+
+import numpy as np
+import torch.nn.functional as F
+import torchaudio.transforms as tat
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config
+from main.inference.realtime.pipeline import Pipeline
+from main.library.utils import circular_write, check_assets, phase_vocoder
+
+class Realtime:
+ """
+ Handles real-time audio block resampling, buffering, VAD, and model inference.
+
+ This class serves as the functional interface for processing streaming raw
+ audio chunks through the designated RVC conversion pipeline. It handles dynamic
+ resampling, safety input volume thresholding, Voice Activity Detection (VAD),
+ TorchGate noise reduction, and custom VST pedalboard effect applications.
+ """
+
+ def __init__(
+ self,
+ model_path,
+ index_path = None,
+ f0_method = "rmvpe",
+ predictor_onnx = False,
+ embedder_model = "hubert_base",
+ embedders_mode = "fairseq",
+ sample_rate = 16000,
+ hop_length = 160,
+ silent_threshold = 0,
+ input_sample_rate = 48000,
+ output_sample_rate = 48000,
+ vad_enabled = False,
+ vad_sensitivity = 3,
+ vad_frame_ms = 30,
+ clean_audio=False,
+ clean_strength=0.7,
+ post_process = False,
+ sid = 0,
+ noise_scale = 0.35,
+ nprobe=1,
+ **kwargs
+ ):
+ """Initializes the backend real-time inference processor."""
+
+ # Check and download required model assets/weights if missing
+ check_assets(
+ f0_method,
+ embedder_model,
+ predictor_onnx=predictor_onnx,
+ embedders_mode=embedders_mode
+ )
+ # Storage for processing pipeline configurations
+ self.model_path = model_path
+ self.index_path = index_path
+ self.f0_method = f0_method
+ self.predictor_onnx = predictor_onnx
+ self.embedder_model = embedder_model
+ self.embedders_mode = embedders_mode
+ self.sample_rate = sample_rate
+ self.hop_length = hop_length
+ self.post_process = post_process
+ self.sid = sid
+ self.noise_scale = noise_scale
+ self.nprobe = nprobe
+ self.kwargs = kwargs
+ # Initialize stream pipelines and buffers to be reallocated later
+ self.pipeline = None
+ self.audio_buffer = None
+ self.convert_buffer = None
+ self.pitch_buffer = None
+ self.pitchf_buffer = None
+ # Sizing boundaries used for context padding and feature offsets
+ self.return_length = 0
+ self.skip_head = 0
+ self.silence_front = 0
+ # Processing properties and hardware settings
+ self.input_sample_rate = input_sample_rate
+ self.output_sample_rate = output_sample_rate
+ self.vad_enabled = vad_enabled
+ self.vad_sensitivity = vad_sensitivity
+ self.vad_frame_ms = vad_frame_ms
+ self.clean_audio = clean_audio
+ self.clean_strength = clean_strength
+ # Convert decibel threshold to raw numerical amplitude sensitivity limit
+ self.input_sensitivity = 10 ** (silent_threshold / 20)
+ self.window_size = sample_rate // 100
+ self.dtype = torch.float16 if config.is_half else torch.float32
+
+ # Conditional initialization for Voice Activity Detection
+ if self.vad_enabled:
+ from main.inference.realtime.vad_utils import VADProcessor
+
+ self.vad = VADProcessor(
+ sensitivity_mode=self.vad_sensitivity,
+ sample_rate=self.sample_rate,
+ frame_duration_ms=self.vad_frame_ms
+ )
+ else: self.vad = None
+
+ # Build structural pedalboard chain if audio post-processing is enabled
+ self.board = self.setup_pedalboard(**self.kwargs) if self.post_process else None
+ # Instantiate core extraction and neural synthesis pipeline handler
+ self.pipeline = Pipeline(
+ weight_root=self.model_path,
+ index_path=self.index_path,
+ f0_method=self.f0_method,
+ predictor_onnx=self.predictor_onnx,
+ embedders_mode=self.embedders_mode,
+ embedder_model=self.embedder_model,
+ noise_scale=self.noise_scale,
+ sample_rate=self.sample_rate,
+ hop_length=self.hop_length,
+ nprobe=self.nprobe,
+ sid=self.sid
+ )
+
+ # Initialize output stream gate noise filter if requested
+ if self.clean_audio:
+ from main.library.audio.noisereduce import TorchGate
+
+ self.tg = TorchGate(
+ self.pipeline.tgt_sr,
+ prop_decrease=self.clean_strength
+ ).to(config.device)
+ else: self.tg = None
+
+ # Define resampling modules for streaming IO
+ self.resample_in = tat.Resample(
+ orig_freq=self.input_sample_rate,
+ new_freq=self.sample_rate,
+ dtype=torch.float32
+ ).to(config.device)
+ self.resample_out = tat.Resample(
+ orig_freq=self.pipeline.tgt_sr,
+ new_freq=self.output_sample_rate,
+ dtype=torch.float32
+ ).to(config.device)
+
+ def setup_pedalboard(self, **kwargs):
+ """
+ Constructs a serial audio effect processing rack using pedalboard components.
+
+ Args:
+ **kwargs: Configuration flags and scalar parameters for effects.
+
+ Returns:
+ An instantiated pedalboard.Pedalboard effect chain.
+ """
+
+ from pedalboard import (
+ Pedalboard,
+ Chorus,
+ Distortion,
+ Reverb,
+ PitchShift,
+ Delay,
+ Limiter,
+ Gain,
+ Bitcrush,
+ Clipping,
+ Compressor,
+ Phaser,
+ HighpassFilter
+ )
+
+ # Initialize base chain with a Highpass filter to eliminate low-end rumble
+ board = Pedalboard([HighpassFilter()])
+
+ if kwargs["chorus"]:
+ board.append(
+ Chorus(
+ depth=kwargs["chorus_depth"],
+ rate_hz=kwargs["chorus_rate"],
+ mix=kwargs["chorus_mix"],
+ centre_delay_ms=kwargs["chorus_delay"],
+ feedback=kwargs["chorus_feedback"]
+ )
+ )
+
+ if kwargs["distortion"]:
+ board.append(
+ Distortion(
+ drive_db=kwargs["distortion_gain"]
+ )
+ )
+
+ if kwargs["reverb"]:
+ board.append(
+ Reverb(
+ room_size=kwargs["reverb_room_size"],
+ damping=kwargs["reverb_damping"],
+ wet_level=kwargs["reverb_wet_level"],
+ dry_level=kwargs["reverb_dry_level"],
+ width=kwargs["reverb_width"],
+ freeze_mode=int(kwargs["reverb_freeze_mode"])
+ )
+ )
+
+ if kwargs["pitch_shift"]:
+ board.append(
+ PitchShift(
+ semitones=kwargs["pitch_shift_semitones"]
+ )
+ )
+
+ if kwargs["delay"]:
+ board.append(
+ Delay(
+ delay_seconds=kwargs["delay_seconds"],
+ feedback=kwargs["delay_feedback"],
+ mix=kwargs["delay_mix"]
+ )
+ )
+
+ if kwargs["compressor"]:
+ board.append(
+ Compressor(
+ threshold_db=kwargs["compressor_threshold"],
+ ratio=kwargs["compressor_ratio"],
+ attack_ms=kwargs["compressor_attack"],
+ release_ms=kwargs["compressor_release"]
+ )
+ )
+
+ if kwargs["limiter"]:
+ board.append(
+ Limiter(
+ threshold_db=kwargs["limiter_threshold"],
+ release_ms=kwargs["limiter_release"]
+ )
+ )
+
+ if kwargs["gain"]:
+ board.append(
+ Gain(
+ gain_db=kwargs["gain_db"]
+ )
+ )
+
+ if kwargs["bitcrush"]:
+ board.append(
+ Bitcrush(
+ bit_depth=kwargs["bitcrush_bit_depth"]
+ )
+ )
+
+ if kwargs["clipping"]:
+ board.append(
+ Clipping(
+ threshold_db=kwargs["clipping_threshold"]
+ )
+ )
+
+ if kwargs["phaser"]:
+ board.append(
+ Phaser(
+ rate_hz=kwargs["phaser_rate_hz"],
+ depth=kwargs["phaser_depth"],
+ centre_frequency_hz=kwargs["phaser_centre_frequency_hz"],
+ feedback=kwargs["phaser_feedback"],
+ mix=kwargs["phaser_mix"]
+ )
+ )
+
+ return board
+
+ def realloc(
+ self,
+ block_frame,
+ extra_frame,
+ crossfade_frame,
+ sola_search_frame
+ ):
+ """
+ Dynamically computes and allocates internal Tensor buffers based on frame layout.
+
+ Args:
+ block_frame: Frames per processing chunk.
+ extra_frame: Extra historical frame lookback padding.
+ crossfade_frame: Frame overlap segment length for mixing.
+ sola_search_frame: Search window size used for SOLA cross-correlation.
+ """
+
+ # Rescale physical hardware frame measurements to the model's internal 16kHz context
+ block_frame_16k = int(
+ block_frame / self.input_sample_rate * self.sample_rate
+ )
+ crossfade_frame_16k = int(
+ crossfade_frame / self.input_sample_rate * self.sample_rate
+ )
+
+ sola_search_frame_16k = int(
+ sola_search_frame / self.input_sample_rate * self.sample_rate
+ )
+ extra_frame_16k = int(
+ extra_frame / self.input_sample_rate * self.sample_rate
+ )
+
+ # Aggregate composite frame sizing required for model inference windowing
+ convert_size_16k = (
+ block_frame_16k +
+ sola_search_frame_16k +
+ extra_frame_16k +
+ crossfade_frame_16k
+ )
+
+ # Align conversion window bounds exactly to integer multiples of the internal window_size
+ if (modulo := convert_size_16k % self.window_size) != 0:
+ convert_size_16k = convert_size_16k + (self.window_size - modulo)
+
+ # Calculate logical feature dimensions based on step size
+ self.convert_feature_size_16k = convert_size_16k // self.window_size
+ self.block_frame_16k = block_frame_16k
+ self.skip_head = extra_frame_16k // self.window_size
+ self.return_length = self.convert_feature_size_16k - self.skip_head
+ self.silence_front = extra_frame_16k - (self.window_size * 5) if self.silence_front else 0
+ # Compute minimum initialization blocks required to fill up sliding buffers
+ self.warmup_blocks = int(np.ceil(convert_size_16k / block_frame_16k)) + 1
+
+ # Physically instantiate processing buffers on target accelerator memory
+ audio_buffer_size = block_frame_16k + crossfade_frame_16k
+
+ self.audio_buffer = torch.zeros(
+ audio_buffer_size,
+ dtype=self.dtype,
+ device=config.device
+ )
+ self.convert_buffer = torch.zeros(
+ convert_size_16k,
+ dtype=self.dtype,
+ device=config.device
+ )
+ self.pitch_buffer = torch.zeros(
+ self.convert_feature_size_16k + 1,
+ dtype=torch.int64,
+ device=config.device
+ )
+ self.pitchf_buffer = torch.zeros(
+ self.convert_feature_size_16k + 1,
+ dtype=self.dtype,
+ device=config.device
+ )
+
+ def inference(
+ self,
+ audio_in,
+ f0_up_key = 0,
+ index_rate = 0.5,
+ protect = 0.5,
+ filter_radius = 3,
+ rms_mix_rate = 1,
+ f0_autotune = False,
+ f0_autotune_strength = 1,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 255.0,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5
+ ):
+ """
+ Runs the streaming audio block payload through the RVC pipeline.
+
+ Args:
+ audio_in: Input NumPy vector containing the current block chunk.
+ f0_up_key: Explicit semitone key shift factor.
+ index_rate: Index retrieval mapping ratio.
+ protect: Speech preservation masking strength factor.
+ filter_radius: Median pitch smoothing window parameter.
+ rms_mix_rate: Volume structural matching index.
+ f0_autotune: Snaps fundamental frequency (F0) to musical notes.
+ f0_autotune_strength: Correction intensity factor for note snapping.
+ proposal_pitch: Automated pitch shift estimation flag via F0 median alignment.
+ proposal_pitch_threshold: Boundary limit value for computed proposal pitch key shift.
+ embedders_mix: Flag to apply composite multiple embeddings.
+ embedders_mix_layers: Target specific layer layout from embedding networks.
+ embedders_mix_ratio: Blending interpolation parameter for combined features.
+
+ Returns:
+ A tuple containing:
+ - Optional[torch.Tensor]: Converted output audio tensor or None if bypassed.
+ - float: Root Mean Square (RMS) volume calculation of the input block.
+ """
+
+ # Resample incoming audio chunk to the pipeline's expected 16kHz processing rate
+ audio_in_16k = self.resample_in(
+ torch.as_tensor(
+ audio_in,
+ dtype=torch.float32,
+ device=config.device
+ )
+ ).to(self.dtype)
+
+ # Enqueue new audio values safely inside the sliding circular storage ring
+ circular_write(audio_in_16k, self.audio_buffer)
+ # Compute the instantaneous root-mean-square amplitude of the current segment
+ vol_t = self.audio_buffer.square().mean().sqrt()
+ vol = max(vol_t.item(), 0)
+
+ tg = self.tg
+ board = self.board
+
+ def inference_with_silent():
+ """Helper to execute silent/dummy inference pass to update internal model memory states."""
+
+ self.pipeline.inference(
+ self.convert_buffer,
+ self.pitch_buffer,
+ self.pitchf_buffer,
+ f0_up_key,
+ index_rate,
+ self.convert_feature_size_16k,
+ self.skip_head,
+ self.return_length,
+ protect,
+ filter_radius,
+ rms_mix_rate,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ tg,
+ board,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ block_size_16k=self.block_frame_16k
+ )
+
+ return None, vol
+
+ # Handle system buffering warmup blocks sequentially without generating audio
+ if self.warmup_blocks > 0:
+ self.warmup_blocks -= 1
+ circular_write(audio_in_16k, self.convert_buffer)
+ return inference_with_silent()
+
+ # Check Voice Activity Detection to bypass calculation during silent intervals
+ if self.vad is not None:
+ is_speech = self.vad.is_speech(audio_in_16k.cpu().numpy().copy())
+ if not is_speech: return inference_with_silent()
+
+ # Gate processing if block volume falls below ambient gate floor sensitivity parameters
+ if vol < self.input_sensitivity: return inference_with_silent()
+ # Update historical conversion tracking buffer
+ circular_write(audio_in_16k, self.convert_buffer)
+
+ # Dispatch audio frames into backend neural acoustic generator
+ audio_model = self.pipeline.inference(
+ self.convert_buffer,
+ self.pitch_buffer,
+ self.pitchf_buffer,
+ f0_up_key,
+ index_rate,
+ self.convert_feature_size_16k,
+ self.skip_head,
+ self.return_length,
+ protect,
+ filter_radius,
+ rms_mix_rate,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ tg,
+ board,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ block_size_16k=self.block_frame_16k
+ )
+
+ # Upsample converted output back to targeted hardware execution sample rates
+ audio_out = self.resample_out(audio_model * vol_t.sqrt())
+ return audio_out, vol
+
+class VoiceChanger:
+ """
+ Manages real-time cross-fading, SOLA alignment, and stream recording.
+
+ This class provides stream stitching routines using the Synchronized Overlap-Add
+ (SOLA) cross-correlation technique or Phase Vocoder to eliminate block-boundary
+ clicking artifacts during live audio transformation.
+ """
+
+ def __init__(
+ self,
+ read_chunk_size,
+ cross_fade_overlap_size,
+ input_sample_rate,
+ output_sample_rate,
+ extra_convert_size,
+ model_path,
+ index_path,
+ f0_method,
+ predictor_onnx,
+ embedder_model,
+ embedders_mode,
+ sample_rate,
+ hop_length,
+ silent_threshold,
+ vad_enabled,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ post_process,
+ sid,
+ noise_scale,
+ nprobe,
+ record_audio,
+ record_audio_path,
+ export_format,
+ **kwargs
+ ):
+ """Initializes the structural layout and overlap windows of the VoiceChanger interface."""
+
+ self.input_sample_rate = input_sample_rate
+ self.output_sample_rate = output_sample_rate
+ # Calculate granular frame sizes based on parameters
+ self.block_frame = read_chunk_size * 128
+ self.crossfade_frame = int(cross_fade_overlap_size * input_sample_rate)
+ self.extra_frame = int(extra_convert_size * input_sample_rate)
+ self.sola_search_frame = input_sample_rate // 100
+ # Uniform tensor utilized inside cross-correlation norm operations
+ self.cor_den_ones = torch.ones(1, 1, self.crossfade_frame, device=config.device, dtype=torch.float32)
+ # Instantiate underlying realtime model engine
+ self.vc_model = Realtime(
+ model_path,
+ index_path,
+ f0_method,
+ predictor_onnx,
+ embedder_model,
+ embedders_mode,
+ sample_rate,
+ hop_length,
+ silent_threshold,
+ self.input_sample_rate,
+ self.output_sample_rate,
+ vad_enabled,
+ vad_sensitivity,
+ vad_frame_ms,
+ clean_audio,
+ clean_strength,
+ post_process,
+ sid,
+ noise_scale,
+ nprobe,
+ **kwargs
+ )
+ # IO configuration details for local session capture
+ self.record_audio = record_audio
+ self.record_audio_path = record_audio_path
+ self.export_format = export_format
+ self.sola_buffer = None
+ # Bootstrap and allocate structural arrays
+ self.vc_model.realloc(
+ self.block_frame,
+ self.extra_frame,
+ self.crossfade_frame,
+ self.sola_search_frame
+ )
+ self.generate_strength()
+ self.setup_soundfile_record()
+
+ def setup_soundfile_record(self):
+ """Initializes the background file recorder for the session."""
+
+ import soundfile as sf
+
+ self.soundfile = sf.SoundFile(
+ self.record_audio_path,
+ mode="w",
+ samplerate=self.output_sample_rate,
+ channels=1,
+ format=self.export_format.lower(),
+ ) if self.record_audio else None
+
+ def generate_strength(self):
+ """Generates mathematical window contours for overlap cross-fading."""
+
+ # Calculate sinusoidal fade-in window coefficients
+ self.fade_in_window = (
+ 0.5 * np.pi * torch.linspace(0.0, 1.0, steps=self.crossfade_frame, device=config.device, dtype=torch.float32)
+ ).sin() ** 2
+ # Complementary inversion yields a matching linear fade-out path
+ self.fade_out_window = 1 - self.fade_in_window
+
+ # Storage for historical phase overlaps
+ self.sola_buffer = torch.zeros(
+ self.crossfade_frame,
+ device=config.device,
+ dtype=torch.float32
+ )
+
+ def process_audio(
+ self,
+ audio_in,
+ f0_up_key = 0,
+ index_rate = 0.5,
+ protect = 0.5,
+ filter_radius = 3,
+ rms_mix_rate = 1,
+ f0_autotune = False,
+ f0_autotune_strength = 1,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 255.0,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5,
+ use_phase_vocoder = True
+ ):
+ """
+ Performs SOLA stitching or Phase Vocoder processing on an audio block.
+
+ Args:
+ audio_in: Array containing streaming input.
+ f0_up_key: Target pitch modifier key.
+ index_rate: Feature retrieval search influence multiplier.
+ protect: Envelope conservation factor parameter.
+ filter_radius: Pitch track filter smooth index bounds.
+ rms_mix_rate: Target loudness preservation mapping index.
+ f0_autotune: Snaps fundamental frequency (F0) to musical notes.
+ f0_autotune_strength: Correction intensity factor for note snapping.
+ proposal_pitch: Automated optimal key estimation flag via F0 median alignment.
+ proposal_pitch_threshold: Boundary ceiling/floor limit for calculated proposal pitch shift key.
+ embedders_mix: Combined multi-embedding processing toggle.
+ embedders_mix_layers: Specific layers targeted for blending extraction.
+ embedders_mix_ratio: Inter-embedding scale mix factor.
+ use_phase_vocoder: Enable phase vocoder..
+
+ Returns:
+ A tuple containing:
+ - np.ndarray: Glitch-free output audio chunk.
+ - float: Detected block level RMS metric values.
+ """
+
+ block_size = audio_in.shape[0]
+ # Fetch output blocks from underlying neural transformer model
+ audio, vol = self.vc_model.inference(
+ audio_in,
+ f0_up_key,
+ index_rate,
+ protect,
+ filter_radius,
+ rms_mix_rate,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio
+ )
+
+ # Handle system-bypass conditions or empty processing responses safely
+ if audio is None:
+ self.sola_buffer.zero_()
+ audio = np.zeros(block_size, dtype=np.float32)
+
+ if self.record_audio and self.soundfile is not None: self.soundfile.write(audio)
+ return audio, vol
+
+ # Identify if current chunk marks the start of a transmission sequence
+ is_onset = not self.sola_buffer.any()
+ # Compute cross-correlation between current stream buffer and past frames using 1D convolution
+ conv_input = audio[None, None, : self.crossfade_frame + self.sola_search_frame].float()
+ cor_nom = F.conv1d(conv_input, self.sola_buffer[None, None, :])
+
+ cor_den = (
+ F.conv1d(
+ conv_input ** 2,
+ self.cor_den_ones
+ ) + 1e-8
+ ).sqrt()
+
+ # Find the optimal overlap point that maximizes signal cross-correlation alignment
+ sola_offset = (cor_nom[0, 0] / cor_den[0, 0]).argmax()
+ audio = audio[sola_offset:]
+
+ # Execute window merging routines depending on current execution paths
+ if use_phase_vocoder and not config.device.startswith(("privateuseone", "ocl")):
+ # Resolve boundary phase discontinuities explicitly using STFT Phase Vocoder processing
+ audio[: self.crossfade_frame] = phase_vocoder(
+ self.sola_buffer,
+ audio[: self.crossfade_frame],
+ self.fade_out_window,
+ self.fade_in_window
+ )
+ else:
+ if is_onset:
+ # Suppress attack transients at the start of speech onset periods to reduce clicks
+ n_hops = block_size // 160
+ if n_hops >= 1:
+ hop_energy = audio[: n_hops * 160].reshape(n_hops, 160).abs().max(dim=1).values
+ peak = hop_energy.max().item()
+ onset_sample = 0
+
+ if peak > 1e-4:
+ above = (hop_energy > 0.1 * peak).nonzero(as_tuple=False)
+ if len(above) > 0: onset_sample = int(above[0].item()) * 160
+ else: onset_sample = 0
+
+ audio[:onset_sample] = 0.0
+ fade_len = min(block_size - onset_sample, self.crossfade_frame)
+ if fade_len > 0: audio[onset_sample : onset_sample + fade_len] *= self.fade_in_window[:fade_len]
+ else:
+ # Apply standard crossfade blending matrices across block junctions
+ audio[: self.crossfade_frame] *= self.fade_in_window
+ audio[: self.crossfade_frame] += self.sola_buffer * self.fade_out_window
+
+ # Enforce consistent audio lengths by padding missing trailing segments if needed
+ min_len = block_size + self.crossfade_frame
+ if audio.shape[0] < min_len: audio = torch.cat([audio, torch.zeros(min_len - audio.shape[0], device=config.device, dtype=audio.dtype)])
+ # Update historical overlap buffers for the next incoming chunk
+ self.sola_buffer[:] = audio[block_size : block_size + self.crossfade_frame]
+ audio_output = audio[:block_size].detach().cpu().numpy()
+
+ # Write converted blocks to local session storage files if enabled
+ if self.record_audio and self.soundfile is not None: self.soundfile.write(audio_output)
+ return audio_output, vol
+
+ @torch.no_grad()
+ def on_request(
+ self,
+ audio_in,
+ f0_up_key = 0,
+ index_rate = 0.5,
+ protect = 0.5,
+ filter_radius = 3,
+ rms_mix_rate = 1,
+ f0_autotune = False,
+ f0_autotune_strength = 1,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 255.0,
+ embedders_mix = False,
+ embedders_mix_layers = 9,
+ embedders_mix_ratio = 0.5,
+ use_phase_vocoder = True
+ ):
+ """
+ Wrapper method that benchmarks processing latency while converting audio blocks.
+
+ Args:
+ All arguments correspond directly to `process_audio` parameters.
+
+ Returns:
+ A tuple containing:
+ - np.ndarray: Stitched transformed output waveform values.
+ - float: Calculated block RMS level metric.
+ - float: Total duration elapsed during calculation (in milliseconds).
+ """
+
+ start = time.perf_counter()
+
+ result, vol = self.process_audio(
+ audio_in,
+ f0_up_key,
+ index_rate,
+ protect,
+ filter_radius,
+ rms_mix_rate,
+ f0_autotune,
+ f0_autotune_strength,
+ proposal_pitch,
+ proposal_pitch_threshold,
+ embedders_mix,
+ embedders_mix_layers,
+ embedders_mix_ratio,
+ use_phase_vocoder
+ )
+
+ end = time.perf_counter()
+ # Compute total latency processing metrics back to the streaming execution pipeline
+ return result, vol, (end - start) * 1000
\ No newline at end of file
diff --git a/main/inference/realtime/vad_utils.py b/main/inference/realtime/vad_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdc7a608148bad8ba3f33961862d5a5a05b5b098
--- /dev/null
+++ b/main/inference/realtime/vad_utils.py
@@ -0,0 +1,138 @@
+import webrtcvad
+
+import numpy as np
+
+class VADProcessor:
+ """
+ A voice activity detector wrapper centered around WebRTC VAD to handle structural multi-channel
+ downmixing, real-time PCM audio conversion, frame chunking, and continuous speech segment parsing.
+ """
+
+ def __init__(
+ self,
+ sensitivity_mode=3,
+ sample_rate=16000,
+ frame_duration_ms=30
+ ):
+ """
+ Initializes the WebRTC VAD instance and calculates frame alignments based on mathematical constraints.
+
+ Args:
+ sensitivity_mode (int): Aggressiveness level filters for filtering out noise (0 to 3). 3 is the most aggressive.
+ sample_rate (int): Audio sampling frequency rate. Must strictly be 8000, 16000, 32000, or 48000 Hz.
+ frame_duration_ms (int): Target time window length per slice context. Must strictly be 10, 20, or 30 ms.
+
+ Raises:
+ ValueError: If sample_rate or frame_duration_ms do not match WebRTC native driver prerequisites.
+ """
+
+ # Validate constraints required by the underlying C-extension implementation of WebRTC VAD
+ if sample_rate not in [8000, 16000, 32000, 48000]: raise ValueError("WebRTC VAD only supports sample rates of 8000, 16000, 32000, or 48000 Hz.")
+ if frame_duration_ms not in [10, 20, 30]: raise ValueError("WebRTC VAD only supports frame lengths of 10, 20, or 30 ms.")
+
+ self.sample_rate = sample_rate
+ self.frame_duration_ms = frame_duration_ms
+
+ self.vad = webrtcvad.Vad(sensitivity_mode)
+ # Calculate exactly how many samples represent the requested milliseconds window length
+ self.frame_length = int(sample_rate * (frame_duration_ms / 1000.0))
+
+ def is_speech(self, audio_chunk):
+ """
+ Processes a raw input chunk of arbitrary length to detect the presence of human speech.
+
+ Args:
+ audio_chunk (np.ndarray): Input floating-point or integer audio array data.
+
+ Returns:
+ bool: True if any sub-frame within the chunk contains active human speech, False otherwise.
+ """
+
+ # Downmix stereo or multi-channel shapes down to standard single-channel mono sequence loops
+ if audio_chunk.ndim > 1 and audio_chunk.shape[1] == 1: audio_chunk = audio_chunk.flatten()
+ elif audio_chunk.ndim > 1: audio_chunk = np.mean(audio_chunk, axis=1)
+
+ # Enforce boundary limitations to guard against potential floating-point out-of-bounds clipping
+ if np.max(np.abs(audio_chunk)) > 1.0: audio_chunk = np.clip(audio_chunk, -1.0, 1.0)
+
+ # Transform normalized -1.0 to 1.0 float data vectors into signed 16-bit Int PCM bytes
+ audio_chunk = (audio_chunk * 32767).astype(np.int16)
+ num_frames = len(audio_chunk) // self.frame_length
+
+ # Apply zero-padding on tiny partial trailing data blocks to perfectly fill out one full expected slice frame
+ if num_frames == 0 and len(audio_chunk) > 0:
+ audio_chunk = np.concatenate((
+ audio_chunk,
+ np.zeros(
+ self.frame_length - len(audio_chunk),
+ dtype=np.int16
+ )
+ ))
+ num_frames = 1
+ elif num_frames == 0 and len(audio_chunk) == 0: return False
+
+ try:
+ # Step through sequential intervals across the byte stream to look for activity markers
+ for i in range(num_frames):
+ start = i * self.frame_length
+ # If the current sub-frame evaluates as speech, flag the entire buffer segment instantly
+ if self.vad.is_speech(
+ audio_chunk[start:start + self.frame_length].tobytes(),
+ self.sample_rate
+ ):
+ return True
+
+ return False
+ except Exception:
+ return False
+
+ def get_speech(self, y, min_speech_duration=0.5):
+ """
+ Parses an extended mono audio array to extract localized timestamps pointing to valid
+ human conversation phrases.
+
+ Args:
+ y (np.ndarray): Complete contiguous floating-point target audio sequence to track.
+ min_speech_duration (float): Minimum duration threshold in seconds required to accept a segment.
+
+ Returns:
+ list: List of dictionary segments with explicit timing parameters, formatted as: [{'start': x, 'end': y}].
+ """
+
+ duration = len(y) / self.sample_rate
+ is_speech_list, segments = [], []
+
+ # Iterate over uniform blocks throughout the audio array sequence
+ for i in range(0, len(y), self.frame_length):
+ chunk = y[i:i + self.frame_length]
+ # Pad the final trailing block with zeros if it falls short of the required frame length
+ if len(chunk) < self.frame_length: chunk = np.pad(chunk, (0, self.frame_length - len(chunk)))
+ # Perform raw evaluation mapping the individual segment slices to binary Boolean arrays
+ is_speech_list.append(self.vad.is_speech((chunk * 32767).astype(np.int16).tobytes(), self.sample_rate))
+
+ start_frame = None
+ # Calculate trailing silence frame threshold equivalent to a 500 ms window
+ silence_threshold_frames = int(500 / self.frame_duration_ms)
+ silence_counter = 0
+
+ # Run state-machine checks across the parsed sequence to establish start/end times
+ for idx, is_speech in enumerate(is_speech_list):
+ if is_speech:
+ if start_frame is None: start_frame = idx
+ silence_counter = 0 # Reset non-activity indicators since speech is actively registered
+ elif start_frame is not None:
+ silence_counter += 1
+ # If continuous silence duration exceeds 500ms, close out the current phrase segment bounds
+ if silence_counter > silence_threshold_frames:
+ start_time = (start_frame * self.frame_duration_ms) / 1000.0
+ end_time = ((idx - silence_counter) * self.frame_duration_ms) / 1000.0
+
+ # Validate the finalized time delta block against the specified minimum duration limits
+ if end_time - start_time >= min_speech_duration: segments.append({"start": start_time, "end": min(end_time, duration)})
+
+ start_frame = None
+ silence_counter = 0
+
+ # Handle edge-case boundaries where speech persists up to the end of the input track data buffer
+ if start_frame is not None: segments.append({"start": (start_frame * self.frame_duration_ms) / 1000.0, "end": duration})
+ return segments
\ No newline at end of file
diff --git a/main/inference/separate_music.py b/main/inference/separate_music.py
new file mode 100644
index 0000000000000000000000000000000000000000..3adfb7d4cff3799d489565beee95f2792079581e
--- /dev/null
+++ b/main/inference/separate_music.py
@@ -0,0 +1,744 @@
+import os
+import sys
+import time
+import argparse
+
+sys.path.append(os.getcwd())
+
+from main.library.utils import strtobool
+from main.library.audio.audio import pydub_load
+from main.library.uvr5_lib.separator import Separator
+from main.app.variables import config, configs, logger, translations, vr_models, demucs_models, mdx_models, karaoke_models, reverb_models, denoise_models
+
+def parse_arguments():
+ """
+ Parses command-line arguments configuring the UVR5 audio separation pipeline.
+
+ Returns:
+ argparse.Namespace: Object containing validated operational configurations.
+ """
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--separate_music", action='store_true')
+ parser.add_argument("--input_path", type=str, required=True)
+ parser.add_argument("--output_dirs", type=str, default="./audios")
+ parser.add_argument("--export_format", type=str, default="wav")
+ parser.add_argument("--model_name", type=str, default="MDXNET_Main")
+ parser.add_argument("--karaoke_model", type=str, default="MDX-Version-1")
+ parser.add_argument("--reverb_model", type=str, default="MDX-Reverb")
+ parser.add_argument("--denoise_model", type=str, default="Normal")
+ parser.add_argument("--sample_rate", type=int, default=44100)
+ parser.add_argument("--shifts", type=int, default=2)
+ parser.add_argument("--batch_size", type=int, default=1)
+ parser.add_argument("--overlap", type=float, default=0.25)
+ parser.add_argument("--aggression", type=int, default=5)
+ parser.add_argument("--hop_length", type=int, default=1024)
+ parser.add_argument("--window_size", type=int, default=512)
+ parser.add_argument("--segments_size", type=int, default=256)
+ parser.add_argument("--post_process_threshold", type=float, default=0.2)
+ parser.add_argument("--enable_tta", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--enable_denoise", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--high_end_process", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--enable_post_process", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--separate_backing", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--separate_reverb", type=lambda x: bool(strtobool(x)), default=False)
+
+ return parser.parse_args()
+
+def main():
+ """Main execution orchestrator routing runtime command-line arguments."""
+
+ args = parse_arguments()
+
+ (
+ input_path,
+ output_dirs,
+ export_format,
+ model_name,
+ karaoke_model,
+ reverb_model,
+ denoise_model,
+ sample_rate,
+ shifts,
+ batch_size,
+ overlap,
+ aggression,
+ hop_length,
+ window_size,
+ segments_size,
+ post_process_threshold,
+ enable_tta,
+ enable_denoise,
+ high_end_process,
+ enable_post_process,
+ separate_backing,
+ separate_reverb
+ ) = (
+ args.input_path,
+ args.output_dirs,
+ args.export_format,
+ args.model_name,
+ args.karaoke_model,
+ args.reverb_model,
+ args.denoise_model,
+ args.sample_rate,
+ args.shifts,
+ args.batch_size,
+ args.overlap,
+ args.aggression,
+ args.hop_length,
+ args.window_size,
+ args.segments_size,
+ args.post_process_threshold,
+ args.enable_tta,
+ args.enable_denoise,
+ args.high_end_process,
+ args.enable_post_process,
+ args.separate_backing,
+ args.separate_reverb
+ )
+
+ separate(
+ input_path,
+ output_dirs,
+ export_format,
+ model_name,
+ karaoke_model,
+ reverb_model,
+ denoise_model,
+ sample_rate,
+ shifts,
+ batch_size,
+ overlap,
+ aggression,
+ hop_length,
+ window_size,
+ segments_size,
+ post_process_threshold,
+ enable_tta,
+ enable_denoise,
+ high_end_process,
+ enable_post_process,
+ separate_backing,
+ separate_reverb
+ )
+
+def separate(
+ input_path,
+ output_dirs,
+ export_format="wav",
+ model_name="MDXNET_Main",
+ karaoke_model="MDX-Version-1",
+ reverb_model="MDX-Reverb",
+ denoise_model="Normal",
+ sample_rate=44100,
+ shifts=2,
+ batch_size=1,
+ overlap=0.25,
+ aggression=5,
+ hop_length=1024,
+ window_size=512,
+ segments_size=256,
+ post_process_threshold=0.2,
+ enable_tta=False,
+ enable_denoise=False,
+ high_end_process=False,
+ enable_post_process=False,
+ separate_backing=False,
+ separate_reverb=False
+):
+ """Orchestrates operational input processing across isolated source audio files or matching folder groups."""
+
+ start_time = time.time()
+ pid_path = os.path.join("assets", "separate_pid.txt")
+
+ with open(pid_path, "w") as pid_file:
+ pid_file.write(str(os.getpid()))
+
+ try:
+ input_path = input_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
+ # Fixed logic: preserve directory path structures correctly if no target file suffix matches
+ output_dirs = os.path.dirname(output_dirs) or output_dirs
+
+ log_data = {
+ translations["audio_path"]: input_path,
+ translations["output_path"]: output_dirs,
+ translations["export_format"]: export_format,
+ translations["shift"]: shifts,
+ translations["segments_size"]: segments_size,
+ translations["overlap"]: overlap,
+ translations["modelname"]: model_name,
+ translations["denoise_mdx"]: enable_denoise,
+ translations["hop_length"]: hop_length,
+ translations["batch_size"]: batch_size,
+ translations["sr"]: sample_rate,
+ translations["separator_backing"]: separate_backing,
+ translations["dereveb_audio"]: separate_reverb,
+ translations["aggression"]: aggression,
+ translations["window_size"]: window_size,
+ translations["post_process_threshold"]: post_process_threshold,
+ translations["enable_tta"]: enable_tta,
+ translations["high_end_process"]: high_end_process,
+ translations["enable_post_process"]: enable_post_process,
+ translations["backing_model_ver"]: karaoke_model,
+ translations["dereveb_model"]: reverb_model,
+ translations["denoise_model"]: denoise_model
+ }
+
+ for key, value in log_data.items():
+ logger.debug(f"{key}: {value}")
+
+ output_files = []
+ # Distinguish flat file entities instances away from structural folder batch loops setup configurations
+ files = [
+ os.path.join(input_path, f)
+ for f in os.listdir(input_path)
+ ] if os.path.isdir(input_path) else [input_path]
+
+ # Sequentially loop through isolated files tracking execution lists inputs objects layers
+ for file in files:
+ if os.path.isfile(file):
+ output_files.append(_separate(
+ input_path,
+ output_dirs,
+ model_name,
+ karaoke_model,
+ reverb_model,
+ denoise_model,
+ export_format,
+ sample_rate,
+ shifts,
+ batch_size,
+ overlap,
+ aggression,
+ hop_length,
+ window_size,
+ segments_size,
+ post_process_threshold,
+ enable_tta,
+ enable_denoise,
+ high_end_process,
+ enable_post_process,
+ separate_backing,
+ separate_reverb
+ ))
+ except Exception as e:
+ # Capture underlying issues and output formatted execution stack errors data frames smoothly
+ logger.error(f"{translations['separator_error']}: {e}")
+ import traceback
+ logger.debug(traceback.format_exc())
+
+ # Destroy PID context files cleanly when pipeline operations close out normally
+ if os.path.exists(pid_path): os.remove(pid_path)
+
+ logger.info(translations["separator_success"].format(elapsed_time=f"{time.time() - start_time:.2f}"))
+ return output_files
+
+def _separate(
+ input_path,
+ output_dirs,
+ model_name,
+ karaoke_model="MDX-Version-1",
+ reverb_model="MDX-Reverb",
+ denoise_model="Normal",
+ export_format="wav",
+ sample_rate=44100,
+ shifts=2,
+ batch_size=1,
+ overlap=0.25,
+ aggression=5,
+ hop_length=1024,
+ window_size=512,
+ segments_size=256,
+ post_process_threshold=0.2,
+ enable_tta=False,
+ enable_denoise=False,
+ high_end_process=False,
+ enable_post_process=False,
+ separate_backing=False,
+ separate_reverb=False
+):
+ """Internal function orchestrating multi-stage structural separation passes sequentially."""
+
+ main_vocals, backing_vocals = None, None
+ # Isolate parent absolute text tokens strings cleanly
+ filename, _ = os.path.splitext(os.path.basename(input_path))
+ output_dirs = os.path.join(output_dirs, filename)
+
+ # Establish isolated local folders containers dynamically mapping onto specific targets
+ os.makedirs(output_dirs, exist_ok=True)
+ clean_file(output_dirs, export_format)
+
+ # Route 1: Initial Source Demixing Phase
+ if model_name in list(demucs_models.keys()):
+ original_vocals, instruments = demucs_main(
+ input_path,
+ output_dirs,
+ model_name,
+ export_format,
+ segments_size,
+ overlap,
+ shifts,
+ sample_rate
+ )
+ elif model_name in list(vr_models.keys()):
+ original_vocals, instruments = vr_main(
+ input_path,
+ output_dirs,
+ vr_models.get(model_name, model_name),
+ export_format,
+ batch_size,
+ window_size,
+ aggression,
+ enable_denoise,
+ denoise_model,
+ enable_tta,
+ enable_post_process,
+ post_process_threshold,
+ high_end_process,
+ sample_rate,
+ )
+ else:
+ original_vocals, instruments = mdx_main(
+ input_path,
+ output_dirs,
+ mdx_models.get(model_name, model_name),
+ export_format,
+ segments_size,
+ overlap,
+ enable_denoise,
+ hop_length,
+ batch_size,
+ sample_rate,
+ )
+
+ # Route 2: Backing Vocals Isolation (Karaoke Step)
+ if separate_backing:
+ if karaoke_model.startswith("MDX"):
+ main_vocals, backing_vocals = mdx_main(
+ original_vocals,
+ output_dirs,
+ karaoke_models.get(karaoke_model, karaoke_model),
+ export_format,
+ segments_size,
+ overlap,
+ enable_denoise,
+ hop_length,
+ batch_size,
+ sample_rate,
+ mode="karaoke"
+ )
+ else:
+ main_vocals, backing_vocals = vr_main(
+ original_vocals,
+ output_dirs,
+ karaoke_models.get(karaoke_model, karaoke_model),
+ export_format,
+ batch_size,
+ window_size,
+ aggression,
+ enable_denoise,
+ denoise_model,
+ enable_tta,
+ enable_post_process,
+ post_process_threshold,
+ high_end_process,
+ sample_rate,
+ mode="karaoke"
+ )
+
+ # Route 3: Reverb Stripping Processing Layer
+ if separate_reverb:
+ dereverb = [original_vocals]
+ if separate_backing: dereverb.append(main_vocals)
+
+ for audio in dereverb:
+ if karaoke_model.startswith("MDX"):
+ _, no_reverb_vocals = mdx_main(
+ audio,
+ output_dirs,
+ reverb_models.get(reverb_model, reverb_model),
+ export_format,
+ segments_size,
+ overlap,
+ enable_denoise,
+ hop_length,
+ batch_size,
+ sample_rate,
+ mode="reverb"
+ )
+ else:
+ _, no_reverb_vocals = vr_main(
+ audio,
+ output_dirs,
+ reverb_models.get(reverb_model, reverb_model),
+ export_format,
+ batch_size,
+ window_size,
+ aggression,
+ enable_denoise,
+ denoise_model,
+ enable_tta,
+ enable_post_process,
+ post_process_threshold,
+ high_end_process,
+ sample_rate,
+ mode="reverb"
+ )
+
+ # Conditionally map updated outputs paths safely over internal tracker targets strings
+ if "Original_Vocals" in os.path.basename(no_reverb_vocals): original_vocals = no_reverb_vocals
+ else: main_vocals = no_reverb_vocals
+
+ return original_vocals, instruments, main_vocals, backing_vocals
+
+def vr_main(
+ input_path,
+ output_dirs,
+ model_name,
+ export_format="wav",
+ batch_size=1,
+ window_size=512,
+ aggression=5,
+ enable_denoise=False,
+ denoise_model="Normal",
+ enable_tta=False,
+ enable_post_process=False,
+ post_process_threshold=0.2,
+ high_end_process=False,
+ sample_rate=44100,
+ mode="original"
+):
+ """Handles inference tasks routing through standard UVR5 VR-Architecture models."""
+
+ exists_file(input_path, output_dirs)
+ logger.info(f"{translations['separator_process_2']}...")
+
+ output_list = separate_main(
+ audio_file=input_path,
+ model_filename=model_name,
+ export_format=export_format,
+ output_dir=output_dirs,
+ batch_size=batch_size,
+ window_size=window_size,
+ aggression=aggression,
+ enable_tta=enable_tta,
+ enable_post_process=enable_post_process,
+ post_process_threshold=post_process_threshold,
+ high_end_process=high_end_process,
+ sample_rate=sample_rate
+ )
+
+ if enable_denoise:
+ denoise_list = []
+ for audio in output_list:
+ audio_path = os.path.join(output_dirs, audio)
+
+ denoise_file = separate_main(
+ audio_file=audio_path,
+ model_filename=denoise_models.get(denoise_model, denoise_model),
+ export_format=export_format,
+ output_dir=output_dirs,
+ batch_size=batch_size,
+ window_size=window_size,
+ aggression=aggression,
+ enable_tta=enable_tta,
+ enable_post_process=enable_post_process,
+ post_process_threshold=post_process_threshold,
+ high_end_process=high_end_process,
+ sample_rate=sample_rate
+ )
+
+ if os.path.exists(audio_path): os.remove(audio_path)
+
+ for file in denoise_file:
+ file_path = os.path.join(output_dirs, file)
+
+ if "_(Noise)_" in file and os.path.exists(file_path): os.remove(file_path)
+ elif "_(No Noise)_" in file:
+ filename = "".join([file.split("_(No Noise)_")[0], ".", export_format])
+ os.rename(file_path, os.path.join(output_dirs, filename))
+
+ denoise_list.append(filename)
+
+ logger.info(translations["separator_success_2"])
+
+ return process_file(
+ denoise_list if enable_denoise else output_list,
+ output_dirs,
+ export_format,
+ mode
+ )
+
+def demucs_main(
+ input_path,
+ output_dirs,
+ model_name,
+ export_format="wav",
+ segments_size=256,
+ overlap=0.25,
+ shifts=2,
+ sample_rate=44100
+):
+ """Handles inference structures processing inputs across Meta Demucs architectures."""
+
+ exists_file(input_path, output_dirs)
+ logger.info(f"{translations['separator_process_2']}...")
+
+ output_list = separate_main(
+ audio_file=input_path,
+ output_dir=output_dirs,
+ model_filename=demucs_models.get(model_name, model_name),
+ export_format=export_format,
+ segment_size=int(segments_size // 4),
+ overlap=overlap,
+ shifts=shifts,
+ sample_rate=sample_rate
+ )
+
+ logger.info(translations["separator_success_2"])
+
+ return process_file(
+ output_list,
+ output_dirs,
+ export_format,
+ mode="4stem"
+ )
+
+def mdx_main(
+ input_path,
+ output_dirs,
+ model_name,
+ export_format="wav",
+ segments_size=256,
+ overlap=0.25,
+ enable_denoise=False,
+ hop_length=1024,
+ batch_size=1,
+ sample_rate=44100,
+ mode="original"
+):
+ """Handles inference tasks routing through modern MDX-Net ONNX structures."""
+
+ exists_file(input_path, output_dirs)
+ logger.info(f"{translations['separator_process_2']}...")
+
+ output_list = separate_main(
+ audio_file=input_path,
+ model_filename=model_name,
+ export_format=export_format,
+ output_dir=output_dirs,
+ segment_size=segments_size,
+ overlap=overlap,
+ batch_size=batch_size,
+ hop_length=hop_length,
+ enable_denoise=enable_denoise,
+ sample_rate=sample_rate
+ )
+
+ logger.info(translations["separator_success_2"])
+
+ return process_file(
+ output_list,
+ output_dirs,
+ export_format,
+ mode
+ )
+
+def process_file(
+ input_list,
+ output_dirs,
+ export_format="wav",
+ mode="original"
+):
+ """Normalizes output suffix conventions and merges structural multi-stem outputs."""
+
+ demucs_inst = []
+ reverb_audio, no_reverb_audio = None, None
+
+ # Instantiate rigid static naming conventions assigned back onto file targets
+ main_audio, backing_audio = (
+ os.path.join(output_dirs, f"Main_Vocals.{export_format}"),
+ os.path.join(output_dirs, f"Backing_Vocals.{export_format}")
+ )
+
+ original_audio, instruments_audio = (
+ os.path.join(output_dirs, f"Original_Vocals.{export_format}"),
+ os.path.join(output_dirs, f"Instruments.{export_format}")
+ )
+
+ # Iterate through file elements inside processing generation blocks array
+ for file in input_list:
+ file_path = os.path.join(output_dirs, file)
+ if not os.path.exists(file_path):
+ logger.warning(translations["not_found"].format(name=file_path))
+
+ # Branch processing strategy behaviors dynamically using runtime tags checks
+ if mode == "original":
+ if "_(Instrumental)_" in file:
+ os.rename(file_path, instruments_audio)
+ elif "_(Vocals)_" in file:
+ os.rename(file_path, original_audio)
+ elif mode == "4stem":
+ if "_(Vocals)_" in file:
+ os.rename(file_path, original_audio)
+ elif "_(Drums)_" in file or "_(Bass)_" in file or "_(Other)_" in file or "_(Guitar)_" in file or "_(Piano)_" in file:
+ demucs_inst.append(file_path)
+ elif mode == "reverb":
+ filename = file.split("_(")[0]
+
+ reverb_audio = os.path.join(
+ output_dirs,
+ "".join([filename, "_Reverb.", export_format])
+ )
+
+ no_reverb_audio = os.path.join(
+ output_dirs,
+ "".join([filename, "_No_Reverb.", export_format])
+ )
+
+ if "_(Reverb)_" in file or "_(Echo)_" in file:
+ os.rename(file_path, reverb_audio)
+ elif "_(No Reverb)_" in file or "_(No Echo)_" in file:
+ os.rename(file_path, no_reverb_audio)
+ elif mode == "karaoke":
+ if "_(Instrumental)_" in file:
+ os.rename(file_path, backing_audio)
+ elif "_(Vocals)_" in file:
+ os.rename(file_path, main_audio)
+
+ # Short-circuit returns mapping specifically onto specialized sub-tasks requirements
+ if mode == "reverb": return reverb_audio, no_reverb_audio
+ if mode == "karaoke": return main_audio, backing_audio
+
+ # Composite continuous instrumentals for Demucs 4-stem outputs
+ if mode == "4stem":
+ demucs_audio = pydub_load(demucs_inst[0])
+ for file in demucs_inst[1:]:
+ demucs_audio = demucs_audio.overlay(pydub_load(file))
+
+ # Export compiled multi-stem track directly as a uniform full instrumentals file
+ demucs_audio.export(instruments_audio, format=export_format)
+ # Drop multi-stem components if configuration explicitly blocks tracking residuals storage
+ if configs.get("del4stem", True):
+ for f in demucs_inst:
+ if os.path.exists(f): os.remove(f)
+
+ return original_audio, instruments_audio
+
+def exists_file(input_path, output_dirs):
+ """Verifies runtime file system integrity context boundaries."""
+
+ if not os.path.exists(input_path):
+ logger.warning(translations["input_not_valid"])
+ sys.exit(1)
+
+ if not os.path.exists(output_dirs):
+ logger.warning(translations["output_not_valid"])
+ sys.exit(1)
+
+def clean_file(output_dirs, export_format):
+ """Cleans up target export extensions inside the target execution folder."""
+
+ for f in os.listdir(output_dirs):
+ if f.endswith(export_format):
+ file_path = os.path.join(output_dirs, f)
+ if os.path.exists(file_path): os.remove(file_path)
+
+def separate_main(
+ audio_file=None,
+ model_filename="UVR-MDX-NET_Main_340.onnx",
+ export_format="wav",
+ output_dir=".",
+ segment_size=256,
+ overlap=0.25,
+ batch_size=1,
+ hop_length=1024,
+ enable_denoise=False,
+ shifts=2,
+ window_size=512,
+ aggression=5,
+ enable_tta=False,
+ enable_post_process=False,
+ post_process_threshold=0.2,
+ high_end_process=False,
+ sample_rate=44100
+):
+ """
+ Directly interfaces with the underlying UVR5 structural Separator object class.
+ Falls back safely onto standardized default parameters upon unexpected failures.
+ """
+
+ try:
+ # Initialize primary separator object using specialized hyperparameter mappings
+
+ separator = Separator(
+ logger=logger,
+ output_dir=output_dir,
+ output_format=export_format,
+ normalization_threshold=0.9,
+ sample_rate=sample_rate,
+ mdx_params={
+ "hop_length": hop_length,
+ "segment_size": segment_size,
+ "overlap": overlap,
+ "batch_size": batch_size,
+ "enable_denoise": enable_denoise
+ },
+ demucs_params={
+ "segment_size": segment_size,
+ "shifts": shifts,
+ "overlap": overlap,
+ "segments_enabled": config.configs.get("demucs_segments_enable", True)
+ },
+ vr_params={
+ "batch_size": batch_size,
+ "window_size": window_size,
+ "aggression": aggression,
+ "enable_tta": enable_tta,
+ "enable_post_process": enable_post_process,
+ "post_process_threshold": post_process_threshold,
+ "high_end_process": high_end_process
+ }
+ )
+ separator.load_model(model_filename=model_filename)
+
+ return separator.separate(audio_file)
+ except:
+ logger.debug(translations["default_setting"])
+ # Standardized robust fallback setup block to prevent hard script crashes
+
+ separator = Separator(
+ logger=logger,
+ output_dir=output_dir,
+ output_format=export_format,
+ normalization_threshold=0.9,
+ sample_rate=44100,
+ mdx_params={
+ "hop_length": 1024,
+ "segment_size": 256,
+ "overlap": 0.25,
+ "batch_size": 1,
+ "enable_denoise": enable_denoise
+ },
+ demucs_params={
+ "segment_size": 128,
+ "shifts": 2,
+ "overlap": 0.25,
+ "segments_enabled": config.configs.get("demucs_segments_enable", True)
+ },
+ vr_params={
+ "batch_size": 1,
+ "window_size": 512,
+ "aggression": 5,
+ "enable_tta": False,
+ "enable_post_process": False,
+ "post_process_threshold": 0.2,
+ "high_end_process": False
+ }
+ )
+ separator.load_model(model_filename=model_filename)
+
+ return separator.separate(audio_file)
+
+if __name__ == "__main__": main()
\ No newline at end of file
diff --git a/main/inference/training/data_utils.py b/main/inference/training/data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..39bcb967cc75d9225db77088e05359827dd02088
--- /dev/null
+++ b/main/inference/training/data_utils.py
@@ -0,0 +1,409 @@
+import os
+import sys
+import torch
+
+import numpy as np
+import torch.utils.data as tdata
+
+from torch.utils.data import DataLoader
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations
+from main.inference.training.mel_processing import spectrogram_torch
+from main.inference.training.utils import load_filepaths_and_text, load_wav_to_torch
+
+class TextAudioLoader(tdata.Dataset):
+ """
+ A PyTorch Dataset that loads audio filepaths, transcriptions, and optional structural
+ pitch labels for voice conversion training pipelines.
+ """
+
+ def __init__(
+ self,
+ hparams,
+ spec_dirs=None,
+ cache_spectrogram=True,
+ pitch_guidance=True,
+ ):
+ """
+ Initializes the dataset configuration and triggers file filtering.
+ """
+
+ self.audiopaths_and_text = load_filepaths_and_text(hparams.training_files)
+ self.max_wav_value = hparams.max_wav_value
+ self.filter_length = hparams.filter_length
+ self.sample_rate = hparams.sample_rate
+ self.hop_length = hparams.hop_length
+ self.win_length = hparams.win_length
+ self.max_text_len = getattr(hparams, "max_text_len", 5000)
+ self.min_text_len = getattr(hparams, "min_text_len", 1)
+ self.cache_spectrogram = cache_spectrogram
+ self.pitch_guidance = pitch_guidance
+ self.spec_dirs = spec_dirs
+ self._filter()
+
+ def _filter(self):
+ """
+ Filters out dataset entries that do not comply with sequence length bounds
+ and pre-computes rough estimate audio length dimensions for bucket sorting.
+ """
+
+ audiopaths_and_text_new, lengths = [], []
+
+ for item in self.audiopaths_and_text:
+ audiopath = item[0]
+ text = item[1]
+ # Validate structural bounds constraints on text token length
+ if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
+ audiopaths_and_text_new.append(item)
+ # Calculate estimated frame lengths based on raw disk file size
+ lengths.append(os.path.getsize(audiopath) // (3 * self.hop_length))
+
+ self.audiopaths_and_text = audiopaths_and_text_new
+ self.lengths = lengths
+
+ def get_sid(self, sid):
+ """
+ Parses and casts a speaker identifier securely into a target LongTensor block.
+
+ Args:
+ sid: Raw speaker identifier.
+
+ Returns:
+ torch.LongTensor: Long Tensor wrapped single-element indexing vector.
+ """
+
+ try:
+ sid = torch.LongTensor([int(sid)])
+ except ValueError:
+ sid = torch.LongTensor([0]) # Fallback defaults to speaker 0 on conversion errors
+
+ return sid
+
+ def get_audio_text_pair(self, audiopath_and_text):
+ """
+ Constructs a runtime training sample dictionary item tuple by unpacking path keys,
+ reading label buffers, and slicing timeline frame misalignments.
+
+ Args:
+ audiopath_and_text (List[str]): Metadata items line parsed from file manifests.
+
+ Returns:
+ Tuple[torch.Tensor, ...]: Packed feature elements for training steps.
+ """
+
+ extra = audiopath_and_text[2:]
+ pitch, pitchf, sid = None, None, None
+
+ if self.pitch_guidance: pitch, pitchf, sid = extra
+ else: sid = extra[0]
+
+ spec, wav = self.get_audio(audiopath_and_text[0])
+ dv = self.get_sid(sid)
+
+ phone, pitch, pitchf = self.get_labels(
+ audiopath_and_text[1],
+ pitch=pitch,
+ pitchf=pitchf
+ )
+
+ len_phone = phone.size()[0]
+ len_spec = spec.size()[-1]
+ # Enforce exact axis sequence alignment across features due to processing edge offsets
+ if len_phone != len_spec:
+ len_min = min(len_phone, len_spec)
+ len_wav = len_min * self.hop_length
+
+ spec, wav, phone = spec[:, :len_min], wav[:, :len_wav], phone[:len_min, :]
+ if self.pitch_guidance: pitch, pitchf = pitch[:len_min], pitchf[:len_min]
+
+ outputs = [spec, wav, phone, dv]
+ if self.pitch_guidance: outputs[3:3] = [pitch, pitchf]
+
+ return tuple(outputs)
+
+ def get_labels(self, phone, pitch=None, pitchf=None):
+ """
+ Loads pre-extracted phonetic hidden features and pitch configurations from numpy files.
+
+ Args:
+ phone (str): Filepath to phoneme representation array.
+ pitch (str, optional): Filepath to integer pitch arrays. Defaults to None.
+ pitchf (str, optional): Filepath to float continuous pitch arrays. Defaults to None.
+
+ Returns:
+ Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: Torched label features.
+ """
+
+ # Feature vectors are typically extracted at double hop sizes, upsample via repeating
+ phone = np.repeat(np.load(phone), 2, axis=0)
+ n_num = min(phone.shape[0], 900) # Hard cap sample sequence ceiling block constraints
+
+ return (
+ torch.FloatTensor(phone[:n_num, :]),
+ torch.LongTensor(np.load(pitch)[:n_num]) if pitch else None,
+ torch.FloatTensor(np.load(pitchf)[:n_num]) if pitchf else None
+ )
+
+ def get_audio(self, filename):
+ """
+ Loads a raw audio waveform and computes/loads its associated spectrogram.
+
+ Args:
+ filename (str): Audio source filename path.
+
+ Returns:
+ Tuple[torch.Tensor, torch.Tensor]: Squeezed Spectrogram and raw normalized waveform.
+ """
+
+ audio, sample_rate = load_wav_to_torch(filename)
+ if sample_rate != self.sample_rate:
+ raise ValueError(translations["sr_does_not_match"].format(sample_rate=sample_rate, sample_rate2=self.sample_rate))
+
+ audio_norm = audio.unsqueeze(0)
+ spec_filename = filename.replace(".wav", ".spec.pt")
+ if self.spec_dirs: spec_filename = os.path.join(self.spec_dirs, os.path.basename(spec_filename))
+
+ def get_spectrogram(audio_norm):
+ return spectrogram_torch(
+ audio_norm,
+ self.filter_length,
+ self.hop_length,
+ self.win_length,
+ center=False
+ ).squeeze(0)
+
+ # Handle persistent disk caching mechanics for spectrogram structures
+ if not self.cache_spectrogram:
+ spec = get_spectrogram(audio_norm)
+ elif os.path.exists(spec_filename):
+ try:
+ spec = torch.load(spec_filename, weights_only=True)
+ except Exception:
+ spec = get_spectrogram(audio_norm)
+ torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
+ else:
+ spec = get_spectrogram(audio_norm)
+ torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
+
+ return spec, audio_norm
+
+ def __getitem__(self, index):
+ return self.get_audio_text_pair(self.audiopaths_and_text[index])
+
+ def __len__(self):
+ return len(self.audiopaths_and_text)
+
+class TextAudioCollate:
+ """
+ Collate function to dynamic-pad and bundle variable-length sequence elements
+ into a uniformly padded tensor batch.
+ """
+
+ def __init__(self, return_ids=False, pitch_guidance=True):
+ self.return_ids = return_ids
+ self.pitch_guidance = pitch_guidance
+
+ def __call__(self, batch):
+ """
+ Sorts the batch by decreasing structural timeline frame lengths,
+ allocates padded tracking tensors, and performs linear target mask copy injections.
+ """
+
+ # Sort batch by decreasing sequence length to satisfy sequence padding constraints down the pipe
+ _, ids_sorted_decreasing = torch.sort(torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True)
+ spec_lengths, wave_lengths = torch.LongTensor(len(batch)), torch.LongTensor(len(batch))
+ spec_padded, wave_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max([x[0].size(1) for x in batch])), torch.FloatTensor(len(batch), 1, max([x[1].size(1) for x in batch]))
+ spec_padded.zero_()
+ wave_padded.zero_()
+
+ max_phone_len = max([x[2].size(0) for x in batch])
+ phone_lengths, phone_padded = torch.LongTensor(len(batch)), torch.FloatTensor(len(batch), max_phone_len, batch[0][2].shape[1])
+ phone_padded.zero_()
+
+ if self.pitch_guidance:
+ pitch_padded, pitchf_padded = torch.LongTensor(len(batch), max_phone_len), torch.FloatTensor(len(batch), max_phone_len)
+ pitch_padded.zero_()
+ pitchf_padded.zero_()
+
+ sid = torch.LongTensor(len(batch))
+ # Iteratively inject sample representations into allocating memory views
+ for i in range(len(ids_sorted_decreasing)):
+ row = batch[ids_sorted_decreasing[i]]
+ spec = row[0]
+
+ spec_padded[i, :, : spec.size(1)] = spec
+ spec_lengths[i] = spec.size(1)
+
+ wave = row[1]
+ wave_padded[i, :, : wave.size(1)] = wave
+ wave_lengths[i] = wave.size(1)
+
+ phone = row[2]
+ phone_padded[i, : phone.size(0), :] = phone
+ phone_lengths[i] = phone.size(0)
+
+ if self.pitch_guidance:
+ pitch = row[3]
+ pitch_padded[i, : pitch.size(0)] = pitch
+ pitchf = row[4]
+ pitchf_padded[i, : pitchf.size(0)] = pitchf
+
+ sid[i] = row[5 if self.pitch_guidance else 3]
+
+ outputs = [phone_padded, phone_lengths, spec_padded, spec_lengths, wave_padded, wave_lengths, sid]
+ if self.pitch_guidance: outputs[2:2] = [pitch_padded, pitchf_padded]
+
+ return tuple(outputs)
+
+class DistributedBucketSampler(tdata.distributed.DistributedSampler):
+ """
+ A distributed sampler that bundles together elements of similar length profiles
+ into variable sequence length clusters ("buckets") to reduce computing resource padding overheads.
+ """
+
+ def __init__(
+ self,
+ dataset,
+ batch_size,
+ boundaries,
+ num_replicas=None,
+ rank=None,
+ shuffle=True
+ ):
+ super().__init__(
+ dataset,
+ num_replicas=num_replicas,
+ rank=rank,
+ shuffle=shuffle
+ )
+ self.lengths = dataset.lengths
+ self.batch_size = batch_size
+ self.boundaries = boundaries
+ self.buckets, self.num_samples_per_bucket = self._create_buckets()
+ self.total_size = sum(self.num_samples_per_bucket)
+ self.num_samples = self.total_size // self.num_replicas
+
+ def _create_buckets(self):
+ """
+ Bins element indices based on historical duration bounds constraints.
+ """
+
+ buckets = [[] for _ in range(len(self.boundaries) - 1)]
+
+ for i in range(len(self.lengths)):
+ idx_bucket = self._bisect(self.lengths[i])
+ if idx_bucket != -1: buckets[idx_bucket].append(i)
+
+ # Drop empty buckets to avoid zero division step bugs
+ for i in range(len(buckets) - 1, -1, -1):
+ if len(buckets[i]) == 0:
+ buckets.pop(i)
+ self.boundaries.pop(i + 1)
+
+ num_samples_per_bucket = []
+
+ for i in range(len(buckets)):
+ len_bucket = len(buckets[i])
+ total_batch_size = self.num_replicas * self.batch_size
+
+ # Pad size calculation calculations to match exact distributed rank dimensions cleanly
+ num_samples_per_bucket.append(
+ len_bucket + ((total_batch_size - (len_bucket % total_batch_size)) % total_batch_size)
+ )
+
+ return buckets, num_samples_per_bucket
+
+ def __iter__(self):
+ g = torch.Generator()
+ g.manual_seed(self.epoch)
+ indices, batches = [], []
+
+ if self.shuffle:
+ for bucket in self.buckets:
+ indices.append(torch.randperm(len(bucket), generator=g).tolist())
+ else:
+ for bucket in self.buckets:
+ indices.append(list(range(len(bucket))))
+
+ for i in range(len(self.buckets)):
+ bucket = self.buckets[i]
+ len_bucket = len(bucket)
+ ids_bucket = indices[i]
+
+ # Balance lists by padding index loops uniformly where remaining values exist
+ rem = self.num_samples_per_bucket[i] - len_bucket
+ ids_bucket = (
+ ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[: (rem % len_bucket)]
+ )[self.rank :: self.num_replicas]
+
+ # Split up individual bucket entries into clean mini-batch size blocks
+ for j in range(len(ids_bucket) // self.batch_size):
+ batches.append([bucket[idx] for idx in ids_bucket[j * self.batch_size : (j + 1) * self.batch_size]])
+
+ # Shuffle the global batch presentation layout if required
+ if self.shuffle: batches = [batches[i] for i in torch.randperm(len(batches), generator=g).tolist()]
+ self.batches = batches
+ assert len(self.batches) * self.batch_size == self.num_samples
+
+ return iter(self.batches)
+
+ def _bisect(self, x, lo=0, hi=None):
+ """
+ Binary search structure lookup utility to trace matching bucket intervals.
+ """
+
+ if hi is None: hi = len(self.boundaries) - 1
+
+ if hi > lo:
+ mid = (hi + lo) // 2
+
+ if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]: return mid
+ elif x <= self.boundaries[mid]: return self._bisect(x, lo, mid)
+ else: return self._bisect(x, mid + 1, hi)
+ else: return -1
+
+ def __len__(self):
+ return self.num_samples // self.batch_size
+
+def get_training_dataloader(config, spec_dir, cache_spectrogram, pitch_guidance = True, architecture = "RVC", batch_size = 8, n_gpus = 1, rank = 0):
+ """
+ Factory helper constructor instantiated to provide configured data loading pipelines.
+
+ Args:
+ config (object): Hparams configuration wrapper namespace block.
+ spec_dir (str): Target folder path directory containing cached components.
+ cache_spectrogram (bool): Toggle option controlling disk write/read cycles.
+ pitch_guidance (bool): Toggle option tracking pitch lines. Defaults to True.
+ architecture (str): Model framework context variant. Defaults to "RVC".
+ batch_size (int): Mini-batch constraint count per device worker. Defaults to 8.
+ n_gpus (int): Total active computational processing nodes. Defaults to 1.
+ rank (int): Host identifier index reference tracking. Defaults to 0.
+
+ Returns:
+ DataLoader: Fully configured PyTorch data loader structure.
+ """
+
+ train_dataset = TextAudioLoader(config.data, spec_dirs=spec_dir, cache_spectrogram=cache_spectrogram, pitch_guidance=pitch_guidance)
+
+ train_loader = DataLoader(
+ train_dataset,
+ num_workers=4,
+ shuffle=False,
+ pin_memory=True,
+ batch_size=1 if architecture != "SVC" else batch_size,
+ collate_fn=TextAudioCollate(pitch_guidance=pitch_guidance),
+ batch_sampler=DistributedBucketSampler(
+ train_dataset,
+ batch_size,
+ [50, 100, 200, 300, 400, 500, 600, 700, 800, 900],
+ num_replicas=n_gpus,
+ rank=rank,
+ shuffle=True
+ ) if architecture != "SVC" else None,
+ persistent_workers=True,
+ prefetch_factor=8
+ )
+
+ return train_loader
\ No newline at end of file
diff --git a/main/inference/training/detector.py b/main/inference/training/detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6d92fb9df48f926474811f82f03530580b1bbc9
--- /dev/null
+++ b/main/inference/training/detector.py
@@ -0,0 +1,177 @@
+import json
+
+def continue_overtrain_detector(training_file_path):
+ """
+ Loads historical raw and smoothed loss metrics for both the generator
+ and discriminator from a JSON monitoring log file.
+
+ Args:
+ training_file_path (str): Path to the target JSON log file.
+
+ Returns:
+ Tuple[List[float], List[float], List[float], List[float]]:
+ - smoothed_loss_gen_history: Historical smoothed generator losses.
+ - loss_gen_history: Historical raw generator losses.
+ - loss_disc_history: Historical raw discriminator losses.
+ - smoothed_loss_disc_history: Historical smoothed discriminator losses.
+ """
+
+ with open(training_file_path, "r") as f:
+ data = json.load(f)
+
+ # Safely extract historical records defaulting to an empty list if not found
+ loss_disc_history, smoothed_loss_disc_history = data.get("loss_disc_history", []), data.get("smoothed_loss_disc_history", [])
+ loss_gen_history, smoothed_loss_gen_history = data.get("loss_gen_history", []), data.get("smoothed_loss_gen_history", [])
+
+ return smoothed_loss_gen_history, loss_gen_history, loss_disc_history, smoothed_loss_disc_history
+
+def check_overtraining(
+ smoothed_loss_history,
+ threshold,
+ epsilon=0.004
+):
+ """
+ Analyzes recent trends in smoothed loss history to flag potential overtraining.
+ Overtraining is identified if the error consistently rises or fails to decline
+ by a significant margin (epsilon) across a given step window (threshold).
+
+ Args:
+ smoothed_loss_history (List[float]): Cumulative list of smoothed loss values.
+ threshold (int): The number of recent trailing elements to verify.
+ epsilon (float, optional): Minimal threshold required to signify
+ legitimate descent. Defaults to 0.004.
+
+ Returns:
+ bool: True if overtraining characteristics are detected, False otherwise.
+ """
+
+ # Not enough data points gathered yet to safely evaluate the lookback window
+ if len(smoothed_loss_history) < threshold + 1: return False
+
+ # Loop backwards through the historical window slice
+ for i in range(-threshold, -1):
+ # Flag immediately if a newer value is strictly higher than the previous one
+ if smoothed_loss_history[i + 1] > smoothed_loss_history[i]:
+ return True
+
+ # If the loss successfully drops by a value >= epsilon, it's a good step
+ if abs(smoothed_loss_history[i + 1] - smoothed_loss_history[i]) >= epsilon:
+ return False
+
+ # True represents a stagnant state where fluctuations remained under epsilon bounds
+ return True
+
+def update_exponential_moving_average(
+ smoothed_loss_history,
+ new_value,
+ smoothing=0.987
+):
+ """
+ Applies and appends an Exponential Moving Average (EMA) smoothing step onto
+ a tracking history array list.
+
+ Args:
+ smoothed_loss_history (List[float]): Cumulative list of historical smoothed losses.
+ new_value (float): Incoming raw scalar value to smooth.
+ smoothing (float, optional): Weight coefficient factor for past data. Defaults to 0.987.
+
+ Returns:
+ float: The newly updated EMA smoothed value.
+ """
+
+ # Initialize using the raw value directly if this is the first item in the list
+ smoothed_value = (
+ new_value
+ if not smoothed_loss_history else
+ (smoothing * smoothed_loss_history[-1] + (1 - smoothing) * new_value)
+ )
+
+ smoothed_loss_history.append(smoothed_value)
+ return smoothed_value
+
+def save_to_json(
+ file_path,
+ loss_disc_history,
+ smoothed_loss_disc_history,
+ loss_gen_history,
+ smoothed_loss_gen_history
+):
+ """
+ Saves the training history arrays into an external JSON log file.
+
+ Args:
+ file_path (str): Destination path where the JSON file will be written.
+ loss_disc_history (List[float]): Raw discriminator losses.
+ smoothed_loss_disc_history (List[float]): Smoothed discriminator losses.
+ loss_gen_history (List[float]): Raw generator losses.
+ smoothed_loss_gen_history (List[float]): Smoothed generator losses.
+ """
+
+ with open(file_path, "w", encoding="utf-8") as f:
+ json.dump({
+ "loss_disc_history": loss_disc_history,
+ "smoothed_loss_disc_history": smoothed_loss_disc_history,
+ "loss_gen_history": loss_gen_history,
+ "smoothed_loss_gen_history": smoothed_loss_gen_history
+ }, f)
+
+def overtraining_detector(
+ smoothed_loss_disc_history,
+ current_loss_disc,
+ smoothed_loss_gen_history,
+ current_loss_gen,
+ overtraining_threshold,
+ consecutive_increases_disc,
+ consecutive_increases_gen
+):
+ """
+ Orchestrates the overtraining check routines for an active optimization step.
+ Smooths new metrics, executes trend checks, and updates anomaly counters.
+
+ Args:
+ smoothed_loss_disc_history (List[float]): Historical smoothed discriminator loss list.
+ current_loss_disc (float): Current step's raw discriminator loss.
+ smoothed_loss_gen_history (List[float]): Historical smoothed generator loss list.
+ current_loss_gen (float): Current step's raw generator loss.
+ overtraining_threshold (int): Base step lookback threshold length window.
+ consecutive_increases_disc (int): Current counter tracking consecutive overtraining anomalies in Discriminator.
+ consecutive_increases_gen (int): Current counter tracking consecutive overtraining anomalies in Generator.
+
+ Returns:
+ Tuple[float, float, bool, bool, int, int]:
+ - smoothed_value_disc: New EMA smoothed discriminator loss scalar.
+ - smoothed_value_gen: New EMA smoothed generator loss scalar.
+ - is_overtraining_disc: Flag checking if discriminator is overtraining.
+ - is_overtraining_gen: Flag checking if generator is overtraining.
+ - consecutive_increases_disc: Updated consecutive counter tracking for discriminator.
+ - consecutive_increases_gen: Updated consecutive counter tracking for generator.
+ """
+
+ # 1. Update Exponential Moving Averages
+ smoothed_value_disc = update_exponential_moving_average(
+ smoothed_loss_disc_history,
+ current_loss_disc
+ )
+
+ smoothed_value_gen = update_exponential_moving_average(
+ smoothed_loss_gen_history,
+ current_loss_gen
+ )
+
+ # 2. Check for overtraining signatures over specified lookback windows
+ is_overtraining_disc = check_overtraining(
+ smoothed_loss_disc_history,
+ overtraining_threshold * 2
+ )
+
+ is_overtraining_gen = check_overtraining(
+ smoothed_loss_gen_history,
+ overtraining_threshold,
+ 0.01
+ )
+
+ # 3. Update or reset the consecutive anomaly counters based on current step evaluation flags
+ consecutive_increases_disc = (consecutive_increases_disc + 1) if is_overtraining_disc else 0
+ consecutive_increases_gen = (consecutive_increases_gen + 1) if is_overtraining_gen else 0
+
+ return smoothed_value_disc, smoothed_value_gen, is_overtraining_disc, is_overtraining_gen, consecutive_increases_disc, consecutive_increases_gen
\ No newline at end of file
diff --git a/main/inference/training/extract_model.py b/main/inference/training/extract_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..89f915fa243c6ca72c15bcdede79c35e6aa869a6
--- /dev/null
+++ b/main/inference/training/extract_model.py
@@ -0,0 +1,118 @@
+import os
+import sys
+import torch
+import hashlib
+import datetime
+
+from collections import OrderedDict
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import logger, translations, config
+from main.inference.training.utils import replace_keys_in_dict
+
+def extract_model(
+ ckpt,
+ sr,
+ pitch_guidance,
+ name,
+ model_path,
+ epoch,
+ step,
+ version,
+ hps,
+ model_author,
+ vocoder,
+ speakers_id,
+ architecture
+):
+ """
+ Extracts the inference-ready weights from a raw training checkpoint, discards
+ unnecessary training blocks (like the posterior encoder `enc_q`), packages structural
+ hyperparameters/metadata, and saves the standalone weights to a targeted disk path.
+
+ Args:
+ ckpt (Dict[str, torch.Tensor]): The raw model state dictionary from training.
+ sr (int): Target audio sampling rate configuration.
+ pitch_guidance (bool): Whether the model uses fundamental frequency (F0) tracking.
+ name (str): The logical name assigned to the model.
+ model_path (str): File destination path to save the compiled model weight file.
+ epoch (int): Total count of epochs processed up to this point.
+ step (int): Total global optimization steps performed.
+ version (str): Model architecture variant or framework version tag.
+ hps (object): Hyperparameters namespace object holding configuration values.
+ model_author (str): The name or tag of the model creator/trainer.
+ vocoder (str): Type of neural vocoder used for synthesis (e.g., HiFi-GAN).
+ speakers_id (int): Dimensional context identifier linked to speaker indexing.
+ architecture (str): Target model core neural network style or configuration flavor.
+ """
+
+ try:
+ logger.info(translations["savemodel"].format(model_dir=model_path, epoch=epoch, step=step))
+ # Ensure the destination parent folders exist
+ os.makedirs(os.path.dirname(model_path), exist_ok=True)
+
+ # Initialize ordered tracking; strip out 'enc_q' since the posterior encoder is only needed for training
+ opt = OrderedDict(
+ weight={
+ key: (
+ value if not config.device.startswith("privateuseone") else value.detach().cpu()
+ ).to(torch.float16 if config.is_half else torch.float32)
+ for key, value in ckpt.items()
+ if "enc_q" not in key
+ }
+ )
+
+ # Pack internal structural network dimensions needed for instant architecture reconstruction
+ opt["config"] = [
+ hps.data.filter_length // 2 + 1,
+ hps.train.segment_size // hps.data.hop_length,
+ hps.model.inter_channels,
+ hps.model.hidden_channels,
+ hps.model.filter_channels,
+ hps.model.n_heads,
+ hps.model.n_layers,
+ hps.model.kernel_size,
+ hps.model.p_dropout,
+ hps.model.resblock,
+ hps.model.resblock_kernel_sizes,
+ hps.model.resblock_dilation_sizes,
+ hps.model.upsample_rates,
+ hps.model.upsample_initial_channel,
+ hps.model.upsample_kernel_sizes,
+ hps.model.spk_embed_dim,
+ hps.model.gin_channels,
+ hps.data.sample_rate
+ ]
+
+ # Inject standard model parameters and historical run tracking tags
+ opt["epoch"] = f"{epoch}epoch"
+ opt["step"] = step
+ opt["sr"] = sr
+ opt["f0"] = int(pitch_guidance)
+ opt["version"] = version
+ opt["creation_date"] = datetime.datetime.now().isoformat()
+ # Create a unique SHA256 integrity hash footprint string bound to this extraction step
+ opt["model_hash"] = hashlib.sha256(f"{str(ckpt)} {epoch} {step} {datetime.datetime.now().isoformat()}".encode()).hexdigest()
+ # Append identifying administrative metadata parameters
+ opt["model_name"] = name
+ opt["author"] = model_author
+ opt["vocoder"] = vocoder
+ opt["speakers_id"] = speakers_id
+ opt["architecture"] = architecture
+
+ # Convert parametrization weight paths back to legacy formats for backward compatibility and save
+ torch.save(
+ replace_keys_in_dict(
+ replace_keys_in_dict(
+ opt,
+ ".parametrizations.weight.original1",
+ ".weight_v"
+ ),
+ ".parametrizations.weight.original0", ".weight_g"
+ ),
+ model_path
+ )
+ except Exception as e:
+ # Catch and report formatting or write exceptions through the centralized logging module
+ logger.error(f"{translations['extract_model_error']}: {e}")
\ No newline at end of file
diff --git a/main/inference/training/losses.py b/main/inference/training/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..0174d40f9f702f2cfc56d4bcca1c07ec57131f5c
--- /dev/null
+++ b/main/inference/training/losses.py
@@ -0,0 +1,145 @@
+def feature_loss(fmap_r, fmap_g):
+ """
+ Computes the Discriminator Feature Matching Loss between real and generated feature maps.
+ This helps the generator learn the intermediate representations captured by the discriminator.
+
+ Args:
+ fmap_r (List[List[torch.Tensor]]): Feature maps extracted from real samples across discriminator layers.
+ fmap_g (List[List[torch.Tensor]]): Feature maps extracted from generated samples across discriminator layers.
+
+ Returns:
+ torch.Tensor: Normalized feature matching loss scaled by a factor of 2.
+ """
+
+ loss = 0
+ # Iterate through each sub-discriminator's feature maps (e.g., Multi-Period / Multi-Scale)
+ for dr, dg in zip(fmap_r, fmap_g):
+ # Iterate through the feature outputs of individual layers
+ for rl, gl in zip(dr, dg):
+ # Detach real features since we only optimize the generator during this step
+ loss += (
+ rl.float().detach() - gl.float()
+ ).abs().mean()
+
+ return loss * 2
+
+def discriminator_loss(disc_real_outputs, disc_generated_outputs):
+ """
+ Computes the Least Squares GAN (LSGAN) loss for the Discriminator network.
+
+ Args:
+ disc_real_outputs (List[torch.Tensor]): Discriminator outputs for real samples.
+ disc_generated_outputs (List[torch.Tensor]): Discriminator outputs for generated samples.
+
+ Returns:
+ Tuple[torch.Tensor, List[float], List[float]]:
+ - Total combined discriminator loss tensor.
+ - List of itemized loss values for real batches.
+ - List of itemized loss values for generated batches.
+ """
+
+ loss = 0
+ r_losses, g_losses = [], []
+
+ # Calculate adversarial errors across all sub-discriminators
+ for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+ dr = dr.float()
+ dg = dg.float()
+
+ r_loss = ((1 - dr) ** 2).mean()
+ g_loss = (dg**2).mean()
+
+ loss += r_loss + g_loss
+ # Collect scalar copies for metrics logging
+ r_losses.append(r_loss.item())
+ g_losses.append(g_loss.item())
+
+ return loss, r_losses, g_losses
+
+def generator_loss(disc_outputs):
+ """
+ Computes the Least Squares GAN (LSGAN) adversarial loss for the Generator network.
+
+ Args:
+ disc_outputs (List[torch.Tensor]): Discriminator outputs for generated samples.
+
+ Returns:
+ Tuple[torch.Tensor, List[torch.Tensor]]:
+ - Total aggregate adversarial generator loss tensor.
+ - List containing unaggregated loss tensors from each sub-discriminator.
+ """
+
+ loss = 0
+ gen_losses = []
+
+ for dg in disc_outputs:
+ l = ((1 - dg.float()) ** 2).mean()
+ gen_losses.append(l)
+ loss += l
+
+ return loss, gen_losses
+
+def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
+ """
+ Computes the Kullback-Leibler (KL) Divergence loss between the posterior
+ distribution (from the encoder) and prior distribution (from the text/phoneme hidden states).
+
+ Args:
+ z_p (torch.Tensor): Latent representation sampled/projected from the posterior.
+ logs_q (torch.Tensor): Log variance of the posterior distribution.
+ m_p (torch.Tensor): Mean of the prior distribution.
+ logs_p (torch.Tensor): Log variance of the prior distribution.
+ z_mask (torch.Tensor): Sequence length padding mask tensor (1s for valid frames, 0s for padding).
+
+ Returns:
+ torch.Tensor: Mask-normalized scalar KL loss tensor.
+ """
+
+ # DirectML does not support training well, and this method requires switching to the CPU.
+ if z_p.device.type == "privateuseone":
+ return kl_loss_cpu(z_p, logs_q, m_p, logs_p, z_mask)
+
+ # Cast to float32 for high numerical precision stability
+ z_p = z_p.float()
+ logs_q = logs_q.float()
+ m_p = m_p.float()
+ logs_p = logs_p.float()
+ z_mask = z_mask.float()
+
+ # Closed-form diagonal Gaussian KL evaluation
+ kl = logs_p - logs_q - 0.5
+ kl += 0.5 * ((z_p - m_p) ** 2) * (-2.0 * logs_p).exp()
+
+ # Zero out padded boundaries and return average over unpadded tokens
+ return (kl * z_mask).sum() / z_mask.sum()
+
+def kl_loss_cpu(z_p, logs_q, m_p, logs_p, z_mask):
+ """
+ A fallback implementation of the KL Divergence loss executed entirely on the CPU.
+ Prevents out-of-memory or operation compatibility exceptions on non-standard device extensions.
+
+ Args:
+ z_p (torch.Tensor): Latent representation.
+ logs_q (torch.Tensor): Log variance of the posterior.
+ m_p (torch.Tensor): Mean of the prior.
+ logs_p (torch.Tensor): Log variance of the prior.
+ z_mask (torch.Tensor): Sequence length padding mask.
+
+ Returns:
+ torch.Tensor: Mask-normalized scalar KL loss mapped back to the original device.
+ """
+
+ orig_device = z_p.device
+ # Detach and cast to CPU to perform stable computation step
+ z_p = z_p.detach().cpu().float()
+ logs_q = logs_q.detach().cpu().float()
+ m_p = m_p.detach().cpu().float()
+ logs_p = logs_p.detach().cpu().float()
+ z_mask = z_mask.detach().cpu().float()
+
+ # Closed-form diagonal Gaussian KL evaluation
+ kl = logs_p - logs_q - 0.5
+ kl += 0.5 * ((z_p - m_p) ** 2) * (-2.0 * logs_p).exp()
+
+ # Return structural reduction result restored back onto the backing hardware device context
+ return ((kl * z_mask).sum() / z_mask.sum()).to(orig_device)
\ No newline at end of file
diff --git a/main/inference/training/mel_processing.py b/main/inference/training/mel_processing.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0aedbde3d1de6e3e9cf8e07a2a3133d962101c4
--- /dev/null
+++ b/main/inference/training/mel_processing.py
@@ -0,0 +1,275 @@
+import os
+import sys
+import torch
+
+sys.path.append(os.getcwd())
+
+from main.library.audio.features import mel
+
+def spectral_normalize_torch(magnitudes):
+ """
+ Applies natural log spectral normalization to a magnitude spectrogram.
+
+ Args:
+ magnitudes (torch.Tensor): Input magnitude spectrogram.
+
+ Returns:
+ torch.Tensor: Log-scaled normalized spectrogram.
+ """
+
+ # Clamp to prevent log(0) and safely compute natural log
+ return (magnitudes.clamp(min=1e-5) * 1.0).log()
+
+def spectral_de_normalize_torch(magnitudes):
+ """
+ Inverts the natural log spectral normalization back to standard magnitudes.
+
+ Args:
+ magnitudes (torch.Tensor): Log-scaled normalized spectrogram.
+
+ Returns:
+ torch.Tensor: Denormalized magnitude spectrogram.
+ """
+
+ return magnitudes.exp() / 1.0
+
+# Cache variables for persistent structures
+stft = None
+mel_basis, hann_window = {}, {}
+
+def spectrogram_torch(
+ y,
+ n_fft,
+ hop_size,
+ win_size,
+ center=False
+):
+ """
+ Computes the linear-frequency magnitude spectrogram from a waveform tensor using STFT.
+
+ Args:
+ y (torch.Tensor): Input audio waveform tensor.
+ n_fft (int): Size of Fourier transform.
+ hop_size (int): Length of hop between STFT windows.
+ win_size (int): Size of the windowing function.
+ center (bool, optional): Whether to center the waveform frames. Defaults to False.
+
+ Returns:
+ torch.Tensor: Magnitude spectrogram on the target audio device.
+ """
+
+ global hann_window, stft
+
+ # Create a unique tracking key for the window cache
+ wnsize_dtype_device = str(win_size) + "_" + str(y.dtype) + "_" + str(y.device)
+ if wnsize_dtype_device not in hann_window:
+ hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+ # Reflectively pad the waveform to handle edge boundaries manually
+ pad = torch.nn.functional.pad(
+ y.unsqueeze(1),
+ (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+ mode="reflect"
+ ).squeeze(1)
+
+ # Use a custom STFT class if running on alternative acceleration backends (OpenCL/PrivateUse1)
+ if y.device.type.startswith(("ocl", "privateuseone")):
+ if stft is None:
+ from main.library.backends.utils import STFT
+
+ stft = STFT(
+ filter_length=n_fft,
+ hop_length=hop_size,
+ win_length=n_fft
+ ).to(y.device)
+
+ spec = stft.transform(
+ pad.to(y.device),
+ eps=1e-6,
+ center=center
+ )
+ else:
+ # Fallback to the standard native PyTorch execution branch
+ spec = torch.stft(
+ pad,
+ n_fft,
+ hop_length=hop_size,
+ win_length=win_size,
+ window=hann_window[wnsize_dtype_device].to(pad.device),
+ center=center,
+ pad_mode="reflect",
+ normalized=False,
+ onesided=True,
+ return_complex=True
+ )
+
+ # Convert complex output to absolute magnitude safely
+ spec = spec.abs().clamp_min_(1e-6)
+
+ return spec.to(y.device)
+
+def spec_to_mel_torch(spec, config):
+ """
+ Projects a linear magnitude spectrogram onto the Mel scale and applies log normalization.
+
+ Args:
+ spec (torch.Tensor): Linear frequency magnitude spectrogram.
+ config: Configuration namespace object containing Mel data attributes.
+
+ Returns:
+ torch.Tensor: Log-Mel spectrogram.
+ """
+
+ global mel_basis
+
+ fmax = config.data.mel_fmax
+ fmax_dtype_device = str(fmax) + "_" + str(spec.dtype) + "_" + str(spec.device)
+ # Initialize and cache the Mel transformation filter matrix if not present
+ if fmax_dtype_device not in mel_basis:
+ mel_basis[fmax_dtype_device] = mel(
+ sr=config.data.sample_rate,
+ n_fft=config.data.filter_length,
+ n_mels=config.data.n_mel_channels,
+ fmin=config.data.mel_fmin,
+ fmax=fmax,
+ dtype=spec.dtype,
+ device=spec.device
+ )
+
+ # Apply the Mel filter bank matrix multiplication and normalize
+ return spectral_normalize_torch(mel_basis[fmax_dtype_device] @ spec)
+
+def mel_spectrogram_torch(y, config, center=False):
+ """
+ Wrapper function converting a raw audio waveform directly into a log-Mel spectrogram.
+
+ Args:
+ y (torch.Tensor): Raw audio waveform.
+ config: Configuration namespace object.
+ center (bool, optional): Whether to center frames during STFT. Defaults to False.
+
+ Returns:
+ torch.Tensor: Log-Mel spectrogram tensor.
+ """
+
+ # Note: Passed positional arguments to spec_to_mel_torch match your layout
+ # but verify config-dependent calls if positional unpacking errors occur.
+ return spec_to_mel_torch(
+ spectrogram_torch(
+ y,
+ config.data.filter_length,
+ config.data.hop_length,
+ config.data.win_length,
+ center
+ ),
+ config
+ )
+
+class MultiScaleMelSpectrogramLoss(torch.nn.Module):
+ """
+ Computes Multi-Scale Mel-Spectrogram Loss between reconstructed and target waveforms
+ across various STFT window and filter scales to capture audio attributes effectively.
+ """
+
+ def __init__(
+ self,
+ sample_rate = 24000,
+ n_mels=[5, 10, 20, 40, 80, 160, 320],
+ window_lengths=[32, 64, 128, 256, 512, 1024, 2048],
+ loss_fn=torch.nn.L1Loss()
+ ):
+ """
+ Initializes multi-scale parameters and storage dictionary banks.
+ """
+
+ super().__init__()
+ self.sample_rate = sample_rate
+ self.loss_fn = loss_fn
+ self.log_base = torch.tensor(10.0).log() # Cache log(10) for base-10 conversion
+ self.stft_params = []
+ self.hann_window = {}
+ self.mel_banks = {}
+ # Combine parameters into paired tuples for the evaluation scale loops
+ self.stft_params = [(mel, win) for mel, win in zip(n_mels, window_lengths)]
+
+ def mel_spectrogram(self, wav, n_mels, window_length):
+ """
+ Extracts a specific scale Mel-spectrogram for multi-scale loss evaluations.
+
+ Args:
+ wav (torch.Tensor): Audio waveform tensor.
+ n_mels (int): Target count of mel channels.
+ window_length (int): Frame length window constraint.
+
+ Returns:
+ torch.Tensor: Linear scale mel spectrogram.
+ """
+
+ dtype_device = str(wav.dtype) + "_" + str(wav.device)
+ win_dtype_device = str(window_length) + "_" + dtype_device
+ mel_dtype_device = str(n_mels) + "_" + dtype_device
+
+ # Initialize and track window vectors per device constraint
+ if win_dtype_device not in self.hann_window:
+ self.hann_window[win_dtype_device] = torch.hann_window(window_length, device=wav.device, dtype=torch.float32)
+
+ # Ensure correct channel squeezing configuration shapes
+ wav = wav.float().squeeze(1)
+ # Offload windowing evaluations to CPU if working under alternative backends
+ if wav.device.type.startswith(("ocl", "privateuseone")):
+ stft = torch.stft(
+ wav.cpu(),
+ n_fft=window_length,
+ hop_length=window_length // 4,
+ window=self.hann_window[win_dtype_device].cpu(),
+ return_complex=True
+ )
+
+ magnitude = stft.abs().clamp_min_(1e-6).to(wav.device, dtype=torch.float32)
+ else:
+ stft = torch.stft(
+ wav,
+ n_fft=window_length,
+ hop_length=window_length // 4,
+ window=self.hann_window[win_dtype_device],
+ return_complex=True
+ )
+
+ magnitude = stft.abs().clamp_min_(1e-6)
+
+ # Lazily instantiate the corresponding scale Mel filter bank
+ if mel_dtype_device not in self.mel_banks:
+ self.mel_banks[mel_dtype_device] = mel(
+ sr=self.sample_rate,
+ n_mels=n_mels,
+ n_fft=window_length,
+ fmin=0,
+ fmax=None,
+ device=wav.device,
+ dtype=torch.float32
+ )
+
+ return self.mel_banks[mel_dtype_device] @ magnitude
+
+ def forward(self, real, fake):
+ """
+ Computes the aggregate multi-scale loss metric sum.
+
+ Args:
+ real (torch.Tensor): Ground-truth target audio waveforms.
+ fake (torch.Tensor): Synthesized generated model audio waveforms.
+
+ Returns:
+ torch.Tensor: Total computed L1/distance error metric scalar.
+ """
+
+ loss = 0.0
+ # Loop through each scale parameter bundle and calculate individual loss terms
+ for p in self.stft_params:
+ # Clamp and convert values from natural log to log10 representations
+ loss += self.loss_fn(
+ self.mel_spectrogram(real, *p).clamp(min=1e-5).log() / self.log_base,
+ self.mel_spectrogram(fake, *p).clamp(min=1e-5).log() / self.log_base
+ )
+
+ return loss
\ No newline at end of file
diff --git a/main/inference/training/train.py b/main/inference/training/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..58b9870dbc0b1f00c490f36f6f51636d7bf11683
--- /dev/null
+++ b/main/inference/training/train.py
@@ -0,0 +1,776 @@
+import os
+import sys
+import glob
+import json
+import torch
+import logging
+import argparse
+import datetime
+import warnings
+
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+from tqdm import tqdm
+from random import randint
+from collections import deque
+from contextlib import nullcontext
+from torch.amp import GradScaler, autocast
+from torch.utils.tensorboard import SummaryWriter
+
+from time import time as ttime
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+sys.path.append(os.getcwd())
+
+# Optimize multi-threading backends based on operating system
+# Use standard system threads (libuv) on POSIX/Linux, fall back to native threads on Windows
+os.environ["USE_LIBUV"] = "0" if sys.platform == "win32" else "1"
+
+from main.library.backends import opencl, xpu
+from main.app.variables import logger, translations
+from main.library.utils import clear_gpu_cache, strtobool
+from main.inference.training.extract_model import extract_model
+from main.inference.training.data_utils import get_training_dataloader
+from main.inference.training.mel_processing import MultiScaleMelSpectrogramLoss, mel_spectrogram_torch, spec_to_mel_torch
+
+from main.library.algorithm import commons
+from main.inference.training import utils, losses, detector
+
+from main.app.variables import config as main_config
+from main.app.variables import configs as main_configs
+
+if not main_config.debug_mode:
+ warnings.filterwarnings("ignore")
+ logging.getLogger("torch").setLevel(logging.ERROR)
+
+def parse_arguments():
+ """
+ Parses CLI configuration parameters for the training workspace, hardware routing,
+ loss function adjustments, and model architecture definitions.
+
+ Returns:
+ argparse.Namespace: Validated command-line argument object.
+ """
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--train", action='store_true')
+ parser.add_argument("--model_name", type=str, required=True)
+ parser.add_argument("--rvc_version", type=str, default="v2")
+ parser.add_argument("--save_every_epoch", type=int, required=True)
+ parser.add_argument("--save_only_latest", type=lambda x: bool(strtobool(x)), default=True)
+ parser.add_argument("--save_every_weights", type=lambda x: bool(strtobool(x)), default=True)
+ parser.add_argument("--total_epoch", type=int, default=300)
+ parser.add_argument("--batch_size", type=int, default=8)
+ parser.add_argument("--gpu", type=str, default="0")
+ parser.add_argument("--pitch_guidance", type=lambda x: bool(strtobool(x)), default=True)
+ parser.add_argument("--g_pretrained_path", type=str, default="")
+ parser.add_argument("--d_pretrained_path", type=str, default="")
+ parser.add_argument("--overtraining_detector", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--overtraining_threshold", type=int, default=50)
+ parser.add_argument("--cleanup", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--cache_data_in_gpu", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--model_author", type=str)
+ parser.add_argument("--vocoder", type=str, default="Default")
+ parser.add_argument("--checkpointing", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--deterministic", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--benchmark", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--optimizer", type=str, default="AdamW")
+ parser.add_argument("--use_custom_reference", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--reference_path", type=str, default="")
+ parser.add_argument("--multiscale_mel_loss", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--use_cosine_annealing_lr", type=lambda x: bool(strtobool(x)), default=False)
+ parser.add_argument("--architecture", type=str, default="RVC")
+ parser.add_argument("--filelist_path", type=str, default="")
+ parser.add_argument("--config_save_path", type=str, default="")
+ parser.add_argument("--spec_dir", type=str, default="")
+ parser.add_argument("--eval_dir", type=str, default="")
+ parser.add_argument("--cache_spectrogram", type=lambda x: bool(strtobool(x)), default=True)
+ parser.add_argument("--save_the_pid", type=lambda x: bool(strtobool(x)), default=True)
+ parser.add_argument("--custom_training", type=lambda x: bool(strtobool(x)), default=False)
+
+ return parser.parse_args()
+
+# Global argument instantiation and tuple unpacking
+args = parse_arguments()
+
+(
+ model_name,
+ save_every_epoch,
+ total_epoch,
+ pretrainG,
+ pretrainD,
+ version,
+ gpus,
+ batch_size,
+ pitch_guidance,
+ save_only_latest,
+ save_every_weights,
+ cache_data_in_gpu,
+ overtraining_detector,
+ overtraining_threshold,
+ cleanup,
+ model_author,
+ vocoder,
+ checkpointing,
+ optimizer_choice,
+ use_custom_reference,
+ reference_path,
+ multiscale_mel_loss,
+ use_cosine_annealing_lr,
+ architecture,
+ filelist_path,
+ config_save_path,
+ spec_dir,
+ eval_dir,
+ cache_spectrogram,
+ save_the_pid,
+ custom_training
+) = (
+ args.model_name,
+ args.save_every_epoch,
+ args.total_epoch,
+ args.g_pretrained_path,
+ args.d_pretrained_path,
+ args.rvc_version,
+ args.gpu,
+ args.batch_size,
+ args.pitch_guidance,
+ args.save_only_latest,
+ args.save_every_weights,
+ args.cache_data_in_gpu,
+ args.overtraining_detector,
+ args.overtraining_threshold,
+ args.cleanup,
+ args.model_author,
+ args.vocoder,
+ args.checkpointing,
+ args.optimizer,
+ args.use_custom_reference,
+ args.reference_path,
+ args.multiscale_mel_loss,
+ args.use_cosine_annealing_lr,
+ args.architecture,
+ args.filelist_path,
+ args.config_save_path,
+ args.spec_dir,
+ args.eval_dir,
+ args.cache_spectrogram,
+ args.save_the_pid,
+ args.custom_training
+)
+
+# Determine global mixed precision strategies
+is_half = main_config.is_half
+if main_config.brain: is_half = True # Enforce mixed precision flags automatically if BF16 acceleration is available
+
+# Setup appropriate discriminator target versions based on the chosen architecture and synthesis tools
+disc_version = version if vocoder not in ["RefineGAN", "BigVGAN"] else "v3"
+
+if architecture == "SVC":
+ disc_version = version if vocoder != "Default" else "v0"
+ pitch_guidance = True # SVC processes always depend on explicit source audio pitch tracking patterns
+
+weights_path = main_configs["weights_path"]
+logs_path = main_configs["logs_path"]
+custom_save_checkpoint_path = None
+
+# Configure file storage contexts. If target identity points to an isolated model name, construct logs path.
+if not os.path.exists(model_name): experiment_dir = os.path.join(logs_path, model_name)
+else:
+ # Explicit absolute workspace paths detected
+ experiment_dir = model_name
+ model_name = os.path.basename(model_name)
+ custom_save_checkpoint_path = weights_path
+
+# Bind execution file structures based on routing priorities
+training_file_path = os.path.join(experiment_dir, "training_data.json")
+checkpoint_path = experiment_dir if custom_save_checkpoint_path is None else custom_save_checkpoint_path
+config_save_path = config_save_path if custom_training else os.path.join(experiment_dir, "config.json")
+filelist_path = filelist_path if custom_training else os.path.join(experiment_dir, "filelist.txt")
+eval_dir = eval_dir if custom_training else os.path.join(experiment_dir, "eval")
+spec_dir = spec_dir if custom_training else None
+
+# Gradient balancing and norm calculation constants
+d_lr_coeff = 1.0
+g_lr_coeff = 1.0
+d_step_per_g_step = 1
+use_clip_grad_value = False
+grad_norm_optim = commons.clip_grad_value if use_clip_grad_value else commons.grad_norm
+
+# Enable hardware acceleration optimizations depending on backend availability
+backend_supported = not main_config.device.startswith(("ocl", "privateuseone")) and not main_config.is_zluda
+torch.backends.cudnn.deterministic = args.deterministic if backend_supported else False
+torch.backends.cudnn.benchmark = args.benchmark if backend_supported else False
+torch.backends.cuda.matmul.allow_tf32 = main_config.tf32 if backend_supported else False
+torch.backends.cudnn.allow_tf32 = main_config.tf32 if backend_supported else False
+
+# Tracker initialization parameters
+global_step = 0
+lowest_value = {"step": 0, "value": float("inf"), "epoch": 0}
+loss_gen_history, smoothed_loss_gen_history, loss_disc_history, smoothed_loss_disc_history = [], [], [], []
+
+# Use historical deques to safely track rolling metric configurations
+avg_losses = {
+ "grad_d_50": deque(maxlen=50),
+ "grad_g_50": deque(maxlen=50),
+ "disc_loss_50": deque(maxlen=50),
+ "adv_loss_50": deque(maxlen=50),
+ "fm_loss_50": deque(maxlen=50),
+ "kl_loss_50": deque(maxlen=50),
+ "mel_loss_50": deque(maxlen=50),
+ "gen_loss_50": deque(maxlen=50)
+}
+
+# Load hyperparameters from file configuration profiles
+with open(config_save_path, "r", encoding="utf-8") as f:
+ config = json.load(f)
+
+config = utils.HParams(**config)
+config.data.training_files = filelist_path
+
+def main():
+ """
+ Orchestrates infrastructure verification, cross-process messaging networks (DDP),
+ dataset sample rate validation, and launches parallel sub-process pools.
+ """
+
+ global smoothed_loss_gen_history, loss_gen_history, loss_disc_history, smoothed_loss_disc_history, gpus
+
+ # Formulate log dictionary mapped against localized string definitions
+ log_data = {
+ translations["modelname"]: model_name,
+ translations["save_every_epoch"]: save_every_epoch,
+ translations["total_e"]: total_epoch,
+ translations["dorg"].format(pretrainG=pretrainG, pretrainD=pretrainD): "",
+ translations["training_version"]: version,
+ "Gpu": gpus,
+ translations["batch_size"]: batch_size,
+ translations["training_f0"]: pitch_guidance,
+ translations["save_only_latest"]: save_only_latest,
+ translations["save_every_weights"]: save_every_weights,
+ translations["cache_in_gpu"]: cache_data_in_gpu,
+ translations["overtraining_detector"]: overtraining_detector,
+ translations["threshold"]: overtraining_threshold,
+ translations["cleanup_training"]: cleanup,
+ translations["memory_efficient_training"]: checkpointing,
+ translations["optimizer"]: optimizer_choice,
+ translations["multiscale_mel_loss"]: multiscale_mel_loss,
+ translations["model_author"].format(model_author=model_author): "",
+ translations["vocoder"]: vocoder,
+ translations["cosine_annealing_lr"]: use_cosine_annealing_lr,
+ translations["architecture"]: architecture
+ }
+
+ for key, value in log_data.items():
+ logger.debug(f"{key}: {value}" if value != "" else f"{key} {value}")
+
+ try:
+ # Establish localized networking values for multi-GPU process communication
+ os.environ["MASTER_ADDR"] = "localhost"
+ os.environ["MASTER_PORT"] = str(randint(20000, 55555))
+
+ # Perform critical verification on dataset sample rate mappings
+ wavs = glob.glob(os.path.join(os.path.join(experiment_dir, "sliced_audios"), "*.wav"))
+ if wavs:
+ _, sr = utils.load_wav_to_torch(wavs[0])
+ if sr != config.data.sample_rate:
+ logger.warning(translations["training_sr"].format(sr_1=config.data.sample_rate, sr_2=sr))
+ sys.exit(1)
+ else:
+ logger.warning(translations["not_found_dataset"])
+ sys.exit(1)
+
+ # Map active runtime hardware device profiles
+ device, gpus, n_gpus = utils.get_device(gpus)
+ logger.info(translations["use_precision"].format(fp=("BF16" if main_config.brain else "FP16") if is_half else "FP32"))
+
+ # Clear historical states if requested
+ if cleanup: utils.cleanup_training(experiment_dir)
+ # Restore rolling historical states for early stopping evaluation
+ if overtraining_detector and os.path.exists(training_file_path): smoothed_loss_gen_history, loss_gen_history, loss_disc_history, smoothed_loss_disc_history = detector.continue_overtrain_detector(training_file_path)
+
+ def start():
+ """Spawns parallel training processes across designated target hardware devices."""
+
+ children = []
+ pid_data = {"process_pids": []}
+ # Retrieve existing structural configurations if tracking execution PIDs
+ if save_the_pid:
+ with open(config_save_path, "r", encoding="utf-8") as f:
+ try:
+ pid_data.update(json.load(f))
+ except json.JSONDecodeError:
+ pass
+
+ # Fork individual sub-processes allocated across independent devices
+ for rank, device_id in enumerate(gpus):
+ subproc = mp.Process(target=run, args=(rank, n_gpus, pretrainG, pretrainD, pitch_guidance, total_epoch, save_every_weights, config, device, device_id, vocoder, checkpointing))
+ children.append(subproc)
+ subproc.start()
+ pid_data["process_pids"].append(subproc.pid)
+
+ # Update the configuration file with the active process IDs
+ if save_the_pid:
+ with open(config_save_path, "w", encoding="utf-8") as f:
+ json.dump(pid_data, f, indent=4)
+
+ # Synchronize threads by waiting for all child processes to complete
+ for i in range(n_gpus):
+ children[i].join()
+
+ start()
+ except Exception as e:
+ logger.error(f"{translations['training_error']} {e}")
+ import traceback
+ logger.debug(traceback.format_exc())
+
+class EpochRecorder:
+ """Calculates and formats execution performance speeds and durations across epoch bounds."""
+
+ def __init__(self):
+ self.last_time = ttime()
+
+ def record(self):
+ """
+ Calculates time elapsed since the last checkpoint and returns a formatted timestamp.
+
+ Returns:
+ str: Log-scannable runtime breakdown statement.
+ """
+
+ now_time = ttime()
+ elapsed_time = now_time - self.last_time
+ self.last_time = now_time
+
+ return translations["time_or_speed_training"].format(current_time=datetime.datetime.now().strftime("%H:%M:%S"), elapsed_time_str=str(datetime.timedelta(seconds=int(round(elapsed_time, 1)))))
+
+def run(rank, n_gpus, pretrainG, pretrainD, pitch_guidance, custom_total_epoch, custom_save_every_weights, config, device, device_id, vocoder, checkpointing):
+ """
+ Initializes distributed communication backends, instantiates neural model modules,
+ restores checkpoints, and manages the training loop across epochs.
+ """
+
+ global global_step, smoothed_value_gen, smoothed_value_disc
+
+ smoothed_value_gen, smoothed_value_disc = 0, 0
+ # Initialize PyTorch Distributed Process Groups
+ dist.init_process_group(
+ backend="gloo" if sys.platform == "win32" or device.type not in ["cuda", "xpu"] else ("xccl" if device.type == "xpu" else "nccl"),
+ init_method="env://",
+ world_size=n_gpus if device.type in ["cuda", "xpu"] else 1,
+ rank=rank if device.type in ["cuda", "xpu"] else 0
+ )
+
+ # Enforce global pseudo-random seed criteria across active hardware targets
+ torch.manual_seed(config.train.seed)
+ if device.type == "cuda": torch.cuda.manual_seed(config.train.seed)
+ elif device.type == "xpu": torch.xpu.manual_seed(config.train.seed)
+ elif device.type == "ocl": opencl.pytorch_ocl.manual_seed_all(config.train.seed)
+
+ # Bind active device visibility scopes per thread
+ if device.type == "cuda": torch.cuda.set_device(device_id)
+ elif device.type == "xpu": torch.xpu.set_device(device_id)
+
+ # Establish localized logging tools exclusively on the primary rank
+ writer_eval = SummaryWriter(log_dir=eval_dir) if rank == 0 else None
+ # Instantiate the data pipeline loader
+ train_loader = get_training_dataloader(config, spec_dir, cache_spectrogram, pitch_guidance, architecture, batch_size, n_gpus, rank)
+
+ if len(train_loader) < 3:
+ logger.warning(translations["not_enough_data"])
+ sys.exit(1)
+
+ # Validate speaker dimension metrics
+ spk_dim = utils.check_speaker_dim(config, checkpoint_path, save_only_latest, pretrainG)
+ config.model.spk_embed_dim = spk_dim
+
+ # Import target structural model blueprints dynamically
+ from main.library.algorithm.discriminators import MultiPeriodDiscriminator
+ from main.library.algorithm.synthesizers import Synthesizer, SynthesizerSVC
+
+ # Select and initialize model architectures
+ net_g, net_d = (
+ (
+ Synthesizer(
+ config.data.filter_length // 2 + 1,
+ config.train.segment_size // config.data.hop_length,
+ **config.model,
+ use_f0=pitch_guidance,
+ sr=config.data.sample_rate,
+ vocoder=vocoder,
+ checkpointing=checkpointing
+ )
+ ) if architecture == "RVC" else (
+ SynthesizerSVC(
+ config.data.filter_length // 2 + 1,
+ config.train.segment_size // config.data.hop_length,
+ **config.model,
+ sr=config.data.sample_rate,
+ vocoder=vocoder,
+ checkpointing=checkpointing
+ )
+ ),
+ MultiPeriodDiscriminator(
+ version=disc_version,
+ use_spectral_norm=config.model.use_spectral_norm,
+ checkpointing=checkpointing
+ )
+ )
+
+ # Migrate models to target hardware acceleration spaces
+ net_g, net_d = (net_g.cuda(device_id), net_d.cuda(device_id)) if device.type == "cuda" else (net_g.xpu(device_id), net_d.xpu(device_id)) if device.type == "xpu" else (net_g.to(device), net_d.to(device))
+ # Instantiate optimization models
+ optim_g, optim_d = utils.get_optimizer(net_g, net_d, optimizer_choice, learning_rate=config.train.learning_rate, betas=config.train.betas, eps=config.train.eps, g_lr_coeff=g_lr_coeff, d_lr_coeff=d_lr_coeff)
+
+ # Bind loss configurations based on configuration criteria
+ fn_mel_loss = MultiScaleMelSpectrogramLoss(sample_rate=config.data.sample_rate) if multiscale_mel_loss else torch.nn.L1Loss()
+ # Wrap model modules with Distributed Data Parallel handlers
+ if device.type.startswith(("cuda", "cpu")): net_g, net_d = (DDP(net_g, device_ids=[device_id]), DDP(net_d, device_ids=[device_id])) if device.type.startswith("cuda") else (DDP(net_g), DDP(net_d))
+
+ scaler_dict = {}
+ try:
+ if rank == 0: logger.info(translations["start_training"])
+
+ # Locate target checkpoint state paths
+ d_path = os.path.join(checkpoint_path, "D_latest.pth") if save_only_latest else utils.latest_checkpoint_path(checkpoint_path, "D_*.pth")
+ g_path = os.path.join(checkpoint_path, "G_latest.pth") if save_only_latest else utils.latest_checkpoint_path(checkpoint_path, "G_*.pth")
+
+ # Attempt to load target parameters
+ _, _, _, epoch_str, scaler_dict = utils.load_checkpoint(d_path, net_d, optim_d)
+ _, _, _, epoch_str, scaler_dict = utils.load_checkpoint(g_path, net_g, optim_g)
+
+ if rank == 0: logger.info(translations["load_checkpoint"].format(d_path=d_path, g_path=g_path))
+
+ epoch_str += 1
+ global_step = (epoch_str - 1) * len(train_loader)
+ except:
+ # Fall back to base foundation files if active checkpoint instances are not found
+ check = ["", "None"]
+ epoch_str, global_step = 1, 0
+ strict = main_configs.get("pretrain_strict", True)
+
+ try:
+ if pretrainG not in check:
+ if rank == 0: logger.info(translations["import_pretrain"].format(dg="G", pretrain=pretrainG))
+
+ ckptG = torch.load(pretrainG, map_location="cpu", weights_only=True)["model"]
+ # Handle SVC speaker embed logic
+ if architecture == "SVC" and "emb_g.weight" not in ckptG: ckptG["emb_g.weight"] = net_g.module.emb_g.weight if hasattr(net_g, "module") else net_g.emb_g.weight
+ net_g.module.load_state_dict(ckptG, strict=strict) if hasattr(net_g, "module") else net_g.load_state_dict(ckptG, strict=strict)
+ del ckptG
+
+ if pretrainD not in check:
+ if rank == 0: logger.info(translations["import_pretrain"].format(dg="D", pretrain=pretrainD))
+
+ ckptD = torch.load(pretrainD, map_location="cpu", weights_only=True)["model"]
+ net_d.module.load_state_dict(ckptD, strict=strict) if hasattr(net_d, "module") else net_d.load_state_dict(ckptD, strict=strict)
+ del ckptD
+ except Exception as e:
+ logger.error(translations["checkpointing_err"])
+ logger.debug(e)
+ sys.exit(1)
+
+ # Initialize learning rate decay schedulers
+ scheduler_g, scheduler_d = utils.get_scheduler(optim_g, optim_d, optimizer_choice, total_epoch=total_epoch, epoch_str=epoch_str, use_cosine_annealing_lr=use_cosine_annealing_lr, lr_decay=config.train.lr_decay)
+
+ # Initialize hardware gradient scalers for half-precision modes
+ if device.type == "xpu" and is_half: xpu.setup_gradscaler()
+ scaler = GradScaler(device=device, enabled=is_half and device.type in ["cuda", "xpu"])
+ cache = []
+
+ # Restore scaling states if continuing from a saved state
+ if len(scaler_dict) > 0: scaler.load_state_dict(scaler_dict)
+ # Generate anchor audios for validation tracing
+ reference = utils.get_reference(train_loader, reference_path, use_custom_reference=use_custom_reference, pitch_guidance=pitch_guidance, rank=rank, device=device)
+
+ # Execute main epoch processing blocks
+ for epoch in range(epoch_str, total_epoch + 1):
+ train_and_evaluate(rank, epoch, config, net_g, net_d, optim_g, optim_d, scaler, train_loader, writer_eval, cache, custom_save_every_weights, custom_total_epoch, device, device_id, reference, fn_mel_loss)
+ scheduler_g.step(); scheduler_d.step()
+
+def train_and_evaluate(rank, epoch, hps, net_g, net_d, optim_g, optim_d, scaler, train_loader, writer, cache, custom_save_every_weights, custom_total_epoch, device, device_id, reference, fn_mel_loss):
+ """Executes step optimizations for both Generator and Discriminator components within an epoch."""
+
+ global global_step, lowest_value, loss_disc, consecutive_increases_gen, consecutive_increases_disc, smoothed_value_gen, smoothed_value_disc
+
+ if epoch == 1:
+ lowest_value = {"step": 0, "value": float("inf"), "epoch": 0}
+ consecutive_increases_gen, consecutive_increases_disc = 0, 0
+
+ # Align dataloader shuffling tracking with epoch counts for standard setups
+ if architecture != "SVC": train_loader.batch_sampler.set_epoch(epoch)
+ net_g.train(); net_d.train()
+
+ # Streamline tensor data pipeline allocations
+ data_iterator, cache = utils.transform_tensor_into_cache(device, device_id, cache_data_in_gpu, cache, train_loader)
+ epoch_recorder = EpochRecorder()
+
+ # Configure mixed precision execution scopes
+ autocast_enabled = is_half and device.type in ["cuda", "xpu"]
+ autocast_dtype = torch.float32 if not autocast_enabled else torch.bfloat16 if main_config.brain else torch.float16
+ autocasts = autocast(device.type, enabled=autocast_enabled, dtype=autocast_dtype) if not device.type.startswith(("ocl", "privateuseone")) else nullcontext()
+
+ with tqdm(total=len(train_loader), leave=False) as pbar:
+ for _, info in data_iterator:
+ info = utils.transforming_computing_devices(info, device, device_id, cache_data_in_gpu)
+ phone, phone_lengths, pitch, pitchf, spec, spec_lengths, wave, sid = utils.get_training_data(info, pitch_guidance)
+
+ # GENERATOR FORWARD PASS
+ with autocasts:
+ y_hat, ids_slice, _, z_mask, (_, z_p, m_p, logs_p, _, logs_q) = net_g(phone, phone_lengths, pitch, pitchf, spec, spec_lengths, sid)
+ wave = commons.slice_segments(wave, ids_slice * config.data.hop_length, config.train.segment_size, dim=3)
+
+ # DISCRIMINATOR OPTIMIZATION STEP
+ for _ in range(d_step_per_g_step):
+ with autocasts:
+ y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, y_hat.detach())
+
+ loss_disc, losses_disc_r, losses_disc_g = losses.discriminator_loss(y_d_hat_r, y_d_hat_g)
+ optim_d.zero_grad()
+
+ if autocast_enabled:
+ scaler.scale(loss_disc).backward()
+ scaler.unscale_(optim_d)
+ grad_norm_d = grad_norm_optim(net_d.parameters())
+ scaler.step(optim_d)
+ else:
+ loss_disc.backward()
+ grad_norm_d = grad_norm_optim(net_d.parameters())
+ optim_d.step()
+
+ # GENERATOR BACKWARD & LOSSES OPTIMIZATION STEP
+ with autocasts:
+ y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(wave, y_hat)
+
+ y_hat_mel = mel_spectrogram_torch(y_hat.float().squeeze(1), config)
+
+ # Route calculation path using targeted error criteria definitions
+ if multiscale_mel_loss: loss_mel = fn_mel_loss(wave, y_hat) * config.train.c_mel / 3.0
+ else: loss_mel = fn_mel_loss(mel_spectrogram_torch(wave.float().squeeze(1), config), y_hat_mel) * config.train.c_mel
+
+ # Aggregate total components for multi-task loss calculation
+ loss_kl = losses.kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * config.train.c_kl
+ loss_fm = losses.feature_loss(fmap_r, fmap_g)
+ loss_gen, losses_gen = losses.generator_loss(y_d_hat_g)
+ loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
+
+ # Update historical global low bounds
+ if loss_gen_all < lowest_value["value"]: lowest_value = {"step": global_step, "value": loss_gen_all, "epoch": epoch}
+
+ optim_g.zero_grad()
+ if autocast_enabled:
+ scaler.scale(loss_gen_all).backward()
+ scaler.unscale_(optim_g)
+ grad_norm_g = grad_norm_optim(net_g.parameters())
+ scaler.step(optim_g)
+ scaler.update()
+ else:
+ loss_gen_all.backward()
+ grad_norm_g = grad_norm_optim(net_g.parameters())
+ optim_g.step()
+
+ global_step += 1
+ # Append calculated step parameters to history tracking arrays
+ avg_losses["grad_d_50"].append(grad_norm_d)
+ avg_losses["grad_g_50"].append(grad_norm_g)
+ avg_losses["disc_loss_50"].append(loss_disc.detach())
+ avg_losses["adv_loss_50"].append(loss_gen.detach())
+ avg_losses["fm_loss_50"].append(loss_fm.detach())
+ avg_losses["kl_loss_50"].append(loss_kl.detach())
+ avg_losses["mel_loss_50"].append(loss_mel.detach())
+ avg_losses["gen_loss_50"].append(loss_gen_all.detach())
+
+ # Export rolling status charts onto summary board records at standard logging boundaries
+ if rank == 0 and global_step % 50 == 0:
+ scalar_dict = {
+ "grad_avg_50/norm_d": sum(avg_losses["grad_d_50"]) / len(avg_losses["grad_d_50"]),
+ "grad_avg_50/norm_g": sum(avg_losses["grad_g_50"]) / len(avg_losses["grad_g_50"]),
+ "loss_avg_50/d/adv": torch.stack(list(avg_losses["disc_loss_50"])).mean(),
+ "loss_avg_50/g/adv": torch.stack(list(avg_losses["adv_loss_50"])).mean(),
+ "loss_avg_50/g/fm": torch.stack(list(avg_losses["fm_loss_50"])).mean(),
+ "loss_avg_50/g/kl": torch.stack(list(avg_losses["kl_loss_50"])).mean(),
+ "loss_avg_50/g/mel": torch.stack(list(avg_losses["mel_loss_50"])).mean(),
+ "loss_avg_50/g/total": torch.stack(list(avg_losses["gen_loss_50"])).mean()
+ }
+
+ utils.summarize(writer=writer, global_step=global_step, scalars=scalar_dict)
+
+ pbar.update(1)
+
+ # Flush active VRAM memory caches safely
+ with torch.no_grad():
+ clear_gpu_cache()
+
+ # EVALUATION, LOGGING, & EXTRACTION CHECKS
+ if rank == 0:
+ mel = spec_to_mel_torch(spec, config)
+ y_mel = commons.slice_segments(mel, ids_slice, config.train.segment_size // config.data.hop_length, dim=3)
+
+ scalar_dict = {
+ "loss/g/total": loss_gen_all,
+ "loss/d/adv": loss_disc,
+ "learning_rate": optim_g.param_groups[0]["lr"],
+ "grad/norm_d": grad_norm_d,
+ "grad/norm_g": grad_norm_g,
+ "loss/g/adv": loss_gen,
+ "loss/g/fm": loss_fm,
+ "loss/g/mel": loss_mel,
+ "loss/g/kl": loss_kl
+ }
+
+ # Expand data maps with nested sub-loss arrays dynamically
+ scalar_dict.update({f"loss/g/{i}": v for i, v in enumerate(losses_gen)})
+ scalar_dict.update({f"loss/d_r/{i}": v for i, v in enumerate(losses_disc_r)})
+ scalar_dict.update({f"loss/d_g/{i}": v for i, v in enumerate(losses_disc_g)})
+
+ image_dict = {
+ "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
+ "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy().copy()),
+ "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
+ }
+
+ # Handle full model saving and validation sample audio synthesis cycles
+ if epoch % save_every_epoch == 0:
+ with autocasts:
+ with torch.no_grad():
+ o = net_g.module.infer(*reference) if hasattr(net_g, "module") else net_g.infer(*reference)
+
+ utils.summarize(writer=writer, global_step=global_step, images=image_dict, scalars=scalar_dict, audios={f"gen/audio_{global_step:07d}": o[0, :, :]}, audio_sample_rate=config.data.sample_rate)
+ else: utils.summarize(writer=writer, global_step=global_step, images=image_dict, scalars=scalar_dict)
+
+ model_add, model_del = [], []
+ done = False
+
+ if rank == 0:
+ # Save structural weights when passing interval checkpoints
+ if epoch % save_every_epoch == 0:
+ checkpoint_suffix = f"{'latest' if save_only_latest else global_step}.pth"
+
+ utils.save_checkpoint(net_g, optim_g, config.train.learning_rate, epoch, os.path.join(checkpoint_path, "G_" + checkpoint_suffix), scaler)
+ utils.save_checkpoint(net_d, optim_d, config.train.learning_rate, epoch, os.path.join(checkpoint_path, "D_" + checkpoint_suffix), scaler)
+
+ if custom_save_every_weights: model_add.append(os.path.join(weights_path, f"{model_name}_{epoch}e_{global_step}s.pth"))
+
+ # Monitor divergence characteristics via Automated Early Stopping Checks
+ if overtraining_detector and epoch > 1:
+ current_loss_disc, current_loss_gen = float(loss_disc), float(lowest_value["value"])
+ loss_disc_history.append(current_loss_disc)
+ loss_gen_history.append(current_loss_gen)
+
+ (
+ smoothed_value_disc,
+ smoothed_value_gen,
+ is_overtraining_disc,
+ is_overtraining_gen,
+ consecutive_increases_disc,
+ consecutive_increases_gen
+ ) = detector.overtraining_detector(
+ smoothed_loss_disc_history,
+ current_loss_disc,
+ smoothed_loss_gen_history,
+ current_loss_gen,
+ overtraining_threshold,
+ consecutive_increases_disc,
+ consecutive_increases_gen
+ )
+
+ if epoch % save_every_epoch == 0: detector.save_to_json(training_file_path, loss_disc_history, smoothed_loss_disc_history, loss_gen_history, smoothed_loss_gen_history)
+ # Check early termination flags based on trend thresholds
+ if is_overtraining_gen and consecutive_increases_gen == overtraining_threshold or is_overtraining_disc and consecutive_increases_disc == (overtraining_threshold * 2):
+ logger.info(translations["overtraining_find"].format(epoch=epoch, smoothed_value_gen=f"{smoothed_value_gen:.3f}", smoothed_value_disc=f"{smoothed_value_disc:.3f}"))
+ done = True
+ else:
+ logger.info(translations["best_epoch"].format(epoch=epoch, smoothed_value_gen=f"{smoothed_value_gen:.3f}", smoothed_value_disc=f"{smoothed_value_disc:.3f}"))
+ # Maintain a clean workspace by purging outdated intermediate weight paths
+ for file in glob.glob(os.path.join(weights_path, f"{model_name}_*e_*s_best_epoch.pth")):
+ model_del.append(file)
+
+ model_add.append(os.path.join(weights_path, f"{model_name}_{epoch}e_{global_step}s_best_epoch.pth"))
+
+ # Standard workflow completion criteria check
+ if epoch >= custom_total_epoch:
+ logger.info(translations["success_training"].format(epoch=epoch, global_step=global_step, loss_gen_all=round(loss_gen_all.item(), 3)))
+ logger.info(translations["training_info"].format(lowest_value_rounded=round(float(lowest_value["value"]), 3), lowest_value_epoch=lowest_value['epoch'], lowest_value_step=lowest_value['step']))
+ model_add.append(os.path.join(weights_path, f"{model_name}_{epoch}e_{global_step}s.pth"))
+ done = True
+
+ # Clean up stale inference models from disk
+ for m in model_del:
+ os.remove(m)
+
+ # Extract targeted production-ready inference weights (.pth)
+ if model_add:
+ ckpt = (net_g.module.state_dict() if hasattr(net_g, "module") else net_g.state_dict())
+ for m in model_add:
+ extract_model(
+ ckpt=ckpt,
+ sr=config.data.sample_rate,
+ pitch_guidance=pitch_guidance,
+ name=model_name,
+ model_path=m,
+ epoch=epoch,
+ step=global_step,
+ version=version,
+ hps=hps,
+ model_author=model_author,
+ vocoder=vocoder,
+ speakers_id=config.sid,
+ architecture=architecture
+ )
+
+ lowest_value_rounded = round(float(lowest_value["value"]), 3)
+ # Output generalized execution summary statistics depending on tracking contexts
+ if epoch > 1 and overtraining_detector:
+ logger.info(
+ translations["model_training_info"].format(
+ model_name=model_name,
+ epoch=epoch,
+ global_step=global_step,
+ epoch_recorder=epoch_recorder.record(),
+ lowest_value_rounded=lowest_value_rounded,
+ lowest_value_epoch=lowest_value['epoch'],
+ lowest_value_step=lowest_value['step'],
+ remaining_epochs_gen=overtraining_threshold - consecutive_increases_gen,
+ remaining_epochs_disc=(overtraining_threshold * 2) - consecutive_increases_disc,
+ smoothed_value_gen=f"{smoothed_value_gen:.3f}",
+ smoothed_value_disc=f"{smoothed_value_disc:.3f}"
+ )
+ )
+ elif epoch > 1 and not overtraining_detector:
+ logger.info(
+ translations["model_training_info_2"].format(
+ model_name=model_name,
+ epoch=epoch,
+ global_step=global_step,
+ epoch_recorder=epoch_recorder.record(),
+ lowest_value_rounded=lowest_value_rounded,
+ lowest_value_epoch=lowest_value['epoch'],
+ lowest_value_step=lowest_value['step']
+ )
+ )
+ else: logger.info(translations["model_training_info_3"].format(model_name=model_name, epoch=epoch, global_step=global_step, epoch_recorder=epoch_recorder.record()))
+
+ logger.debug(f"loss_gen_all: {loss_gen_all} loss_gen: {loss_gen} loss_fm: {loss_fm} loss_mel: {loss_mel} loss_kl: {loss_kl}")
+
+ # Post-training cleanup routine upon triggering termination flags
+ if done:
+ if save_the_pid:
+ with open(config_save_path, "r", encoding="utf-8") as pid_file:
+ pid_data = json.load(pid_file)
+
+ with open(config_save_path, "w", encoding="utf-8") as pid_file:
+ pid_data.pop("process_pids", None)
+ json.dump(pid_data, pid_file, indent=4)
+
+ if os.path.exists(os.path.join(experiment_dir, "train_pid.txt")): os.remove(os.path.join(experiment_dir, "train_pid.txt"))
+
+ sys.exit(0)
+
+ with torch.no_grad():
+ clear_gpu_cache()
+
+if __name__ == "__main__":
+ # Enforce spawn multiprocessing method for thread-safe cross-GPU allocation bounds
+ mp.set_start_method("spawn")
+ main()
\ No newline at end of file
diff --git a/main/inference/training/utils.py b/main/inference/training/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a053aa9cfb904219ab0cefad41bbd48e0b450030
--- /dev/null
+++ b/main/inference/training/utils.py
@@ -0,0 +1,651 @@
+import os
+import re
+import sys
+import glob
+import torch
+
+import numpy as np
+import soundfile as sf
+import matplotlib.pyplot as plt
+
+from random import shuffle
+from collections import OrderedDict
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config, translations, logger
+
+MATPLOTLIB_FLAG = False
+
+def optimizer_state_dict_cpu(optimizer):
+ """
+ Moves all tensors within the optimizer's state dictionary to the CPU.
+
+ Args:
+ optimizer (torch.optim.Optimizer): The PyTorch optimizer object.
+
+ Returns:
+ dict: A deep copy of the optimizer's state dict with all tensors on CPU.
+ """
+
+ import copy
+
+ # Deep copy the state to prevent modifying the original optimizer state
+ opt_state = copy.deepcopy(optimizer.state_dict())
+ # Iterate through optimizer state values and detach tensors to CPU
+ for state in opt_state["state"].values():
+ for k, v in state.items():
+ if torch.is_tensor(v):
+ state[k] = v.detach().cpu()
+
+ return opt_state
+
+def replace_keys_in_dict(d, old_key_part, new_key_part):
+ """
+ Recursively replaces substrings within the keys of a dictionary or OrderedDict.
+
+ Args:
+ d (dict or OrderedDict): The dictionary to process.
+ old_key_part (str): The substring to be replaced.
+ new_key_part (str): The substring to replace with.
+
+ Returns:
+ dict or OrderedDict: A new dictionary with updated keys.
+ """
+
+ # Maintain OrderedDict type if the input is an OrderedDict
+ updated_dict = OrderedDict() if isinstance(d, OrderedDict) else {}
+
+ for key, value in d.items():
+ # Recursively apply to nested dictionaries, otherwise keep the value
+ updated_dict[(
+ # Replace the substring if the key is a string
+ key.replace(old_key_part, new_key_part) if isinstance(key, str) else key
+ )] = (
+ replace_keys_in_dict(value, old_key_part, new_key_part) if isinstance(value, dict) else value
+ )
+
+ return updated_dict
+
+def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
+ """
+ Loads a model and optional optimizer state from a checkpoint file.
+ Handles legacy weight key conversions (.weight_v / .weight_g) to the newer parametrization format.
+
+ Args:
+ checkpoint_path (str): Path to the checkpoint file (.pth).
+ model (torch.nn.Module): The model instance to load weights into.
+ optimizer (torch.optim.Optimizer, optional): The optimizer instance. Defaults to None.
+ load_opt (int, optional): Flag to determine whether to load optimizer state (1 to load). Defaults to 1.
+
+ Returns:
+ tuple: (model, optimizer, learning_rate, iteration, scaler_state)
+ """
+
+ # Verify checkpoint file existence
+ assert os.path.isfile(checkpoint_path), translations["not_found_checkpoint"].format(checkpoint_path=checkpoint_path)
+
+ # Load checkpoint safely and map keys from old formats to PyTorch's parametrization standard
+ checkpoint_dict = replace_keys_in_dict(
+ replace_keys_in_dict(
+ torch.load(checkpoint_path, map_location="cpu", weights_only=True),
+ ".weight_v",
+ ".parametrizations.weight.original1"
+ ),
+ ".weight_g",
+ ".parametrizations.weight.original0"
+ )
+
+ # Map checkpoint weights into current state dict framework, falling back to current if missing
+ new_state_dict = {
+ k: checkpoint_dict["model"].get(k, v)
+ for k, v in (model.module.state_dict() if hasattr(model, "module") else model.state_dict()).items()
+ }
+
+ # Load state dict non-strictly to tolerate missing/extra keys
+ model.module.load_state_dict(new_state_dict, strict=False) if hasattr(model, "module") else model.load_state_dict(new_state_dict, strict=False)
+ # Optional loading of the optimizer state
+ if optimizer and load_opt == 1: optimizer.load_state_dict(checkpoint_dict.get("optimizer", {}))
+ logger.debug(translations["save_checkpoint"].format(checkpoint_path=checkpoint_path, checkpoint_dict=checkpoint_dict['iteration']))
+
+ return (
+ model,
+ optimizer,
+ checkpoint_dict.get("learning_rate", 0),
+ checkpoint_dict["iteration"],
+ checkpoint_dict.get("scaler", {})
+ )
+
+def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path, scaler):
+ """
+ Saves the current training states (model, optimizer, iteration, lr, scaler) to a file.
+ Converts modern PyTorch parametrization keys back to legacy weight keys for compatibility.
+
+ Args:
+ model (torch.nn.Module): The model instance.
+ optimizer (torch.optim.Optimizer): The optimizer instance.
+ learning_rate (float): Current learning rate.
+ iteration (int): Current training iteration/step.
+ checkpoint_path (str): Target file path where checkpoint will be saved.
+ scaler (torch.cuda.amp.GradScaler): Gradient scaler for mixed precision training.
+ """
+
+ state_dict = (model.module.state_dict() if hasattr(model, "module") else model.state_dict())
+ # Move weights/states to CPU if specialized computing devices are used
+ if config.device.startswith("privateuseone"):
+ model_state = {k: v.detach().cpu() for k, v in state_dict.items()}
+ model_optimizer = optimizer_state_dict_cpu(optimizer)
+ else:
+ model_state = state_dict
+ model_optimizer = optimizer.state_dict()
+
+ # Revert newer serialization keys back to legacy format before saving for backward compatibility
+ torch.save(
+ replace_keys_in_dict(
+ replace_keys_in_dict({
+ "model": model_state,
+ "iteration": iteration,
+ "optimizer": model_optimizer,
+ "learning_rate": learning_rate,
+ "scaler": scaler.state_dict()
+ }, ".parametrizations.weight.original1", ".weight_v"),
+ ".parametrizations.weight.original0", ".weight_g"
+ ),
+ checkpoint_path
+ )
+
+ logger.info(translations["save_model"].format(checkpoint_path=checkpoint_path, iteration=iteration))
+
+def summarize(
+ writer,
+ global_step,
+ scalars={},
+ histograms={},
+ images={},
+ audios={},
+ audio_sample_rate=22050
+):
+ """
+ Logs multi-modal training metrics and summaries to TensorBoard.
+
+ Args:
+ writer (torch.utils.tensorboard.SummaryWriter): TensorBoard SummaryWriter instance.
+ global_step (int): Current global step in training.
+ scalars (dict, optional): Dict of scalar metrics {tag: value}. Defaults to {}.
+ histograms (dict, optional): Dict of histogram metrics {tag: values}. Defaults to {}.
+ images (dict, optional): Dict of image tensors {tag: image}. Defaults to {}.
+ audios (dict, optional): Dict of audio waveforms {tag: audio}. Defaults to {}.
+ audio_sample_rate (int, optional): Sampling rate for audio logs. Defaults to 22050.
+ """
+
+ for k, v in scalars.items():
+ writer.add_scalar(k, v, global_step)
+
+ for k, v in histograms.items():
+ writer.add_histogram(k, v, global_step)
+
+ for k, v in images.items():
+ writer.add_image(k, v, global_step, dataformats="HWC")
+
+ for k, v in audios.items():
+ writer.add_audio(k, v, global_step, audio_sample_rate)
+
+def latest_checkpoint_path(dir_path, regex="G_*.pth"):
+ """
+ Finds the latest saved checkpoint in a directory based on numerical sorting of its filename.
+
+ Args:
+ dir_path (str): Path to the directory containing checkpoints.
+ regex (str, optional): Search wildcard pattern. Defaults to "G_*.pth".
+
+ Returns:
+ str or None: Path to the latest checkpoint file, or None if none found.
+ """
+
+ checkpoints = sorted(
+ glob.glob(
+ os.path.join(dir_path, regex)
+ ),
+ key=lambda f: int("".join(filter(str.isdigit, f))) # Sort purely by the digits found in the filename
+ )
+ return checkpoints[-1] if checkpoints else None
+
+def plot_spectrogram_to_numpy(spectrogram):
+ """
+ Converts a spectrogram tensor into an RGB numpy image array using Matplotlib.
+
+ Args:
+ spectrogram (np.ndarray or torch.Tensor): The 2D spectrogram grid.
+
+ Returns:
+ np.ndarray: An RGB image representation of the spectrogram with shape (H, W, 3).
+ """
+
+ global MATPLOTLIB_FLAG
+
+ # Switch backend to headless 'Agg' to avoid GUI overhead during distributed training
+ if not MATPLOTLIB_FLAG:
+ plt.switch_backend("Agg")
+ MATPLOTLIB_FLAG = True
+
+ fig, ax = plt.subplots(figsize=(10, 2))
+ plt.colorbar(ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none"), ax=ax)
+ plt.xlabel("Frames")
+ plt.ylabel("Channels")
+ plt.tight_layout()
+ fig.canvas.draw()
+ plt.close(fig)
+
+ # Attempt to extract raw buffer as RGB array; fallback if canvas rendering differs
+ try:
+ data = np.array(
+ fig.canvas.renderer.buffer_rgba(),
+ dtype=np.uint8
+ ).reshape(
+ fig.canvas.get_width_height()[::-1] + (4,)
+ )[:, :, :3]
+ except:
+ data = np.fromstring(
+ fig.canvas.tostring_rgb(),
+ dtype=np.uint8,
+ sep=""
+ ).reshape(
+ fig.canvas.get_width_height()[::-1] + (3,)
+ )
+
+ return data
+
+def load_wav_to_torch(full_path):
+ """
+ Loads an audio file from a path into a PyTorch FloatTensor.
+
+ Args:
+ full_path (str): Path to the audio file.
+
+ Returns:
+ tuple: (torch.FloatTensor of audio data, int sample rate)
+ """
+
+ data, sample_rate = sf.read(full_path, dtype=np.float32)
+ return torch.FloatTensor(data.astype(np.float32)), sample_rate
+
+def load_filepaths_and_text(filename, split="|"):
+ """
+ Read a text file containing data paths and convert it into a list.
+
+ Args:
+ filename (str): Path to the metadata file.
+ split (str, optional): String delimiter. Defaults to "|".
+
+ Returns:
+ list of list: A parsed grid containing components of each line.
+ """
+
+ with open(filename, encoding="utf-8") as f:
+ return [line.strip().split(split) for line in f]
+
+class HParams:
+ """
+ A Hyperparameters configuration container class wrapper that converts nested dictionaries
+ into an object wrapper, enabling attribute-style access (e.g., hps.model.lr).
+ """
+
+ def __init__(self, **kwargs):
+ for k, v in kwargs.items():
+ # Recursively turn dictionaries into child HParams objects
+ self[k] = HParams(**v) if isinstance(v, dict) else v
+
+ def keys(self):
+ return self.__dict__.keys()
+
+ def items(self):
+ return self.__dict__.items()
+
+ def values(self):
+ return self.__dict__.values()
+
+ def __len__(self):
+ return len(self.__dict__)
+
+ def __getitem__(self, key):
+ return self.__dict__[key]
+
+ def __setitem__(self, key, value):
+ self.__dict__[key] = value
+
+ def __contains__(self, key):
+ return key in self.__dict__
+
+ def __repr__(self):
+ return repr(self.__dict__)
+
+def get_device(gpus):
+ """
+ Resolves the targeted hardware acceleration device context based on config and string requests.
+
+ Args:
+ gpus (str): String identifier for allocation (e.g., "0-1-2" or "-").
+
+ Returns:
+ tuple: (torch.device object, list of active GPU IDs, int count of devices)
+ """
+
+ if gpus == "-":
+ device, gpus = torch.device("cpu"), [0]
+ n_gpus = 1
+ logger.warning(translations["not_gpu"])
+ elif config.device.startswith("cuda"):
+ device, gpus = torch.device("cuda"), [int(item) for item in gpus.split("-")]
+ n_gpus = len(gpus)
+ elif config.device.startswith("xpu"):
+ device, gpus = torch.device("xpu"), [int(item) for item in gpus.split("-")]
+ n_gpus = len(gpus)
+ elif config.device.startswith("ocl"):
+ device, gpus = torch.device("ocl"), [int(item) for item in gpus.split("-")]
+ n_gpus = len(gpus)
+ elif config.device.startswith("privateuseone"):
+ device, gpus = torch.device("privateuseone"), [int(item) for item in gpus.split("-")]
+ n_gpus = len(gpus)
+ elif config.device.startswith("mps"):
+ device, gpus = torch.device("mps"), [0]
+ n_gpus = 1
+ else:
+ device, gpus = torch.device("cpu"), [0]
+ n_gpus = 1
+ logger.warning(translations["not_gpu"])
+
+ return device, gpus, n_gpus
+
+def get_optimizer(net_g, net_d, optimizer_choice = "AdamW", learning_rate = 1e-4, betas = [0.8, 0.99], eps = 1e-9, g_lr_coeff = 1.0, d_lr_coeff = 1.0):
+ """
+ Initializes and selects the optimization engines for both Generator (net_g) and Discriminator (net_d).
+
+ Args:
+ net_g (torch.nn.Module): Generator model.
+ net_d (torch.nn.Module): Discriminator model.
+ optimizer_choice (str, optional): Optimization algorithm name. Defaults to "AdamW".
+ learning_rate (float, optional): Base learning rate. Defaults to 1e-4.
+ betas (list, optional): Beta coefficients for Adam-based optimizers. Defaults to [0.8, 0.99].
+ eps (float, optional): Term added to the denominator to improve numerical stability. Defaults to 1e-9.
+ g_lr_coeff (float, optional): Multiplier scaling factor for Generator learning rate. Defaults to 1.0.
+ d_lr_coeff (float, optional): Multiplier scaling factor for Discriminator learning rate. Defaults to 1.0.
+
+ Returns:
+ tuple: (optim_g, optim_d) instances
+ """
+
+ # Dynamic optimizer factory imports based on user choice
+ if optimizer_choice == "AnyPrecisionAdamW" and config.brain:
+ from main.library.optimizers.anyprecision_optimizer import AnyPrecisionAdamW
+
+ optimizer_optim = AnyPrecisionAdamW
+ elif optimizer_choice == "RAdam":
+ from torch.optim import RAdam
+
+ optimizer_optim = RAdam
+ elif optimizer_choice == "AdaBelief":
+ from main.library.optimizers.adabelief import AdaBelief
+
+ optimizer_optim = AdaBelief
+ else:
+ from torch.optim import AdamW
+
+ optimizer_optim = AdamW
+
+ optim_g = optimizer_optim(net_g.parameters(), learning_rate * g_lr_coeff, betas=betas if not optimizer_choice.startswith("AdaBelief") else 1e-8, eps=eps)
+ optim_d = optimizer_optim(net_d.parameters(), learning_rate * d_lr_coeff, betas=betas if not optimizer_choice.startswith("AdaBelief") else 1e-8, eps=eps)
+
+ return optim_g, optim_d
+
+def get_scheduler(optim_g, optim_d, optimizer_choice = "AdamW", total_epoch = 100, epoch_str = 1, use_cosine_annealing_lr = False, lr_decay = 0.999875):
+ """
+ Creates learning rate schedules for the Generator and Discriminator optimizers.
+
+ Args:
+ optim_g (torch.optim.Optimizer): Optimizer for Generator.
+ optim_d (torch.optim.Optimizer): Optimizer for Discriminator.
+ optimizer_choice (str, optional): Name of chosen optimizer. Defaults to "AdamW".
+ total_epoch (int, optional): Max training epoch target for Cosine Annealing. Defaults to 100.
+ epoch_str (int, optional): Starting epoch offset. Defaults to 1.
+ use_cosine_annealing_lr (bool, optional): Force cosine schedule. Defaults to False.
+ lr_decay (float, optional): Multiplicative decay gamma factor for ExponentialLR. Defaults to 0.999875.
+
+ Returns:
+ tuple: (scheduler_g, scheduler_d) Learning rate schedulers.
+ """
+
+ # Apply CosineAnnealing fallback conditions, otherwise default to ExponentialLR decay
+ if optimizer_choice == "AdaBelief" or use_cosine_annealing_lr:
+ scheduler_g = torch.optim.lr_scheduler.CosineAnnealingLR(optim_g, T_max=total_epoch, eta_min=1e-6, last_epoch=epoch_str - 2)
+ scheduler_d = torch.optim.lr_scheduler.CosineAnnealingLR(optim_d, T_max=total_epoch, eta_min=1e-6, last_epoch=epoch_str - 2)
+ else:
+ scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=lr_decay, last_epoch=epoch_str - 2)
+ scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=lr_decay, last_epoch=epoch_str - 2)
+
+ return scheduler_g, scheduler_d
+
+def cleanup_training(experiment_dir):
+ """
+ Cleans up training checkpoints, index tracks, and evaluation directories inside an experiment directory.
+
+ Args:
+ experiment_dir (str): Base root path of the experiment directory.
+ """
+
+ for root, dirs, files in os.walk(experiment_dir, topdown=False):
+ # Remove target training artifact files
+ for name in files:
+ if name.endswith((".0", ".pth", ".index")): os.remove(os.path.join(root, name))
+
+ # Empty and strip out structural 'eval' metric logging directories
+ for name in dirs:
+ if name == "eval":
+ folder_path = os.path.join(root, name)
+
+ for item in os.listdir(folder_path):
+ item_path = os.path.join(folder_path, item)
+ if os.path.isfile(item_path): os.remove(item_path)
+
+ os.rmdir(folder_path)
+
+def get_training_data(info, pitch_guidance):
+ """
+ Deconstructs a data loader's batch tensor list into cleanly named variables based on pitch features.
+
+ Args:
+ info (list): List of tensors yielded by the DataLoader pipeline batch.
+ pitch_guidance (bool): Flag indicating if pitch/F0 is used for training.
+
+ Returns:
+ tuple: (phone, phone_lengths, pitch, pitchf, spec, spec_lengths, wave, sid)
+ """
+
+ phone, phone_lengths = info[0], info[1]
+ # Map indexes conditionally based on whether F0/pitch is part of the batch vectors
+ if pitch_guidance:
+ pitch, pitchf = info[2], info[3]
+ spec, spec_lengths, wave, sid = info[4], info[5], info[6], info[8]
+ else:
+ pitch = pitchf = None
+ spec, spec_lengths, wave, sid = info[2], info[3], info[4], info[6]
+
+ return phone, phone_lengths, pitch, pitchf, spec, spec_lengths, wave, sid
+
+def transform_tensor_into_cache(device, device_id, cache_data_in_gpu, cache, train_loader):
+ """
+ Caches an entire training data loader directly into a hardware device memory (GPU/XPU/etc.)
+ to maximize iteration speed, or falls back to an standard iterator if caching is disabled.
+
+ Args:
+ device (torch.device): Device target context wrapper.
+ device_id (int or torch.device): Local computing accelerator rank index.
+ cache_data_in_gpu (bool): Flag to trigger total cache storage in device memory.
+ cache (list): Existing cache list placeholder matrix.
+ train_loader (torch.utils.data.DataLoader): Standard training batch data loader.
+
+ Returns:
+ tuple: (data_iterator object, updated cache list structures)
+ """
+
+ if device.type == "cuda" and cache_data_in_gpu: # CUDA execution branch caching
+ data_iterator = cache
+
+ if cache == []:
+ for batch_idx, info in enumerate(train_loader):
+ cache.append(
+ (batch_idx, [
+ tensor.cuda(device_id, non_blocking=True)
+ for tensor in info
+ ])
+ )
+ else:
+ shuffle(cache)
+ elif device.type == "xpu" and cache_data_in_gpu: # Intel XPU execution branch caching
+ data_iterator = cache
+
+ if cache == []:
+ for batch_idx, info in enumerate(train_loader):
+ cache.append(
+ (batch_idx, [
+ tensor.xpu(device_id, non_blocking=True)
+ for tensor in info
+ ])
+ )
+ else:
+ shuffle(cache)
+ elif device.type in ["privateuseone", "ocl"] and cache_data_in_gpu: # OpenCL & Custom Device (privateuseone) execution branch caching
+ data_iterator = cache
+
+ if cache == []:
+ for batch_idx, info in enumerate(train_loader):
+ cache.append(
+ (batch_idx, [
+ tensor.to(device_id if device.type == "ocl" else device, non_blocking=True)
+ for tensor in info
+ ])
+ )
+ else:
+ shuffle(cache)
+ else: # Default fallback: streaming directly from CPU/Disk data loader every epoch
+ data_iterator = enumerate(train_loader)
+
+ return data_iterator, cache
+
+def transforming_computing_devices(info, device, device_id, cache_data_in_gpu):
+ """
+ Moves an in-flight batch data tensor collection to the target device memory
+ on-the-fly (only if memory-wide caching was not utilized).
+
+ Args:
+ info (list): List of tensors from data step.
+ device (torch.device): Target hardware device configuration.
+ device_id (int or torch.device): Local execution device ID reference.
+ cache_data_in_gpu (bool): Flag indicating if dataset was pre-cached.
+
+ Returns:
+ list: Moved tensor collections.
+ """
+
+ # Process only if tensors are not already cached in GPU memory
+ if device.type == "cuda" and not cache_data_in_gpu:
+ info = [
+ tensor.cuda(device_id, non_blocking=True)
+ for tensor in info
+ ]
+ elif device.type == "xpu" and not cache_data_in_gpu:
+ info = [
+ tensor.xpu(device_id, non_blocking=True)
+ for tensor in info
+ ]
+ elif device.type in ["privateuseone", "ocl"] and not cache_data_in_gpu:
+ info = [
+ tensor.to(device_id if device.type == "ocl" else device, non_blocking=True)
+ for tensor in info
+ ]
+ else:
+ # Fallback tracking for CPU or generalized transfers
+ info = [
+ tensor.to(device)
+ for tensor in info
+ ]
+
+ return info
+
+def get_reference(train_loader, reference_path, use_custom_reference = False, pitch_guidance = True, rank = 0, device = "cpu"):
+ """
+ Fetches reference feature states used to evaluate validation or visual steps.
+ Loads from a static feature directory if specified, otherwise grabs the next standard batch.
+
+ Args:
+ train_loader (torch.utils.data.DataLoader): Active fallback training loader pipeline.
+ reference_path (str): File folder tracking custom reference features (`feats.npy`).
+ use_custom_reference (bool, optional): Force load static file metrics. Defaults to False.
+ pitch_guidance (bool, optional): Model relies on explicit F0 values. Defaults to True.
+ rank (int, optional): Distributed processing ID. Defaults to 0.
+ device (str, optional): Target destination execution string. Defaults to "cpu".
+
+ Returns:
+ tuple: Tensor references containing (phone, phone_lengths, pitch, pitchf, sid/wave)
+ """
+
+ # Load custom reference numpy files from disk if requested and files exist
+ if use_custom_reference and os.path.isfile(os.path.join(reference_path, "feats.npy")):
+ # Clean up tracking names using regex
+ if rank == 0: logger.info(translations["using_reference"].format(reference_name=re.sub(r'_v\d+_(?:[A-Za-z0-9_]+?)_(True|False)$', '', os.path.basename(reference_path))))
+ phone = np.repeat(np.load(os.path.join(reference_path, "feats.npy")), 2, axis=0)
+
+ reference = (
+ torch.FloatTensor(phone).unsqueeze(0).to(device),
+ torch.LongTensor([phone.shape[0]]).to(device),
+ torch.LongTensor(np.load(os.path.join(reference_path, "pitch_coarse.npy"))[:-1]).unsqueeze(0).to(device) if pitch_guidance else None,
+ torch.FloatTensor(np.load(os.path.join(reference_path, "pitch_fine.npy"))[:-1]).unsqueeze(0).to(device) if pitch_guidance else None,
+ torch.LongTensor([0]).to(device)
+ )
+ else:
+ # Default fallback: dynamically pluck the next available elements inside the train data loader
+ info = next(iter(train_loader))
+ reference = (info[0].to(device), info[1].to(device))
+
+ if pitch_guidance: reference += (info[2].to(device), info[3].to(device), info[8].to(device))
+ else: reference += (None, None, info[6].to(device))
+
+ return reference
+
+def check_speaker_dim(hps, checkpoint_path, save_only_latest, pretrainG):
+ """
+ Infers the dimensional size configuration of the speaker embedding block.
+ Checks the hyperparameter configuration, falling back to reading actual tensor shape dimensions
+ from available checkpoints or pretrained generator targets.
+
+ Args:
+ hps (HParams): Active hyperparameters object configuration instance.
+ checkpoint_path (str): Target directory tracking saved model versions.
+ save_only_latest (bool): Flag checking if tracking targets are strictly isolated to latest file.
+ pretrainG (str): Filepath location indicating backing base pretrained model.
+
+ Returns:
+ int: Total speaker embedding dimension count.
+ """
+
+ spk_dim = hps.model.spk_embed_dim
+ # Attempt to fall back to speaker identity parameter counts
+ try:
+ spk_dim = hps.sid
+ except Exception as e:
+ logger.debug(e)
+
+ # Inspect the model checkpoint architecture weights directly to extract exact embedding dimensions
+ try:
+ g_path = os.path.join(checkpoint_path, "G_latest.pth")
+ last_g = g_path if save_only_latest and os.path.exists(g_path) else latest_checkpoint_path(checkpoint_path, "G_*.pth")
+ chk_path = (last_g if last_g else (pretrainG if pretrainG not in ["", "None"] else None))
+
+ if chk_path:
+ ckpt = torch.load(chk_path, map_location="cpu", weights_only=True)
+ # Read shape dimensions of global embedding weights matrix
+ spk_dim = ckpt["model"]["emb_g.weight"].shape[0]
+ del ckpt
+ except Exception as e:
+ logger.debug(e)
+
+ return spk_dim
\ No newline at end of file
diff --git a/main/library/algorithm/attentions.py b/main/library/algorithm/attentions.py
new file mode 100644
index 0000000000000000000000000000000000000000..80bee493c25b8409e7793467f54fac7ddb38e6e9
--- /dev/null
+++ b/main/library/algorithm/attentions.py
@@ -0,0 +1,347 @@
+import math
+import torch
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+class MultiHeadAttention(nn.Module):
+ """
+ Multi-Head Attention module with support for relative position representations,
+ proximal bias, and ONNX-friendly exporting. Designed primarily for 1D sequences
+ (e.g., speech or text processing) using Conv1D operators.
+ """
+
+ def __init__(
+ self,
+ channels,
+ out_channels,
+ n_heads,
+ p_dropout=0.0,
+ window_size=None,
+ heads_share=True,
+ block_length=None,
+ proximal_bias=False,
+ proximal_init=False,
+ onnx=False
+ ):
+ """
+ Initializes the MultiHeadAttention module.
+
+ Args:
+ channels (int): Number of input channels (hidden dimension).
+ out_channels (int): Number of output channels.
+ n_heads (int): Number of attention heads.
+ p_dropout (float): Dropout probability. Defaults to 0.0.
+ window_size (int, optional): Window size for relative position embeddings. Defaults to None.
+ heads_share (bool): If True, all heads share the same relative embeddings. Defaults to True.
+ block_length (int, optional): Maximum distance constraint for block-sparse masking. Defaults to None.
+ proximal_bias (bool): If True, applies log-distance penalty to local attention. Defaults to False.
+ proximal_init (bool): If True, initializes key weights using query weights. Defaults to False.
+ onnx (bool): If True, uses ONNX-compatible tensor operations. Defaults to False.
+ """
+
+ super().__init__()
+ assert channels % n_heads == 0
+ # Attribute assignments
+ self.channels = channels
+ self.out_channels = out_channels
+ self.n_heads = n_heads
+ self.k_channels = channels // n_heads # Dimension per head
+ self.window_size = window_size
+ self.block_length = block_length
+ self.proximal_bias = proximal_bias
+ # Linear projections implemented via 1x1 convolutions
+ self.conv_q = nn.Conv1d(channels, channels, 1)
+ self.conv_k = nn.Conv1d(channels, channels, 1)
+ self.conv_v = nn.Conv1d(channels, channels, 1)
+ self.conv_o = nn.Conv1d(channels, out_channels, 1)
+ self.drop = nn.Dropout(p_dropout)
+
+ # Dynamic routing of functions based on ONNX export mode
+ self._get_relative_embeddings = self._get_relative_embeddings_onnx if onnx else self._get_relative_embeddings_torch
+ self._relative_position_to_absolute_position = self._relative_position_to_absolute_position_onnx if onnx else self._relative_position_to_absolute_position_torch
+ self._absolute_position_to_relative_position = self._absolute_position_to_relative_position_onnx if onnx else self._absolute_position_to_relative_position_torch
+
+ # Initialize relative position embeddings if window_size is specified
+ if window_size is not None:
+ n_heads_rel = 1 if heads_share else n_heads
+ rel_stddev = self.k_channels**-0.5 # Scale initialization standard deviation
+
+ # Relative embeddings for keys and values
+ self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+ self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+
+ # Weight initialization using Xavier uniform
+ nn.init.xavier_uniform_(self.conv_q.weight)
+ nn.init.xavier_uniform_(self.conv_k.weight)
+ nn.init.xavier_uniform_(self.conv_v.weight)
+ nn.init.xavier_uniform_(self.conv_o.weight)
+
+ # Initialize Key projection to copy Query projection for localized bias initialization
+ if proximal_init:
+ with torch.no_grad():
+ self.conv_k.weight.copy_(self.conv_q.weight)
+ self.conv_k.bias.copy_(self.conv_q.bias)
+
+ def forward(self, x, c, attn_mask=None):
+ """
+ Forward pass for Multi-Head Attention.
+
+ Args:
+ x (torch.Tensor): Query input tensor.
+ c (torch.Tensor): Key/Value input tensor.
+ attn_mask (torch.Tensor, optional): Mask tensor. Defaults to None.
+
+ Returns:
+ torch.Tensor: Output tensor.
+ """
+
+ # Project inputs to query, key, and value representations
+ q, k, v = self.conv_q(x), self.conv_k(c), self.conv_v(c)
+ # Calculate attention mechanism
+ x, self.attn = self.attention(q, k, v, mask=attn_mask)
+ # Final linear projection to destination channels
+ return self.conv_o(x)
+
+ def attention(self, query, key, value, mask=None):
+ """
+ Computes scaled dot-product attention with optional relative positioning and masking.
+
+ Args:
+ query (torch.Tensor): Query tensor.
+ key (torch.Tensor): Key tensor.
+ value (torch.Tensor): Value tensor.
+ mask (torch.Tensor, optional): Attention mask tensor. Defaults to None.
+
+ Returns:
+ Tuple[torch.Tensor, torch.Tensor]: Context tensor and attention probability weights.
+ """
+
+ b, d, t_s, t_t = (*key.size(), query.size(2))
+ # Reshape and transpose for multi-head attention
+ query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+ key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+ value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+ # Compute base attention scores
+ scores = (query / math.sqrt(self.k_channels)) @ key.transpose(-2, -1)
+
+ # Incorporate relative position representations for keys
+ if self.window_size:
+ assert t_s == t_t
+ # Extract sliced embeddings, apply relative matrix multiplication and shift to absolute positions
+ scores += self._relative_position_to_absolute_position(
+ self._matmul_with_relative_keys(
+ query / math.sqrt(self.k_channels),
+ self._get_relative_embeddings(
+ self.emb_rel_k,
+ t_s
+ )
+ )
+ )
+
+ # Apply localized distance-based log penalty
+ if self.proximal_bias:
+ assert t_s == t_t
+ scores += self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+
+ # Apply external masking (e.g., sequence padding)
+ if mask is not None:
+ scores = scores.masked_fill(mask == 0, -1e4)
+ # Apply local block-diagonal constraints (banded attention)
+ if self.block_length:
+ assert (t_s == t_t)
+ block_mask = (
+ torch.ones_like(scores)
+ .triu(-self.block_length) # Keep upper band
+ .tril(self.block_length) # Keep lower band
+ )
+ scores = scores.masked_fill(block_mask == 0, -1e4)
+
+ # Softmax normalize and apply dropout to obtain final probabilities
+ p_attn = self.drop(F.softmax(scores, dim=-1))
+ # Compute context vector
+ output = p_attn @ value
+
+ # Incorporate relative position representations for values
+ if self.window_size:
+ output += self._matmul_with_relative_values(
+ self._absolute_position_to_relative_position(
+ p_attn
+ ),
+ self._get_relative_embeddings(
+ self.emb_rel_v,
+ t_s
+ )
+ )
+
+ # Permute and reshape back to 1D channel format.
+ return output.transpose(2, 3).contiguous().view(b, d, t_t), p_attn
+
+ def _matmul_with_relative_values(self, x, y):
+ """Helper for relative value matrix multiplication."""
+
+ return x @ y.unsqueeze(0)
+
+ def _matmul_with_relative_keys(self, x, y):
+ """Helper for relative key matrix multiplication."""
+
+ return x @ y.unsqueeze(0).transpose(-2, -1)
+
+ def _get_relative_embeddings_onnx(self, relative_embeddings, length):
+ """Slices relative embeddings to match current sequence length (ONNX compatible)."""
+
+ pad_length = (length - (self.window_size + 1)).clamp(min=0)
+ slice_start_position = ((self.window_size + 1) - length).clamp(min=0)
+
+ # Padding via concatenation to workaround ONNX padding limitations
+ x = torch.cat([torch.zeros(relative_embeddings.size(0), pad_length, relative_embeddings.size(2), device=relative_embeddings.device, dtype=relative_embeddings.dtype), relative_embeddings], dim=1)
+ relative_embeddings = torch.cat([x, torch.zeros(x.size(0), pad_length, x.size(2), device=x.device, dtype=x.dtype)], dim=1)
+
+ return relative_embeddings[:, slice_start_position:(slice_start_position + 2 * length - 1)]
+
+ def _get_relative_embeddings_torch(self, relative_embeddings, length):
+ """Slices relative embeddings to match current sequence length (PyTorch native)."""
+
+ pad_length = max(length - (self.window_size + 1), 0)
+ slice_start_position = max((self.window_size + 1) - length, 0)
+ # Native functional padding
+ if pad_length > 0:
+ relative_embeddings = F.pad(
+ relative_embeddings,
+ [0, 0, pad_length, pad_length, 0, 0],
+ )
+
+ return relative_embeddings[:, slice_start_position:(slice_start_position + 2 * length - 1)]
+
+ def _relative_position_to_absolute_position_onnx(self, x):
+ """Converts a relative representation matrix to absolute coordinates (ONNX compatible)."""
+
+ batch, heads, length, _ = x.size()
+ # Reshaping trick via manual padding to perform coordinate shifting
+ pad = self.padding(x, [0, 1]).view([batch, heads, length * 2 * length])
+ return self.padding(pad, [0, length - 1]).view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1 :]
+
+ def _relative_position_to_absolute_position_torch(self, x):
+ """Converts a relative representation matrix to absolute coordinates using structural padding."""
+
+ batch, heads, length, _ = x.size()
+ # Tensor flattening via padding trick to transform relative indices to absolute
+ pad = F.pad(x, [0, 1, 0, 0, 0, 0, 0, 0]).view([batch, heads, length * 2 * length])
+ return F.pad(pad, [0, length - 1, 0, 0, 0, 0]).view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1 :]
+
+ def _absolute_position_to_relative_position_onnx(self, x):
+ """Converts absolute coordinate metrics to relative position coordinates (ONNX compatible)."""
+
+ batch, heads, length, _ = x.size()
+
+ pad = self.padding(x, [0, length - 1]).view([batch, heads, length**2 + length * (length - 1)])
+ return self.padding(pad, [length, 0]).view([batch, heads, length, 2 * length])[:, :, :, 1:]
+
+ def _absolute_position_to_relative_position_torch(self, x):
+ """Converts absolute coordinate metrics to relative position coordinates using structural padding."""
+
+ batch, heads, length, _ = x.size()
+
+ pad = F.pad(x, [0, length - 1, 0, 0, 0, 0, 0, 0]).view([batch, heads, length**2 + length * (length - 1)])
+ return F.pad(pad, [length, 0, 0, 0, 0, 0]).view([batch, heads, length, 2 * length])[:, :, :, 1:]
+
+ def _attention_bias_proximal(self, length):
+ """Generates a localized attention bias matrix where penalty increases with distance."""
+
+ r = torch.arange(length, dtype=torch.float32)
+ diff = r.unsqueeze(0) - r.unsqueeze(1) # Matrix of structural distance gaps
+ return -diff.abs().log1p().unsqueeze(0).unsqueeze(0)
+
+ def padding(self, x, pad):
+ """Alternative zero padding function using concatenation to ensure ONNX support."""
+
+ left, right = pad[-2], pad[-1]
+ shape = list(x.shape[:-1])
+
+ # Concatenate zeros to the edges of the last dimension
+ x = torch.cat([torch.zeros(*shape, left, device=x.device, dtype=x.dtype), x], dim=-1)
+ x = torch.cat([x, torch.zeros(*shape, right, device=x.device, dtype=x.dtype)], dim=-1)
+
+ return x
+
+class FFN(nn.Module):
+ """
+ Feed-Forward Network (FFN) using 1D convolutions with support for
+ causal or identical padding, custom activations, and masking.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ filter_channels,
+ kernel_size,
+ p_dropout=0.0,
+ activation=None,
+ causal=False
+ ):
+ """
+ Initializes the FFN module.
+
+ Args:
+ in_channels (int): Number of input channels.
+ out_channels (int): Number of output channels.
+ filter_channels (int): Intermediate hidden layer channels.
+ kernel_size (int): Size of the convolutional kernel.
+ p_dropout (float): Dropout probability. Defaults to 0.0.
+ activation (str, optional): Type of activation ("gelu" or "relu"). Defaults to None.
+ causal (bool): If True, applies causal padding to prevent looking into future steps. Defaults to False.
+ """
+
+ super().__init__()
+ # 1D Convolution operations acting as position-wise layers
+ self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+ self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+ self.drop = nn.Dropout(p_dropout)
+ self.kernel_size = kernel_size
+ self.activation = activation
+ # Route padding technique conditionally
+ self.padding_fn = self._causal_padding if causal else self._same_padding
+
+ def forward(self, x, x_mask):
+ """
+ Forward pass for the Feed-Forward Network.
+
+ Args:
+ x (torch.Tensor): Input tensor.
+ x_mask (torch.Tensor): Mask tensor.
+
+ Returns:
+ torch.Tensor: Output tensor.
+ """
+
+ # First linear transformation layer with structured padding and sequence masking
+ x = self.conv_1(self.padding_fn(x * x_mask))
+ # Non-linear activation combined with dropout regularization
+ x = self.drop(self._apply_activation(x))
+ # Second linear transformation layer
+ x = self.conv_2(self.padding_fn(x * x_mask))
+
+ # Enforce zeros on masked elements in the final output
+ return x * x_mask
+
+ def _apply_activation(self, x):
+ """Applies configured non-linear function (supports custom fast approximate GELU or ReLU)."""
+ if self.activation == "gelu": return x * (1.702 * x).sigmoid() # Fast GeLU approximation formula
+ return x.relu()
+
+ def _causal_padding(self, x):
+ """Pads the input exclusively on the left to enforce temporal causality."""
+
+ return F.pad(
+ x,
+ [self.kernel_size, 0, 0, 0, 0, 0]
+ )
+
+ def _same_padding(self, x):
+ """Pads symmetrically on both edges to preserve equivalent sequence length."""
+
+ return F.pad(
+ x,
+ [(self.kernel_size - 1) // 2, self.kernel_size // 2, 0, 0, 0, 0],
+ )
\ No newline at end of file
diff --git a/main/library/algorithm/commons.py b/main/library/algorithm/commons.py
new file mode 100644
index 0000000000000000000000000000000000000000..2407a4f5bbd6579f4dc57146acde385c9dc1df86
--- /dev/null
+++ b/main/library/algorithm/commons.py
@@ -0,0 +1,235 @@
+import torch
+import functools
+
+def init_weights(m, mean=0.0, std=0.01):
+ """
+ Applies standard normal initialization parameters across target convolution layer elements.
+
+ Args:
+ m (nn.Module): Layer target instance node context.
+ mean (float, optional): Mean initialization factor. Defaults to 0.0.
+ std (float, optional): Variance scale standard deviation. Defaults to 0.01.
+ """
+
+ if m.__class__.__name__.find("Conv") != -1: m.weight.data.normal_(mean, std)
+
+def get_padding(kernel_size, dilation=1):
+ """
+ Calculates exact causal zero padding width to maintain temporal spatial resolution.
+
+ Args:
+ kernel_size (int): Size of the convolution kernel.
+ dilation (int, optional): Dilation factor coefficient rate. Defaults to 1.
+
+ Returns:
+ int: Required symmetric padding width.
+ """
+
+ return int((kernel_size * dilation - dilation) / 2)
+
+def slice_segments(x, ids_str, segment_size = 4, dim = 2):
+ """
+ Slices segments of a fixed size from a tensor based on given start indices.
+
+ Args:
+ x (torch.Tensor): Input tensor of shape (B, T) for dim=2 or (B, C, T) for dim=3.
+ ids_str (torch.Tensor): Start indices for each batch element, shape (B,).
+ segment_size (int, optional): The length of the sliced segment. Defaults to 4.
+ dim (int, optional): Dimension variant support. Must be 2 or 3. Defaults to 2.
+
+ Returns:
+ torch.Tensor: The sliced tensor with segment length equal to `segment_size`.
+ """
+
+ # Initialize the output tensor template based on the chosen dimension format
+ if dim == 2: ret = torch.zeros_like(x[:, :segment_size])
+ elif dim == 3: ret = torch.zeros_like(x[:, :, :segment_size]) # dim == 3
+
+ # Slice each batch element individually using the specific start index
+ for i in range(x.size(0)):
+ idx_str = ids_str[i].item()
+ idx_end = idx_str + segment_size
+
+ if dim == 2: ret[i] = x[i, idx_str:idx_end]
+ else: ret[i] = x[i, :, idx_str:idx_end]
+
+ return ret
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+ """
+ Randomly selects and slices contiguous segments from a batched sequence tensor.
+
+ Args:
+ x (torch.Tensor): Input tensor of shape (B, C, T).
+ x_lengths (torch.Tensor or int, optional): Actual lengths of sequences in the batch.
+ Defaults to the total time length T.
+ segment_size (int, optional): Desired segment length. Defaults to 4.
+
+ Returns:
+ tuple: (ret, ids_str)
+ - ret (torch.Tensor): Randomly sliced segments of shape (B, C, segment_size).
+ - ids_str (torch.Tensor): Generated random starting indices of shape (B,).
+ """
+
+ b, _, t = x.size()
+ if x_lengths is None: x_lengths = t
+
+ # Calculate the upper bound for the random start indices
+ ids_str_max = x_lengths - segment_size + 1
+ # Generate random start indices uniformly across the valid window range
+ ids_str = (torch.rand([b], device=x.device) * ids_str_max).to(dtype=torch.long)
+ # Perform the slice operation using the 3D tensor path
+ ret = slice_segments(x, ids_str, segment_size, dim=3)
+
+ return ret, ids_str
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+ """
+ Gated activation unit combining element-wise addition, Tanh, and Sigmoid layers.
+
+ Designed for TorchScript compilation to achieve high-performance fusion kernels.
+
+ Args:
+ input_a (torch.Tensor): First input activation map tensor.
+ input_b (torch.Tensor): Second input activation map tensor (e.g., condition/bias).
+ n_channels (torch.Tensor): A tensor containing the split channel index at index 0.
+
+ Returns:
+ torch.Tensor: Gated multiplied activations.
+ """
+
+ n_channels_int = n_channels[0]
+ # Fuse element-wise addition to save memory bandwidth
+ in_act = input_a + input_b
+
+ # Split channels into main activation branch and gating branch
+ t_act = in_act[:, :n_channels_int, :].tanh()
+ s_act = in_act[:, n_channels_int:, :].sigmoid()
+
+ # Apply gating mechanism via multiplication
+ acts = t_act * s_act
+ return acts
+
+def sequence_mask(length, max_length = None):
+ """
+ Generates a binary mask tensor for batches of variable-length sequences.
+
+ Args:
+ length (torch.Tensor): Tensor indicating actual lengths of sequences, shape (B,).
+ max_length (int, optional): Upper bound ceiling for the mask grid. Defaults to length.max().
+
+ Returns:
+ torch.Tensor: Boolean mask tensor of shape (B, max_length), where valid positions are True.
+ """
+
+ if max_length is None: max_length = length.max()
+ # Construct mask via broadcasting comparison: [max_length] matrix vs [B, 1] row matrix
+ return torch.arange(max_length, dtype=length.dtype, device=length.device).unsqueeze(0) < length.unsqueeze(1)
+
+def clip_grad_value(parameters, clip_value=None, norm_type=2):
+ """
+ Clips the gradients of an iterable of parameters and returns the accumulated norm.
+
+ Args:
+ parameters (Iterable[torch.Tensor] or torch.Tensor): Model weights/parameters.
+ clip_value (float, optional): Maximum allowed absolute gradient value. Clips in-place to [-clip_value, clip_value] if provided.
+ norm_type (float or int, optional): Type of the p-norm to compute. Defaults to 2.
+
+ Returns:
+ float: Total computed p-norm of the parameters' gradients prior to clipping.
+ """
+
+ if isinstance(parameters, torch.Tensor): parameters = [parameters]
+ norm_type = float(norm_type)
+
+ if clip_value is not None: clip_value = float(clip_value)
+ total_norm = 0
+
+ for p in list(filter(lambda p: p.grad is not None, parameters)): # Filter parameter list to extract only those with active gradients
+ # Accumulate total gradient norm using step-by-step un-averaged sum powers
+ total_norm += (p.grad.data.norm(norm_type)).item() ** norm_type
+ # Perform absolute value clamping in-place on target gradient
+ if clip_value is not None: p.grad.data.clamp_(min=-clip_value, max=clip_value)
+
+ # Return the global root of total accumulated norms
+ return total_norm ** (1.0 / norm_type)
+
+def grad_norm(parameters, norm_type = 2.0):
+ """
+ Computes the total vector p-norm over gradients of all parameters treated as a single vector.
+
+ Args:
+ parameters (Iterable[torch.Tensor] or torch.Tensor): Model weights/parameters.
+ norm_type (float or int, optional): Type of the p-norm to compute. Defaults to 2.0.
+
+ Returns:
+ float: The calculated global gradient vector norm. Returns 0.0 if no gradients are found.
+ """
+
+ if isinstance(parameters, torch.Tensor): parameters = [parameters]
+ # Extract active gradient tensors
+ parameters = [p for p in parameters if p.grad is not None]
+
+ if not parameters: return 0.0
+ # Stack norms of individual parameter tensors and compute overall vector norm safely
+ # Uses PyTorch native linalg engine to prevent underflow/overflow scaling issues
+ return torch.linalg.vector_norm(torch.stack([p.grad.norm(norm_type) for p in parameters]), ord=norm_type).item()
+
+def rescale_conv(conv, reference):
+ """
+ Rescales the weights and biases of a convolutional layer based on a reference standard deviation.
+
+ This function normalizes the weight tensor of a convolutional layer so that
+ its standard deviation matches a target reference scale, adjusted by a square root.
+ It prevents exploding or vanishing gradients during initialization.
+
+ Args:
+ conv (torch.nn.modules.conv._ConvNd): The convolutional layer to rescale.
+ reference (float): The target reference value (e.g., standard deviation of a baseline layer).
+ """
+
+ # Calculate the scaling factor based on the ratio of the current weight's standard deviation to the reference.
+ # The square root (** 0.5) is used here to dampen the scaling adjustment.
+ scale = (conv.weight.std().detach() / reference) ** 0.5
+ # In-place division of weights to adjust the variance/standard deviation
+ conv.weight.data /= scale
+ # If the layer uses a bias, scale it by the exact same factor to maintain relative magnitude
+ if conv.bias is not None: conv.bias.data /= scale
+
+def rescale_module(module, reference):
+ """
+ Recursively finds and rescales all convolutional layers within a PyTorch module.
+
+ Args:
+ module (torch.nn.Module): The root PyTorch module containing sub-modules to rescale.
+ reference (float): The target reference standard deviation passed to `rescale_conv`.
+ """
+
+ # Iterate through all child and sub-child modules recursively
+ for sub in module.modules():
+ # Check if the sub-module is a 1D or 2D standard or transposed convolutional layer
+ if isinstance(sub, (torch.nn.Conv1d, torch.nn.ConvTranspose1d, torch.nn.Conv2d, torch.nn.ConvTranspose2d)): rescale_conv(sub, reference)
+
+def capture_init(init):
+ """
+ A decorator to capture the positional and keyword arguments passed to a class `__init__` method.
+
+ This is highly useful for serialization, logging, or re-instantiating the object
+ later with its exact initial configuration.
+
+ Args:
+ init (callable): The original `__init__` method of a class.
+
+ Returns:
+ callable: The wrapped `__init__` method that stores arguments before executing.
+ """
+
+ @functools.wraps(init)
+ def __init__(self, *args, **kwargs):
+ # Store a copy of the initialization arguments as a tuple and dictionary in the instance self
+ self._init_args_kwargs = (args, kwargs)
+ # Call the original __init__ constructor to proceed with normal object setup
+ init(self, *args, **kwargs)
+
+ return __init__
\ No newline at end of file
diff --git a/main/library/algorithm/discriminators.py b/main/library/algorithm/discriminators.py
new file mode 100644
index 0000000000000000000000000000000000000000..d30a7bbf14572946a4d7f82716e625a68258b35f
--- /dev/null
+++ b/main/library/algorithm/discriminators.py
@@ -0,0 +1,279 @@
+import os
+import sys
+import torch
+import torch.nn.functional as F
+
+from torch.utils.checkpoint import checkpoint
+from torch.nn.utils.parametrizations import spectral_norm, weight_norm
+
+sys.path.append(os.getcwd())
+
+from main.library.algorithm.commons import get_padding
+from main.library.algorithm.residuals import LRELU_SLOPE
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+ """
+ Multi-Period Discriminator (MPD) architecture that wraps multiple sub-discriminators
+ including Scale (DiscriminatorS), Period (DiscriminatorP), and Resolution/Multi-Scale
+ Mel-Spectrogram (DiscriminatorR) variants. Used widely in adversarial audio synthesis (e.g., HiFi-GAN).
+ """
+
+ def __init__(self, version, use_spectral_norm=False, checkpointing=False):
+ """
+ Initializes the MultiPeriodDiscriminator ensemble based on a predefined configuration version.
+
+ Args:
+ version (str): Configuration version ("v0", "v1", "v2", or "v3").
+ use_spectral_norm (bool): If True, applies spectral normalization instead of weight normalization. Defaults to False.
+ checkpointing (bool): If True, utilizes gradient checkpointing to save memory during training. Defaults to False.
+ """
+
+ super(MultiPeriodDiscriminator, self).__init__()
+ self.checkpointing = checkpointing
+ # Configure periods and resolutions based on version criteria
+ if version == "v0":
+ periods = [2, 3, 5, 7, 11]
+ resolutions = []
+ elif version == "v1":
+ periods = [2, 3, 5, 7, 11, 17]
+ resolutions = []
+ elif version == "v2":
+ periods = [2, 3, 5, 7, 11, 17, 23, 37]
+ resolutions = []
+ elif version == "v3":
+ periods = [2, 3, 5, 7, 11]
+ resolutions = [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]]
+ else:
+ raise ValueError(f"Unknown MultiPeriodDiscriminator version: {version}")
+
+ # Construct sub-discriminators list dynamically
+ self.discriminators = torch.nn.ModuleList(
+ [DiscriminatorS(use_spectral_norm=use_spectral_norm)] +
+ [DiscriminatorP(p, use_spectral_norm=use_spectral_norm) for p in periods] +
+ [DiscriminatorR(r, use_spectral_norm=use_spectral_norm) for r in resolutions]
+ )
+
+ def forward(self, y, y_hat):
+ """
+ Evaluates both real ground-truth audio and generated audio across all sub-discriminators.
+
+ Args:
+ y (torch.Tensor): Real audio waveform tensor.
+ y_hat (torch.Tensor): Generated/Synthetic audio waveform tensor.
+
+ Returns:
+ Tuple containing:
+ - y_d_rs (List[torch.Tensor]): Real validation scores from each discriminator.
+ - y_d_gs (List[torch.Tensor]): Fake validation scores from each discriminator.
+ - fmap_rs (List[List[torch.Tensor]]): Feature maps extracted from real audio inputs.
+ - fmap_gs (List[List[torch.Tensor]]): Feature maps extracted from generated audio inputs.
+ """
+
+ y_d_rs, y_d_gs, fmap_rs, fmap_gs = [], [], [], []
+
+ for d in self.discriminators:
+ # Conditional routing to leverage activation checkpointing during backpropagation memory optimization
+ if self.training and self.checkpointing:
+ y_d_r, fmap_r = checkpoint(d, y, use_reentrant=False)
+ y_d_g, fmap_g = checkpoint(d, y_hat, use_reentrant=False)
+ else:
+ y_d_r, fmap_r = d(y)
+ y_d_g, fmap_g = d(y_hat)
+
+ y_d_rs.append(y_d_r); fmap_rs.append(fmap_r)
+ y_d_gs.append(y_d_g); fmap_gs.append(fmap_g)
+
+ return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+class DiscriminatorS(torch.nn.Module):
+ """
+ Multi-Scale / Sub-Scale Discriminator (MSD) variant acting on raw 1D audio sequences at full-scale.
+ """
+
+ def __init__(self, use_spectral_norm=False):
+ """
+ Initializes 1D convolutional layers with group configurations for localized pattern matching.
+
+ Args:
+ use_spectral_norm (bool): Normalization technique flag switch.
+ """
+
+ super(DiscriminatorS, self).__init__()
+ norm_f = spectral_norm if use_spectral_norm else weight_norm
+ # 1D Convolutional blocks with varying kernel lengths and grouped architectures
+ self.convs = torch.nn.ModuleList([
+ norm_f(torch.nn.Conv1d(1, 16, 15, 1, padding=7)),
+ norm_f(torch.nn.Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+ norm_f(torch.nn.Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+ norm_f(torch.nn.Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+ norm_f(torch.nn.Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+ norm_f(torch.nn.Conv1d(1024, 1024, 5, 1, padding=2))
+ ])
+ self.conv_post = norm_f(torch.nn.Conv1d(1024, 1, 3, 1, padding=1))
+ self.lrelu = torch.nn.LeakyReLU(LRELU_SLOPE)
+
+ def forward(self, x):
+ """
+ Args:
+ x (torch.Tensor): Audio waveform input tensor.
+
+ Returns:
+ Tuple[torch.Tensor, List[torch.Tensor]]: Flattened validity matrix score and intermediate feature map list.
+ """
+
+ fmap = []
+ for conv in self.convs:
+ x = self.lrelu(conv(x))
+ fmap.append(x)
+
+ x = self.conv_post(x)
+ fmap.append(x)
+
+ # Flatten features from dimension 1 to the end for categorical classification loss evaluation
+ return x.flatten(1, -1), fmap
+
+class DiscriminatorP(torch.nn.Module):
+ """
+ Period Discriminator that reshapes 1D audio signals into structural 2D planes
+ based on a defined period interval to evaluate periodic patterns.
+ """
+
+ def __init__(self, period, kernel_size=5, use_spectral_norm=False):
+ """
+ Args:
+ period (int): Periodic interval slicing window step.
+ kernel_size (int): Height dimension kernel size. Defaults to 5.
+ use_spectral_norm (bool): Normalization switch indicator.
+ """
+
+ super(DiscriminatorP, self).__init__()
+ self.period = period
+ norm_f = spectral_norm if use_spectral_norm else weight_norm
+ # 2D Convolutions utilizing a vertical strip kernel to treat periodic frames as image columns
+ self.convs = torch.nn.ModuleList([
+ norm_f(
+ torch.nn.Conv2d(
+ in_ch,
+ out_ch,
+ (kernel_size, 1),
+ (stride, 1),
+ padding=(get_padding(kernel_size, 1), 0)
+ )
+ )
+ for in_ch, out_ch, stride in zip(
+ [1, 32, 128, 512, 1024],
+ [32, 128, 512, 1024, 1024],
+ [3, 3, 3, 3, 1]
+ )
+ ])
+ self.conv_post = norm_f(torch.nn.Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+ self.lrelu = torch.nn.LeakyReLU(LRELU_SLOPE)
+
+ def forward(self, x):
+ """
+ Args:
+ x (torch.Tensor): Audio tensor.
+
+ Returns:
+ Tuple[torch.Tensor, List[torch.Tensor]]: Validated classification tensor and accumulated feature maps.
+ """
+
+ fmap = []
+ b, c, t = x.shape
+ # Pad sequence length to be perfectly divisible by the target period before 2D transformation
+ if t % self.period != 0: x = F.pad(x, (0, (self.period - (t % self.period))), "reflect")
+ # Reshape 1D data sequence to 2D image matrix
+ x = x.view(b, c, -1, self.period)
+
+ for conv in self.convs:
+ x = self.lrelu(conv(x))
+ fmap.append(x)
+
+ x = self.conv_post(x)
+ fmap.append(x)
+ return x.flatten(1, -1), fmap
+
+class DiscriminatorR(torch.nn.Module):
+ """
+ Resolution Discriminator that processes transformed spectrogram magnitudes
+ instead of raw raw time-domain audio sequences.
+ """
+
+ def __init__(self, resolution, use_spectral_norm=False):
+ """
+ Args:
+ resolution (List[int]): STFT configuration constants [n_fft, hop_length, win_length].
+ use_spectral_norm (bool): Normalization technique selection flag.
+ """
+
+ super().__init__()
+ self.resolution = resolution
+ self.lrelu_slope = LRELU_SLOPE # Specific localized slope modifier for LeakyReLU in Resolution domain
+ norm_f = spectral_norm if use_spectral_norm else weight_norm
+
+ # 2D Convolutions utilizing horizontal contextual kernels for time-frequency feature extraction
+ self.convs = torch.nn.ModuleList([
+ norm_f(torch.nn.Conv2d( 1, 32, (3, 9), padding=(1, 4))),
+ norm_f(torch.nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
+ norm_f(torch.nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
+ norm_f(torch.nn.Conv2d(32, 32, (3, 9), stride=(1, 2), padding=(1, 4))),
+ norm_f(torch.nn.Conv2d(32, 32, (3, 3), padding=(1, 1)))
+ ])
+ self.conv_post = norm_f(torch.nn.Conv2d(32, 1, (3, 3), padding=(1, 1)))
+
+ def forward(self, x):
+ """
+ Args:
+ x (torch.Tensor): Waveform audio signal.
+
+ Returns:
+ Tuple[torch.Tensor, List[torch.Tensor]]: Flattened evaluation result matrix and spectral feature tracking logs.
+ """
+
+ fmap = []
+ # Calculate Magnitude Spectrogram and append a dummy channel index
+ x = self.spectrogram(x).unsqueeze(1)
+
+ for layer in self.convs:
+ x = F.leaky_relu(layer(x), self.lrelu_slope)
+ fmap.append(x)
+
+ x = self.conv_post(x)
+ fmap.append(x)
+
+ return x.flatten(1, -1), fmap
+
+ def spectrogram(self, x):
+ """
+ Helper method computing the short-time Fourier transform (STFT) magnitude map.
+ Handles alternative hardware devices (like OpenCL / privateuseone backends) by falling back safely to CPU.
+
+ Args:
+ x (torch.Tensor): Raw time-series sequence data tensor.
+
+ Returns:
+ torch.Tensor: Linear amplitude spectrogram representation tensor.
+ """
+
+ n_fft, hop_length, win_length = self.resolution
+ pad = int((n_fft - hop_length) / 2)
+
+ # Detect non-standard backends to safeguard STFT compatibility errors
+ is_not_cuda = x.device.type in ["privateuseone", "ocl"]
+ # Execute Short-Time Fourier Transform
+ stft = torch.stft(
+ F.pad( # Apply structured reflection boundaries and squeeze out the single channel
+ x.cpu() if is_not_cuda else x,
+ (pad, pad),
+ mode="reflect"
+ ).squeeze(1),
+ n_fft=n_fft,
+ hop_length=hop_length,
+ win_length=win_length,
+ window=torch.ones(win_length, device="cpu" if is_not_cuda else x.device),
+ center=False,
+ return_complex=True
+ )
+
+ # Extract Euclidean norm magnitude from complex projections and cast back to the native input execution device
+ return torch.view_as_real(stft).norm(p=2, dim=-1).to(x.device)
\ No newline at end of file
diff --git a/main/library/algorithm/encoders.py b/main/library/algorithm/encoders.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e73552b21b794afddbab4f9ac00924557487e5e
--- /dev/null
+++ b/main/library/algorithm/encoders.py
@@ -0,0 +1,326 @@
+import os
+import sys
+import math
+import torch
+
+sys.path.append(os.getcwd())
+
+from main.library.algorithm.modules import WaveNet
+from main.library.algorithm.commons import sequence_mask
+from main.library.algorithm.normalization import LayerNorm
+from main.library.algorithm.attentions import MultiHeadAttention, FFN
+
+class Encoder(torch.nn.Module):
+ """
+ Transformer-style Encoder backbone block consisting of Multi-Head Attention,
+ Feed-Forward Networks (FFN), residual connections, and Layer Normalization.
+ Commonly utilized for processing sequence states in sequential text-to-speech models.
+ """
+
+ def __init__(
+ self,
+ hidden_channels,
+ filter_channels,
+ n_heads,
+ n_layers,
+ kernel_size=1,
+ p_dropout=0.0,
+ window_size=10,
+ onnx=False,
+ **kwargs
+ ):
+ """
+ Initializes the stack of identical Encoder blocks.
+
+ Args:
+ hidden_channels (int): Input and output hidden dimensionality of the model.
+ filter_channels (int): Intermediate channel depth within the FFN layer block.
+ n_heads (int): Count of parallel self-attention execution heads.
+ n_layers (int): The overall number of cascading module block repetitions.
+ kernel_size (int): Convolution kernel dimension for FFN blocks. Defaults to 1.
+ p_dropout (float): Dropout regularizer probability. Defaults to 0.0.
+ window_size (int): Context boundary range for localized position embeddings. Defaults to 10.
+ onnx (bool): Toggles ONNX-compatible functional paths inside modules. Defaults to False.
+ """
+
+ super().__init__()
+ self.hidden_channels = hidden_channels
+ self.n_layers = n_layers
+ self.drop = torch.nn.Dropout(p_dropout)
+ # Multi-Head Attention layers stack
+ self.attn_layers = torch.nn.ModuleList([
+ MultiHeadAttention(
+ hidden_channels,
+ hidden_channels,
+ n_heads,
+ p_dropout=p_dropout,
+ window_size=window_size,
+ onnx=onnx
+ )
+ for _ in range(n_layers)
+ ])
+
+ # Normalization layer stack prior to passing intermediate features forward
+ self.norm_layers_1 = torch.nn.ModuleList([
+ LayerNorm(
+ hidden_channels,
+ onnx=onnx
+ )
+ for _ in range(n_layers)
+ ])
+
+ # Position-wise Feed-Forward Network layer stack
+ self.ffn_layers = torch.nn.ModuleList([
+ FFN(
+ hidden_channels,
+ hidden_channels,
+ filter_channels,
+ kernel_size,
+ p_dropout=p_dropout
+ )
+ for _ in range(n_layers)
+ ])
+
+ # Secondary normalization layers following FFN transforms
+ self.norm_layers_2 = torch.nn.ModuleList([
+ LayerNorm(
+ hidden_channels,
+ onnx=onnx
+ )
+ for _ in range(n_layers)
+ ])
+
+ def forward(self, x, x_mask):
+ """
+ Executes sequential transformation computations over hidden sequence maps.
+
+ Args:
+ x (torch.Tensor): Hidden states vector sequence tensor.
+ x_mask (torch.Tensor): Binary temporal mask padding tensor.
+
+ Returns:
+ torch.Tensor: Transformed hidden states sequence matrix.
+ """
+
+ # Outer-product step to cast sequence masks into relational 2D matrix planes
+ attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+ x = x * x_mask # Initialize zero-out on padding entries
+
+ # Iterate forward propagation layers along the Transformer hierarchy
+ for i in range(self.n_layers):
+ # Block 1: Self-Attention layer joined with a standard residual shortcut path
+ x = self.norm_layers_1[i](x + self.drop(self.attn_layers[i](x, x, attn_mask)))
+ # Block 2: Feature-scaling FFN block paired with a standard residual shortcut path
+ x = self.norm_layers_2[i](x + self.drop(self.ffn_layers[i](x, x_mask)))
+
+ return x * x_mask
+
+class TextEncoder(torch.nn.Module):
+ """
+ Phoneme-level textual text encoder that captures textual representations
+ and optionally pairs them with explicit fundamental frequency (F0) tracking metrics.
+ """
+
+ def __init__(
+ self,
+ out_channels,
+ hidden_channels,
+ filter_channels,
+ n_heads,
+ n_layers,
+ kernel_size,
+ p_dropout,
+ embedding_dim,
+ f0=True,
+ onnx=False
+ ):
+ """
+ Initializes the linguistic text representation encoder block mapping.
+
+ Args:
+ out_channels (int): Channel capacity for projection parameters (mean and variance).
+ hidden_channels (int): Hidden dimension width.
+ filter_channels (int): Depth size inside internal FFN channels.
+ n_heads (int): Total attention head components.
+ n_layers (int): Depth repetitions count for core structural encoder layers.
+ kernel_size (int): FFN layer 1D conv width span.
+ p_dropout (float): Dropout probability.
+ embedding_dim (int): Vector width scale of the text phoneme features.
+ f0 (bool): Flag to instantiate structural pitch token embeddings. Defaults to True.
+ onnx (bool): ONNX translation compatibility marker. Defaults to False.
+ """
+
+ super(TextEncoder, self).__init__()
+ self.hidden_channels = hidden_channels
+ self.out_channels = out_channels
+ self.lrelu = torch.nn.LeakyReLU(0.1, inplace=True)
+ # Phoneme textual mapping layer
+ self.emb_phone = torch.nn.Linear(embedding_dim, hidden_channels)
+ # Quantized F0 pitch state representation layer
+ self.emb_pitch = torch.nn.Embedding(256, hidden_channels) if f0 else None
+ # Core feature encoder backbone
+ self.encoder = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, float(p_dropout), onnx=onnx)
+ # Prediction head tracking standard deviation logs and mean parameters
+ self.proj = torch.nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+ def forward(self, phone, pitch, lengths):
+ """
+ Extracts multi-variate statistical distributions representing sequence segments.
+
+ Args:
+ phone (torch.Tensor): Continuous phoneme tensor representation of shape (B, T, embedding_dim).
+ pitch (torch.Tensor, optional): Discrete fundamental frequency indices of shape (B, T).
+ lengths (torch.Tensor): Sequence duration measurements tensor of shape (B,).
+
+ Returns:
+ Tuple containing:
+ - m (torch.Tensor): Projected distribution mean map tensor of shape (B, out_channels, T).
+ - logs (torch.Tensor): Variance standard deviation logarithmic mapping scale of shape (B, out_channels, T).
+ - x_mask (torch.Tensor): Output padding tracking logic array of shape (B, 1, T).
+ """
+
+ x = self.emb_phone(phone)
+ if self.emb_pitch is not None: x += self.emb_pitch(pitch)
+
+ # Scale tensor inputs by hidden width radical prior to shifting axes format
+ x = self.lrelu(x * math.sqrt(self.hidden_channels)).transpose(1, -1)
+ # Generate masking criteria to discard padding indices during calculations
+ x_mask = sequence_mask(lengths, x.size(2)).unsqueeze(1).to(x.dtype)
+ # Project contextual embeddings, then split into mean (m) and log variance (logs) matrices
+ m, logs = (self.proj(self.encoder(x * x_mask, x_mask)) * x_mask).split(self.out_channels, dim=1)
+
+ return m, logs, x_mask
+
+class TextEncoderSVC(torch.nn.Module):
+ """
+ Specialized Content-to-Vocal feature variant mapping module tailored for Singing Voice Conversion (SVC).
+ Accepts raw pre-extracted audio content feature markers (e.g., ContentVec, Hubert).
+ """
+
+ def __init__(
+ self,
+ out_channels,
+ hidden_channels,
+ filter_channels,
+ n_heads,
+ n_layers,
+ kernel_size,
+ p_dropout,
+ onnx=False
+ ):
+ """Initializes the SVC Text/Content Encoder block."""
+
+ super().__init__()
+ self.hidden_channels = hidden_channels
+ self.out_channels = out_channels
+ self.f0_emb = torch.nn.Embedding(256, hidden_channels)
+ self.proj = torch.nn.Conv1d(hidden_channels, out_channels * 2, 1)
+ # Core encoder configured with a narrower window size for specialized voice audio tracking
+ self.encoder = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, float(p_dropout), window_size=4, onnx=onnx)
+
+ def forward(self, x, x_mask, f0=None, noise_scale=1.0):
+ """
+ Args:
+ x (torch.Tensor): Extracted audio content features matrix.
+ x_mask (torch.Tensor): Tensor indicating explicit segment sequence length positions.
+ f0 (torch.Tensor): Discrete F0 frame metrics arrays mapping pitch profiles.
+ noise_scale (float): Scale adjustment factor for Gaussian stochastic reparameterization. Defaults to 1.0.
+
+ Returns:
+ Tuple containing:
+ - z (torch.Tensor): Latent token sample array generated through reparameterization.
+ - m (torch.Tensor): Calculated content probability mean distributions.
+ - logs (torch.Tensor): Calculated content probability logarithmic variance tracking scales.
+ - x_mask (torch.Tensor): Sequence masking configuration logic matrix.
+ """
+
+ # Inject structural musical/vocal pitch embedding markers onto raw acoustic features
+ x = x + self.f0_emb(f0).transpose(1, 2)
+
+ # Compute output parameter statistics
+ m, logs = (self.proj(self.encoder(x * x_mask, x_mask)) * x_mask).split(self.out_channels, dim=1)
+ # Draw random standard Normal distribution variables and apply the Reparameterization Trick
+ z = (m + torch.randn_like(m) * logs.exp() * noise_scale) * x_mask
+
+ return z, m, logs, x_mask
+
+class PosteriorEncoder(torch.nn.Module):
+ """
+ Posterior Encoder module using non-causal WaveNet blocks to map acoustic targets
+ (e.g., linear spectrogram magnitudes or Mel features) into prior latent spaces.
+ Typically used in VAE-based frameworks like VITS.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ gin_channels=0
+ ):
+ """
+ Initializes the Posterior WaveNet Spectrogram Feature Encoder.
+
+ Args:
+ in_channels (int): Input acoustic map format channels (e.g., linear spec bins).
+ out_channels (int): Output dimensionality for latent vectors.
+ hidden_channels (int): Hidden channel layer volume within internal layers.
+ kernel_size (int): Size of 1D dilated casual convolutional kernels.
+ dilation_rate (int): Geometric structural spacing scaling multiplier base.
+ n_layers (int): Comprehensive layer length inside the WaveNet core block.
+ gin_channels (int): Global conditioning channel length (e.g., speaker embeddings). Defaults to 0.
+ """
+
+ super(PosteriorEncoder, self).__init__()
+ self.out_channels = out_channels
+ # Initial dimension adjustment module
+ self.pre = torch.nn.Conv1d(in_channels, hidden_channels, 1)
+ # Dilated residual convolution block backbone
+ self.enc = WaveNet(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+ # Output mean and variance parameter estimation projection layer
+ self.proj = torch.nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+ def forward(self, x, x_lengths, g = None):
+ """
+ Transforms acoustic spectrum values into sound distribution variables.
+
+ Args:
+ x (torch.Tensor): Linear audio spectrogram values sequence matrix.
+ x_lengths (torch.Tensor): Real unpadded step sequence time vectors.
+ g (torch.Tensor, optional): Speaker identity condition vectors matrix. Defaults to None.
+
+ Returns:
+ Tuple containing:
+ - z (torch.Tensor): Sampled random output latent target vectors.
+ - m (torch.Tensor): Target feature standard mean values projection.
+ - logs (torch.Tensor): Projected logarithmic standard deviation scale variance.
+ - x_mask (torch.Tensor): Tracking array indicating temporal sequence lengths.
+ """
+
+ # Form sequence masking filters
+ x_mask = sequence_mask(x_lengths, x.size(2)).unsqueeze(1).to(x.dtype)
+
+ # Run multi-layer forward execution pipelines
+ m, logs = (
+ self.proj(
+ self.enc(
+ self.pre(x) * x_mask,
+ x_mask,
+ g=g
+ )
+ ) * x_mask
+ ).split(self.out_channels, dim=1)
+
+ # Perform random latent generation using standard VAE reparameterization
+ return (m + torch.randn_like(m) * logs.exp()) * x_mask, m, logs, x_mask
+
+ def remove_weight_norm(self):
+ """
+ Unbinds and removes weight normalization tracking constants from internal layers
+ to optimize execution performance during inference.
+ """
+
+ self.enc.remove_weight_norm()
\ No newline at end of file
diff --git a/main/library/algorithm/faisssearch.py b/main/library/algorithm/faisssearch.py
new file mode 100644
index 0000000000000000000000000000000000000000..04ea991d51bd92e5322f635e2fbc226cb3a1229c
--- /dev/null
+++ b/main/library/algorithm/faisssearch.py
@@ -0,0 +1,173 @@
+import os
+import sys
+import faiss
+import torch
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations, logger
+
+class IndexWrapper:
+ """
+ A lightweight wrapper for loading, converting, and searching FAISS indexes using PyTorch tensors.
+
+ Supports:
+ - Reading a FAISS index
+ - Converting vectors to tensors
+ - Performing brute-force L2 distance search with PyTorch
+ """
+
+ def __init__(self, index_path, nprobe = 1, device="cuda", is_half=False, faiss_cpu=False, clamp=1e-8):
+ """
+ Initializes the IndexWrapper with paths, hardware options, and calculation parameters.
+
+ Args:
+ index_path (str): File system path to the serialized `.index` FAISS file.
+ nprobe (int): Number of clusters to query during Voronoi cells lookup. Defaults to 1.
+ device (str): Destination hardware string ("cuda" or "cpu"). Defaults to "cuda".
+ is_half (bool): If True, uses FP16 precision instead of FP32. Defaults to False.
+ faiss_cpu (bool): Explicitly forces the lookup strategy to rely on CPU. Defaults to False.
+ clamp (float): Lower constraint threshold value to avoid zero/negative distances. Defaults to 1e-8.
+ """
+
+ self.index_path = index_path
+ self.nprobe = nprobe
+ self.device = device
+ self.clamp = clamp
+ # In-memory structural database placeholders
+ self.index = None
+ self.big_npy = None
+ self.b_norms = None
+ self.big_tensor = None
+ # Data type definition mapping
+ self.dtype = torch.float16 if is_half else torch.float32
+ # Dynamically map the main search execution routing function path
+ self.search = self._search_cpu if faiss_cpu else self._search_gpu
+
+ def read_index(self):
+ """
+ Loads the FAISS index file from storage and extracts underlying target vector arrays.
+
+ Returns:
+ Tuple containing:
+ - index (faiss.Index or None): Initialized FAISS index struct.
+ - big_npy (np.ndarray or None): Matrix of full dataset vectors.
+ """
+
+ if self.index_path != "" and os.path.exists(self.index_path):
+ try:
+ # Load serialized structural mapping records from disk
+ index = faiss.read_index(self.index_path)
+ # Reconstruct full flat float arrays representing all registered indices
+ big_npy = index.reconstruct_n(0, index.ntotal)
+ except Exception as e:
+ logger.error(translations["read_faiss_index_error"].format(e=e))
+ index = big_npy = None
+ else: index = big_npy = None
+
+ # Set search probe cluster configuration depth limits if parsing was successful.
+ # Basically, it is only useful when you perform the search using the CPU.
+ if index is not None: index.nprobe = self.nprobe
+
+ self.index = index
+ self.big_npy = big_npy
+
+ return index, big_npy
+
+ def read_index_tensor(self):
+ """
+ Initializes structural index resources and maps feature arrays directly to VRAM.
+
+ Returns:
+ Tuple containing:
+ - big_tensor (torch.Tensor or None): High speed VRAM feature table tensor.
+ - b_norms (torch.Tensor or None): Precomputed horizontal squared L2 norm vectors.
+ """
+
+ self.read_index()
+
+ if self.index is None or self.big_npy is None: self.big_tensor, self.b_norms = None, None
+ else:
+ # Transfer flat vector arrays to GPU for accelerated matrix ops
+ self.big_tensor = torch.from_numpy(self.big_npy).to(self.dtype).to(self.device).contiguous()
+ # Precompute the squared L2 norm matrix to optimize distance calculation processes.
+ self.b_norms = (self.big_tensor ** 2).sum(dim=-1, keepdim=True).T.contiguous()
+
+ return self.big_tensor, self.b_norms
+
+ def setup_cpu(self, query, k=8):
+ """
+ Emergency fallback setup triggered when VRAM bounds are exceeded.
+ Frees up GPU spaces and routes operations back to host resources.
+
+ Args:
+ query (torch.Tensor): Vector query input data tensor.
+ k (int): Number of top items to fetch. Defaults to 8.
+
+ Returns:
+ Tuple[torch.Tensor, torch.Tensor]: Search metrics score outputs from CPU execution fallback.
+ """
+
+ logger.warning(translations["index_warn"])
+ # Permanently shift standard execution mapping to safe CPU paths
+ self.search = self._search_cpu
+ self.b_norms = None
+
+ # Dynamically call garbage collections to purge stranded allocations
+ from main.library.utils import clear_gpu_cache
+ clear_gpu_cache()
+
+ return self._search_cpu(query, k)
+
+ def _search_gpu(self, query, k=8):
+ """
+ Executes fast batched vector distance queries natively on VRAM devices
+ utilizing algebraic expansions
+
+ Args:
+ query (torch.Tensor): Query vectors matrix tensor.
+ k (int): Total near neighbors tracking depth. Defaults to 8.
+
+ Returns:
+ Tuple containing:
+ - distances (torch.Tensor): Calculated smallest scalar distances matrix.
+ - indices (torch.Tensor): Index positions pointing to closest target vectors.
+ """
+
+ with torch.inference_mode():
+ try:
+ # 1. Compute squared norms of the input queries: ||A||^2
+ q_norm = (query ** 2).sum(dim=-1, keepdim=True)
+
+ # 2. Perform matrix multiplication trick: distances = beta * ||B||^2 + alpha * (A @ B^T)
+ # Compiles down to a single optimized fused GPU calculation block
+ distances = torch.addmm(self.b_norms, query, self.big_tensor.T, alpha=-2.0, beta=1.0)
+
+ # 3. Complete expansion: distances = (||B||^2 - 2AB^T) + ||A||^2
+ distances.add_(q_norm)
+ distances.clamp_(min=self.clamp) # Clamp to handle rounding anomalies
+
+ # 4. Extract smallest elements by utilizing TopK over negative fields
+ scores, indices = torch.topk(-distances, k=k, dim=-1)
+ return -scores, indices
+ except (torch.OutOfMemoryError, RuntimeError):
+ # Gracefully intercept hardware runtime execution errors and trigger CPU routing routines
+ return self.setup_cpu(query, k)
+
+ def _search_cpu(self, query, k=8):
+ """
+ Standard fallback execution method processing similarity queries over host system CPU spaces.
+
+ Args:
+ query (torch.Tensor): Vector query input tensor.
+ k (int): Target neighbor retrieval matching limit. Defaults to 8.
+
+ Returns:
+ Tuple[torch.Tensor, torch.Tensor]: Score results array and associated mapping indices locations.
+ """
+
+ # Execute traditional index structure searching out of NumPy array references
+ score, ix = self.index.search(query.cpu().numpy(), k)
+
+ # Cast results back to native environment storage targets
+ return torch.from_numpy(score).to(self.dtype).to(self.device), torch.from_numpy(ix).to(self.device).long()
\ No newline at end of file
diff --git a/main/library/algorithm/modules.py b/main/library/algorithm/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf46304b55d384b413f49afcb48d03bdad85b2e7
--- /dev/null
+++ b/main/library/algorithm/modules.py
@@ -0,0 +1,154 @@
+import os
+import sys
+import torch
+
+import torch.nn.utils.parametrize as parametrize
+
+sys.path.append(os.getcwd())
+
+from main.library.algorithm.commons import fused_add_tanh_sigmoid_multiply
+
+class WaveNet(torch.nn.Module):
+ """
+ A 1D Dilated Non-Causal WaveNet architecture acting as a residual sequence
+ backbone. It utilizes Gated Activation Units (tanh and sigmoid multiplication)
+ and supports global conditioning (e.g., speaker or noise embeddings).
+ """
+
+ def __init__(
+ self,
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ gin_channels=0,
+ p_dropout=0
+ ):
+ """
+ Initializes the WaveNet architecture layer blocks.
+
+ Args:
+ hidden_channels (int): Input and output hidden channel size.
+ kernel_size (int): Size of the 1D convolution kernel. Must be an odd number.
+ dilation_rate (int): Base dilation factor geometric growth multiplier.
+ n_layers (int): Total depth number of dilated convolutional layers.
+ gin_channels (int): Channels for global conditioning input. Defaults to 0 (disabled).
+ p_dropout (float): Dropout probability. Defaults to 0.0.
+ """
+
+ super(WaveNet, self).__init__()
+ assert kernel_size % 2 == 1
+ self.hidden_channels = hidden_channels
+ self.kernel_size = (kernel_size,)
+ self.dilation_rate = dilation_rate
+ self.n_layers = n_layers
+ self.gin_channels = gin_channels
+ self.p_dropout = p_dropout
+ # Kept as a tensor block to serve as a channel delimiter indicator for custom fused operators
+ self.n_channels_tensor = torch.IntTensor([hidden_channels])
+ self.in_layers = torch.nn.ModuleList()
+ self.res_skip_layers = torch.nn.ModuleList()
+ self.drop = torch.nn.Dropout(p_dropout)
+
+ # Optional projection layer for global condition embeddings (e.g., Speaker IDs)
+ if gin_channels:
+ self.cond_layer = torch.nn.utils.parametrizations.weight_norm(
+ torch.nn.Conv1d(
+ gin_channels,
+ 2 * hidden_channels * n_layers,
+ 1
+ ),
+ name="weight"
+ )
+
+ # Precompute dilation sizes and matching padding limits to keep temporal lengths aligned
+ dilations = [dilation_rate**i for i in range(n_layers)]
+ paddings = [(kernel_size * d - d) // 2 for d in dilations]
+
+ # Construct cascading WaveNet layer blocks
+ for i in range(n_layers):
+ # Dilated convolution layer projecting to gated channels
+ self.in_layers.append(
+ torch.nn.utils.parametrizations.weight_norm(
+ torch.nn.Conv1d(
+ hidden_channels,
+ 2 * hidden_channels,
+ kernel_size,
+ dilation=dilations[i],
+ padding=paddings[i]
+ ),
+ name="weight"
+ )
+ )
+
+ # Determine structural channels for residual and skip connection pathways
+ # The final layer only needs to output residual features (hidden_channels)
+ res_skip_channels = (hidden_channels if i == n_layers - 1 else 2 * hidden_channels)
+
+ self.res_skip_layers.append(
+ torch.nn.utils.parametrizations.weight_norm(
+ torch.nn.Conv1d(
+ hidden_channels,
+ res_skip_channels,
+ 1
+ ),
+ name="weight"
+ )
+ )
+
+ def forward(self, x, x_mask, g=None):
+ """
+ Executes WaveNet forward propagation over sequences.
+
+ Args:
+ x (torch.Tensor): Input hidden representation tensor.
+ x_mask (torch.Tensor): Temporal tracking binary mask tensor.
+ g (torch.Tensor, optional): Global conditioning embedding. Defaults to None.
+
+ Returns:
+ torch.Tensor: Aggregated sequence tensor maps.
+ """
+
+ # Allocate accumulation storage initialized to zeros
+ output = x.clone().zero_()
+ # Pre-project global condition embeddings across all layers simultaneously if present
+ g = self.cond_layer(g) if g is not None else None
+
+ for i in range(self.n_layers):
+ # Pass input data through the dilated 1D convolution layer
+ x_in = self.in_layers[i](x)
+ # Extract the unique chunk of conditional parameters designated for the current layer block
+ g_l = (g[:, i * 2 * self.hidden_channels : (i + 1) * 2 * self.hidden_channels, :] if g is not None else 0)
+
+ # Run dropout regularizer followed by 1x1 projection mapping
+ # Apply Gated Activation Unit (GAU): tanh(x_conv + g_tanh) * sigmoid(x_conv + g_sigmoid)
+ # Implemented as a highly optimized fused function to minimize GPU memory traffic
+ res_skip_acts = self.res_skip_layers[i](self.drop(fused_add_tanh_sigmoid_multiply(x_in, g_l, self.n_channels_tensor)))
+
+ if i < self.n_layers - 1:
+ # Mid layer paths: update the core representation for the next block using residual values
+ x = (x + res_skip_acts[:, : self.hidden_channels, :]) * x_mask
+ # Accumulate the remaining section directly into the global skip connection track
+ output = output + res_skip_acts[:, self.hidden_channels :, :]
+ else: output = output + res_skip_acts # Final layer path: accumulate the remaining output states entirely into the skip connections
+
+ return output * x_mask
+
+ def remove_weight_norm(self):
+ """
+ Removes weight normalization from all convolution layers.
+ Compatible with both PyTorch modern 'parametrizations' API and legacy hooks
+ to ensure seamless inference execution.
+ """
+
+ if self.gin_channels != 0:
+ if hasattr(self.cond_layer, "parametrizations") and "weight" in self.cond_layer.parametrizations: parametrize.remove_parametrizations(self.cond_layer, "weight", leave_parametrized=True)
+ else: torch.nn.utils.remove_weight_norm(self.cond_layer)
+
+ for l in self.in_layers:
+ if hasattr(l, "parametrizations") and "weight" in l.parametrizations: parametrize.remove_parametrizations(l, "weight", leave_parametrized=True)
+ else: torch.nn.utils.remove_weight_norm(l)
+
+ for l in self.res_skip_layers:
+ if hasattr(l, "parametrizations") and "weight" in l.parametrizations: parametrize.remove_parametrizations(l, "weight", leave_parametrized=True)
+ else: torch.nn.utils.remove_weight_norm(l)
\ No newline at end of file
diff --git a/main/library/algorithm/normalization.py b/main/library/algorithm/normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..b94a157878b8c29ef5293a69d1e824467ee9b6e6
--- /dev/null
+++ b/main/library/algorithm/normalization.py
@@ -0,0 +1,198 @@
+import torch
+
+import torch.nn.functional as F
+
+class LayerNorm(torch.nn.Module):
+ """
+ Custom Layer Normalization module designed for channel-first tensors.
+ It dynamically selects between a standard PyTorch execution path and an
+ ONNX-friendly path with static shape definitions to facilitate model export.
+ """
+
+ def __init__(self, channels, eps=1e-5, onnx=False):
+ """
+ Initializes the LayerNorm module and sets the appropriate forward path.
+
+ Args:
+ channels (int): Expected number of channels in the input tensor.
+ eps (float): A value added to the denominator for numerical stability. Defaults to 1e-5.
+ onnx (bool): If True, configures the module to use static shape constraints for ONNX export.
+ """
+
+ super().__init__()
+ self.channels = channels
+ self.eps = eps
+ # Learnable scale (gamma) and shift (beta) parameters initialized to 1s and 0s
+ self.gamma = torch.nn.Parameter(torch.ones(channels))
+ self.beta = torch.nn.Parameter(torch.zeros(channels))
+ # Dynamic forward routing based on targeted runtime compiler environment
+ self.forward = self._forward_onnx if onnx else self._forward_torch
+
+ def _forward_onnx(self, x):
+ """
+ ONNX-compatible forward path using a explicit static channel length constraint.
+ """
+
+ # Swap axes to channel-last representation
+ x = x.transpose(1, -1)
+
+ # Normalize across the fixed static channel dimension parameter
+ return F.layer_norm(
+ x,
+ (self.channels,),
+ self.gamma,
+ self.beta,
+ self.eps
+ ).transpose(1, -1) # Restore axis format back
+
+ def _forward_torch(self, x):
+ """
+ Standard PyTorch forward path tracking shape dimensions dynamically.
+ """
+
+ # Swap axes to channel-last representation
+ x = x.transpose(1, -1)
+
+ # Normalize across the runtime-evaluated dynamic dimension length
+ return F.layer_norm(
+ x,
+ (x.size(-1),),
+ self.gamma,
+ self.beta,
+ self.eps
+ ).transpose(1, -1) # Restore axis format back
+
+class Fp32LayerNorm(torch.nn.LayerNorm):
+ """
+ A Layer Normalization wrapper that forces operations to execute in Float32 precision.
+ Prevents underflow/overflow artifacts when running models under low-precision mix (AMP/FP16).
+ """
+
+ def __init__(
+ self,
+ *args,
+ **kwargs
+ ):
+ """Initializes the base torch.nn.LayerNorm wrapper module."""
+
+ super().__init__(*args, **kwargs)
+
+ def forward(self, input):
+ """
+ Casts inputs to full FP32 resolution before evaluating layer norm criteria.
+
+ Args:
+ input (torch.Tensor): Input tensor of arbitrary shape.
+
+ Returns:
+ torch.Tensor: Normalized tensor matched back to the input's original data type.
+ """
+
+ # Upscale weights, biases, and activation features to 32-bit floating point matrix maps
+ output = F.layer_norm(
+ input.float(),
+ self.normalized_shape,
+ self.weight.float() if self.weight is not None else None,
+ self.bias.float() if self.bias is not None else None,
+ self.eps
+ )
+
+ # Downscale precision state safely back to the tensor format passed by the caller
+ return output.type_as(input)
+
+class Fp32GroupNorm(torch.nn.GroupNorm):
+ """
+ A Group Normalization wrapper that forces operations to execute in Float32 precision.
+ Ensures mathematical stability across separate split feature sub-channels under mixed-precision.
+ """
+
+ def __init__(
+ self,
+ *args,
+ **kwargs
+ ):
+ """Initializes the base torch.nn.GroupNorm wrapper module."""
+
+ super().__init__(*args, **kwargs)
+
+ def forward(self, input):
+ """
+ Casts inputs to full FP32 resolution before evaluating group norm criteria.
+
+ Args:
+ input (torch.Tensor): Input tensor of arbitrary shape.
+
+ Returns:
+ torch.Tensor: Group-normalized tensor recast back to the input's native format.
+ """
+
+ # Execute Group Normalization calculations at 32-bit resolution to protect scaling logic
+ output = F.group_norm(
+ input.float(),
+ self.num_groups,
+ self.weight.float() if self.weight is not None else None,
+ self.bias.float() if self.bias is not None else None,
+ self.eps
+ )
+
+ return output.type_as(input)
+
+class Fp32GroupNormTranspose(torch.nn.GroupNorm):
+ """
+ A specialized Group Normalization wrapper executing at Float32 precision
+ tailored specifically for channel-last sequence formats.
+ Swap the internal dimensions to comply with PyTorch's default GroupNorm constraint.
+ """
+
+ def __init__(
+ self,
+ *args,
+ **kwargs
+ ):
+ """Initializes the underlying GroupNorm module infrastructure."""
+
+ super().__init__(
+ *args,
+ **kwargs
+ )
+
+ def forward(self, input):
+ """
+ Transposes input sequence steps, computes group norms in FP32, and restores original axes.
+
+ Args:
+ input (torch.Tensor): Channel-last sequence tensor.
+
+ Returns:
+ torch.Tensor: Transposed and normalized sequence tensor matching shape.
+ """
+
+ # Rearrange layout to match standard channel-first expectations
+ input = input.transpose(1, 2)
+
+ # Cast tensors to full 32-bit float mappings and run Group Norm functions
+ output = F.group_norm(
+ input.float(),
+ self.num_groups,
+ self.weight.float() if self.weight is not None else None,
+ self.bias.float() if self.bias is not None else None,
+ self.eps
+ ).transpose(1, 2) # Revert output structures back to original axis layout
+
+ return output.type_as(input)
+
+class LayerScale(torch.nn.Module):
+ def __init__(
+ self,
+ channels,
+ init = 0,
+ channel_last=False
+ ):
+ super().__init__()
+ self.channel_last = channel_last
+ self.scale = torch.nn.Parameter(torch.zeros(channels, requires_grad=True))
+ self.scale.data[:] = init
+
+ def forward(self, x):
+ if self.channel_last: return self.scale * x
+ else: return self.scale[:, None] * x
\ No newline at end of file
diff --git a/main/library/algorithm/residuals.py b/main/library/algorithm/residuals.py
new file mode 100644
index 0000000000000000000000000000000000000000..db292900ac259f5c81aa45633d268ca12150d546
--- /dev/null
+++ b/main/library/algorithm/residuals.py
@@ -0,0 +1,275 @@
+import os
+import sys
+import torch
+
+import torch.nn.functional as F
+import torch.nn.utils.parametrize as parametrize
+
+from itertools import chain
+from torch.nn.utils import remove_weight_norm
+from torch.nn.utils.parametrizations import weight_norm
+
+sys.path.append(os.getcwd())
+
+from main.library.algorithm.modules import WaveNet
+from main.library.algorithm.commons import get_padding, init_weights
+
+LRELU_SLOPE = 0.1
+
+def create_conv1d_layer(channels, kernel_size, dilation):
+ """
+ Helper function to instantiate a 1D Convolutional layer wrapped with weight normalization
+ and automatically calculated padding to preserve sequence length.
+ """
+
+ return weight_norm(
+ torch.nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ 1,
+ dilation=dilation,
+ padding=get_padding(kernel_size, dilation)
+ )
+ )
+
+def apply_mask(tensor, mask):
+ """
+ Applies a binary tracking sequence mask to clear out values in padding positions.
+ """
+
+ return tensor * mask if mask is not None else tensor
+
+class ResBlock(torch.nn.Module):
+ """
+ Standard Residual Block containing a series of dilated 1D Convolutions with
+ LeakyReLU activations, acting as a secondary alternative backbone layer.
+ """
+
+ def __init__(
+ self,
+ channels,
+ kernel_size=3,
+ dilations=(1, 3, 5)
+ ):
+ """
+ Initializes convolutional block stacks.
+
+ Args:
+ channels (int): Input and output channel dimensionality.
+ kernel_size (int): Convolution kernel width. Defaults to 3.
+ dilations (List[int]): Dilation multipliers for each parallel layer sequence. Defaults to (1, 3, 5).
+ """
+
+ super().__init__()
+ self.convs1 = self._create_convs(channels, kernel_size, dilations)
+ self.convs2 = self._create_convs(channels, kernel_size, [1] * len(dilations))
+
+ @staticmethod
+ def _create_convs(channels, kernel_size, dilations):
+ """
+ Generates weight-normalized conv layers and applies model parameter weight initializations.
+ """
+
+ layers = torch.nn.ModuleList([
+ create_conv1d_layer(channels, kernel_size, d)
+ for d in dilations
+ ])
+ layers.apply(init_weights)
+
+ return layers
+
+ def forward(self, x):
+ """
+ Args:
+ x (torch.Tensor): Input hidden state matrix.
+ """
+
+ for conv1, conv2 in zip(self.convs1, self.convs2):
+ x = conv2(F.leaky_relu(conv1(F.leaky_relu(x, LRELU_SLOPE)), LRELU_SLOPE)) + x
+
+ return x
+
+ def remove_weight_norm(self):
+ """Removes weight normalization configurations across all internal layers."""
+
+ for conv in chain(self.convs1, self.convs2):
+ if hasattr(conv, "parametrizations") and "weight" in conv.parametrizations: parametrize.remove_parametrizations(conv, "weight", leave_parametrized=True)
+ else: remove_weight_norm(conv)
+
+class Flip(torch.nn.Module):
+ """
+ A bi-directional flow layer that inverts/flips the sequence order along the channel dimension.
+ Ensures that alternative parts of the split features interact across sequential flow steps.
+ """
+
+ def forward(
+ self,
+ x,
+ *args,
+ reverse=False,
+ **kwargs
+ ):
+ """
+ Flips the channel axis order.
+
+ Args:
+ x (torch.Tensor): Feature tensor.
+ reverse (bool): Execution direction trigger flag. Defaults to False.
+
+ Returns:
+ - If forward (reverse=False): Tuple containing the flipped tensor and a zero log-determinant.
+ - If backward (reverse=True): Flipped tensor only.
+ """
+
+ x = x.flip([1]) # Flip across dimension 1 (Channels)
+
+ if not reverse: return x, torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+ else: return x
+
+class ResidualCouplingBlock(torch.nn.Module):
+ """
+ Multi-layer Normalizing Flow block containing alternating Residual Coupling Layers
+ and Channel-Flipping layers. This handles complex probability distribution mappings
+ invertibly (e.g., from linear specs to latents and back) in VITS architectures.
+ """
+
+ def __init__(
+ self,
+ channels,
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ n_flows=4,
+ gin_channels=0
+ ):
+ """
+ Initializes standard flows consisting of sequential Coupling Layers paired with Flips.
+ """
+
+ super(ResidualCouplingBlock, self).__init__()
+ self.channels = channels
+ self.hidden_channels = hidden_channels
+ self.kernel_size = kernel_size
+ self.dilation_rate = dilation_rate
+ self.n_layers = n_layers
+ self.n_flows = n_flows
+ self.gin_channels = gin_channels
+ self.flows = torch.nn.ModuleList()
+
+ for _ in range(n_flows):
+ self.flows.append(
+ ResidualCouplingLayer(
+ channels,
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ gin_channels=gin_channels,
+ mean_only=True
+ )
+ )
+ self.flows.append(Flip())
+
+ def forward(self, x, x_mask, g = None, reverse = False):
+ """
+ Propagates sequence transformations through the flow network.
+
+ Args:
+ x (torch.Tensor): Input hidden vector matrix.
+ x_mask (torch.Tensor): Binary temporal mask padding tensor.
+ g (torch.Tensor, optional): Global speaker embedding condition context.
+ reverse (bool): If True, executes the inverse mathematical mapping path. Defaults to False.
+ """
+
+ if not reverse:
+ # Forward propagation passes consecutively from step 0 to end
+ for flow in self.flows:
+ x, _ = flow(x, x_mask, g=g, reverse=reverse)
+ else:
+ # Inverse backward path processes blocks in strict backwards order
+ for flow in reversed(self.flows):
+ x = flow.forward(x, x_mask, g=g, reverse=reverse)
+
+ return x
+
+ def remove_weight_norm(self):
+ """Removes weight normalization from internal WaveNet layers."""
+ for i in range(self.n_flows):
+ self.flows[i * 2].remove_weight_norm()
+
+class ResidualCouplingLayer(torch.nn.Module):
+ """
+ An affine or additive coupling layer split across the channel axis.
+ One half remains unchanged and generates parameters (mean/variance) via WaveNet
+ to transform the remaining half, keeping the transformation entirely invertible.
+ """
+
+ def __init__(
+ self,
+ channels,
+ hidden_channels,
+ kernel_size,
+ dilation_rate,
+ n_layers,
+ p_dropout=0,
+ gin_channels=0,
+ mean_only=False
+ ):
+ """Initializes components for the specific Coupling Layer segment."""
+
+ assert channels % 2 == 0
+ super().__init__()
+ self.channels = channels
+ self.hidden_channels = hidden_channels
+ self.kernel_size = kernel_size
+ self.dilation_rate = dilation_rate
+ self.n_layers = n_layers
+ self.half_channels = channels // 2
+ self.mean_only = mean_only
+
+ # Dimension matching input layer
+ self.pre = torch.nn.Conv1d(self.half_channels, hidden_channels, 1)
+ # Deep internal processing module
+ self.enc = WaveNet(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
+ # Output projection head: switches size depending on whether variance tracking logs are calculated
+ self.post = torch.nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+
+ # Initialize final projection parameters to zero so the layer acts as an identity mapping initially
+ self.post.weight.data.zero_()
+ self.post.bias.data.zero_()
+
+ def forward(self, x, x_mask, g=None, reverse=False):
+ """
+ Executes coupling steps to transform split tensors.
+
+ Args:
+ x (torch.Tensor): Audio latent/mel representation tensor of shape (B, C, T).
+ x_mask (torch.Tensor): Sequence masking configuration logic matrix (B, 1, T).
+ g (torch.Tensor, optional): Conditioning vector matrix. Defaults to None.
+ reverse (bool): Direction switch flag (False = forward, True = inverse reconstruction).
+ """
+
+ # Split features equally along the channel index dimension
+ x0, x1 = x.split([self.half_channels] * 2, 1)
+ # Pass the first half through WaveNet blocks to compute transformation parameters
+ stats = self.post(self.enc((self.pre(x0) * x_mask), x_mask, g=g)) * x_mask
+
+ # Extract shift (m) and scale scale logs parameters accordingly
+ if not self.mean_only: m, logs = stats.split([self.half_channels] * 2, 1)
+ else:
+ m = stats
+ logs = torch.zeros_like(m) # Zero scale logs map implies an additive coupling mode
+
+ if not reverse:
+ # Forward transform
+ return torch.cat([x0, (m + x1 * logs.exp() * x_mask)], 1), logs.sum(dim=[1, 2])
+ else:
+ # Inverse exact reconstruction
+ return torch.cat([x0, ((x1 - m) * (-logs).exp() * x_mask)], 1)
+
+ def remove_weight_norm(self):
+ """Propagates normalization stripping commands downstream to WaveNet layers."""
+
+ self.enc.remove_weight_norm()
\ No newline at end of file
diff --git a/main/library/algorithm/synthesizers.py b/main/library/algorithm/synthesizers.py
new file mode 100644
index 0000000000000000000000000000000000000000..95b4a0c918fd624b0626ca29d383b7df5aee6857
--- /dev/null
+++ b/main/library/algorithm/synthesizers.py
@@ -0,0 +1,446 @@
+import os
+import sys
+import torch
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import logger, translations
+from main.library.algorithm.residuals import ResidualCouplingBlock
+from main.library.algorithm.encoders import TextEncoder, PosteriorEncoder, TextEncoderSVC
+from main.library.algorithm.commons import slice_segments, rand_slice_segments, sequence_mask
+
+class Synthesizer(torch.nn.Module):
+ """
+ Synthesizer module for Voice Conversion (commonly used in RVC inference/training pipelines).
+ Transforms source speech representations into target speaker characteristics
+ using a PosteriorEncoder, Flow model, and dynamic Vocoder selections.
+ """
+
+ def __init__(
+ self,
+ spec_channels,
+ segment_size,
+ inter_channels,
+ hidden_channels,
+ filter_channels,
+ n_heads,
+ n_layers,
+ kernel_size,
+ p_dropout,
+ resblock,
+ resblock_kernel_sizes,
+ resblock_dilation_sizes,
+ upsample_rates,
+ upsample_initial_channel,
+ upsample_kernel_sizes,
+ spk_embed_dim,
+ gin_channels,
+ sr,
+ use_f0,
+ text_enc_hidden_dim=768,
+ vocoder="Default",
+ checkpointing=False,
+ onnx=False,
+ **kwargs
+ ):
+ """Initializes components of the RVC Synthesizer architecture."""
+
+ super(Synthesizer, self).__init__()
+ self.segment_size = segment_size
+ self.use_f0 = use_f0
+
+ # Feature/Text Encoder processes linguistic/content units into prior distributions
+ self.enc_p = TextEncoder(
+ inter_channels,
+ hidden_channels,
+ filter_channels,
+ n_heads,
+ n_layers,
+ kernel_size,
+ float(p_dropout),
+ text_enc_hidden_dim,
+ f0=use_f0,
+ onnx=onnx
+ )
+
+ # Dynamic mapping setup for targeted Audio Waveform Generators (Vocoders)
+ if use_f0:
+ if vocoder == "RefineGAN":
+ from main.library.generators.refinegan import RefineGANGenerator
+
+ logger.info(translations["use_vocoders"].format(name="REFINEGAN"))
+
+ self.dec = RefineGANGenerator(
+ sample_rate=sr,
+ upsample_rates=upsample_rates,
+ num_mels=inter_channels,
+ checkpointing=checkpointing
+ )
+ elif vocoder == "BigVGAN":
+ from main.library.generators.bigvgan import BigVGANGenerator
+
+ logger.info(translations["use_vocoders"].format(name="BIGVGAN"))
+
+ self.dec = BigVGANGenerator(
+ in_channel=inter_channels,
+ upsample_initial_channel=upsample_initial_channel,
+ upsample_rates=upsample_rates,
+ upsample_kernel_sizes=upsample_kernel_sizes,
+ resblock_kernel_sizes=resblock_kernel_sizes,
+ resblock_dilations=resblock_dilation_sizes,
+ gin_channels=gin_channels,
+ sample_rate=sr,
+ harmonic_num=0,
+ )
+ elif vocoder in ["MRF-HiFi-GAN", "MRF HiFi-GAN"]:
+ from main.library.generators.mrf_hifigan import HiFiGANMRFGenerator
+
+ logger.info(translations["use_vocoders"].format(name="MRF-HIFIGAN"))
+
+ self.dec = HiFiGANMRFGenerator(
+ in_channel=inter_channels,
+ upsample_initial_channel=upsample_initial_channel,
+ upsample_rates=upsample_rates,
+ upsample_kernel_sizes=upsample_kernel_sizes,
+ resblock_kernel_sizes=resblock_kernel_sizes,
+ resblock_dilations=resblock_dilation_sizes,
+ gin_channels=gin_channels,
+ sample_rate=sr,
+ harmonic_num=8,
+ checkpointing=checkpointing
+ )
+ else:
+ from main.library.generators.nsf_hifigan import HiFiGANNSFGenerator
+
+ logger.info(translations["use_vocoders"].format(name="NSF-HIFIGAN"))
+
+ self.dec = HiFiGANNSFGenerator(
+ inter_channels,
+ resblock_kernel_sizes,
+ resblock_dilation_sizes,
+ upsample_rates,
+ upsample_initial_channel,
+ upsample_kernel_sizes,
+ gin_channels=gin_channels,
+ sr=sr,
+ checkpointing=checkpointing,
+ harmonic_num=0
+ )
+ else:
+ from main.library.generators.hifigan import HiFiGANGenerator
+
+ logger.info(translations["use_vocoders"].format(name="HIFIGAN"))
+
+ self.dec = HiFiGANGenerator(
+ inter_channels,
+ resblock_kernel_sizes,
+ resblock_dilation_sizes,
+ upsample_rates,
+ upsample_initial_channel,
+ upsample_kernel_sizes,
+ gin_channels=gin_channels
+ )
+
+ # Posterior Encoder extracts latent traits from target linear spectrograms during training
+ self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
+ # Normalizing Flow refines complex posteriors into simpler prior distributions invertibly
+ self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels)
+ # Target Speaker Identity Lookup Table
+ self.emb_g = torch.nn.Embedding(spk_embed_dim, gin_channels)
+
+ def remove_weight_norm(self):
+ """Strips weight normalization flags to optimize execution performance for production."""
+
+ for module in [self.dec, self.flow, self.enc_q]:
+ module.remove_weight_norm()
+
+ @torch.jit.ignore
+ def forward(self, phone, phone_lengths, pitch = None, pitchf = None, y = None, y_lengths = None, ds = None):
+ """Runs the Variational Inference forward training loop for Voice Conversion."""
+
+ g = self.emb_g(ds).unsqueeze(-1) # Global conditioning
+ m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+
+ if y is not None:
+ # Extract posterior representation from target speech spectrogram (y)
+ z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+ # Map target latents into the flow space
+ z_p = self.flow(z, y_mask, g=g)
+
+ # Slice acoustic segments randomly to preserve bounded GPU memory tracking
+ z_slice, ids_slice = rand_slice_segments(z, y_lengths, self.segment_size)
+ f0_slice = slice_segments(pitchf, ids_slice, self.segment_size, 2) if self.use_f0 else None
+
+ # Generate target converted audio waveform slice
+ return (
+ self.dec(
+ z_slice,
+ f0_slice,
+ g=g
+ ),
+ ids_slice,
+ x_mask,
+ y_mask,
+ (z, z_p, m_p, logs_p, m_q, logs_q)
+ )
+ else: return None, None, x_mask, None, (None, None, m_p, logs_p, None, None)
+
+ @torch.jit.export
+ def infer(self, phone, phone_lengths, pitch = None, nsff0 = None, sid = None, rate = None):
+ """Executes source-to-target voice conversion inference within PyTorch."""
+
+ g = self.emb_g(sid).unsqueeze(-1)
+ m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+
+ # Sample latent vectors from source content priors using reparameterization
+ z_p = (m_p + logs_p.exp() * torch.randn_like(m_p) * 0.66666) * x_mask
+
+ if rate is not None:
+ # Adjusts speech speed/tempo offset allocations
+ head = int(z_p.shape[2] * (1.0 - rate.item()))
+ z_p, x_mask = z_p[:, :, head:], x_mask[:, :, head:]
+ if self.use_f0: nsff0 = nsff0[:, head:]
+
+ # Pass priors through Inverse Flow to reconstruct converted target latents
+ z = self.flow(z_p, x_mask, g=g, reverse=True)
+ return self.dec(z * x_mask, nsff0, g=g)
+
+ def onnx_infer(self, phone, phone_lengths, sid = None, rate = None, pitch = None, nsff0 = None):
+ """Alternative target inference route utilizing ONNX-exportable tracing steps."""
+
+ g = self.emb_g(sid).unsqueeze(-1)
+ m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+
+ # Sample latent vectors from source content priors using reparameterization
+ z_p = (m_p + logs_p.exp() * torch.randn_like(m_p) * 0.66666) * x_mask
+
+ # Floor and casting are explicitly typed for static tensor indexing compliance in ONNX runtimes
+ head = torch.floor(z_p.size(2) * (1.0 - rate)).long()
+ z_p, x_mask = z_p[:, :, head:], x_mask[:, :, head:]
+ if self.use_f0: nsff0 = nsff0[:, head:]
+
+ # Pass priors through Inverse Flow to reconstruct converted target latents
+ z = self.flow(z_p, x_mask, g=g, reverse=True)
+ return self.dec(z * x_mask, nsff0, g=g)
+
+class SynthesizerSVC(torch.nn.Module):
+ """
+ Synthesizer module customized specifically for Singing Voice Conversion (SVC).
+ Processes dense acoustic speech feature vectors (e.g., ContentVec/HuBERT) instead of text tokens
+ and synchronizes source expressions with the target singer's voice profile.
+ """
+
+ def __init__(
+ self,
+ spec_channels,
+ segment_size,
+ inter_channels,
+ hidden_channels,
+ filter_channels,
+ n_heads,
+ n_layers,
+ kernel_size,
+ p_dropout,
+ resblock,
+ resblock_kernel_sizes,
+ resblock_dilation_sizes,
+ upsample_rates,
+ upsample_initial_channel,
+ upsample_kernel_sizes,
+ spk_embed_dim,
+ gin_channels,
+ sr,
+ text_enc_hidden_dim=768,
+ vocoder="Default",
+ checkpointing=False,
+ onnx=False,
+ noise_scale=0.35,
+ **kwargs
+ ):
+ """Initializes architectural modules for Singing Voice Conversion pipelines."""
+
+ super().__init__()
+ self.segment_size = segment_size
+ self.noise_scale = noise_scale
+ self.sr = sr
+ # Content/Acoustic feature representation encoder
+ self.enc_p = TextEncoderSVC(
+ inter_channels,
+ hidden_channels,
+ filter_channels=filter_channels,
+ n_heads=n_heads,
+ n_layers=n_layers,
+ kernel_size=kernel_size,
+ p_dropout=p_dropout,
+ onnx=onnx
+ )
+
+ # Dynamic mapping setup for targeted Audio Waveform Generators (Vocoders)
+ if vocoder == "RefineGAN":
+ from main.library.generators.refinegan import RefineGANGenerator
+
+ logger.info(translations["use_vocoders"].format(name="REFINEGAN"))
+
+ self.dec = RefineGANGenerator(
+ sample_rate=sr,
+ upsample_rates=upsample_rates,
+ num_mels=inter_channels,
+ checkpointing=checkpointing
+ )
+ elif vocoder == "BigVGAN":
+ from main.library.generators.bigvgan import BigVGANGenerator
+
+ logger.info(translations["use_vocoders"].format(name="BIGVGAN"))
+
+ self.dec = BigVGANGenerator(
+ in_channel=inter_channels,
+ upsample_initial_channel=upsample_initial_channel,
+ upsample_rates=upsample_rates,
+ upsample_kernel_sizes=upsample_kernel_sizes,
+ resblock_kernel_sizes=resblock_kernel_sizes,
+ resblock_dilations=resblock_dilation_sizes,
+ gin_channels=gin_channels,
+ sample_rate=sr,
+ harmonic_num=0,
+ )
+ elif vocoder in ["MRF-HiFi-GAN", "MRF HiFi-GAN"]:
+ from main.library.generators.mrf_hifigan import HiFiGANMRFGenerator
+
+ logger.info(translations["use_vocoders"].format(name="MRF-HIFIGAN"))
+
+ self.dec = HiFiGANMRFGenerator(
+ in_channel=inter_channels,
+ upsample_initial_channel=upsample_initial_channel,
+ upsample_rates=upsample_rates,
+ upsample_kernel_sizes=upsample_kernel_sizes,
+ resblock_kernel_sizes=resblock_kernel_sizes,
+ resblock_dilations=resblock_dilation_sizes,
+ gin_channels=gin_channels,
+ sample_rate=sr,
+ harmonic_num=8,
+ checkpointing=checkpointing
+ )
+ else:
+ from main.library.generators.nsf_hifigan import HiFiGANNSFGenerator
+
+ logger.info(translations["use_vocoders"].format(name="NSF-HIFIGAN"))
+
+ self.dec = HiFiGANNSFGenerator(
+ inter_channels,
+ resblock_kernel_sizes,
+ resblock_dilation_sizes,
+ upsample_rates,
+ upsample_initial_channel,
+ upsample_kernel_sizes,
+ gin_channels=gin_channels,
+ sr=sr,
+ checkpointing=checkpointing,
+ harmonic_num=8
+ )
+
+ # Discrete Voiced/Unvoiced parameter tracking table
+ self.emb_uv = torch.nn.Embedding(2, hidden_channels)
+ self.emb_g = torch.nn.Embedding(spk_embed_dim, gin_channels)
+ # Projection layer to map external content feature vectors into hidden channels
+ self.pre = torch.nn.Conv1d(text_enc_hidden_dim, hidden_channels, kernel_size=5, padding=2)
+ self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
+ self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+
+ def remove_weight_norm(self):
+ """Removes weight normalization flags across submodules."""
+
+ for module in [self.dec, self.flow, self.enc_q]:
+ module.remove_weight_norm()
+
+ def forward(self, phone, phone_lengths, pitch = None, pitchf = None, y = None, y_lengths = None, ds = None):
+ """Runs the SVC forward training loop using content representations."""
+
+ g = self.emb_g(ds.unsqueeze(0) if ds.dim() == 1 else ds).transpose(1, 2)
+ phone = phone.transpose(1, 2) # Reshape layout to channel-first format
+
+ x_mask = sequence_mask(phone_lengths, phone.size(2)).unsqueeze(1).to(phone.dtype)
+ # Combine projected content embeddings with fundamental voiced/unvoiced sequence flags
+ x = self.pre(phone) * x_mask + self.emb_uv((pitchf > 0.0).long()).transpose(1, 2)
+
+ _, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=pitch)
+ z, m_q, logs_q, spec_mask = self.enc_q(y, y_lengths, g=g)
+
+ z_p = self.flow(z, spec_mask, g=g)
+ z_slice, ids_slice = rand_slice_segments(z, y_lengths, self.segment_size)
+
+ return (
+ self.dec(
+ z_slice,
+ g=g,
+ f0=slice_segments(pitchf, ids_slice, self.segment_size, 2)
+ ),
+ ids_slice,
+ x_mask,
+ spec_mask,
+ (z, z_p, m_p, logs_p, m_q, logs_q)
+ )
+
+ @torch.no_grad()
+ def infer(self, phone, phone_lengths, pitch = None, nsff0 = None, sid = None, rate = None):
+ """Transforms source vocal content into targeted singer outputs during SVC inference."""
+
+ g = self.emb_g(sid.unsqueeze(0) if sid.dim() == 1 else sid).transpose(1, 2)
+ phone = phone.transpose(1, 2)
+
+ x_mask = sequence_mask(phone_lengths, phone.size(2)).unsqueeze(1).to(phone.dtype)
+ x = self.pre(phone) * x_mask + self.emb_uv((nsff0 > 0.0).long()).transpose(1, 2)
+
+ # Extract content prior conditions adjusted via noise scale factors
+ z_p, _, _, c_mask = self.enc_p(x, x_mask, f0=pitch, noise_scale=self.noise_scale)
+
+ if rate is not None:
+ head = int(z_p.shape[2] * (1.0 - rate.item()))
+ z_p, c_mask, nsff0 = z_p[:, :, head:], c_mask[:, :, head:], nsff0[:, head:]
+
+ # Convert priors to singer latents through the Inverse Flow block
+ z = self.flow(z_p, c_mask, g=g, reverse=True)
+ o = self.dec(z * c_mask, g=g, f0=nsff0)
+
+ # Linearly interpolate audio duration dimensions if the output length drifts from hop targets
+ target_len = phone_lengths * (self.sr // 100)
+ if o.shape[-1] != target_len:
+ o = torch.nn.functional.interpolate(
+ o,
+ size=target_len,
+ mode="linear",
+ align_corners=False
+ )
+
+ return o
+
+ @torch.no_grad()
+ def onnx_infer(self, phone, phone_lengths, sid = None, rate = None, pitch = None, nsff0 = None):
+ """ONNX-friendly singing voice conversion route utilizing tracking tracing operators."""
+
+ g = self.emb_g(sid.unsqueeze(0) if sid.dim() == 1 else sid).transpose(1, 2)
+ phone = phone.transpose(1, 2)
+
+ x_mask = sequence_mask(phone_lengths, phone.size(2)).unsqueeze(1).to(phone.dtype)
+ x = self.pre(phone) * x_mask + self.emb_uv((nsff0 > 0.0).long()).transpose(1, 2)
+
+ # Extract content prior conditions adjusted via noise scale factors
+ z_p, _, _, c_mask = self.enc_p(x, x_mask, f0=pitch, noise_scale=self.noise_scale)
+
+ head = torch.floor(z_p.size(2) * (1.0 - rate)).long()
+ z_p, c_mask, nsff0 = z_p[:, :, head:], c_mask[:, :, head:], nsff0[:, head:]
+
+ # Convert priors to singer latents through the Inverse Flow block
+ z = self.flow(z_p, c_mask, g=g, reverse=True)
+ o = self.dec(z * c_mask, g=g, f0=nsff0)
+
+ # Linearly interpolate audio duration dimensions if the output length drifts from hop targets
+ target_len = phone_lengths * (self.sr // 100)
+ if o.shape[-1] != target_len:
+ o = torch.nn.functional.interpolate(
+ o,
+ size=target_len,
+ mode="linear",
+ align_corners=False
+ )
+
+ return o
\ No newline at end of file
diff --git a/main/library/algorithm/viterbi.py b/main/library/algorithm/viterbi.py
new file mode 100644
index 0000000000000000000000000000000000000000..97896610f4cbfa5cd24e1a09f2331012f2bbfda2
--- /dev/null
+++ b/main/library/algorithm/viterbi.py
@@ -0,0 +1,202 @@
+import torch
+
+import numpy as np
+import numba as nb
+
+@nb.njit(cache=True)
+def _viterbi(log_prob, log_trans_T, log_p_init):
+ """
+ Core Numba-accelerated Viterbi algorithm for a single sequence.
+
+ Args:
+ log_prob (np.ndarray): Log emission probabilities of shape (n_steps, n_states).
+ log_trans_T (np.ndarray): Transposed log transition matrix of shape (n_states, n_states).
+ log_p_init (np.ndarray): Log initial state probabilities of shape (n_states,).
+
+ Returns:
+ tuple: (state, best_val)
+ - state (np.ndarray): Most likely sequence of state indices of shape (n_steps,).
+ - best_val (float): The maximum log probability of the best path.
+ """
+
+ n_steps, n_states = log_prob.shape
+ # Allocate memory for tracking optimal paths and backpointers
+ state = np.empty(n_steps, dtype=np.int16)
+ ptr = np.empty((n_steps, n_states), dtype=np.int16)
+
+ # Buffers to hold log probabilities for forward dynamic programming
+ prev = np.empty(n_states, dtype=log_prob.dtype)
+ curr = np.empty(n_states, dtype=log_prob.dtype)
+
+ # Initialization step (t = 0)
+ for j in range(n_states):
+ prev[j] = log_prob[0, j] + log_p_init[j]
+
+ # Recursion step over time steps
+ for t in range(1, n_steps):
+ for j in range(n_states):
+ row = log_trans_T[j]
+ # Find the max log probability coming from the previous states
+ best_val = prev[0] + row[0]
+ best_idx = 0
+
+ for i in range(1, n_states):
+ v = prev[i] + row[i]
+ if v > best_val: best_val, best_idx = v, i
+
+ # Store the computed max value and its corresponding backpointer
+ curr[j] = log_prob[t, j] + best_val
+ ptr[t, j] = best_idx
+
+ # Swap buffers for the next time step iteration
+ prev, curr = curr, prev
+
+ # Termination step: find the best final state
+ best_idx = 0
+ best_val = prev[0]
+
+ for j in range(1, n_states):
+ v = prev[j]
+ if v > best_val: best_val, best_idx = v, j
+
+ # Backtracking path reconstruction
+ state[-1] = best_idx
+ for t in range(n_steps - 2, -1, -1):
+ state[t] = ptr[t + 1, state[t + 1]]
+
+ return state, best_val
+
+def viterbi_np(prob, transition, p_init=None, return_logp=False, eps=1e-7):
+ """
+ Computes the most likely sequence of states using the Viterbi algorithm (NumPy version).
+
+ Supports both single sequences and batched inputs.
+
+ Args:
+ prob (np.ndarray): Emission probabilities.
+ transition (np.ndarray): Transition matrix.
+ p_init (np.ndarray, optional): Initial state probabilities. Defaults to uniform distribution.
+ return_logp (bool, optional): Whether to return the log probability of the paths. Defaults to False.
+ eps (float, optional): Epsilon value to prevent log(0). Defaults to 1e-7.
+
+ Returns:
+ np.ndarray or tuple: Best state path sequence, optionally paired with its path log-probability.
+
+ Raises:
+ ValueError: If array dimensions do not align, or if probability invariants are violated.
+ """
+
+ n_states, _ = prob.shape[-2:]
+
+ # Input validation and error raising
+ if transition.shape != (n_states, n_states): raise ValueError(f"Transition matrix shape must be {(n_states, n_states)}, got {transition.shape}")
+ if np.any(transition < 0) or not np.allclose(transition.sum(axis=1), 1): raise ValueError("Transition matrix must be non-negative and each row must sum to 1.")
+ if np.any(prob < 0) or np.any(prob > 1): raise ValueError("Emission probabilities must be bounded between 0 and 1.")
+
+ # Initialize or validate prior distribution
+ if p_init is None: p_init = np.full(n_states, 1.0 / n_states, dtype=prob.dtype)
+ elif (np.any(p_init < 0) or not np.allclose(p_init.sum(), 1) or p_init.shape != (n_states,)): raise ValueError(f"p_init must be non-negative, sum to 1, and have shape {(n_states,)}, got shape {p_init.shape if hasattr(p_init, 'shape') else 'unknown'}")
+
+ # Convert to log-space safely using epsilon clipping
+ log_trans = np.ascontiguousarray(np.log(transition + eps)).T
+ log_prob = np.ascontiguousarray(np.log(prob + eps))
+ log_p_init = np.ascontiguousarray(np.log(p_init + eps))
+
+ # Single sequence execution path
+ if log_prob.ndim == 2: states, logp = _viterbi(log_prob.T, log_trans, log_p_init)
+ else:
+ # Batched inputs execution path
+ batch_shape = log_prob.shape[:-2]
+ n_steps = log_prob.shape[-1]
+
+ # Flatten leading batch dimensions to a single dimension for looping
+ flat = log_prob.reshape(-1, n_states, n_steps)
+ states = np.empty((flat.shape[0], n_steps), dtype=np.int16)
+ logp = np.empty(flat.shape[0], dtype=log_prob.dtype)
+
+ for i in range(flat.shape[0]):
+ s, lp = _viterbi(flat[i].T, log_trans, log_p_init)
+ states[i] = s
+ logp[i] = lp
+
+ # Reshape the output back to match the original batch structure
+ states = states.reshape(*batch_shape, n_steps)
+ logp = logp.reshape(batch_shape)
+
+ if return_logp: return states, logp
+ return states
+
+def viterbi(prob, transition, p_init=None, return_logp=False, eps=1e-7):
+ """
+ Computes the most likely sequence of states using the Viterbi algorithm (PyTorch version).
+
+ Args:
+ prob (torch.Tensor): Emission probabilities.
+ transition (torch.Tensor): Transition matrix.
+ p_init (torch.Tensor, optional): Initial state probabilities. Defaults to uniform distribution.
+ return_logp (bool, optional): Whether to return the log probability of the paths. Defaults to False.
+ eps (float, optional): Epsilon value to prevent log(0). Defaults to 1e-7.
+
+ Returns:
+ torch.Tensor or tuple: Best state path sequence as torch.int64, optionally paired with its path log-probability tensor.
+
+ Raises:
+ ValueError: If tensor dimensions do not match, or if probability conditions are violated.
+ """
+
+ if not prob.device.type.startswith(("cuda", "cpu")):
+ # When tested on other backends, it yielded inaccurate results or failed to work, necessitating a fallback to NumPy.
+ states_np, logp_np = viterbi_np(
+ prob.detach().cpu().numpy(),
+ transition.detach().cpu().numpy(),
+ p_init.detach().cpu().numpy() if p_init is not None else p_init,
+ return_logp=True,
+ eps=eps
+ )
+ states_pt = torch.as_tensor(states_np, device=prob.device, dtype=torch.int32)
+
+ if return_logp: return states_pt, torch.as_tensor(logp_np, device=prob.device)
+ return states_pt
+
+ n_states = prob.shape[-2]
+ n_steps = prob.shape[-1]
+ batch_shape = prob.shape[:-2]
+
+ # Input validations for tensors
+ if transition.shape != (n_states, n_states): raise ValueError(f"Transition matrix shape must be {(n_states, n_states)}, got {transition.shape}")
+ if torch.any(transition < 0) or not torch.allclose(transition.sum(dim=-1), torch.tensor(1.0, dtype=transition.dtype, device=transition.device)): raise ValueError("Transition matrix must be non-negative and each row must sum to 1.")
+ if torch.any(prob < 0) or torch.any(prob > 1): raise ValueError("Emission probabilities must be bounded between 0 and 1.")
+
+ # Initialize or validate prior distribution
+ if p_init is None: p_init = torch.full((n_states,), 1.0 / n_states, dtype=prob.dtype, device=prob.device)
+ elif torch.any(p_init < 0) or not torch.allclose(p_init.sum(), torch.tensor(1.0, dtype=p_init.dtype, device=p_init.device)) or p_init.shape != (n_states,): raise ValueError
+
+ # Stable calculation via log-space with clamping minimum boundaries
+ log_trans = transition.clamp_min(eps).log()
+ log_prob = prob.clamp_min(eps).log()
+ log_p_init = p_init.clamp_min(eps).log()
+
+ # Dynamic programming matrices
+ value = torch.zeros((*batch_shape, n_steps, n_states), dtype=prob.dtype, device=prob.device)
+ ptr = torch.zeros((*batch_shape, n_steps, n_states), dtype=torch.long, device=prob.device)
+ # Base initialization step (t = 0)
+ value[..., 0, :] = log_prob[..., :, 0] + log_p_init
+ log_trans_expanded = log_trans.unsqueeze(0)
+
+ # Vectorized forward pass across batched grids
+ for t in range(1, n_steps):
+ # Adding broadcasted transitions and taking max over previous states
+ max_vals, argmax_indices = (value[..., t - 1, :].unsqueeze(-1) + log_trans_expanded).max(dim=-2)
+
+ ptr[..., t, :] = argmax_indices
+ value[..., t, :] = log_prob[..., :, t] + max_vals
+
+ # Construct the state trajectories placeholder
+ states = torch.zeros((*batch_shape, n_steps), dtype=torch.long, device=prob.device)
+ states[..., -1] = value[..., -1, :].argmax(dim=-1)
+ # Backward dynamic decoding pass
+ for t in range(n_steps - 2, -1, -1):
+ states[..., t] = ptr[..., t + 1, :].gather(dim=-1, index=states[..., t + 1].unsqueeze(-1)).squeeze(-1)
+
+ if return_logp: return states.to(torch.int64), value[..., -1, :].gather(dim=-1, index=states[..., -1].unsqueeze(-1)).squeeze(-1)
+ return states.to(torch.int64)
\ No newline at end of file
diff --git a/main/library/audio/audio.py b/main/library/audio/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6a534699d5a425f3f7d0e6781a5f8f59ffe9cf1
--- /dev/null
+++ b/main/library/audio/audio.py
@@ -0,0 +1,238 @@
+import os
+import sys
+import librosa
+
+import numpy as np
+import soundfile as sf
+
+from pydub import AudioSegment
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations
+
+def load_audio(
+ file,
+ sample_rate=16000,
+ formant_shifting=False,
+ formant_qfrency=0.8,
+ formant_timbre=0.8,
+ flatten=True,
+ res_type="soxr_vhq",
+ dtype = np.float64,
+ return_sr = False,
+ mode = "soundfile"
+):
+ """
+ Load an audio file using one of several available backends.
+
+ Supported backends:
+ - soundfile : Fast and accurate for PCM-based formats.
+ - librosa : Broad codec support through audioread.
+ - ffmpeg : Maximum compatibility with compressed formats.
+
+ If the selected backend fails, the remaining backends are tried
+ automatically before reporting an error.
+
+ Processing steps:
+ 1. Load audio.
+ 2. Convert stereo/multi-channel audio to mono.
+ 3. Resample to the requested sample rate.
+ 4. Optionally apply formant shifting.
+ 5. Optionally flatten the output array.
+
+ Args:
+ file: Path to the audio file.
+ sample_rate: Desired output sample rate.
+ formant_shifting: Enable formant shifting.
+ formant_qfrency: Formant quefrency in milliseconds.
+ formant_timbre: Formant distortion factor.
+ flatten: Flatten the output array.
+ res_type: librosa resampling algorithm.
+ dtype: Output numpy dtype.
+ return_sr: Return the sample rate together with the audio.
+ mode: Preferred loading backend.
+
+ Returns:
+ np.ndarray or (np.ndarray, int)
+ """
+
+ try:
+ # Remove accidental whitespace and quotes from the file path.
+ file = file.strip().strip('"').strip("\n").strip('"').strip()
+ if not os.path.isfile(file): raise FileNotFoundError(translations["not_found"].format(name=file))
+
+ try:
+ if mode == "soundfile":
+ # Native PCM decoder with excellent performance.
+ audio, sr = sf.read(file, dtype=dtype)
+ elif mode == "librosa":
+ # librosa keeps the original sample rate when sr=None.
+ audio, sr = librosa.load(file, sr=None, dtype=dtype)
+ elif mode == "ffmpeg":
+ # FFmpeg provides the broadest codec compatibility.
+
+ from main.library.audio import ffmpeg
+
+ audio, _ = (
+ ffmpeg.input(file, threads=0)
+ .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sample_rate)
+ .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+ )
+ audio = np.frombuffer(audio, dtype=np.float32).astype(dtype=dtype)
+ sr = sample_rate
+ else:
+ raise ValueError("This backend is not supported; please choose from ['soundfile', 'librosa', 'ffmpeg'].")
+ except:
+ # Automatically fall back to the remaining loaders.
+ modes = ["soundfile", "librosa", "ffmpeg"]
+ if mode in modes: modes.remove(mode)
+
+ for m in modes:
+ try:
+ return load_audio(
+ file,
+ sample_rate=sample_rate,
+ formant_shifting=formant_shifting,
+ formant_qfrency=formant_qfrency,
+ formant_timbre=formant_timbre,
+ flatten=flatten,
+ res_type=res_type,
+ dtype=dtype,
+ return_sr=return_sr,
+ mode=m
+ )
+ except:
+ pass
+ raise RuntimeError
+
+ # Convert multi-channel audio into mono.
+ # Channels may be stored as (channels, samples)
+ # or (samples, channels) depending on the backend.
+ if audio.ndim > 1: audio = audio.mean(axis=0, dtype=dtype) if audio.shape[0] < audio.shape[1] else audio.mean(axis=1, dtype=dtype)
+
+ # Resample only when necessary.
+ # soxr_vhq provides very high-quality sinc resampling.
+ if sr is not None and sr != sample_rate:
+ audio = librosa.resample(
+ audio,
+ orig_sr=sr,
+ target_sr=sample_rate,
+ res_type=res_type
+ )
+ sr = sample_rate
+
+ if formant_shifting:
+ from main.library.audio.stftpitchshift import StftPitchShift
+
+ pitchshifter = StftPitchShift(
+ framesize=1024,
+ hopsize=32, # Small hop size minimizes phase discontinuities.
+ samplerate=sample_rate
+ )
+ # Preserve pitch while modifying vocal timbre.
+ audio = pitchshifter.shiftpitch(
+ audio,
+ factors=1,
+ quefrency=formant_qfrency * 1e-3,
+ distortion=formant_timbre
+ )
+
+ audio = audio.flatten() if flatten else audio # Ensure the output is a contiguous 1-D array.
+ except Exception as e:
+ raise RuntimeError(f"{translations['errors_loading_audio']}: {e}")
+
+ if return_sr: return audio, sr
+ return audio
+
+def pydub_load(input_path, volume = None):
+ """
+ Load audio using pydub.
+
+ This loader is mainly intended for operations that rely on
+ AudioSegment, such as volume adjustment or exporting.
+
+ Args:
+ input_path: Audio file path.
+ volume: Gain in dB.
+
+ Returns:
+ AudioSegment
+ """
+
+ try:
+ # Use dedicated decoders for common formats when available.
+ if input_path.endswith(".wav"): audio = AudioSegment.from_wav(input_path)
+ elif input_path.endswith(".mp3"): audio = AudioSegment.from_mp3(input_path)
+ elif input_path.endswith(".ogg"): audio = AudioSegment.from_ogg(input_path)
+ else: audio = AudioSegment.from_file(input_path)
+ except:
+ # Fall back to FFmpeg's automatic format detection.
+ audio = AudioSegment.from_file(input_path)
+
+ return audio if volume is None else (audio + volume)
+
+def cut(audio, sr, db_thresh=-60, min_interval=250):
+ """
+ Split audio into voiced segments.
+
+ Silent regions below the threshold are removed while preserving
+ the original timeline information.
+
+ Args:
+ audio: Input waveform.
+ sr: Sample rate.
+ db_thresh: Silence threshold in dBFS.
+ Lower values detect quieter speech.
+ min_interval: Minimum silence duration (ms) required to
+ split the audio.
+
+ Returns:
+ list
+ """
+
+ from main.inference.preprocess.slicer2 import Slicer2
+
+ slicer = Slicer2(
+ sr=sr,
+ threshold=db_thresh,
+ min_interval=min_interval
+ )
+
+ return slicer.slice2(audio)
+
+def restore(segments, total_len, dtype=np.float32):
+ """
+ Restore the original timeline after segmented processing.
+
+ Any removed silent regions are replaced with zeros so the final
+ waveform matches the original duration.
+
+ Args:
+ segments: List of (start, end, processed_audio).
+ total_len: Original waveform length.
+ dtype: Output dtype.
+
+ Returns:
+ np.ndarray
+ """
+
+ out = []
+ last_end = 0
+
+ for start, end, processed_seg in segments:
+ if start > last_end: # Restore skipped silent regions.
+ out.append(
+ np.zeros(start - last_end, dtype=dtype)
+ )
+
+ out.append(processed_seg) # Insert the processed segment.
+ last_end = end
+
+ # Restore trailing silence.
+ if last_end < total_len:
+ out.append(
+ np.zeros(total_len - last_end, dtype=dtype)
+ )
+
+ return np.concatenate(out, axis=-1)
\ No newline at end of file
diff --git a/main/library/audio/audio_processing.py b/main/library/audio/audio_processing.py
new file mode 100644
index 0000000000000000000000000000000000000000..ade8baff219a761fd848ee03439d3c804999f18f
--- /dev/null
+++ b/main/library/audio/audio_processing.py
@@ -0,0 +1,282 @@
+import os
+import sys
+import librosa
+
+import numpy as np
+import scipy.signal as signal
+
+sys.path.append(os.getcwd())
+
+def rms(x, eps=1e-9):
+ """
+ Compute the root mean square (RMS) level of an audio signal.
+
+ Args:
+ x: Input audio signal.
+ eps: Small constant for numerical stability.
+
+ Returns:
+ float: RMS value.
+ """
+
+ return np.sqrt(np.mean(x**2) + eps)
+
+def dc_remove(x):
+ """
+ Remove the DC offset from an audio signal.
+
+ Args:
+ x: Input audio signal.
+
+ Returns:
+ np.ndarray: Zero-centered audio.
+ """
+
+ return x - np.mean(x)
+
+def normalize_rms(x, target=0.08):
+ """
+ Normalize the signal to a target RMS level.
+
+ Args:
+ x: Input audio signal.
+ target: Desired RMS level.
+
+ Returns:
+ np.ndarray: RMS-normalized audio.
+ """
+
+ r = rms(x)
+ return x if r < 1e-6 else x * (target / r)
+
+def soft_limiter(x, th=0.98):
+ """
+ Apply a soft limiter to prevent clipping.
+
+ Samples below the threshold remain unchanged, while peaks are
+ compressed smoothly instead of hard-clipped.
+
+ Args:
+ x: Input audio signal.
+ th: Limiter threshold.
+
+ Returns:
+ np.ndarray: Limited audio.
+ """
+
+ return np.where(
+ np.abs(x) < th,
+ x,
+ th * np.sign(x) + (x - th * np.sign(x)) / (1 + ((x - th) / (1 - th))**2)
+ )
+
+def preprocess(
+ audio,
+ target_sr=16000
+):
+ """
+ Preprocess audio before inference.
+
+ Processing steps:
+ 1. Remove DC offset.
+ 2. Apply a high-pass filter.
+ 3. Normalize RMS.
+ 4. Reduce stationary noise.
+ 5. Normalize peak amplitude.
+
+ Args:
+ audio: Input waveform.
+ target_sr: Audio sample rate.
+
+ Returns:
+ np.ndarray: Processed audio.
+ """
+
+ # Remove DC offset and low-frequency rumble.
+ x = dc_remove(audio)
+
+ # High-pass filter at 80 Hz.
+ # Removes DC drift, microphone rumble, wind noise and handling vibrations
+ # while preserving the fundamental frequencies of most speech.
+ x = signal.filtfilt(*signal.butter(2, 80 / (target_sr / 2), btype="high"), x)
+
+ # Normalize loudness before denoising.
+ x = normalize_rms(x, 0.06)
+
+ # Suppress stationary background noise.
+ x = spectral_denoise_np(x, alpha=1.0)
+
+ # Normalize peak amplitude.
+ x /= (np.max(np.abs(x)) + 1e-9) * 0.99
+ return x.astype(np.float32)
+
+def spectral_denoise_np(
+ audio,
+ n_fft=4096,
+ hop=1024,
+ alpha=2.5
+):
+ """
+ Reduce stationary noise using spectral Wiener filtering.
+
+ Noise statistics are estimated from relatively quiet frames,
+ then used to attenuate the noise spectrum.
+
+ Args:
+ audio: Input waveform.
+ n_fft: FFT size.
+ hop: Hop size.
+ alpha: Noise suppression strength.
+
+ Returns:
+ np.ndarray: Denoised audio.
+ """
+
+ if hop is None: hop = n_fft // 4
+
+ # Compute STFT.
+ stft = librosa.stft(
+ audio,
+ n_fft=n_fft,
+ hop_length=hop,
+ window="hann"
+ )
+
+ mag = np.abs(stft)
+ phase = np.angle(stft)
+
+ # Detect relatively silent frames.
+ energy = np.mean(mag**2, axis=0)
+ silent = energy < np.percentile(energy, 30)
+
+ # Estimate the average spectrum from relatively quiet frames.
+ # These frames are assumed to contain mostly stationary background noise.
+ noise = (
+ np.median(mag[:, silent], axis=1, keepdims=True)
+ if np.any(silent)
+ else np.median(mag, axis=1, keepdims=True)
+ )
+
+ # Wiener-like spectral gain.
+ # alpha controls the amount of noise suppression:
+ # alpha < 1.0 : weaker denoising
+ # alpha = 1.0 : balanced
+ # alpha > 2.0 : stronger suppression but may introduce artifacts
+ mask = (
+ mag**2 /
+ (mag**2 + alpha * (noise**2) + 1e-9)
+ )
+
+ clean = mask * mag
+
+ # Reconstruct waveform.
+ out = librosa.istft(
+ clean * np.exp(1j * phase),
+ hop_length=hop,
+ length=len(audio)
+ )
+
+ return out.astype(np.float32)
+
+def output_eq(audio, sr):
+ """
+ Apply a fixed multi-band equalizer.
+
+ The EQ slightly attenuates low frequencies while enhancing
+ speech presence and high-frequency clarity.
+
+ Args:
+ audio: Input waveform.
+ sr: Sample rate.
+
+ Returns:
+ np.ndarray: Equalized audio.
+ """
+
+ out = audio.copy()
+
+ bands = [
+ (0, 60, -8.0), # Sub-bass. Strong attenuation removes HVAC hum, microphone vibration and unnecessary low-frequency energy.
+ (60, 200, -3.0), # Bass. Slight cut reduces muddiness and boominess in speech.
+ (200, 1000, 0.0), # Midrange. Keep natural vocal body unchanged.
+ (1000, 3500, +2.5), # Presence. Boost intelligibility by emphasizing consonants.
+ (3500, 8000, +3.0), # High presence. Improve clarity and articulation.
+ (8000, 16000, +1.5) # Air. Add brightness without excessive harshness.
+ ]
+
+ for low, high, g in bands:
+ gain = 10 ** (g / 20)
+
+ if low == 0:
+ b, a = signal.butter(2, high / (sr / 2), "low")
+ else:
+ b, a = signal.butter(2, [low / (sr / 2), high / (sr / 2)], "band")
+
+ band = signal.filtfilt(b, a, audio)
+ out += (gain - 1.0) * band # Blend the adjusted frequency band back into the signal.
+
+ return out
+
+def presence_exciter(audio, sr, strength=0.03):
+ """
+ Add harmonic excitation to improve vocal presence.
+
+ Args:
+ audio: Input waveform.
+ sr: Sample rate.
+ strength: Exciter intensity.
+
+ Returns:
+ np.ndarray: Enhanced audio.
+ """
+
+ # Isolate the upper vocal harmonics.
+ # Frequencies around 4–9 kHz contribute to speech clarity,
+ # articulation and perceived detail.
+ b, a = signal.butter(2, [4000 / (sr / 2), 9000 / (sr / 2)], "band")
+ band = signal.filtfilt(b, a, audio)
+
+ # Generate harmonics using soft saturation.
+ exc = np.sign(band) * (1 - np.exp(-np.abs(band) * 4.0))
+ return audio + strength * exc
+
+def postprocess(
+ audio,
+ sr=48000
+):
+ """
+ Postprocess audio after inference.
+
+ Processing steps:
+ 1. Remove DC offset.
+ 2. High-pass filtering.
+ 3. Spectral denoising.
+ 4. Multi-band EQ.
+ 5. Harmonic excitation.
+ 6. Soft limiting.
+ 7. Peak normalization.
+
+ Args:
+ audio: Input waveform.
+ sr: Sample rate.
+
+ Returns:
+ np.ndarray: Final processed audio.
+ """
+
+ # Remove DC and low-frequency rumble.
+ x = dc_remove(audio)
+ x = signal.filtfilt(*signal.butter(2, 60 / (sr / 2), "high"), x)
+
+ # Reduce residual background noise.
+ x = spectral_denoise_np(x)
+ # Shape the frequency response.
+ x = output_eq(x, sr)
+ # Restore brightness and articulation.
+ x = presence_exciter(x, sr)
+ # Prevent clipping.
+ x = soft_limiter(x, 0.98)
+
+ # Normalize output level.
+ x /= (np.max(np.abs(x)) + 1e-9) * 0.99
+ return x.astype(np.float32)
\ No newline at end of file
diff --git a/main/library/audio/features.py b/main/library/audio/features.py
new file mode 100644
index 0000000000000000000000000000000000000000..34511fede461e6f4035d3f159830cd4c3d73ff9a
--- /dev/null
+++ b/main/library/audio/features.py
@@ -0,0 +1,311 @@
+import torch
+
+import torch.nn.functional as F
+
+def frame(x, frame_length, hop_length, axis = -1):
+ """
+ Slice a tensor into overlapping frames using stride tricks.
+
+ Args:
+ x (torch.Tensor): Input tensor containing the signal data.
+ frame_length (int): Number of samples in each frame.
+ hop_length (int): Number of samples between consecutive frames.
+ axis (int, optional): Axis along which framing is applied. Defaults to the last axis (-1).
+ """
+
+ if x.shape[axis] < frame_length or hop_length < 1: raise ValueError("Target axis length must be >= frame_length and hop_length >= 1")
+ axis = axis % x.ndim
+
+ # Move target axis to the last dimension for easier processing
+ if axis != x.ndim - 1: x = x.movedim(axis, -1)
+
+ # Compute output shape and stride configuration and Create framed tensor without copying memory
+ xw = torch.as_strided(x, size=x.shape[:-1] + (1 + (x.shape[-1] - frame_length) // hop_length, frame_length), stride=x.stride()[:-1] + (hop_length * x.stride()[-1], x.stride()[-1]))
+
+ # Restore original axis order if needed
+ if axis != x.ndim - 1: xw = xw.movedim(-2, axis)
+
+ return xw
+
+def rms(
+ y,
+ frame_length = 2048,
+ hop_length = 512,
+ center = True,
+ pad_mode = "constant",
+ dtype = torch.float32,
+ device = "cpu"
+):
+ """
+ Compute the Root Mean Square (RMS) energy of an audio signal.
+
+ Args:
+ y (torch.Tensor or numpy.ndarray): Input audio signal.
+ frame_length (int, optional): Length of each analysis frame. Defaults to 2048.
+ hop_length (int, optional): Number of samples between frames. Defaults to 512.
+ center (bool, optional): If True, pad the signal so frames are centered. Defaults to True.
+ pad_mode (str, optional): Padding mode used when centering. Defaults to "constant".
+ dtype (torch.dtype, optional): Tensor data type. Defaults to torch.float32.
+ device (str, optional): Target device for computation. Defaults to "cpu".
+ """
+ # Convert input to tensor and move to target device/dtype
+ y = y.to(device=device, dtype=dtype) if torch.is_tensor(y) else torch.from_numpy(y.copy()).to(device=device, dtype=dtype)
+
+ return frame(
+ torch.nn.functional.pad(y, (frame_length // 2, frame_length // 2), mode=pad_mode) if center else y, # Pad signal so frames are centered
+ frame_length=frame_length,
+ hop_length=hop_length
+ ).square().mean(dim=-1, keepdim=True).sqrt().mT # Compute mean square energy per frame and convert power to RMS
+
+def change_rms(
+ source_audio,
+ source_rate,
+ target_audio,
+ target_rate,
+ rate = 1.0,
+ device = "cpu"
+):
+ """
+ Adjust the RMS level of target_audio to match the RMS of source_audio, with a given blending rate.
+
+ Args:
+ source_audio: The source audio signal as a Torch Tensor.
+ source_rate: The sampling rate of the source audio.
+ target_audio: The target audio signal to adjust.
+ target_rate: The sampling rate of the target audio.
+ rate: The blending rate between the source and target RMS levels.
+ dtype (torch.dtype, optional): Tensor data type. Defaults to torch.float32.
+ device (str, optional): Target device for computation. Defaults to "cpu".
+ """
+
+ # Convert NumPy input to a float tensor if necessary.
+ if not torch.is_tensor(target_audio): target_audio = torch.from_numpy(target_audio).to(device=device, dtype=torch.float32)
+
+ # Compute and upsample the target RMS envelope.
+ rms2 = F.interpolate(
+ rms(
+ y=target_audio,
+ frame_length=target_rate // 2 * 2,
+ hop_length=target_rate // 2,
+ device=device
+ ).float().unsqueeze(0),
+ size=target_audio.shape[0],
+ mode="linear"
+ ).squeeze()
+
+ # Scale the target audio using the blended RMS ratio.
+ return target_audio * (
+ F.interpolate(
+ rms(
+ y=source_audio,
+ frame_length=source_rate // 2 * 2,
+ hop_length=source_rate // 2,
+ device=device
+ ).float().unsqueeze(0),
+ size=target_audio.shape[0],
+ mode="linear"
+ ).squeeze().pow(1 - rate) * rms2.maximum(torch.zeros_like(rms2) + 1e-6).pow(rate - 1)
+ )
+
+def mel(
+ sr,
+ n_fft,
+ n_mels = 128,
+ fmin = 0.0,
+ fmax = None,
+ htk = False,
+ norm = "slaney",
+ dtype = torch.float32,
+ device = None
+):
+ """
+ This function is used to generate a tensor mel directly, instead of a array mel.
+ Construct a Mel filter bank.
+
+ Args:
+ sr: Sample rate.
+ n_fft: FFT size.
+ n_mels: Number of Mel bands.
+ fmin: Minimum frequency (Hz).
+ fmax: Maximum frequency (Hz). Defaults to Nyquist.
+ htk: Use the HTK Mel scale.
+ norm: Filter normalization method ("slaney" or numeric norm).
+ dtype: Tensor dtype.
+ device: Target device.
+
+ Returns:
+ torch.Tensor: Mel filter bank with shape (n_mels, n_fft // 2 + 1).
+ """
+
+ if fmax is None: fmax = float(sr) / 2
+ n_mels = int(n_mels)
+
+ weights = torch.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype, device=device)
+
+ # FFT bin center frequencies.
+ fftfreqs = torch.fft.rfftfreq(n_fft, d=1.0 / sr, device=device).to(dtype)
+
+ # Mel band edge frequencies.
+ mel_f = mel_to_hz(
+ torch.linspace(
+ hz_to_mel(fmin, htk=htk, dtype=dtype, device=device),
+ hz_to_mel(fmax, htk=htk, dtype=dtype, device=device),
+ n_mels + 2,
+ dtype=dtype,
+ device=device
+ ),
+ htk=htk,
+ dtype=dtype,
+ device=device
+ )
+
+ # Compute triangular filter slopes.
+ fdiff = mel_f.diff()
+ ramps = mel_f.unsqueeze(1) - fftfreqs.unsqueeze(0)
+
+ lower = -ramps[:-2] / fdiff[:-1].unsqueeze(1)
+ weights = lower.minimum(ramps[2:] / fdiff[1:].unsqueeze(1)).clamp(min=0)
+
+ # Normalize filter areas if requested.
+ if isinstance(norm, str):
+ if norm == "slaney": weights *= (2.0 / (mel_f[2:n_mels + 2] - mel_f[:n_mels])).unsqueeze(1)
+ else: raise ValueError(f"Unsupported normalization mode: {norm!r}. Expected 'slaney' or a numeric norm.")
+ else: weights = normalize(weights, norm=norm, axis=-1)
+
+ return weights
+
+def hz_to_mel(frequencies, htk=False, dtype=torch.float32, device=None):
+ """
+ Convert frequencies from Hertz to the Mel scale.
+
+ Args:
+ frequencies: Frequency values in Hz.
+ htk: Use the HTK conversion formula.
+ dtype: Tensor dtype.
+ device: Target device.
+
+ Returns:
+ torch.Tensor: Mel values.
+ """
+
+ frequencies = torch.as_tensor(frequencies, dtype=dtype, device=device)
+ # HTK Mel conversion.
+ if htk: return 2595.0 * (1.0 + frequencies / 700.0).log10()
+
+ # Fill in the linear part
+ f_min = 0.0
+ f_sp = 200.0 / 3
+
+ mels = (frequencies - f_min) / f_sp
+
+ # Fill in the log-scale part
+
+ min_log_hz = 1000.0 # beginning of log region (Hz)
+ min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
+
+ logstep = torch.tensor(6.4, dtype=dtype, device=device).log() / 27.0 # step size for log region
+
+ return torch.where(frequencies >= min_log_hz, min_log_mel + (frequencies / min_log_hz).log() / logstep, mels)
+
+def mel_to_hz(mels, htk=False, dtype=torch.float32, device=None):
+ """
+ Convert Mel values back to Hertz.
+
+ Args:
+ mels: Mel values.
+ htk: Use the HTK conversion formula.
+ dtype: Tensor dtype.
+ device: Target device.
+
+ Returns:
+ torch.Tensor: Frequencies in Hz.
+ """
+
+ mels = torch.as_tensor(mels, dtype=dtype, device=device)
+ if htk: return 700.0 * (10.0 ** (mels / 2595.0) - 1.0) # HTK inverse conversion.
+
+ # Fill in the linear scale
+
+ f_min = 0.0
+ f_sp = 200.0 / 3
+ freqs = f_min + f_sp * mels
+
+ # And now the nonlinear scale
+
+ min_log_hz = 1000.0 # beginning of log region (Hz)
+ min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
+ logstep = torch.tensor(6.4, dtype=dtype, device=device).log() / 27.0 # step size for log region
+
+ return torch.where(mels >= min_log_mel, min_log_hz * (logstep * (mels - min_log_mel)).exp(), freqs)
+
+def normalize(S, norm=torch.inf, axis=0, threshold=None, fill=None):
+ """
+ Normalize a tensor along a specified axis.
+
+ Supports L∞, L-∞, L0, and arbitrary positive Lp norms.
+
+ Args:
+ S: Input tensor.
+ norm: Normalization type.
+ axis: Axis along which to normalize.
+ threshold: Minimum norm before considering the vector too small.
+ fill: Handling strategy for small norms.
+ None -> leave unchanged.
+ False -> output zeros.
+ True -> fill with a uniform normalized vector.
+
+ Returns:
+ torch.Tensor: Normalized tensor.
+ """
+
+ S = torch.as_tensor(S)
+
+ # Determine numerical threshold.
+ if threshold is None:
+ x = torch.as_tensor(S)
+ threshold = torch.finfo(x.dtype if x.dtype in (torch.float16, torch.float32, torch.float64, torch.bfloat16) else torch.float32).tiny
+ elif threshold <= 0: raise ValueError("threshold must be greater than 0.")
+
+ if fill not in [None, False, True]: raise ValueError("fill must be one of: None, False, or True.")
+ if not torch.all(torch.isfinite(S)): raise ValueError("Input tensor contains NaN or Inf values.")
+
+ # All norms only depend on magnitude, let's do that first
+ mag = S.abs().to(torch.float32)
+ # For max/min norms, filling with 1 works
+ fill_norm = 1
+
+ # Compute vector norm.
+ if norm is None: return S
+ elif norm == torch.inf: length = mag.amax(dim=axis, keepdim=True)
+ elif norm == -torch.inf: length = mag.amin(dim=axis, keepdim=True)
+ elif norm == 0:
+ if fill is True: raise ValueError("fill=True is not supported when norm=0.")
+ length = (mag > 0).sum(dim=axis, keepdim=True, dtype=mag.dtype)
+ elif isinstance(norm, (int, float)) and norm > 0:
+ length = (mag ** norm).sum(dim=axis, keepdim=True) ** (1.0 / norm)
+ fill_norm = (mag.numel() ** (-1.0 / norm)) if axis is None else (mag.shape[axis] ** (-1.0 / norm))
+ else: raise ValueError(f"Unsupported norm: {norm!r}. Expected None, ±torch.inf, 0, or a positive numeric value.")
+
+ # Detect vectors with extremely small norms.
+ small_idx = length < threshold
+
+ if fill is None:
+ # Leave small indices un-normalized
+ length = length.clone()
+ length[small_idx] = 1.0
+ Snorm = S / length
+ elif fill:
+ # If we have a non-zero fill value, we locate those entries by
+ # doing a nan-divide.
+ # If S was finite, then length is finite (except for small positions)
+ length = length.clone()
+ length[small_idx] = torch.nan
+ Snorm = torch.nan_to_num(S / length, nan=fill_norm)
+ else:
+ # Set small values to zero by doing an inf-divide.
+ # This is safe (by IEEE-754) as long as S is finite.
+ length = length.clone()
+ length[small_idx] = torch.inf
+ Snorm = S / length
+
+ return Snorm
\ No newline at end of file
diff --git a/main/library/audio/ffmpeg.py b/main/library/audio/ffmpeg.py
new file mode 100644
index 0000000000000000000000000000000000000000..995702396c40891b6fa51ed56270741958623d18
--- /dev/null
+++ b/main/library/audio/ffmpeg.py
@@ -0,0 +1,1050 @@
+import os
+import copy
+import hashlib
+import subprocess
+
+from collections import namedtuple
+from collections.abc import Iterable
+
+# Named tuple representing an edge in the Directed Acyclic Graph (DAG)
+DagEdge = namedtuple(
+ 'DagEdge',
+ [
+ 'downstream_node',
+ 'downstream_label',
+ 'upstream_node',
+ 'upstream_label',
+ 'upstream_selector',
+ ],
+)
+
+class KwargReprNode:
+ """A DAG node that is identified and hashed based on its arguments and upstream connections."""
+
+ def __init__(self, incoming_edge_map, name, args=None, kwargs=None):
+ """Initialize a KwargReprNode.
+
+ Args:
+ incoming_edge_map (dict): Mapping of downstream labels to upstream node connections.
+ name (str): Name of the node operation.
+ args (list, optional): Positional arguments. Defaults to None.
+ kwargs (dict, optional): Keyword arguments. Defaults to None.
+ """
+
+ self._incoming_edge_map = incoming_edge_map
+ self.name = name
+ self.args = args or []
+ self.kwargs = kwargs or {}
+ # The hash is pre-computed during initialization since nodes are immutable components of the graph
+ self._hash = self._build_hash()
+
+ def _get_upstream_hashes(self):
+ """Collect hash components from all incoming edges to maintain structural integrity."""
+
+ hashes = []
+ # Iterate through every dependency to ensure any upstream change updates this node's identity
+ for downstream_label, upstream_info in self.incoming_edge_map.items():
+ upstream_node, upstream_label, upstream_selector = upstream_info
+ # Collect unique identity aspects of the incoming connection
+ hashes.extend([
+ hash(downstream_label),
+ hash(upstream_node),
+ hash(upstream_label),
+ hash(upstream_selector),
+ ])
+
+ return hashes
+
+ def _get_inner_hash(self):
+ """Generate a hash component from the local arguments and keyword arguments."""
+
+ return get_hash({
+ 'args': self.args,
+ 'kwargs': self.kwargs,
+ })
+
+ def _build_hash(self):
+ """Combine upstream dependency hashes and internal attribute hashes into a single unique integer."""
+
+ hashes = self._get_upstream_hashes()
+ hashes.append(self._get_inner_hash())
+
+ return get_hash_int(hashes) # Convert the structural MD5 hex string into a native Python integer
+
+ def __hash__(self):
+ return self._hash
+
+ def __eq__(self, other):
+ # Two nodes are identical if they belong to the same class and have matching graph-dependency hashes
+ return isinstance(other, self.__class__) and hash(self) == hash(other)
+
+ def __repr__(self):
+ return self.long_repr()
+
+ @property
+ def incoming_edge_map(self):
+ return self._incoming_edge_map
+
+ @property
+ def incoming_edges(self):
+ """Get a list of DagEdge structures representing connections leading into this node."""
+
+ return get_incoming_edges(self, self.incoming_edge_map)
+
+ @property
+ def short_hash(self):
+ """Return a 12-character hex string representing the absolute hash of the node."""
+
+ return format(abs(hash(self)), 'x')[:12]
+
+ @property
+ def short_repr(self):
+ return self.name
+
+ def long_repr(self, include_hash=True):
+ """
+ Format a detailed signature of the node, resembling a Python function call.
+
+ Args:
+ include_hash (bool): Whether to append the unique short hash token.
+ """
+ # Convert positional arguments into their standard string representations
+ formatted = [repr(arg) for arg in self.args]
+
+ # Order keyword arguments alphabetically to prevent hash mismatches caused by key shuffling
+ formatted.extend(
+ '{}={!r}'.format(key, self.kwargs[key])
+ for key in sorted(self.kwargs)
+ )
+
+ # Build function-style string: e.g., "vflip(input_0)"
+ result = '{}({})'.format(
+ self.name,
+ ', '.join(formatted),
+ )
+
+ if include_hash: # Append unique hash signature tracking when requested (useful for debugging complex graph routing)
+ result += ' <{}>'.format(self.short_hash)
+
+ return result
+
+class Stream:
+ """Represents a data wrapper/reference originating from a specific outlet (label) of a Node."""
+
+ def __init__(
+ self,
+ upstream_node,
+ upstream_label,
+ node_types,
+ upstream_selector=None,
+ ):
+ """
+ Initialize a Stream object.
+
+ Raises:
+ TypeError: If the upstream node does not match the valid allowed types.
+ """
+
+ # Strict validation ensuring streams only originate from permitted nodes
+ if not is_of_types(upstream_node, node_types): raise TypeError(f"Upstream node must be an instance of {node_types}, got {type(upstream_node).__name__}.")
+
+ self.node = upstream_node
+ self.label = upstream_label
+ self.selector = upstream_selector
+
+ def __hash__(self):
+ return get_hash_int([
+ hash(self.node),
+ hash(self.label),
+ ])
+
+ def __eq__(self, other):
+ return isinstance(other, Stream) and hash(self) == hash(other)
+
+ def __repr__(self):
+ # Exclude hash from internal node signature to keep output human-readable
+ node_repr = self.node.long_repr(include_hash=False)
+
+ # Append optional selectors like stream specifiers (e.g., ':v' or ':a') if active
+ selector = ''
+ if self.selector:
+ selector = ':{}'.format(self.selector)
+
+ return '{}[{!r}{}] <{}>'.format(
+ node_repr,
+ self.label,
+ selector,
+ self.node.short_hash,
+ )
+
+ def __getitem__(self, index):
+ """
+ Sub-select or extract parts of a stream (e.g., audio/video selector string).
+
+ Raises:
+ ValueError: If a selector option is already present on this stream.
+ TypeError: If the index specifier is not a string type.
+ """
+
+ # Prevent chaining selectors twice (e.g., stream['v']['a'])
+ if self.selector is not None: raise ValueError(f"Stream already has a selector pattern applied: {self.selector!r}.")
+ # Bracket operators require a string parameter identifier
+ if not isinstance(index, str): raise TypeError(f"Stream selector key must be a string descriptor, not {type(index).__name__}.")
+
+ # Request the parent node to output a modified stream view containing the selector pattern
+ return self.node.stream(
+ label=self.label,
+ selector=index,
+ )
+
+ @property
+ def audio(self):
+ """Shortcut property to select the audio layer ('a') of the current stream."""
+
+ return self['a']
+
+ @property
+ def video(self):
+ """Shortcut property to select the video layer ('v') of the current stream."""
+
+ return self['v']
+
+class Node(KwargReprNode):
+ """An operation node within the DAG capable of verifying input thresholds and stream mappings."""
+
+ @classmethod
+ def check_input_len(cls, stream_map, min_inputs, max_inputs):
+ """
+ Validate whether the quantity of provided streams falls within acceptable bounds.
+
+ Raises:
+ ValueError: If the total inputs violate minimum or maximum boundaries.
+ """
+
+ stream_count = len(stream_map)
+
+ # Check against lower bounds if configured
+ if min_inputs is not None and stream_count < min_inputs: raise ValueError(f"Insufficient input streams provided. Expected at least {min_inputs}, got {stream_count}.")
+ # Check against upper bounds if configured
+ if max_inputs is not None and stream_count > max_inputs: raise ValueError(f"Too many input streams provided. Expected at most {max_inputs}, got {stream_count}.")
+
+ @classmethod
+ def check_input_types(cls, stream_map, incoming_stream_types):
+ """
+ Ensure all incoming streams match the target schema classifications.
+
+ Raises:
+ TypeError: If any input stream encounters a type validation failure.
+ """
+
+ for stream in stream_map.values():
+ if not is_of_types(stream, incoming_stream_types): raise TypeError(f"Invalid stream type detected: {type(stream).__name__}. Expected one of: {incoming_stream_types}.")
+
+ @classmethod
+ def get_incoming_edge_map(cls, stream_map):
+ """Convert a stream map configuration dictionary into a functional graph structure schema."""
+
+ edge_map = {}
+ # Deconstruct Stream object metadata to formulate direct dictionary connections
+ for downstream_label, upstream in stream_map.items():
+ edge_map[downstream_label] = (
+ upstream.node,
+ upstream.label,
+ upstream.selector,
+ )
+
+ return edge_map
+
+ def __init__(
+ self,
+ stream_spec,
+ name,
+ incoming_stream_types,
+ outgoing_stream_type,
+ min_inputs,
+ max_inputs,
+ args=None,
+ kwargs=None,
+ ):
+ """Initialize a Node component while processing validation and parent setups."""
+
+ self._outgoing_stream_type = outgoing_stream_type
+ # Parse unstructured stream collections (lists, items, dicts) into explicit mappings
+ stream_map = get_stream_map(stream_spec)
+
+ # Perform topological sanity checks before instantiating node structures
+ self.check_input_len(
+ stream_map,
+ min_inputs,
+ max_inputs,
+ )
+
+ self.check_input_types(
+ stream_map,
+ incoming_stream_types,
+ )
+
+ # Initialize base KwargReprNode properties using the parsed incoming edge mapping configuration
+ super().__init__(
+ self.get_incoming_edge_map(stream_map),
+ name,
+ args=args,
+ kwargs=kwargs,
+ )
+
+ def stream(self, label=None, selector=None):
+ """Instantiate a structured downstream wrapper linked directly to this specific operational node."""
+
+ return self._outgoing_stream_type(
+ self,
+ label,
+ upstream_selector=selector,
+ )
+
+ def __getitem__(self, item):
+ """Enable clean bracket slicing notation to fetch structured internal stream segments."""
+
+ if isinstance(item, slice):
+ return self.stream(
+ label=item.start, # The 'start' token tracks the label index identifier
+ selector=item.stop, # The 'stop' token tracks optional selector variants
+ )
+
+ return self.stream(label=item)
+
+class FilterableStream(Stream):
+ """A stream variant specifically designated for feeding into multimedia processing filters."""
+
+ def __init__(
+ self,
+ upstream_node,
+ upstream_label,
+ upstream_selector=None,
+ ):
+ # Filterable streams can only originate from file inputs or existing filter blocks
+ super().__init__(
+ upstream_node,
+ upstream_label,
+ {InputNode, FilterNode},
+ upstream_selector,
+ )
+
+class InputNode(Node):
+ """Represents a primary external data source entry node (such as media files) in the graph."""
+
+ def __init__(self, name, args=None, kwargs=None):
+ super().__init__(
+ stream_spec=None, # Inputs have no prior parent streams inside the DAG layout
+ name=name,
+ incoming_stream_types={},
+ outgoing_stream_type=FilterableStream,
+ min_inputs=0,
+ max_inputs=0,
+ args=args,
+ kwargs=kwargs,
+ )
+
+ @property
+ def short_repr(self):
+ # Extract the underlying base filename for easy visual log inspections
+ return os.path.basename(self.kwargs['filename'])
+
+class FilterNode(Node):
+ """Represents an processing filter stage modification inside the multimedia graph execution path."""
+
+ def __init__(
+ self,
+ stream_spec,
+ name,
+ max_inputs=1,
+ args=None,
+ kwargs=None,
+ ):
+ super().__init__(
+ stream_spec=stream_spec,
+ name=name,
+ incoming_stream_types={FilterableStream},
+ outgoing_stream_type=FilterableStream,
+ min_inputs=1,
+ max_inputs=max_inputs,
+ args=args,
+ kwargs=kwargs,
+ )
+
+ def _get_filter(self, outgoing_edges):
+ """Generate a formatted string string representing this filter component for CLI flags."""
+
+ args = self.args
+ kwargs = self.kwargs
+
+ # Special handling for ffmpeg's split filters: the output count dictates its main parameter argument
+ if self.name in ('split', 'asplit'):
+ args = [len(outgoing_edges)]
+
+ # Escape key/value pairings to ensure robust parsing by ffmpeg's complex graph layout engine
+ escaped_args = [
+ escape_chars(value, "\\'=:")
+ for value in args
+ ]
+
+ # Escape key/value pairings to ensure robust parsing by ffmpeg's complex graph layout engine
+ escaped_kwargs = {}
+ for key, value in kwargs.items():
+ escaped_kwargs[
+ escape_chars(key, "\\'=:")
+ ] = escape_chars(value, "\\'=:")
+
+ params = []
+ # Merge positional components into the main parameter collection
+ params.extend(
+ escape_chars(value, "\\'=:")
+ for value in escaped_args
+ )
+
+ # Merge and sort keyword components into key=value strings
+ params.extend(
+ '{}={}'.format(key, escaped_kwargs[key])
+ for key in sorted(escaped_kwargs)
+ )
+
+ # Construct the final initialization flag: name=param1:param2...
+ result = escape_chars(self.name, "\\'=:")
+ if params: result += '={}'.format(':'.join(params))
+
+ # Perform comprehensive terminal-safe escaping for final graph injections
+ return escape_chars(result, "\\'[],;")
+
+class OutputNode(Node):
+ """An end-point node indicating file target streams or rendering locations within the active graph."""
+
+ def __init__(self, stream, name, args=None, kwargs=None):
+ super().__init__(
+ stream_spec=stream,
+ name=name,
+ incoming_stream_types={FilterableStream},
+ outgoing_stream_type=OutputStream,
+ min_inputs=1,
+ max_inputs=None,
+ args=args,
+ kwargs=kwargs,
+ )
+
+ @property
+ def short_repr(self):
+ return os.path.basename(self.kwargs['filename'])
+
+class OutputStream(Stream):
+ """A terminal stream subtype tracking routing structures into endpoint configurations."""
+
+ def __init__(
+ self,
+ upstream_node,
+ upstream_label,
+ upstream_selector=None,
+ ):
+ # Terminal streams route towards rendering components, global configurations, or merging adapters
+ super().__init__(
+ upstream_node,
+ upstream_label,
+ {OutputNode, GlobalNode, MergeOutputsNode},
+ upstream_selector,
+ )
+
+class MergeOutputsNode(Node):
+ """A specialized pipeline component combining an arbitrary cluster of distinct Output streams."""
+
+ def __init__(self, streams, name):
+ super().__init__(
+ stream_spec=streams,
+ name=name,
+ incoming_stream_types={OutputStream},
+ outgoing_stream_type=OutputStream,
+ min_inputs=1,
+ max_inputs=None,
+ )
+
+class GlobalNode(Node):
+ """Applies global-scope flag directives targeting runtime execution blocks."""
+
+ def __init__(self, stream, name, args=None, kwargs=None):
+ super().__init__(
+ stream_spec=stream,
+ name=name,
+ incoming_stream_types={OutputStream},
+ outgoing_stream_type=OutputStream,
+ min_inputs=1,
+ max_inputs=1,
+ args=args,
+ kwargs=kwargs,
+ )
+
+def get_incoming_edges(downstream_node, incoming_edge_map):
+ """Build structural DagEdge instances out of basic dictionary tracking formats."""
+
+ edges = []
+ for downstream_label, upstream_info in incoming_edge_map.items():
+ upstream_node, upstream_label, upstream_selector = upstream_info
+ # Convert decoupled tracking items into functional immutable namedtuple objects
+ edges.append(
+ DagEdge(
+ downstream_node,
+ downstream_label,
+ upstream_node,
+ upstream_label,
+ upstream_selector,
+ )
+ )
+
+ return edges
+
+def get_outgoing_edges(upstream_node, outgoing_edge_map):
+ """Synthesize downstream out-edge definitions derived from topological tracking indices."""
+
+ edges = []
+ # Iterate sorting key tags to maintain absolute consistency during parameter compiling
+ for upstream_label, downstream_infos in sorted(outgoing_edge_map.items()):
+ for downstream_info in downstream_infos:
+ downstream_node, downstream_label, downstream_selector = downstream_info
+
+ edges.append(
+ DagEdge(
+ downstream_node,
+ downstream_label,
+ upstream_node,
+ upstream_label,
+ downstream_selector # Mapped as downstream target variant selector reference
+ )
+ )
+
+ return edges
+
+def is_of_types(obj, types):
+ """Validate if target object instances fit within custom typing collections."""
+
+ return any(isinstance(obj, item) for item in types)
+
+def get_stream_map(stream_spec):
+ """
+ Normalize input definitions, lists, or mapping tables into a reliable, uniform dictionary structure.
+
+ Raises:
+ TypeError: If the provided stream specifier type is unmappable.
+ """
+
+ if stream_spec is None:
+ return {}
+
+ # Wrap a standalone Stream into a default unified single-item dictionary mapping
+ if isinstance(stream_spec, Stream):
+ return {None: stream_spec}
+
+ # Map sequential index listings directly to integer lookup definitions
+ if isinstance(stream_spec, (list, tuple)):
+ return dict(enumerate(stream_spec))
+
+ # Return valid mappings directly
+ if isinstance(stream_spec, dict):
+ return stream_spec
+
+ raise TypeError(f"Cannot parse stream specification from type: {type(stream_spec).__name__}.")
+
+def get_stream_map_nodes(stream_map):
+ """
+ Extract operational core nodes from processed uniform stream structures.
+
+ Raises:
+ TypeError: If an element within the stream tracking map is not a valid Stream.
+ """
+
+ nodes = []
+ for stream in stream_map.values():
+ if not isinstance(stream, Stream):
+ raise TypeError(f"Expected a Stream instance inside stream map, found: {type(stream).__name__}.")
+
+ nodes.append(stream.node)
+
+ return nodes
+
+def get_stream_spec_nodes(stream_spec):
+ """High-level translation parsing general input targets directly into a sequence of source nodes."""
+
+ return get_stream_map_nodes(
+ get_stream_map(stream_spec)
+ )
+
+def stream_operator(stream_classes=None, name=None):
+ """Decorator factory helper to bind specific operations directly into targeted Stream instances."""
+
+ stream_classes = stream_classes or {Stream}
+
+ def decorator(func):
+ # Override target context names using custom names when specified
+ func_name = name or func.__name__
+ # Dynamically attach the utility function method onto the specified Stream target structures
+ for stream_class in stream_classes:
+ setattr(stream_class, func_name, func)
+
+ return func
+
+ return decorator
+
+def filter_operator(name=None):
+ """Decorator targeting FilterableStream objects exclusively."""
+
+ return stream_operator(
+ stream_classes={FilterableStream},
+ name=name,
+ )
+
+def output_operator(name=None):
+ """Decorator targeting OutputStream objects exclusively."""
+
+ return stream_operator(
+ stream_classes={OutputStream},
+ name=name,
+ )
+
+def input(filename, **kwargs):
+ """
+ Generate a clean, standardized multimedia file entry input node structure.
+
+ Raises:
+ ValueError: If both 'f' alias and 'format' keyword are provided.
+ """
+
+ kwargs['filename'] = filename
+ # Extract short-hand format descriptors (e.g., input('file.mp4', f='mp4'))
+ fmt = kwargs.pop('f', None)
+
+ if fmt:
+ # Enforce consistency to avoid colliding options
+ if 'format' in kwargs: raise ValueError("Ambiguous formatting options: both 'f' alias and 'format' key were supplied.")
+ kwargs['format'] = fmt
+
+ # Return the open streaming line reference pointing to this source node instance
+ return InputNode(
+ input.__name__,
+ kwargs=kwargs,
+ ).stream()
+
+@output_operator()
+def global_args(stream, *args):
+ """Inject unstructured custom parameter flags onto the parent multimedia stream target."""
+
+ return GlobalNode(
+ stream,
+ global_args.__name__,
+ args,
+ ).stream()
+
+@output_operator()
+def overwrite_output(stream):
+ """Append the implicit '-y' execution instruction to override present file paths."""
+
+ return GlobalNode(
+ stream,
+ overwrite_output.__name__,
+ ['-y'],
+ ).stream()
+
+@output_operator()
+def merge_outputs(*streams):
+ """Aggregate multi-stream branches back into a single operational interface node context."""
+
+ return MergeOutputsNode(
+ streams,
+ merge_outputs.__name__,
+ ).stream()
+
+@filter_operator()
+def output(*streams_and_filename, **kwargs):
+ """
+ Compile distinct processed layers onto an absolute file location definition.
+
+ Raises:
+ ValueError: If filename arguments are missing or invalid, or if file format options collide.
+ """
+
+ streams_and_filename = list(streams_and_filename)
+ # Pop file strings from trailing argument values if a named filename kwarg is missing
+ if 'filename' not in kwargs:
+ if not isinstance(streams_and_filename[-1], str): raise ValueError("Output node declaration requires a destination file path string.")
+ kwargs['filename'] = streams_and_filename.pop()
+
+ fmt = kwargs.pop('f', None)
+
+ if fmt:
+ if 'format' in kwargs: raise ValueError("Ambiguous formatting options: both 'f' alias and 'format' key were supplied.")
+ kwargs['format'] = fmt
+
+ return OutputNode(
+ streams_and_filename,
+ output.__name__,
+ kwargs=kwargs,
+ ).stream()
+
+def escape_chars(text, chars):
+ """Cleanse runtime specific parameter string strings by prepending escape sequences."""
+
+ text = str(text)
+ # De-duplicate character targets
+ chars = list(set(chars))
+
+ # Backslashes must be escaped first before injecting additions to prevent recursive looping bugs
+ if '\\' in chars:
+ chars.remove('\\')
+ chars.insert(0, '\\')
+
+ for char in chars:
+ text = text.replace(char, '\\' + char)
+
+ return text
+
+def recursive_repr(item):
+ """Recursively formats structures into deterministic strings suitable for reliable hashing pipelines."""
+
+ if isinstance(item, str):
+ return item
+
+ # Resolve arrays iteratively
+ if isinstance(item, list):
+ return '[{}]'.format(
+ ', '.join(recursive_repr(x) for x in item)
+ )
+
+ # Alphabetize lookup dictionaries to keep state evaluations reliable
+ if isinstance(item, dict):
+ return '{{{}}}'.format(
+ ', '.join(
+ '{}: {}'.format(
+ recursive_repr(key),
+ recursive_repr(item[key]),
+ )
+ for key in sorted(item)
+ )
+ )
+
+ return repr(item)
+
+def get_hash(item):
+ """Generate a unique MD5 hex signature for a python component structural state tracking layout."""
+
+ value = recursive_repr(item).encode('utf-8')
+ return hashlib.md5(value).hexdigest()
+
+def get_hash_int(item):
+ """Generate an integer-based signature equivalent extracted out of base hex data frames."""
+
+ return int(get_hash(item), 16)
+
+@output_operator()
+def run(
+ stream_spec,
+ cmd='ffmpeg',
+ capture_stdout=False,
+ capture_stderr=False,
+ input=None,
+ quiet=False,
+ overwrite_output=False,
+ cwd=None,
+):
+ """
+ Block-execute a multimedia stream graph directly within host system process utilities.
+
+ Raises:
+ RuntimeError: If the process tracking encounters non-zero error execution states.
+ """
+
+ # Spawns process structures using internal async engine setups
+ process = run_async(
+ stream_spec,
+ cmd=cmd,
+ pipe_stdin=input is not None,
+ pipe_stdout=capture_stdout,
+ pipe_stderr=capture_stderr,
+ quiet=quiet,
+ overwrite_output=overwrite_output,
+ cwd=cwd,
+ )
+
+ # Perform synchronous block reads capturing standard runtime buffers
+ out, err = process.communicate(input)
+ retcode = process.poll()
+
+ if retcode: raise RuntimeError(f"Process execution failed with exit code {retcode}. Error details: {err.decode() if err else ''}")
+ return out, err
+
+@output_operator()
+def run_async(
+ stream_spec,
+ cmd='ffmpeg',
+ pipe_stdin=False,
+ pipe_stdout=False,
+ pipe_stderr=False,
+ quiet=False,
+ overwrite_output=False,
+ cwd=None
+):
+ """Spawns an asynchronous sub-process engine layer without blocking program execution paths."""
+
+ # Build out final list array arguments mapping the whole graph setup
+ args = compile(stream_spec, cmd=cmd, overwrite_output=overwrite_output)
+ # Map target standard descriptor piping flags
+ stdin_stream = subprocess.PIPE if pipe_stdin else None
+ stdout_stream = subprocess.PIPE if pipe_stdout else None
+ stderr_stream = subprocess.PIPE if pipe_stderr else None
+
+ if quiet: # Redirect logs into internal blackholes when quiet mode is enabled
+ stdout_stream = subprocess.DEVNULL
+ stderr_stream = subprocess.STDOUT
+
+ return subprocess.Popen(args, stdin=stdin_stream, stdout=stdout_stream, stderr=stderr_stream, cwd=cwd)
+
+@output_operator()
+def compile(stream_spec, cmd='ffmpeg', overwrite_output=False):
+ """Translate active graph connections directly into valid terminal terminal array directives."""
+
+ # Ensure command string definitions adapt into uniform lists
+ if isinstance(cmd, str): cmd = [cmd]
+ elif not isinstance(cmd, list): cmd = list(cmd)
+
+ return cmd + get_args(stream_spec, overwrite_output=overwrite_output)
+
+@output_operator()
+def get_args(stream_spec, overwrite_output=False):
+ """Construct full list configurations mapping out topological dependency flags sequentially."""
+
+ nodes = get_stream_spec_nodes(stream_spec)
+ # Sort active nodes via deep-first sorting to trace dependencies accurately
+ sorted_nodes, outgoing_edge_maps = topo_sort(nodes)
+ args, input_nodes, output_nodes, global_nodes, filter_nodes = [], [], [], [], []
+ # Distribute heterogeneous node entries into specific classification lists
+ for node in sorted_nodes:
+ if isinstance(node, InputNode): input_nodes.append(node)
+ elif isinstance(node, OutputNode): output_nodes.append(node)
+ elif isinstance(node, GlobalNode): global_nodes.append(node)
+ elif isinstance(node, FilterNode): filter_nodes.append(node)
+
+ # Auto-index primitive file entry node names map sequentially: (node, None) -> "0", "1", ...
+ stream_name_map = {(node, None): str(index) for index, node in enumerate(input_nodes)}
+ for node in input_nodes:
+ args.extend(get_input_args(node))
+
+ # Process and build internal complex filter instructions string blocks
+ filter_arg = get_filter_arg(filter_nodes, outgoing_edge_maps, stream_name_map)
+ if filter_arg: args.extend(['-filter_complex', filter_arg])
+
+ # Append downstream export definitions
+ for node in output_nodes:
+ args.extend(get_output_args(node, stream_name_map))
+
+ # Append final global flag directives
+ for node in global_nodes:
+ args.extend(list(node.args))
+
+ # Inject global overwrite flags directly at the end when requested
+ if overwrite_output: args.append('-y')
+ return args
+
+def get_filter_arg(filter_nodes, outgoing_edge_maps, stream_name_map):
+ """Assemble complex multiline filter graph configurations safely merged with semicolon delimiters."""
+
+ allocate_filter_stream_names(filter_nodes, outgoing_edge_maps, stream_name_map)
+ return ';'.join([get_filter_spec(node, outgoing_edge_maps[node], stream_name_map) for node in filter_nodes])
+
+def allocate_filter_stream_names(filter_nodes, outgoing_edge_maps, stream_name_map):
+ """
+ Sequentially assign distinct alphanumeric labels (e.g., [s0]) onto multi-branch outputs.
+
+ Raises:
+ ValueError: If an individual branch breaks validation by binding duplicate edge rules.
+ """
+
+ stream_count = 0
+
+ for upstream_node in filter_nodes:
+ outgoing_edge_map = outgoing_edge_maps[upstream_node]
+
+ for upstream_label, downstreams in sorted(outgoing_edge_map.items()):
+ # If multiple targets request a label without an explicit split filter, raise an issue
+ if len(downstreams) > 1: raise ValueError(f"Stream split conflict: Label {upstream_label!r} of node {upstream_node.name} is mapped to multiple downstream endpoints without an explicit split filter.")
+
+ # Assign internal stream identity tags sequentially (e.g., s0, s1, s2...)
+ stream_name_map[(upstream_node, upstream_label)] = 's{}'.format(stream_count)
+ stream_count += 1
+
+def get_filter_spec(node, outgoing_edge_map, stream_name_map):
+ """Format inputs, operations, and intermediate targets into structural filter specification tokens."""
+
+ incoming_edges = node.incoming_edges
+ outgoing_edges = get_outgoing_edges(node, outgoing_edge_map)
+
+ inputs = [format_input_stream_name(stream_name_map, edge) for edge in incoming_edges] # Format incoming tokens: e.g., "[0:v]" or "[s0]"
+ outputs = [format_output_stream_name(stream_name_map, edge) for edge in outgoing_edges] # Format destination tokens: e.g., "[s1]"
+
+ return '{}{}{}'.format(''.join(inputs), node._get_filter(outgoing_edges), ''.join(outputs))
+
+def get_input_args(input_node):
+ """
+ Generate CLI command-line parameters dedicated to initializing specific source files.
+
+ Raises:
+ ValueError: If the current operational node structure type mismatches input specifications.
+ """
+
+ if input_node.name != input.__name__: raise ValueError(f"Expected input execution context block, but encountered node: {input_node.name}")
+
+ kwargs = copy.copy(input_node.kwargs)
+ filename = kwargs.pop('filename')
+ fmt = kwargs.pop('format', None)
+ video_size = kwargs.pop('video_size', None)
+ args = []
+
+ # Map format flags sequentially
+ if fmt: args.extend(['-f', fmt])
+ # Map video resolution array tuples down into string formats
+ if video_size: args.extend(['-video_size', '{}x{}'.format(video_size[0], video_size[1])])
+
+ # Convert auxiliary parameters into standard command options
+ args.extend(convert_kwargs_to_cmd_line_args(kwargs))
+ # Target file paths are always introduced after their respective metadata modifiers
+ args.extend(['-i', filename])
+
+ return args
+
+def get_output_args(node, stream_name_map):
+ """
+ Generate final execution mappings and specific compression/bitrate arguments targeting outputs.
+
+ Raises:
+ ValueError: If node schemas violate expected conventions or lose input connections entirely.
+ """
+
+ if node.name != output.__name__: raise ValueError(f"Expected output execution context block, but encountered node: {node.name}")
+ if not node.incoming_edges: raise ValueError(f"Output node {node.long_repr()} has no valid incoming streams to export.")
+
+ args = []
+ # Compile stream routing definitions into explicit map declarations
+ for edge in node.incoming_edges:
+ stream_name = format_input_stream_name(stream_name_map, edge, is_final_arg=True)
+ # Avoid explicit mapping declarations for plain standalone root streams
+ if stream_name != '0' or len(node.incoming_edges) > 1: args.extend(['-map', stream_name])
+
+ kwargs = copy.copy(node.kwargs)
+ filename = kwargs.pop('filename')
+
+ # Convert generic keywords into explicit formatting keys
+ if 'format' in kwargs: args.extend(['-f', kwargs.pop('format')])
+ if 'video_bitrate' in kwargs: args.extend(['-b:v', str(kwargs.pop('video_bitrate'))])
+ if 'audio_bitrate' in kwargs: args.extend(['-b:a', str(kwargs.pop('audio_bitrate'))])
+ # Process resolution parameters
+ if 'video_size' in kwargs:
+ video_size = kwargs.pop('video_size')
+ # Map structural arrays down into size tokens
+ if not isinstance(video_size, str) and isinstance(video_size, Iterable): video_size = '{}x{}'.format(video_size[0], video_size[1])
+
+ args.extend(['-video_size', video_size])
+
+ # Unroll remaining kwargs configurations
+ args.extend(convert_kwargs_to_cmd_line_args(kwargs))
+ args.append(filename)
+
+ return args
+
+def convert_kwargs_to_cmd_line_args(kwargs):
+ """Convert arbitrary key-value pairs into explicit terminal hyphen prefix flags."""
+
+ args = []
+
+ for key in sorted(kwargs):
+ value = kwargs[key]
+ # Handle list/iterable argument sets mapped under identical flag names
+ if isinstance(value, Iterable) and not isinstance(value, str):
+ for item in value:
+ args.append('-{}'.format(key))
+ if item is not None: args.append(str(item))
+
+ continue
+
+ # Map basic standard parameters
+ args.append('-{}'.format(key))
+ if value is not None: args.append(str(value))
+
+ return args
+
+def format_input_stream_name(stream_name_map, edge, is_final_arg=False):
+ """Translate edge data maps into exact bracket syntax indices required by the terminal engine execution."""
+
+ prefix = stream_name_map[(edge.upstream_node, edge.upstream_label)]
+ suffix = ''
+
+ # Append optional sub-selectors like stream indices or tracks
+ if edge.upstream_selector: suffix = ':{}'.format(edge.upstream_selector)
+ # Bare unbracketed stream tokens are preferred for raw mapped input roots
+ if is_final_arg and isinstance(edge.upstream_node, InputNode): return '{}{}'.format(prefix, suffix)
+
+ # Standard stream tokens require bracket enclosures inside complex filters
+ return '[{}{}]'.format(prefix, suffix)
+
+def format_output_stream_name(stream_name_map, edge):
+ """Format downstream reference identifiers for use within complex filter strings."""
+
+ return '[{}]'.format(stream_name_map[(edge.upstream_node, edge.upstream_label)])
+
+def topo_sort(downstream_nodes):
+ """
+ Perform a depth-first search Topological Sort to establish execution order.
+
+ Raises:
+ RuntimeError: If a cyclic graph relationship (loop) is identified.
+ """
+
+ marked_nodes = set() # Tracks nodes currently on the active stack path to spot cycles
+ sorted_nodes = [] # Stores the final resolved execution stack sequentially
+ outgoing_edge_maps = {} # Tracks reverse mappings pointing upstream nodes to downstreams
+
+ def visit(
+ upstream_node,
+ upstream_label,
+ downstream_node,
+ downstream_label,
+ downstream_selector=None
+ ):
+ # A cycle is found if an upstream node matches an item on the current stack path
+ if upstream_node in marked_nodes: raise RuntimeError(f"Cyclic dependency detected! The graph contains a loop involving node: {upstream_node.name}")
+
+ # Update the reverse outgoing mapping layout data frames
+ if downstream_node is not None:
+ outgoing_edge_map = outgoing_edge_maps.setdefault(
+ upstream_node,
+ {}
+ )
+
+ outgoing_edges = outgoing_edge_map.setdefault(
+ upstream_label,
+ []
+ )
+
+ outgoing_edges.append((downstream_node, downstream_label, downstream_selector))
+
+ # Skip nodes that have already been fully resolved and sorted
+ if upstream_node in sorted_nodes: return
+ # Add the node to the active stack path
+ marked_nodes.add(upstream_node)
+
+ # Recursively visit all upstream dependencies first
+ for edge in upstream_node.incoming_edges:
+ visit(edge.upstream_node, edge.upstream_label, edge.downstream_node, edge.downstream_label, edge.upstream_selector)
+
+ # Remove the node from the active stack path and commit it to the sorted list
+ marked_nodes.remove(upstream_node)
+ sorted_nodes.append(upstream_node)
+
+ # Convert the raw array input parameters into initial pending items
+ pending_nodes = [(node, None) for node in downstream_nodes]
+ # Process all pending root nodes
+ while pending_nodes:
+ upstream_node, upstream_label = pending_nodes.pop()
+ visit(upstream_node, upstream_label, None, None)
+
+ return sorted_nodes, outgoing_edge_maps
\ No newline at end of file
diff --git a/main/library/audio/noisereduce.py b/main/library/audio/noisereduce.py
new file mode 100644
index 0000000000000000000000000000000000000000..74b51254100d604cfddec0f8a2831a912f448a27
--- /dev/null
+++ b/main/library/audio/noisereduce.py
@@ -0,0 +1,298 @@
+import os
+import sys
+import torch
+
+from torch.nn.functional import conv1d, conv2d
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config
+
+@torch.no_grad()
+def temperature_sigmoid(x, x0, temp_coeff):
+ """
+ Computes a temperature-scaled sigmoid function for soft-thresholding.
+
+ Args:
+ x (Tensor): Input tensor (activation values).
+ x0 (float/Tensor): Threshold center offset (inflection point).
+ temp_coeff (float): Scaling factor controlling the slope steepness.
+
+ Returns:
+ Tensor: Smooth sigmoidal mask values bounded between 0.0 and 1.0.
+ """
+
+ # Scaling by temperature controls mask softness; prevents hard binary clipping artifacts
+ return ((x - x0) / temp_coeff).sigmoid()
+
+@torch.no_grad()
+def linspace(start, stop, num = 50, endpoint = True, **kwargs):
+ """
+ Generates a linear sequence of evenly spaced values over a specified interval.
+
+ Args:
+ start (float): The starting value of the sequence.
+ stop (float): The end value of the sequence.
+ num (int): Number of samples to generate. Defaults to 50.
+ endpoint (bool): If True, includes the stop value. Defaults to True.
+ **kwargs: Additional tensor configuration options (e.g., dtype, device).
+
+ Returns:
+ Tensor: 1D linear spacing tensor.
+ """
+
+ return (
+ torch.linspace(
+ start,
+ stop,
+ num,
+ **kwargs
+ )
+ ) if endpoint else (
+ torch.linspace( # If endpoint is excluded, generate num + 1 elements and drop the trailing bound element
+ start,
+ stop,
+ num + 1,
+ **kwargs
+ )[:-1]
+ )
+
+@torch.no_grad()
+def amp_to_db(x, eps=torch.finfo(torch.float32).eps, top_db=40):
+ """
+ Converts a linear amplitude spectrogram to Decibel (dB) scale.
+
+ Args:
+ x (Tensor): Magnitude spectrogram tensor.
+ eps (float): Small epsilon floor value to prevent log10(0) domain errors.
+ top_db (float): Dynamic range threshold in dB relative to the peak magnitude.
+
+ Returns:
+ Tensor: Logarithmic scale dB representation capped at the dynamic floor.
+ """
+
+ # Compute 20 * log10(Amplitude) with an epsilon floor protection
+ x_db = 20 * (x + eps).log10()
+
+ # Apply dynamic range clamping based on the maximum global peak value along the last axis
+ return x_db.max(
+ (x_db.max(-1).values - top_db).unsqueeze(-1)
+ )
+
+class TorchGate(torch.nn.Module):
+ """A PyTorch-native Audio Spectral Gate for stationary and non-stationary noise reduction."""
+
+ @torch.no_grad()
+ def __init__(
+ self,
+ sr,
+ nonstationary = False,
+ n_std_thresh_stationary = 1.5,
+ n_thresh_nonstationary = 1.3,
+ temp_coeff_nonstationary = 0.1,
+ n_movemean_nonstationary = 20,
+ prop_decrease = 1.0,
+ n_fft = 1024,
+ win_length = None,
+ hop_length = None,
+ freq_mask_smooth_hz = 500,
+ time_mask_smooth_ms = 50
+ ):
+ """
+ A PyTorch module that applies a spectral gate to an input signal.
+
+ Args:
+ sr {int} -- Sample rate of the input signal.
+ nonstationary {bool} -- Whether to use non-stationary or stationary masking (default: {False}).
+ n_std_thresh_stationary {float} -- Number of standard deviations above mean to threshold noise for stationary masking (default: {1.5}).
+ n_thresh_nonstationary {float} -- Number of multiplies above smoothed magnitude spectrogram. for non-stationary masking (default: {1.3}).
+ temp_coeff_nonstationary {float} -- Temperature coefficient for non-stationary masking (default: {0.1}).
+ n_movemean_nonstationary {int} -- Number of samples for moving average smoothing in non-stationary masking (default: {20}).
+ prop_decrease {float} -- Proportion to decrease signal by where the mask is zero (default: {1.0}).
+ n_fft {int} -- Size of FFT for STFT (default: {1024}).
+ win_length {[int]} -- Window length for STFT. If None, defaults to `n_fft` (default: {None}).
+ hop_length {[int]} -- Hop length for STFT. If None, defaults to `win_length` // 4 (default: {None}).
+ freq_mask_smooth_hz {float} -- Frequency smoothing width for mask (in Hz). If None, no smoothing is applied (default: {500}).
+ time_mask_smooth_ms {float} -- Time smoothing width for mask (in ms). If None, no smoothing is applied (default: {50}).
+ """
+
+ super().__init__()
+ self.sr = sr
+ self.n_fft = n_fft
+ self.nonstationary = nonstationary
+ self.prop_decrease = prop_decrease
+ self.time_mask_smooth_ms = time_mask_smooth_ms
+ self.freq_mask_smooth_hz = freq_mask_smooth_hz
+ self.n_thresh_nonstationary = n_thresh_nonstationary
+ self.n_std_thresh_stationary = n_std_thresh_stationary
+ self.temp_coeff_nonstationary = temp_coeff_nonstationary
+ self.n_movemean_nonstationary = n_movemean_nonstationary
+
+ # Enforce range safety invariant for the noise attenuation coefficient
+ assert 0.0 <= prop_decrease <= 1.0
+ self.stft = None
+ self.win_length = self.n_fft if win_length is None else win_length
+ self.hop_length = self.win_length // 4 if hop_length is None else hop_length
+ self.register_buffer("smoothing_filter", self._generate_mask_smoothing_filter())
+ # torch.stft/istft is unsupported on OpenCL and DirectML, use the custom implementation instead.
+ self.forward = self._forward_other_backends if config.device.startswith(("ocl", "privateuseone")) else self._forward_torch
+
+ @torch.no_grad()
+ def _generate_mask_smoothing_filter(self):
+ """
+ A PyTorch module that applies a spectral gate to an input signal using the STFT.
+
+ Returns:
+ smoothing_filter (torch.Tensor): a 2D tensor representing the smoothing filter,
+ with shape (n_grad_freq, n_grad_time), where n_grad_freq is the number of frequency
+ bins to smooth and n_grad_time is the number of time frames to smooth.
+ If both self.freq_mask_smooth_hz and self.time_mask_smooth_ms are None, returns None.
+ """
+
+ if self.freq_mask_smooth_hz is None and self.time_mask_smooth_ms is None: return None
+
+ # Derive the kernel radius length along the frequency axis based on bin resolution width
+ n_grad_freq = (1 if self.freq_mask_smooth_hz is None else int(self.freq_mask_smooth_hz / (self.sr / (self.n_fft / 2))))
+ if n_grad_freq < 1: raise ValueError(f"freq_mask_smooth_hz needs to be at least {int((self.sr / (self.n_fft / 2)))} Hz")
+
+ # Derive the kernel radius length along the time axis based on the hop length stride window (ms)
+ n_grad_time = (1 if self.time_mask_smooth_ms is None else int(self.time_mask_smooth_ms / ((self.hop_length / self.sr) * 1000)))
+ if n_grad_time < 1: raise ValueError(f"time_mask_smooth_ms needs to be at least {int((self.hop_length / self.sr) * 1000)} ms")
+ if n_grad_time == 1 and n_grad_freq == 1: return None
+
+ # Build a 2D smoothing filter mask by calculating the outer product of two triangular linspace windows
+ smoothing_filter = torch.outer(
+ torch.cat([
+ linspace(0, 1, n_grad_freq + 1, endpoint=False),
+ linspace(1, 0, n_grad_freq + 2)
+ ])[1:-1],
+ torch.cat([
+ linspace(0, 1, n_grad_time + 1, endpoint=False),
+ linspace(1, 0, n_grad_time + 2)
+ ])[1:-1]
+ ).unsqueeze(0).unsqueeze(0)
+
+ # Unit-normalize the coefficient matrix sums to guarantee strict energy preservation boundaries
+ return smoothing_filter / smoothing_filter.sum()
+
+ @torch.no_grad()
+ def _stationary_mask(self, X_db):
+ """Calculates a Boolean voice activity gate mask based on stationary noise statistics."""
+
+ # Extract mean profiles and standard deviations across the time frame dimension (final axis)
+ std_freq_noise, mean_freq_noise = torch.std_mean(X_db, dim=-1)
+
+ # Elements exceeding the statistical floor (mean + n * std) are passed; others are flagged as noise
+ return X_db > (mean_freq_noise + std_freq_noise * self.n_std_thresh_stationary).unsqueeze(2)
+
+ @torch.no_grad()
+ def _nonstationary_mask(self, X_abs):
+ """Calculates a soft gain mask tracking fast-shifting non-stationary noise floors."""
+
+ # Utilize a 1D convolution rolling boxcar filter along the time axis to map local moving averages
+ X_smoothed = (
+ conv1d(
+ X_abs.reshape(-1, 1, X_abs.shape[-1]),
+ torch.ones(
+ self.n_movemean_nonstationary,
+ dtype=X_abs.dtype,
+ device=X_abs.device
+ ).view(1, 1, -1),
+ padding="same"
+ ).view(X_abs.shape) / self.n_movemean_nonstationary
+ )
+
+ # Compute signal-to-noise ratio deviations and pass them into the temperature-scaled sigmoid gate
+ return temperature_sigmoid(
+ ((X_abs - X_smoothed) / X_smoothed),
+ self.n_thresh_nonstationary,
+ self.temp_coeff_nonstationary
+ )
+
+ def _forward_torch(self, x):
+ """Processes gating functionality using PyTorch native STFT/iSTFT primitives."""
+
+ # Transform 1D time-series signals into a 2D complex time-frequency representation via STFT
+ X = torch.stft(
+ x,
+ n_fft=self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_length,
+ return_complex=True,
+ pad_mode="constant",
+ center=True,
+ window=torch.hann_window(self.win_length).to(x.device)
+ )
+
+ # Extract the signal masks based on chosen stationarity tracking configuration modes
+ sig_mask = self._nonstationary_mask(X.abs()) if self.nonstationary else self._stationary_mask(amp_to_db(X.abs()))
+ # Scale and project the mask tracking space into attenuation limits configured by prop_decrease
+ sig_mask = self.prop_decrease * (sig_mask.float() * 1.0 - 1.0) + 1.0
+
+ # Apply 2D spatial convolution filtering to resolve sharp threshold boundaries and block phase variance errors
+ if self.smoothing_filter is not None:
+ sig_mask = conv2d(
+ sig_mask.unsqueeze(1),
+ self.smoothing_filter.to(sig_mask.dtype),
+ padding="same"
+ )
+
+ # Execute point-wise complex spectral multiplication to attenuate noise-dominant frames
+ Y = X * sig_mask.squeeze(1)
+
+ # Reconstruct filtered 2D spectral components back into time-domain continuous wave signals
+ return (
+ torch.istft(
+ Y,
+ n_fft=self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_length,
+ center=True,
+ window=torch.hann_window(self.win_length).to(Y.device)
+ ).to(dtype=x.dtype)
+ )
+
+ def _forward_other_backends(self, x):
+ """Processes gating functionality using PyTorch native STFT/iSTFT primitives."""
+
+ # Lazy-instantiate the vendor-optimized custom backend STFT handler block if uninitialized
+ if self.stft is None:
+ from main.library.backends.utils import STFT
+
+ self.stft = STFT(
+ filter_length=self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_length,
+ pad_mode="constant"
+ ).to(x.device)
+
+ # Explicitly decouple magnitudes and phase components to handle non-complex hardware registers
+ X, phase = self.stft.transform(
+ x,
+ eps=1e-9,
+ return_phase=True
+ )
+
+ # Extract the signal masks based on chosen stationarity tracking configuration modes
+ sig_mask = self._nonstationary_mask(X.abs()) if self.nonstationary else self._stationary_mask(amp_to_db(X.abs()))
+ # Scale and project the mask tracking space into attenuation limits configured by prop_decrease
+ sig_mask = self.prop_decrease * (sig_mask.float() * 1.0 - 1.0) + 1.0
+
+ # Apply 2D spatial convolution filtering to resolve sharp threshold boundaries and block phase variance errors
+ if self.smoothing_filter is not None:
+ sig_mask = conv2d(
+ sig_mask.unsqueeze(1),
+ self.smoothing_filter.to(sig_mask.dtype),
+ padding="same"
+ )
+
+ # Execute point-wise complex spectral multiplication to attenuate noise-dominant frames
+ Y = X * sig_mask.squeeze(1)
+
+ # Reconstruct filtered 2D spectral components back into time-domain continuous wave signals
+ return (
+ self.stft.inverse(
+ Y,
+ phase
+ )
+ )
\ No newline at end of file
diff --git a/main/library/audio/sounddevice.py b/main/library/audio/sounddevice.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b00539c9861144086c41e966a0dd3ae8afeb042
--- /dev/null
+++ b/main/library/audio/sounddevice.py
@@ -0,0 +1,1570 @@
+import os
+import sys
+import struct
+import locale
+import atexit
+import platform
+import threading
+import contextlib
+import _cffi_backend
+
+import numpy as np
+
+from numbers import Integral
+from ctypes.util import find_library
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import configs, translations
+
+portaudiolib = None
+
+# Initialize FFI for PortAudio binding
+ffi = _cffi_backend.FFI('portaudio',
+ _version = 0x2601,
+ _types = b'\x00\x00\x76\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x79\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x1C\x0D\x00\x00\x8D\x03\x00\x00\x00\x0F\x00\x00\x7B\x0D\x00\x00\x00\x0F\x00\x00\x80\x0D\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x88\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x88\x0D\x00\x00\x07\x01\x00\x00\x07\x01\x00\x00\x01\x01\x00\x00\x00\x0F\x00\x00\x88\x0D\x00\x00\x00\x0F\x00\x00\x21\x0D\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x01\x0B\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x82\x03\x00\x00\x1F\x11\x00\x00\x0E\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x01\x00\x00\x07\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x0A\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x03\x00\x00\x1F\x11\x00\x00\x1F\x11\x00\x00\x0E\x01\x00\x00\x0A\x01\x00\x00\x0A\x01\x00\x00\x52\x03\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x2E\x11\x00\x00\x07\x01\x00\x00\x07\x01\x00\x00\x0A\x01\x00\x00\x0E\x01\x00\x00\x0A\x01\x00\x00\x34\x11\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x11\x00\x00\x07\x11\x00\x00\x0A\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x11\x00\x00\x8D\x03\x00\x00\x0A\x01\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x07\x11\x00\x00\x6B\x03\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x4B\x11\x00\x00\x07\x11\x00\x00\x0A\x01\x00\x00\x7F\x03\x00\x00\x0A\x01\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x0D\x00\x00\x00\x0F\x00\x00\x69\x0D\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x8D\x0D\x00\x00\x7D\x03\x00\x00\x8B\x03\x00\x00\x0A\x01\x00\x00\x00\x0F\x00\x00\x8D\x0D\x00\x00\x60\x11\x00\x00\x0A\x01\x00\x00\x00\x0F\x00\x00\x8D\x0D\x00\x00\x09\x01\x00\x00\x00\x0F\x00\x00\x8D\x0D\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x8D\x0D\x00\x00\x07\x11\x00\x00\x09\x01\x00\x00\x07\x11\x00\x00\x09\x01\x00\x00\x07\x11\x00\x00\x00\x0F\x00\x00\x01\x09\x00\x00\x77\x03\x00\x00\x02\x09\x00\x00\x00\x0B\x00\x00\x7A\x03\x00\x00\x03\x09\x00\x00\x7C\x03\x00\x00\x04\x09\x00\x00\x00\x09\x00\x00\x02\x0B\x00\x00\x05\x09\x00\x00\x81\x03\x00\x00\x06\x09\x00\x00\x07\x09\x00\x00\x03\x0B\x00\x00\x04\x0B\x00\x00\x08\x09\x00\x00\x05\x0B\x00\x00\x06\x0B\x00\x00\x89\x03\x00\x00\x02\x01\x00\x00\x01\x03\x00\x00\x15\x01\x00\x00\x6E\x03\x00\x00\x00\x01',
+ _globals = (b'\x00\x00\x11\x23PaMacCore_GetChannelName',0,b'\x00\x00\x5F\x23PaMacCore_SetupChannelMap',0,b'\x00\x00\x64\x23PaMacCore_SetupStreamInfo',0,b'\x00\x00\x23\x23PaWasapi_IsLoopback',0,b'\x00\x00\x5A\x23PaWasapi_UpdateDeviceList',0,b'\x00\x00\x41\x23Pa_AbortStream',0,b'\x00\x00\x41\x23Pa_CloseStream',0,b'\x00\x00\x5A\x23Pa_GetDefaultHostApi',0,b'\x00\x00\x5A\x23Pa_GetDefaultInputDevice',0,b'\x00\x00\x5A\x23Pa_GetDefaultOutputDevice',0,b'\x00\x00\x5A\x23Pa_GetDeviceCount',0,b'\x00\x00\x00\x23Pa_GetDeviceInfo',0,b'\x00\x00\x0E\x23Pa_GetErrorText',0,b'\x00\x00\x5A\x23Pa_GetHostApiCount',0,b'\x00\x00\x03\x23Pa_GetHostApiInfo',0,b'\x00\x00\x09\x23Pa_GetLastHostErrorInfo',0,b'\x00\x00\x2A\x23Pa_GetSampleSize',0,b'\x00\x00\x18\x23Pa_GetStreamCpuLoad',0,b'\x00\x00\x06\x23Pa_GetStreamHostApiType',0,b'\x00\x00\x0B\x23Pa_GetStreamInfo',0,b'\x00\x00\x5C\x23Pa_GetStreamReadAvailable',0,b'\x00\x00\x18\x23Pa_GetStreamTime',0,b'\x00\x00\x5C\x23Pa_GetStreamWriteAvailable',0,b'\x00\x00\x5A\x23Pa_GetVersion',0,b'\x00\x00\x16\x23Pa_GetVersionText',0,b'\x00\x00\x26\x23Pa_HostApiDeviceIndexToDeviceIndex',0,b'\x00\x00\x1B\x23Pa_HostApiTypeIdToHostApiIndex',0,b'\x00\x00\x5A\x23Pa_Initialize',0,b'\x00\x00\x1E\x23Pa_IsFormatSupported',0,b'\x00\x00\x41\x23Pa_IsStreamActive',0,b'\x00\x00\x41\x23Pa_IsStreamStopped',0,b'\x00\x00\x37\x23Pa_OpenDefaultStream',0,b'\x00\x00\x2D\x23Pa_OpenStream',0,b'\x00\x00\x44\x23Pa_ReadStream',0,b'\x00\x00\x4E\x23Pa_SetStreamFinishedCallback',0,b'\x00\x00\x68\x23Pa_Sleep',0,b'\x00\x00\x41\x23Pa_StartStream',0,b'\x00\x00\x41\x23Pa_StopStream',0,b'\x00\x00\x5A\x23Pa_Terminate',0,b'\x00\x00\x49\x23Pa_WriteStream',0,b'\xFF\xFF\xFF\x0BeAudioCategoryAlerts',4,b'\xFF\xFF\xFF\x0BeAudioCategoryCommunications',3,b'\xFF\xFF\xFF\x0BeAudioCategoryGameChat',8,b'\xFF\xFF\xFF\x0BeAudioCategoryGameEffects',6,b'\xFF\xFF\xFF\x0BeAudioCategoryGameMedia',7,b'\xFF\xFF\xFF\x0BeAudioCategoryMedia',11,b'\xFF\xFF\xFF\x0BeAudioCategoryMovie',10,b'\xFF\xFF\xFF\x0BeAudioCategoryOther',0,b'\xFF\xFF\xFF\x0BeAudioCategorySoundEffects',5,b'\xFF\xFF\xFF\x0BeAudioCategorySpeech',9,b'\xFF\xFF\xFF\x0BeStreamOptionMatchFormat',2,b'\xFF\xFF\xFF\x0BeStreamOptionNone',0,b'\xFF\xFF\xFF\x0BeStreamOptionRaw',1,b'\xFF\xFF\xFF\x0BeThreadPriorityAudio',1,b'\xFF\xFF\xFF\x0BeThreadPriorityCapture',2,b'\xFF\xFF\xFF\x0BeThreadPriorityDistribution',3,b'\xFF\xFF\xFF\x0BeThreadPriorityGames',4,b'\xFF\xFF\xFF\x0BeThreadPriorityNone',0,b'\xFF\xFF\xFF\x0BeThreadPriorityPlayback',5,b'\xFF\xFF\xFF\x0BeThreadPriorityProAudio',6,b'\xFF\xFF\xFF\x0BeThreadPriorityWindowManager',7,b'\xFF\xFF\xFF\x0BpaAL',9,b'\xFF\xFF\xFF\x0BpaALSA',8,b'\xFF\xFF\xFF\x0BpaASIO',3,b'\xFF\xFF\xFF\x0BpaAbort',2,b'\xFF\xFF\xFF\x1FpaAsioUseChannelSelectors',1,b'\xFF\xFF\xFF\x0BpaAudioScienceHPI',14,b'\xFF\xFF\xFF\x0BpaBadBufferPtr',-9972,b'\xFF\xFF\xFF\x0BpaBadIODeviceCombination',-9993,b'\xFF\xFF\xFF\x0BpaBadStreamPtr',-9988,b'\xFF\xFF\xFF\x0BpaBeOS',10,b'\xFF\xFF\xFF\x0BpaBufferTooBig',-9991,b'\xFF\xFF\xFF\x0BpaBufferTooSmall',-9990,b'\xFF\xFF\xFF\x0BpaCanNotReadFromACallbackStream',-9977,b'\xFF\xFF\xFF\x0BpaCanNotReadFromAnOutputOnlyStream',-9975,b'\xFF\xFF\xFF\x0BpaCanNotWriteToACallbackStream',-9976,b'\xFF\xFF\xFF\x0BpaCanNotWriteToAnInputOnlyStream',-9974,b'\xFF\xFF\xFF\x1FpaClipOff',1,b'\xFF\xFF\xFF\x0BpaComplete',1,b'\xFF\xFF\xFF\x0BpaContinue',0,b'\xFF\xFF\xFF\x0BpaCoreAudio',5,b'\xFF\xFF\xFF\x1FpaCustomFormat',65536,b'\xFF\xFF\xFF\x0BpaDeviceUnavailable',-9985,b'\xFF\xFF\xFF\x0BpaDirectSound',1,b'\xFF\xFF\xFF\x1FpaDitherOff',2,b'\xFF\xFF\xFF\x1FpaFloat32',1,b'\xFF\xFF\xFF\x1FpaFormatIsSupported',0,b'\xFF\xFF\xFF\x1FpaFramesPerBufferUnspecified',0,b'\xFF\xFF\xFF\x0BpaHostApiNotFound',-9979,b'\xFF\xFF\xFF\x0BpaInDevelopment',0,b'\xFF\xFF\xFF\x0BpaIncompatibleHostApiSpecificStreamInfo',-9984,b'\xFF\xFF\xFF\x0BpaIncompatibleStreamHostApi',-9973,b'\xFF\xFF\xFF\x1FpaInputOverflow',2,b'\xFF\xFF\xFF\x0BpaInputOverflowed',-9981,b'\xFF\xFF\xFF\x1FpaInputUnderflow',1,b'\xFF\xFF\xFF\x0BpaInsufficientMemory',-9992,b'\xFF\xFF\xFF\x1FpaInt16',8,b'\xFF\xFF\xFF\x1FpaInt24',4,b'\xFF\xFF\xFF\x1FpaInt32',2,b'\xFF\xFF\xFF\x1FpaInt8',16,b'\xFF\xFF\xFF\x0BpaInternalError',-9986,b'\xFF\xFF\xFF\x0BpaInvalidChannelCount',-9998,b'\xFF\xFF\xFF\x0BpaInvalidDevice',-9996,b'\xFF\xFF\xFF\x0BpaInvalidFlag',-9995,b'\xFF\xFF\xFF\x0BpaInvalidHostApi',-9978,b'\xFF\xFF\xFF\x0BpaInvalidSampleRate',-9997,b'\xFF\xFF\xFF\x0BpaJACK',12,b'\xFF\xFF\xFF\x0BpaMME',2,b'\xFF\xFF\xFF\x1FpaMacCoreChangeDeviceParameters',1,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityHigh',1024,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityLow',768,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityMax',0,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityMedium',512,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityMin',256,b'\xFF\xFF\xFF\x1FpaMacCoreFailIfConversionRequired',2,b'\xFF\xFF\xFF\x1FpaMacCoreMinimizeCPU',257,b'\xFF\xFF\xFF\x1FpaMacCoreMinimizeCPUButPlayNice',256,b'\xFF\xFF\xFF\x1FpaMacCorePlayNice',0,b'\xFF\xFF\xFF\x1FpaMacCorePro',1,b'\xFF\xFF\xFF\x1FpaNeverDropInput',4,b'\xFF\xFF\xFF\x1FpaNoDevice',-1,b'\xFF\xFF\xFF\x0BpaNoError',0,b'\xFF\xFF\xFF\x1FpaNoFlag',0,b'\xFF\xFF\xFF\x1FpaNonInterleaved',2147483648,b'\xFF\xFF\xFF\x0BpaNotInitialized',-10000,b'\xFF\xFF\xFF\x0BpaNullCallback',-9989,b'\xFF\xFF\xFF\x0BpaOSS',7,b'\xFF\xFF\xFF\x1FpaOutputOverflow',8,b'\xFF\xFF\xFF\x1FpaOutputUnderflow',4,b'\xFF\xFF\xFF\x0BpaOutputUnderflowed',-9980,b'\xFF\xFF\xFF\x1FpaPlatformSpecificFlags',4294901760,b'\xFF\xFF\xFF\x1FpaPrimeOutputBuffersUsingStreamCallback',8,b'\xFF\xFF\xFF\x1FpaPrimingOutput',16,b'\xFF\xFF\xFF\x0BpaSampleFormatNotSupported',-9994,b'\xFF\xFF\xFF\x0BpaSoundManager',4,b'\xFF\xFF\xFF\x0BpaStreamIsNotStopped',-9982,b'\xFF\xFF\xFF\x0BpaStreamIsStopped',-9983,b'\xFF\xFF\xFF\x0BpaTimedOut',-9987,b'\xFF\xFF\xFF\x1FpaUInt8',32,b'\xFF\xFF\xFF\x0BpaUnanticipatedHostError',-9999,b'\xFF\xFF\xFF\x1FpaUseHostApiSpecificDeviceSpecification',-2,b'\xFF\xFF\xFF\x0BpaWASAPI',13,b'\xFF\xFF\xFF\x0BpaWDMKS',11,b'\xFF\xFF\xFF\x0BpaWinWasapiAutoConvert',64,b'\xFF\xFF\xFF\x0BpaWinWasapiExclusive',1,b'\xFF\xFF\xFF\x0BpaWinWasapiExplicitSampleFormat',32,b'\xFF\xFF\xFF\x0BpaWinWasapiPolling',8,b'\xFF\xFF\xFF\x0BpaWinWasapiRedirectHostProcessor',2,b'\xFF\xFF\xFF\x0BpaWinWasapiThreadPriority',16,b'\xFF\xFF\xFF\x0BpaWinWasapiUseChannelMask',4),
+ _struct_unions = ((b'\x00\x00\x00\x7D\x00\x00\x00\x02$PaMacCoreStreamInfo',b'\x00\x00\x2B\x11size',b'\x00\x00\x1C\x11hostApiType',b'\x00\x00\x2B\x11version',b'\x00\x00\x2B\x11flags',b'\x00\x00\x61\x11channelMap',b'\x00\x00\x2B\x11channelMapSize'),(b'\x00\x00\x00\x75\x00\x00\x00\x02PaAsioStreamInfo',b'\x00\x00\x2B\x11size',b'\x00\x00\x1C\x11hostApiType',b'\x00\x00\x2B\x11version',b'\x00\x00\x2B\x11flags',b'\x00\x00\x8A\x11channelSelectors'),(b'\x00\x00\x00\x77\x00\x00\x00\x02PaDeviceInfo',b'\x00\x00\x01\x11structVersion',b'\x00\x00\x88\x11name',b'\x00\x00\x01\x11hostApi',b'\x00\x00\x01\x11maxInputChannels',b'\x00\x00\x01\x11maxOutputChannels',b'\x00\x00\x21\x11defaultLowInputLatency',b'\x00\x00\x21\x11defaultLowOutputLatency',b'\x00\x00\x21\x11defaultHighInputLatency',b'\x00\x00\x21\x11defaultHighOutputLatency',b'\x00\x00\x21\x11defaultSampleRate'),(b'\x00\x00\x00\x7A\x00\x00\x00\x02PaHostApiInfo',b'\x00\x00\x01\x11structVersion',b'\x00\x00\x1C\x11type',b'\x00\x00\x88\x11name',b'\x00\x00\x01\x11deviceCount',b'\x00\x00\x01\x11defaultInputDevice',b'\x00\x00\x01\x11defaultOutputDevice'),(b'\x00\x00\x00\x7C\x00\x00\x00\x02PaHostErrorInfo',b'\x00\x00\x1C\x11hostApiType',b'\x00\x00\x69\x11errorCode',b'\x00\x00\x88\x11errorText'),(b'\x00\x00\x00\x7F\x00\x00\x00\x02PaStreamCallbackTimeInfo',b'\x00\x00\x21\x11inputBufferAdcTime',b'\x00\x00\x21\x11currentTime',b'\x00\x00\x21\x11outputBufferDacTime'),(b'\x00\x00\x00\x81\x00\x00\x00\x02PaStreamInfo',b'\x00\x00\x01\x11structVersion',b'\x00\x00\x21\x11inputLatency',b'\x00\x00\x21\x11outputLatency',b'\x00\x00\x21\x11sampleRate'),(b'\x00\x00\x00\x82\x00\x00\x00\x02PaStreamParameters',b'\x00\x00\x01\x11device',b'\x00\x00\x01\x11channelCount',b'\x00\x00\x2B\x11sampleFormat',b'\x00\x00\x21\x11suggestedLatency',b'\x00\x00\x07\x11hostApiSpecificStreamInfo'),(b'\x00\x00\x00\x85\x00\x00\x00\x02PaWasapiStreamInfo',b'\x00\x00\x2B\x11size',b'\x00\x00\x1C\x11hostApiType',b'\x00\x00\x2B\x11version',b'\x00\x00\x2B\x11flags',b'\x00\x00\x2B\x11channelMask',b'\x00\x00\x8C\x11hostProcessorOutput',b'\x00\x00\x8C\x11hostProcessorInput',b'\x00\x00\x87\x11threadPriority',b'\x00\x00\x84\x11streamCategory',b'\x00\x00\x86\x11streamOption')),
+ _enums = (b'\x00\x00\x00\x78\x00\x00\x00\x15PaErrorCode\x00paNoError,paNotInitialized,paUnanticipatedHostError,paInvalidChannelCount,paInvalidSampleRate,paInvalidDevice,paInvalidFlag,paSampleFormatNotSupported,paBadIODeviceCombination,paInsufficientMemory,paBufferTooBig,paBufferTooSmall,paNullCallback,paBadStreamPtr,paTimedOut,paInternalError,paDeviceUnavailable,paIncompatibleHostApiSpecificStreamInfo,paStreamIsStopped,paStreamIsNotStopped,paInputOverflowed,paOutputUnderflowed,paHostApiNotFound,paInvalidHostApi,paCanNotReadFromACallbackStream,paCanNotWriteToACallbackStream,paCanNotReadFromAnOutputOnlyStream,paCanNotWriteToAnInputOnlyStream,paIncompatibleStreamHostApi,paBadBufferPtr',b'\x00\x00\x00\x1C\x00\x00\x00\x16PaHostApiTypeId\x00paInDevelopment,paDirectSound,paMME,paASIO,paSoundManager,paCoreAudio,paOSS,paALSA,paAL,paBeOS,paWDMKS,paJACK,paWASAPI,paAudioScienceHPI',b'\x00\x00\x00\x7E\x00\x00\x00\x16PaStreamCallbackResult\x00paContinue,paComplete,paAbort',b'\x00\x00\x00\x83\x00\x00\x00\x16PaWasapiFlags\x00paWinWasapiExclusive,paWinWasapiRedirectHostProcessor,paWinWasapiUseChannelMask,paWinWasapiPolling,paWinWasapiThreadPriority,paWinWasapiExplicitSampleFormat,paWinWasapiAutoConvert',b'\x00\x00\x00\x84\x00\x00\x00\x16PaWasapiStreamCategory\x00eAudioCategoryOther,eAudioCategoryCommunications,eAudioCategoryAlerts,eAudioCategorySoundEffects,eAudioCategoryGameEffects,eAudioCategoryGameMedia,eAudioCategoryGameChat,eAudioCategorySpeech,eAudioCategoryMovie,eAudioCategoryMedia',b'\x00\x00\x00\x86\x00\x00\x00\x16PaWasapiStreamOption\x00eStreamOptionNone,eStreamOptionRaw,eStreamOptionMatchFormat',b'\x00\x00\x00\x87\x00\x00\x00\x16PaWasapiThreadPriority\x00eThreadPriorityNone,eThreadPriorityAudio,eThreadPriorityCapture,eThreadPriorityDistribution,eThreadPriorityGames,eThreadPriorityPlayback,eThreadPriorityProAudio,eThreadPriorityWindowManager'),
+ _typenames = (b'\x00\x00\x00\x75PaAsioStreamInfo',b'\x00\x00\x00\x01PaDeviceIndex',b'\x00\x00\x00\x77PaDeviceInfo',b'\x00\x00\x00\x01PaError',b'\x00\x00\x00\x78PaErrorCode',b'\x00\x00\x00\x01PaHostApiIndex',b'\x00\x00\x00\x7APaHostApiInfo',b'\x00\x00\x00\x1CPaHostApiTypeId',b'\x00\x00\x00\x7CPaHostErrorInfo',b'\x00\x00\x00\x7DPaMacCoreStreamInfo',b'\x00\x00\x00\x2BPaSampleFormat',b'\x00\x00\x00\x8DPaStream',b'\x00\x00\x00\x52PaStreamCallback',b'\x00\x00\x00\x2BPaStreamCallbackFlags',b'\x00\x00\x00\x7EPaStreamCallbackResult',b'\x00\x00\x00\x7FPaStreamCallbackTimeInfo',b'\x00\x00\x00\x6BPaStreamFinishedCallback',b'\x00\x00\x00\x2BPaStreamFlags',b'\x00\x00\x00\x81PaStreamInfo',b'\x00\x00\x00\x82PaStreamParameters',b'\x00\x00\x00\x21PaTime',b'\x00\x00\x00\x83PaWasapiFlags',b'\x00\x00\x00\x8CPaWasapiHostProcessorCallback',b'\x00\x00\x00\x84PaWasapiStreamCategory',b'\x00\x00\x00\x85PaWasapiStreamInfo',b'\x00\x00\x00\x86PaWasapiStreamOption',b'\x00\x00\x00\x87PaWasapiThreadPriority',b'\x00\x00\x00\x2BPaWinWaveFormatChannelMask',b'\x00\x00\x00\x8BSInt32'),
+)
+
+# Load PortAudio library depending on the OS platform
+if portaudiolib is None:
+ try:
+ # Try finding standard PortAudio installations
+ for libname in ('portaudio', 'bin\\libportaudio-2.dll', 'lib/libportaudio.dylib'):
+ libname = find_library(libname)
+ if libname is not None: break
+ else: raise OSError(translations['portaudio_not_found'])
+
+ portaudiolib = ffi.dlopen(libname)
+ except OSError:
+ # Fallback to local binaries if not found in system paths
+ if platform.system() == 'Darwin': libname = 'libportaudio.dylib'
+ elif platform.system() == 'Windows':
+ if platform.machine().lower() in ('arm64', 'aarch64'): platform_suffix = 'arm64'
+ else: platform_suffix = platform.architecture()[0]
+
+ libname = 'libportaudio' + platform_suffix + ('-asio.dll' if 'SD_ENABLE_ASIO' in os.environ else '.dll')
+ else: raise
+
+ libdir = configs.get("binary_path", os.path.join("assets", "binary"))
+ libpath = os.path.join(libdir, libname)
+
+ # Unpack binary from a pre-packaged pickle file if it doesn't exist
+ if not os.path.exists(libpath):
+ target_content = None
+
+ with open(os.path.join(libdir, "portaudiolib.bin"), "rb") as f:
+ while 1:
+ name_len_bytes = f.read(4)
+ if not name_len_bytes: break
+
+ current = f.read(struct.unpack("I", name_len_bytes)[0]).decode('utf-8')
+ data_len = struct.unpack("I", f.read(4))[0]
+
+ if current == libname:
+ target_content = f.read(data_len)
+ break
+ else: f.seek(data_len, 1)
+
+ if target_content is None:
+ raise FileNotFoundError(f"Library '{libname}' not found inside packaged resources.")
+
+ with open(libpath, "wb") as w:
+ w.write(target_content)
+
+ portaudiolib = ffi.dlopen(os.path.abspath(libpath))
+
+initialized = 0
+last_callback = None
+sampleformats = {'float32': portaudiolib.paFloat32, 'int32': portaudiolib.paInt32, 'int24': portaudiolib.paInt24, 'int16': portaudiolib.paInt16, 'int8': portaudiolib.paInt8, 'uint8': portaudiolib.paUInt8}
+
+def play(data, samplerate=None, mapping=None, blocking=False, loop=False, **kwargs):
+ """
+ Play audio data using an output stream.
+
+ Args:
+ data (array-like): The audio data to play.
+ samplerate (int, optional): The sampling frequency. Defaults to None.
+ mapping (list/tuple, optional): Channel mapping configuration. Defaults to None.
+ blocking (bool): If True, wait until playback finishes. Defaults to False.
+ loop (bool): If True, loop the audio data indefinitely. Defaults to False.
+ **kwargs: Additional stream parameters.
+ """
+
+ ctx = CallbackContext(loop=loop)
+ ctx.frames = ctx.check_data(data, mapping, kwargs.get('device'))
+
+ def callback(outdata, frames, time, status):
+ assert len(outdata) == frames
+ ctx.callback_enter(status, outdata)
+ ctx.write_outdata(outdata)
+ ctx.callback_exit()
+
+ ctx.start_stream(OutputStream, samplerate, ctx.output_channels, ctx.output_dtype, callback, blocking, prime_output_buffers_using_stream_callback=False, **kwargs)
+
+def rec(frames=None, samplerate=None, channels=None, dtype=None, out=None, mapping=None, blocking=False, **kwargs):
+ """
+ Record audio data using an input stream.
+
+ Args:
+ frames (int, optional): Number of frames to record. Defaults to None.
+ samplerate (int, optional): The sampling frequency. Defaults to None.
+ channels (int, optional): Number of input channels. Defaults to None.
+ dtype (str/type, optional): Data type of the recorded audio. Defaults to None.
+ out (array-like, optional): Pre-allocated buffer to store recorded audio. Defaults to None.
+ mapping (list/tuple, optional): Channel mapping configuration. Defaults to None.
+ blocking (bool): If True, wait until recording finishes. Defaults to False.
+ **kwargs: Additional stream parameters.
+
+ Returns:
+ array-like: The array containing the recorded audio data.
+ """
+
+ ctx = CallbackContext()
+ out, ctx.frames = ctx.check_out(out, frames, channels, dtype, mapping)
+
+ def callback(indata, frames, time, status):
+ assert len(indata) == frames
+ ctx.callback_enter(status, indata)
+ ctx.read_indata(indata)
+ ctx.callback_exit()
+
+ ctx.start_stream(InputStream, samplerate, ctx.input_channels,
+ ctx.input_dtype, callback, blocking, **kwargs)
+ return out
+
+def playrec(data, samplerate=None, channels=None, dtype=None, out=None, input_mapping=None, output_mapping=None, blocking=False, **kwargs):
+ """
+ Simultaneously play and record audio data using a duplex stream.
+
+ Args:
+ data (array-like): Audio data for playback.
+ samplerate (int, optional): The sampling frequency. Defaults to None.
+ channels (int, optional): Number of input channels. Defaults to None.
+ dtype (str/type, optional): Data type for recording. Defaults to None.
+ out (array-like, optional): Pre-allocated buffer for recorded data. Defaults to None.
+ input_mapping (list/tuple, optional): Input channel mapping. Defaults to None.
+ output_mapping (list/tuple, optional): Output channel mapping. Defaults to None.
+ blocking (bool): If True, wait until the operation completes. Defaults to False.
+ **kwargs: Additional stream parameters.
+
+ Raises:
+ ValueError: If input and output frame counts do not match.
+
+ Returns:
+ array-like: The array containing the recorded audio data.
+ """
+
+ ctx = CallbackContext()
+ output_frames = ctx.check_data(data, output_mapping, kwargs.get('device'))
+
+ if dtype is None: dtype = ctx.data.dtype
+ out, input_frames = ctx.check_out(out, output_frames, channels, dtype, input_mapping)
+
+ if input_frames != output_frames: raise ValueError("Input and output frame sizes must match for playrec operation.")
+ ctx.frames = input_frames
+
+ def callback(indata, outdata, frames, time, status):
+ assert len(indata) == len(outdata) == frames
+ ctx.callback_enter(status, indata)
+ ctx.read_indata(indata)
+ ctx.write_outdata(outdata)
+ ctx.callback_exit()
+
+ ctx.start_stream(Stream, samplerate, (ctx.input_channels, ctx.output_channels), (ctx.input_dtype, ctx.output_dtype), callback, blocking, prime_output_buffers_using_stream_callback=False, **kwargs)
+ return out
+
+def wait(ignore_errors=True):
+ """
+ Wait for the active playback/recording stream to finish.
+
+ Args:
+ ignore_errors (bool): If True, ignore stream processing errors during wait. Defaults to True.
+ """
+
+ if last_callback: return last_callback.wait(ignore_errors)
+
+def stop(ignore_errors=True):
+ """
+ Stop and close the currently active stream.
+
+ Args:
+ ignore_errors (bool): If True, suppress errors during shutdown. Defaults to True.
+ """
+
+ if last_callback:
+ last_callback.stream.stop(ignore_errors)
+ last_callback.stream.close(ignore_errors)
+
+def get_status():
+ """
+ Get the status flags of the last active stream callback.
+
+ Raises:
+ RuntimeError: If no active stream or callback history exists.
+
+ Returns:
+ PaCallbackFlags: The status flags of the stream.
+ """
+
+ if last_callback: return last_callback.status
+ else: raise RuntimeError("No active stream or callback status found.")
+
+def get_stream():
+ """
+ Retrieve the last active stream object.
+
+ Raises:
+ RuntimeError: If no stream has been opened or initialized yet.
+
+ Returns:
+ StreamBase: The active stream instance.
+ """
+
+ if last_callback: return last_callback.stream
+ else: raise RuntimeError("No active stream found.")
+
+def query_devices(device=None, kind=None):
+ """
+ Query information about host audio devices.
+
+ Args:
+ device (int/str, optional): Device index or name pattern. Defaults to None.
+ kind (str, optional): Filter devices by 'input' or 'output'. Defaults to None.
+
+ Raises:
+ ValueError: If 'kind' is invalid, or selected device does not support specified 'kind'.
+ PortAudioError: If PortAudio fails to fetch information for the device index.
+
+ Returns:
+ dict/DeviceList: Information dictionary of the specified device, or a list of all devices.
+ """
+
+ if kind not in ('input', 'output', None): raise ValueError(f"Invalid device kind: {kind!r}. Expected 'input', 'output', or None.")
+ if device is None and kind is None: return DeviceList(query_devices(i) for i in range(check(portaudiolib.Pa_GetDeviceCount())))
+
+ device = get_device_id(device, kind, raise_on_error=True)
+ info = portaudiolib.Pa_GetDeviceInfo(device)
+
+ if not info: raise PortAudioError(f"Failed to query device information for index: {device}")
+ assert info.structVersion == 2
+ name_bytes = ffi_string(info.name)
+
+ # Handle OS-specific string encodings for device names
+ try:
+ name = name_bytes.decode('utf-8')
+ except UnicodeDecodeError:
+ api_idx = portaudiolib.Pa_HostApiTypeIdToHostApiIndex
+
+ if info.hostApi in (api_idx(portaudiolib.paDirectSound), api_idx(portaudiolib.paMME)): name = name_bytes.decode('mbcs')
+ elif info.hostApi == api_idx(portaudiolib.paASIO): name = name_bytes.decode(locale.getpreferredencoding())
+ else: raise
+
+ device_dict = {
+ 'name': name,
+ 'index': device,
+ 'hostapi': info.hostApi,
+ 'max_input_channels': info.maxInputChannels,
+ 'max_output_channels': info.maxOutputChannels,
+ 'default_low_input_latency': info.defaultLowInputLatency,
+ 'default_low_output_latency': info.defaultLowOutputLatency,
+ 'default_high_input_latency': info.defaultHighInputLatency,
+ 'default_high_output_latency': info.defaultHighOutputLatency,
+ 'default_samplerate': info.defaultSampleRate,
+ }
+
+ if kind and device_dict['max_' + kind + '_channels'] < 1: raise ValueError(f"Device '{device_dict['name']}' does not support {kind} channels.")
+ return device_dict
+
+def query_hostapis(index=None):
+ """
+ Query information about Host APIs supported by the system.
+
+ Args:
+ index (int, optional): The index of the Host API. Defaults to None.
+
+ Raises:
+ PortAudioError: If details cannot be retrieved for the given Host API index.
+
+ Returns:
+ dict/tuple: Dictionary info of the API, or a tuple containing info of all host APIs.
+ """
+
+ if index is None: return tuple(query_hostapis(i) for i in range(check(portaudiolib.Pa_GetHostApiCount())))
+ info = portaudiolib.Pa_GetHostApiInfo(index)
+ if not info: raise PortAudioError(f"Failed to fetch host API info for index: {index}")
+
+ assert info.structVersion == 1
+ return {
+ 'name': ffi_string(info.name).decode(),
+ 'devices': [portaudiolib.Pa_HostApiDeviceIndexToDeviceIndex(index, i) for i in range(info.deviceCount)],
+ 'default_input_device': info.defaultInputDevice,
+ 'default_output_device': info.defaultOutputDevice,
+ }
+
+def check_input_settings(device=None, channels=None, dtype=None, extra_settings=None, samplerate=None):
+ """
+ Validate if the provided input configuration is natively supported by PortAudio.
+
+ Args:
+ device (int/str, optional): Target device.
+ channels (int, optional): Requested number of input channels.
+ dtype (str/type, optional): Desired sample format.
+ extra_settings (cdata, optional): Host API specific configurations.
+ samplerate (int, optional): Target sampling frequency.
+ """
+
+ parameters, dtype, _, samplerate = get_stream_parameters('input', device=device, channels=channels, dtype=dtype, latency=None, extra_settings=extra_settings, samplerate=samplerate)
+ check(portaudiolib.Pa_IsFormatSupported(parameters, ffi.NULL, samplerate))
+
+def check_output_settings(device=None, channels=None, dtype=None, extra_settings=None, samplerate=None):
+ """
+ Validate if the provided output configuration is natively supported by PortAudio.
+
+ Args:
+ device (int/str, optional): Target device.
+ channels (int, optional): Requested number of output channels.
+ dtype (str/type, optional): Desired sample format.
+ extra_settings (cdata, optional): Host API specific configurations.
+ samplerate (int, optional): Target sampling frequency.
+ """
+
+ parameters, dtype, _, samplerate = get_stream_parameters('output', device=device, channels=channels, dtype=dtype, latency=None, extra_settings=extra_settings, samplerate=samplerate)
+ check(portaudiolib.Pa_IsFormatSupported(ffi.NULL, parameters, samplerate))
+
+def sleep(msec):
+ """
+ Put the current executing thread to sleep for a specified duration using PortAudio.
+
+ Args:
+ msec (int): Duration to sleep in milliseconds.
+ """
+
+ portaudiolib.Pa_Sleep(msec)
+
+def get_portaudio_version():
+ """
+ Get the version identifier and textual release details of PortAudio.
+
+ Returns:
+ tuple: An (int, str) containing the version code and description text.
+ """
+
+ return portaudiolib.Pa_GetVersion(), ffi_string(portaudiolib.Pa_GetVersionText()).decode()
+
+class StreamBase:
+ def __init__(self, kind, samplerate=None, blocksize=None, device=None, channels=None, dtype=None, latency=None, extra_settings=None, callback=None, finished_callback=None, clip_off=None, dither_off=None, never_drop_input=None, prime_output_buffers_using_stream_callback=None, userdata=None, wrap_callback=None):
+ """
+ Initialize the base stream with relevant parameters.
+
+ Raises:
+ ValueError: If sample rates between input and output configurations differ in a duplex setup.
+ PortAudioError: If metadata cannot be acquired for the opened stream.
+ """
+
+ assert kind in ('input', 'output', 'duplex')
+ assert wrap_callback in ('array', 'buffer', None)
+ stream_flags = portaudiolib.paNoFlag
+
+ # Fallback to defaults if specific parameters aren't configured
+ if blocksize is None: blocksize = default.blocksize
+ if clip_off is None: clip_off = default.clip_off
+ if dither_off is None: dither_off = default.dither_off
+ if never_drop_input is None: never_drop_input = default.never_drop_input
+ if prime_output_buffers_using_stream_callback is None: prime_output_buffers_using_stream_callback = default.prime_output_buffers_using_stream_callback
+ if clip_off: stream_flags |= portaudiolib.paClipOff
+ if dither_off: stream_flags |= portaudiolib.paDitherOff
+ if never_drop_input: stream_flags |= portaudiolib.paNeverDropInput
+ if prime_output_buffers_using_stream_callback: stream_flags |= portaudiolib.paPrimeOutputBuffersUsingStreamCallback
+
+ # Handle stream configuration parameter retrieval depending on stream nature
+ if kind == 'duplex':
+ idevice, odevice = split(device)
+ ichannels, ochannels = split(channels)
+ idtype, odtype = split(dtype)
+ ilatency, olatency = split(latency)
+ iextra, oextra = split(extra_settings)
+
+ iparameters, idtype, isize, isamplerate = get_stream_parameters('input', idevice, ichannels, idtype, ilatency, iextra, samplerate)
+ oparameters, odtype, osize, osamplerate = get_stream_parameters('output', odevice, ochannels, odtype, olatency, oextra, samplerate)
+
+ self._dtype = idtype, odtype
+ self._device = iparameters.device, oparameters.device
+ self._channels = iparameters.channelCount, oparameters.channelCount
+ self._samplesize = isize, osize
+
+ if isamplerate != osamplerate: raise ValueError
+ else: samplerate = isamplerate
+ else:
+ parameters, self._dtype, self._samplesize, samplerate = get_stream_parameters(kind, device, channels, dtype, latency, extra_settings, samplerate)
+ self._device = parameters.device
+ self._channels = parameters.channelCount
+ iparameters = ffi.NULL
+ oparameters = ffi.NULL
+
+ if kind == 'input': iparameters = parameters
+ elif kind == 'output': oparameters = parameters
+
+ ffi_callback = ffi.callback('PaStreamCallback', error=portaudiolib.paAbort)
+
+ # Map dynamic callback functions based on the data formatting requirements ('array' or 'buffer')
+ if callback is None: callback_ptr = ffi.NULL
+ elif kind == 'input' and wrap_callback == 'buffer':
+ @ffi_callback
+ def callback_ptr(iptr, optr, frames, time, status, _):
+ data = mbuffer(iptr, frames, self._channels, self._samplesize)
+ return _wrap_callback(callback, data, frames, time, status)
+ elif kind == 'input' and wrap_callback == 'array':
+ @ffi_callback
+ def callback_ptr(iptr, optr, frames, time, status, _):
+ data = array(mbuffer(iptr, frames, self._channels, self._samplesize), self._channels, self._dtype)
+ return _wrap_callback(callback, data, frames, time, status)
+ elif kind == 'output' and wrap_callback == 'buffer':
+ @ffi_callback
+ def callback_ptr(iptr, optr, frames, time, status, _):
+ data = mbuffer(optr, frames, self._channels, self._samplesize)
+ return _wrap_callback(callback, data, frames, time, status)
+ elif kind == 'output' and wrap_callback == 'array':
+ @ffi_callback
+ def callback_ptr(iptr, optr, frames, time, status, _):
+ data = array(mbuffer(optr, frames, self._channels, self._samplesize), self._channels, self._dtype)
+ return _wrap_callback(callback, data, frames, time, status)
+ elif kind == 'duplex' and wrap_callback == 'buffer':
+ @ffi_callback
+ def callback_ptr(iptr, optr, frames, time, status, _):
+ idata, odata = mbuffer(iptr, frames, self._channels[0], self._samplesize[0]), mbuffer(optr, frames, self._channels[1], self._samplesize[1])
+ return _wrap_callback(callback, idata, odata, frames, time, status)
+ elif kind == 'duplex' and wrap_callback == 'array':
+ @ffi_callback
+ def callback_ptr(iptr, optr, frames, time, status, _):
+ idata, odata = array(mbuffer(iptr, frames, self._channels[0], self._samplesize[0]), self._channels[0], self._dtype[0]), array(mbuffer(optr, frames, self._channels[1], self._samplesize[1]), self._channels[1], self._dtype[1])
+ return _wrap_callback(callback, idata, odata, frames, time, status)
+ else: callback_ptr = ffi.cast('PaStreamCallback*', callback)
+
+ self._callback = callback_ptr
+ if userdata is None: userdata = ffi.NULL
+
+ self._ptr = ffi.new('PaStream**')
+ check(portaudiolib.Pa_OpenStream(self._ptr, iparameters, oparameters, samplerate, blocksize, stream_flags, callback_ptr, userdata), self.__class__.__name__)
+
+ self._ptr = self._ptr[0]
+ self._blocksize = blocksize
+
+ info = portaudiolib.Pa_GetStreamInfo(self._ptr)
+ if not info: raise PortAudioError("Could not retrieve info from opened PortAudio stream.")
+ self._samplerate = info.sampleRate
+
+ if not oparameters: self._latency = info.inputLatency
+ elif not iparameters: self._latency = info.outputLatency
+ else: self._latency = info.inputLatency, info.outputLatency
+
+ if finished_callback:
+ if isinstance(finished_callback, ffi.CData): self._finished_callback = finished_callback
+ else:
+ def finished_callback_wrapper(_):
+ return finished_callback()
+
+ self._finished_callback = ffi.callback('PaStreamFinishedCallback', finished_callback_wrapper)
+
+ check(portaudiolib.Pa_SetStreamFinishedCallback(self._ptr, self._finished_callback))
+
+ _ptr = ffi.NULL
+
+ @property
+ def samplerate(self):
+ return self._samplerate
+
+ @property
+ def blocksize(self):
+ return self._blocksize
+
+ @property
+ def device(self):
+ return self._device
+
+ @property
+ def channels(self):
+ return self._channels
+
+ @property
+ def dtype(self):
+ return self._dtype
+
+ @property
+ def samplesize(self):
+ return self._samplesize
+
+ @property
+ def latency(self):
+ return self._latency
+
+ @property
+ def active(self):
+ if self.closed: return False
+ return check(portaudiolib.Pa_IsStreamActive(self._ptr)) == 1
+
+ @property
+ def stopped(self):
+ if self.closed: return True
+ return check(portaudiolib.Pa_IsStreamStopped(self._ptr)) == 1
+
+ @property
+ def closed(self):
+ return self._ptr == ffi.NULL
+
+ @property
+ def time(self):
+ time = portaudiolib.Pa_GetStreamTime(self._ptr)
+ if not time: raise PortAudioError("Failed to fetch current stream timeline position.")
+ return time
+
+ @property
+ def cpu_load(self):
+ return portaudiolib.Pa_GetStreamCpuLoad(self._ptr)
+
+ def __enter__(self):
+ self.start()
+ return self
+
+ def __exit__(self, *args):
+ self.stop()
+ self.close()
+
+ def start(self):
+ """Start the processing on the active stream."""
+
+ err = portaudiolib.Pa_StartStream(self._ptr)
+ if err != portaudiolib.paStreamIsNotStopped: check(err)
+
+ def stop(self, ignore_errors=True):
+ """Stop processing audio on the stream."""
+
+ err = portaudiolib.Pa_StopStream(self._ptr)
+ if not ignore_errors: check(err)
+
+ def abort(self, ignore_errors=True):
+ """Abort audio processing immediately on the stream."""
+
+ err = portaudiolib.Pa_AbortStream(self._ptr)
+ if not ignore_errors: check(err)
+
+ def close(self, ignore_errors=True):
+ """Close the stream and release internal processing allocations."""
+
+ err = portaudiolib.Pa_CloseStream(self._ptr)
+ self._ptr = ffi.NULL
+ if not ignore_errors: check(err)
+
+class InputStreamBase(StreamBase):
+ """Stream subclass specific to processing input captures."""
+
+ @property
+ def read_available(self):
+ return check(portaudiolib.Pa_GetStreamReadAvailable(self._ptr))
+
+ def _raw_read(self, frames):
+ """
+ Read synchronous buffer samples directly out from an input pipeline.
+
+ Args:
+ frames (int): Target frame chunk size.
+
+ Returns:
+ tuple: (buffer, bool) buffer data chunk and input overflow flag status.
+ """
+
+ channels, _ = split(self._channels)
+ samplesize, _ = split(self._samplesize)
+
+ data = ffi.new('signed char[]', channels * samplesize * frames)
+ err = portaudiolib.Pa_ReadStream(self._ptr, data, frames)
+
+ if err == portaudiolib.paInputOverflowed: overflowed = True
+ else:
+ check(err)
+ overflowed = False
+
+ return ffi.mbuffer(data), overflowed
+
+class RawInputStream(InputStreamBase):
+ """Raw byte-oriented variation of an Input Stream structure."""
+
+ def __init__(self, samplerate=None, blocksize=None, device=None, channels=None, dtype=None, latency=None, extra_settings=None, callback=None, finished_callback=None, clip_off=None, dither_off=None, never_drop_input=None, prime_output_buffers_using_stream_callback=None):
+ StreamBase.__init__(self, kind='input', wrap_callback='buffer', **remove_self(locals()))
+
+ read = InputStreamBase._raw_read
+
+class OutputStreamBase(StreamBase):
+ @property
+ def write_available(self):
+ return check(portaudiolib.Pa_GetStreamWriteAvailable(self._ptr))
+
+ def _raw_write(self, data):
+ """
+ Write synchronous sample buffer objects onto an output pipeline.
+
+ Args:
+ data (bufferable): Buffer byte structure containing frame samples.
+
+ Raises:
+ ValueError: If sample data chunk dimensions are malformed.
+
+ Returns:
+ bool: True if output underflow occurred, False otherwise.
+ """
+
+ try:
+ data = ffi.from_buffer(data)
+ except AttributeError:
+ pass
+ except TypeError:
+ pass
+
+ _, samplesize = split(self._samplesize)
+ _, channels = split(self._channels)
+
+ samples, remainder = divmod(len(data), samplesize)
+ if remainder: raise ValueError("Buffer data size is not a multiple of the sample size.")
+
+ frames, remainder = divmod(samples, channels)
+ if remainder: raise ValueError("Total sample count is not a multiple of the channel count.")
+
+ err = portaudiolib.Pa_WriteStream(self._ptr, data, frames)
+
+ if err == portaudiolib.paOutputUnderflowed: underflowed = True
+ else:
+ check(err)
+ underflowed = False
+
+ return underflowed
+
+class RawOutputStream(OutputStreamBase):
+ """Raw byte-oriented variation of an Output Stream structure."""
+
+ def __init__(self, samplerate=None, blocksize=None, device=None, channels=None, dtype=None, latency=None, extra_settings=None, callback=None, finished_callback=None, clip_off=None, dither_off=None, never_drop_input=None, prime_output_buffers_using_stream_callback=None):
+ StreamBase.__init__(self, kind='output', wrap_callback='buffer', **remove_self(locals()))
+
+ write = OutputStreamBase._raw_write
+
+class RawStream(RawInputStream, RawOutputStream):
+ """A full-duplex raw byte stream providing concurrent IO pipelines."""
+
+ def __init__(self, samplerate=None, blocksize=None, device=None, channels=None, dtype=None, latency=None, extra_settings=None, callback=None, finished_callback=None, clip_off=None, dither_off=None, never_drop_input=None, prime_output_buffers_using_stream_callback=None):
+ StreamBase.__init__(self, kind='duplex', wrap_callback='buffer', **remove_self(locals()))
+
+class InputStream(InputStreamBase):
+ def __init__(self, samplerate=None, blocksize=None, device=None, channels=None, dtype=None, latency=None, extra_settings=None, callback=None, finished_callback=None, clip_off=None, dither_off=None, never_drop_input=None, prime_output_buffers_using_stream_callback=None):
+ """
+ Initialize an input stream.
+
+ Args:
+ samplerate (float): The desired sampling rate in Hz.
+ blocksize (int): The number of frames passed to the stream callback.
+ device (int or str): The device index or name.
+ channels (int): The number of channels.
+ dtype (str or numpy.dtype): The data type of the audio buffer.
+ latency (float or str): The desired latency in seconds or 'low'/'high'.
+ extra_settings (object): API-specific settings object.
+ callback (callable): The callback function for audio processing.
+ finished_callback (callable): Callback called when stream finishes.
+ clip_off (bool): Disable clipping of out-of-range samples.
+ dither_off (bool): Disable dithering.
+ never_drop_input (bool): Overflow management strategy.
+ prime_output_buffers_using_stream_callback (bool): Output priming control.
+ """
+
+ # Call the base class initializer for the input stream
+ StreamBase.__init__(self, kind='input', wrap_callback='array', **remove_self(locals()))
+
+ def read(self, frames):
+ """
+ Read data from the input stream.
+
+ Args:
+ frames (int): The number of frames to read.
+
+ Returns:
+ tuple: A tuple containing the audio data (numpy.ndarray) and an overflow indicator (bool).
+ """
+
+ dtype, _ = split(self._dtype)
+ channels, _ = split(self._channels)
+
+ data, overflowed = InputStreamBase._raw_read(self, frames)
+ data = array(data, channels, dtype)
+
+ return data, overflowed
+
+class OutputStream(OutputStreamBase):
+ def __init__(self, samplerate=None, blocksize=None, device=None, channels=None, dtype=None, latency=None, extra_settings=None, callback=None, finished_callback=None, clip_off=None, dither_off=None, never_drop_input=None, prime_output_buffers_using_stream_callback=None):
+ """
+ Initialize an output stream.
+
+ Args:
+ samplerate (float): The desired sampling rate in Hz.
+ blocksize (int): The number of frames passed to the stream callback.
+ device (int or str): The device index or name.
+ channels (int): The number of channels.
+ dtype (str or numpy.dtype): The data type of the audio buffer.
+ latency (float or str): The desired latency in seconds or 'low'/'high'.
+ extra_settings (object): API-specific settings object.
+ callback (callable): The callback function for audio processing.
+ finished_callback (callable): Callback called when stream finishes.
+ clip_off (bool): Disable clipping of out-of-range samples.
+ dither_off (bool): Disable dithering.
+ never_drop_input (bool): Overflow management strategy.
+ prime_output_buffers_using_stream_callback (bool): Output priming control.
+ """
+
+ # Call the base class initializer for the output stream
+ StreamBase.__init__(self, kind='output', wrap_callback='array', **remove_self(locals()))
+
+ def write(self, data):
+ """
+ Write data to the output stream.
+
+ Args:
+ data (array_like): The audio data to be played back.
+
+ Returns:
+ bool: An underflow indicator.
+ """
+
+ data = np.asarray(data)
+ _, dtype = split(self._dtype)
+ _, channels = split(self._channels)
+
+ # Enforce the audio data to be 2-dimensional (frames, channels)
+ if data.ndim < 2: data = data.reshape(-1, 1)
+ elif data.ndim > 2: raise ValueError("Audio data must be 1-dimensional or 2-dimensional.")
+
+ # Validate that data dimensions match configured channel counts
+ if data.shape[1] != channels: raise ValueError(f"Channel mismatch: data has {data.shape[1]} channels, stream expects {channels}.")
+ # Ensure the underlying data type matches the hardware requirements
+ if data.dtype != dtype: raise TypeError(f"Data type mismatch: data is {data.dtype}, stream expects {dtype}.")
+ # C-contiguous layout is mandatory for direct memory access via CFFI
+ if not data.flags.c_contiguous: raise TypeError("Audio data buffer must be C-contiguous.")
+
+ return OutputStreamBase._raw_write(self, data)
+
+class Stream(InputStream, OutputStream):
+ def __init__(self, samplerate=None, blocksize=None, device=None, channels=None, dtype=None, latency=None, extra_settings=None, callback=None, finished_callback=None, clip_off=None, dither_off=None, never_drop_input=None, prime_output_buffers_using_stream_callback=None):
+ """Initialize a duplex stream.
+
+ Args:
+ samplerate (float): The desired sampling rate in Hz.
+ blocksize (int): The number of frames passed to the stream callback.
+ device (int, str or tuple): The device index/name, or a tuple of (input, output).
+ channels (int or tuple): Number of channels, or a tuple of (input, output).
+ dtype (str, numpy.dtype or tuple): Data type, or a tuple of (input, output).
+ latency (float, str or tuple): Latency setting, or a tuple of (input, output).
+ extra_settings (object or tuple): Settings object, or a tuple of (input, output).
+ callback (callable): The callback function for audio processing.
+ finished_callback (callable): Callback called when stream finishes.
+ clip_off (bool): Disable clipping of out-of-range samples.
+ dither_off (bool): Disable dithering.
+ never_drop_input (bool): Overflow management strategy.
+ prime_output_buffers_using_stream_callback (bool): Output priming control.
+ """
+
+ # Call the base class initializer for a duplex stream configuration
+ StreamBase.__init__(self, kind='duplex', wrap_callback='array', **remove_self(locals()))
+
+class DeviceList(tuple):
+ """A list of available audio devices."""
+ __slots__ = ()
+
+ def __repr__(self):
+ """Return a formatted string representing all available devices."""
+
+ # Get default input/output device indexes from PortAudio
+ idev = get_device_id(default.device['input'], 'input')
+ odev = get_device_id(default.device['output'], 'output')
+ # Calculate padding size for clean string formatting alignment
+ digits = len(str(portaudiolib.Pa_GetDeviceCount() - 1))
+ hostapi_names = [hostapi['name'] for hostapi in query_hostapis()]
+
+ def get_mark(idx):
+ # Helper to assign visual flags: '>' for input, '<' for output, '*' for duplex
+ return (' ', '>', '<', '*')[(idx == idev) + 2 * (idx == odev)]
+
+ # Generate a multiline string for every audio device found in the structure
+ text = '\n'.join('{mark} {idx:{dig}} {name}, {ha} ({ins} in, {outs} out)'.format(mark=get_mark(info['index']), idx=info['index'], dig=digits, name=info['name'], ha=hostapi_names[info['hostapi']], ins=info['max_input_channels'], outs=info['max_output_channels']) for info in self)
+ return text
+
+class CallbackFlags:
+ """Flags passed to the stream callback function."""
+ __slots__ = '_flags'
+
+ def __init__(self, flags=0x0):
+ """
+ Initialize callback flags.
+
+ Args:
+ flags (int): Integer bitmask containing PortAudio status flags.
+ """
+
+ self._flags = flags
+
+ def __repr__(self):
+ flags = str(self)
+ return f''
+
+ def __str__(self):
+ # Format names cleanly by swapping underscores with spaces
+ return ', '.join(name.replace('_', ' ') for name in dir(self) if not name.startswith('_') and getattr(self, name))
+
+ def __bool__(self):
+ return bool(self._flags)
+
+ def __ior__(self, other):
+ # Implement bitwise in-place OR assignments (e.g., flags |= other)
+ if not isinstance(other, CallbackFlags): return NotImplemented
+ self._flags |= other._flags
+ return self
+
+ @property
+ def input_underflow(self):
+ return self._hasflag(portaudiolib.paInputUnderflow)
+
+ @input_underflow.setter
+ def input_underflow(self, value):
+ self._updateflag(portaudiolib.paInputUnderflow, value)
+
+ @property
+ def input_overflow(self):
+ return self._hasflag(portaudiolib.paInputOverflow)
+
+ @input_overflow.setter
+ def input_overflow(self, value):
+ self._updateflag(portaudiolib.paInputOverflow, value)
+
+ @property
+ def output_underflow(self):
+ return self._hasflag(portaudiolib.paOutputUnderflow)
+
+ @output_underflow.setter
+ def output_underflow(self, value):
+ self._updateflag(portaudiolib.paOutputUnderflow, value)
+
+ @property
+ def output_overflow(self):
+ return self._hasflag(portaudiolib.paOutputOverflow)
+
+ @output_overflow.setter
+ def output_overflow(self, value):
+ self._updateflag(portaudiolib.paOutputOverflow, value)
+
+ @property
+ def priming_output(self):
+ return self._hasflag(portaudiolib.paPrimingOutput)
+
+ def _hasflag(self, flag):
+ # Perform binary bit-mask validation
+ return bool(self._flags & flag)
+
+ def _updateflag(self, flag, value):
+ # Flip bits depending on target boolean flag value
+ if value: self._flags |= flag
+ else: self._flags &= ~flag
+
+class InputOutputPair:
+ """Helper class to store configurations for both input and output settings."""
+ _indexmapping = {'input': 0, 'output': 1}
+
+ def __init__(self, parent, default_attr):
+ """
+ Initialize an input-output pair.
+
+ Args:
+ parent (object): Reference to the owner container.
+ default_attr (str): Name of the fallback attribute string.
+ """
+
+ self._pair = [None, None]
+ self._parent = parent
+ self._default_attr = default_attr
+
+ def __getitem__(self, index):
+ # Map indices 'input'/'output' or integers to internal list lookup
+ index = self._indexmapping.get(index, index)
+ value = self._pair[index]
+ # Return fallback configuration if local context value is undefined
+ if value is None: value = getattr(self._parent, self._default_attr)[index]
+ return value
+
+ def __setitem__(self, index, value):
+ index = self._indexmapping.get(index, index)
+ self._pair[index] = value
+
+ def __repr__(self):
+ return '[{0[0]!r}, {0[1]!r}]'.format(self)
+
+class default:
+ """Global default settings container."""
+
+ _pairs = 'device', 'channels', 'dtype', 'latency', 'extra_settings'
+ device = (None, None)
+ _default_channels = (None, None)
+ channels = _default_channels
+ _default_dtype = 'float32', 'float32'
+ dtype = _default_dtype
+ _default_latency = 'high', 'high'
+ latency = _default_latency
+ _default_extra_settings = (None, None)
+ extra_settings = _default_extra_settings
+ samplerate = None
+ blocksize = portaudiolib.paFramesPerBufferUnspecified
+ clip_off = False
+ dither_off = False
+ never_drop_input = False
+ prime_output_buffers_using_stream_callback = False
+
+ def __init__(self):
+ # Wrap configurations in InputOutputPairs dynamically
+ for attr in self._pairs:
+ vars(self)[attr] = InputOutputPair(self, '_default_' + attr)
+
+ def __setattr__(self, name, value):
+ # Route attribute writing to target proxy items or internal dictionary storage
+ if name in self._pairs: getattr(self, name)._pair[:] = split(value)
+ elif name in dir(self) and name != 'reset': object.__setattr__(self, name, value)
+ else: raise AttributeError(f"'{type(self).__name__}' object has no attribute '{repr(name)}'")
+
+ @property
+ def _default_device(self):
+ # Dynamically retrieve host OS default interfaces
+ return (portaudiolib.Pa_GetDefaultInputDevice(), portaudiolib.Pa_GetDefaultOutputDevice())
+
+ @property
+ def hostapi(self):
+ return check(portaudiolib.Pa_GetDefaultHostApi())
+
+ def reset(self):
+ vars(self).clear()
+ self.__init__()
+
+# Instantiate singleton unless running inside a fake test execution ecosystem
+if not hasattr(ffi, 'I_AM_FAKE'):
+ _default_instance = default()
+ default = _default_instance
+
+class PortAudioError(Exception):
+ """Exception raised for errors returned from PortAudio API operations."""
+ def __init__(self, *args):
+ super().__init__(*args)
+
+class CallbackStop(Exception):
+ """Exception to cleanly stop audio stream loop inside python callbacks."""
+ def __init__(self, *args):
+ super().__init__(*args)
+
+class CallbackAbort(Exception):
+ """Exception to immediately terminate audio stream processing queues."""
+ def __init__(self, *args):
+ super().__init__(*args)
+
+class AsioSettings:
+ """ASIO driver-specific host settings."""
+
+ def __init__(self, channel_selectors):
+ if isinstance(channel_selectors, int): raise TypeError("channel_selectors must be an iterable/list of integers, not a single integer.")
+
+ # Instantiate continuous C memory space variables via FFI
+ self._selectors = ffi.new('int[]', channel_selectors)
+ self._streaminfo = ffi.new('PaAsioStreamInfo*', dict(size=ffi.sizeof('PaAsioStreamInfo'), hostApiType=portaudiolib.paASIO, version=1, flags=portaudiolib.paAsioUseChannelSelectors, channelSelectors=self._selectors))
+
+class CoreAudioSettings:
+ """CoreAudio-specific host settings (macOS)."""
+
+ def __init__(self, channel_map=None, change_device_parameters=False, fail_if_conversion_required=False, conversion_quality='max'):
+ """
+ Initialize CoreAudio settings.
+
+ Args:
+ channel_map (list of int): Custom hardware channel remapping sequence.
+ change_device_parameters (bool): Permission flag to mutate OS hardware parameters.
+ fail_if_conversion_required (bool): Assert strict match without backend sampling resampling.
+ conversion_quality (str): Quality configuration mapping ('min' to 'max').
+ """
+
+ conversion_dict = {
+ 'min': portaudiolib.paMacCoreConversionQualityMin,
+ 'low': portaudiolib.paMacCoreConversionQualityLow,
+ 'medium': portaudiolib.paMacCoreConversionQualityMedium,
+ 'high': portaudiolib.paMacCoreConversionQualityHigh,
+ 'max': portaudiolib.paMacCoreConversionQualityMax,
+ }
+
+ if isinstance(channel_map, int): raise TypeError("channel_map must be an iterable/list of integers, not a single integer.")
+
+ try:
+ self._flags = conversion_dict[conversion_quality.lower()]
+ except (KeyError, AttributeError):
+ raise ValueError(f"Invalid conversion quality. Must be one of: {repr(list(conversion_dict))}")
+
+ if change_device_parameters: self._flags |= portaudiolib.paMacCoreChangeDeviceParameters
+ if fail_if_conversion_required: self._flags |= portaudiolib.paMacCoreFailIfConversionRequired
+
+ self._streaminfo = ffi.new('PaMacCoreStreamInfo*')
+ portaudiolib.PaMacCore_SetupStreamInfo(self._streaminfo, self._flags)
+
+ if channel_map is not None:
+ self._channel_map = ffi.new('SInt32[]', channel_map)
+ if len(self._channel_map) == 0: raise TypeError
+ portaudiolib.PaMacCore_SetupChannelMap(self._streaminfo, self._channel_map, len(self._channel_map))
+
+class WasapiSettings:
+ """WASAPI-specific host settings (Windows)."""
+
+ def __init__(self, exclusive=False, auto_convert=False, explicit_sample_format=False):
+ """
+ Initialize WASAPI configurations.
+
+ Args:
+ exclusive (bool): Request exclusive hardware audio access panel locks.
+ auto_convert (bool): Allow auto fallback formatting standardizations.
+ explicit_sample_format (bool): Skip automatic system channel conversion pipelines.
+ """
+
+ flags = 0x0
+ if exclusive: flags |= portaudiolib.paWinWasapiExclusive
+ if auto_convert: flags |= portaudiolib.paWinWasapiAutoConvert
+ if explicit_sample_format: flags |= portaudiolib.paWinWasapiExplicitSampleFormat
+
+ self._streaminfo = ffi.new('PaWasapiStreamInfo*', dict(size=ffi.sizeof('PaWasapiStreamInfo'), hostApiType=portaudiolib.paWASAPI, version=1, flags=flags,))
+
+class CallbackContext:
+ """Context manager and data processor for handling audio stream callbacks."""
+
+ frame = 0
+ frames = 0
+ input_channels = output_channels = None
+ input_dtype = output_dtype = None
+ silent_channels = None
+
+ def __init__(self, loop=False):
+ """
+ Initialize the callback context.
+
+ Args:
+ loop (bool): Whether to loop the playback data continuously.
+ """
+
+ self.loop = loop
+ self.event = threading.Event()
+ self.status = CallbackFlags()
+
+ def check_data(self, data, mapping, device):
+ """
+ Validate and prepare output playback data, channels, and custom mapping.
+
+ Args:
+ data (array_like): The input audio data buffer.
+ mapping (list of int): Custom output channel indices mapping.
+ device (int or str): Target output audio device identifier.
+
+ Returns:
+ int: The total number of frames in the provided audio data.
+ """
+
+ data = np.asarray(data)
+
+ # Enforce audio data matrix constraints
+ if data.ndim < 2: data = data.reshape(-1, 1)
+ elif data.ndim > 2: raise ValueError("Audio data matrix must be 1D or 2D.")
+
+ frames, channels = data.shape
+ dtype = check_dtype(data.dtype)
+
+ mapping_is_explicit = mapping is not None
+ mapping, channels = check_mapping(mapping, channels)
+
+ # Verify that explicit matrix column count matches target mappings
+ if data.shape[1] == 1: pass
+ elif data.shape[1] != len(mapping): raise ValueError(f"Data column count ({data.shape[1]}) mismatch with mapping size ({len(mapping)}).")
+
+ # Fallback handling for default mono array matching stereo devices
+ if (mapping_is_explicit and np.array_equal(mapping, [0]) and query_devices(device, 'output')['max_output_channels'] >= 2): channels = 2
+
+ # Extract channels that should be padded with silent waveforms
+ silent_channels = np.setdiff1d(np.arange(channels), mapping)
+ if len(mapping) + len(silent_channels) != channels: raise ValueError("Total active and silent channels do not match stream configuration.")
+
+ self.data = data
+ self.output_channels = channels
+ self.output_dtype = dtype
+ self.output_mapping = mapping
+ self.silent_channels = silent_channels
+
+ return frames
+
+ def check_out(self, out, frames, channels, dtype, mapping):
+ """
+ Validate or dynamically pre-allocate memory buffers for recording audio.
+
+ Args:
+ out (numpy.ndarray or None): Destination output array or None to auto-allocate.
+ frames (int or None): Expected total recording frames.
+ channels (int or None): Desired input channels.
+ dtype (str or numpy.dtype): Target audio structure type.
+ mapping (list of int): Custom input channel mappings.
+
+ Returns:
+ tuple: A tuple containing the destination array (numpy.ndarray) and total frames (int).
+ """
+
+ if out is None:
+ if frames is None: raise TypeError("The 'frames' parameter must be provided if 'out' is None.")
+ if channels is None: channels = default.channels['input']
+
+ if channels is None:
+ if mapping is None: raise TypeError("Either 'channels' or 'mapping' must be provided to determine the layout.")
+ else: channels = len(np.atleast_1d(mapping))
+
+ if dtype is None: dtype = default.dtype['input']
+
+ try:
+ # Pre-allocate continuous contiguous C-buffer memory array blocks
+ out = np.empty((frames, channels), dtype, order='C')
+ except TypeError as e:
+ if not isinstance(frames, Integral): raise TypeError("The 'frames' parameter must be an integer.")
+ if not isinstance(channels, Integral): raise TypeError("The 'channels' parameter must be an integer.")
+ raise e
+ else:
+ frames, channels = out.shape
+ dtype = out.dtype
+
+ dtype = check_dtype(dtype)
+ mapping, channels = check_mapping(mapping, channels)
+ if out.shape[1] != len(mapping): raise ValueError(f"Output array column count ({out.shape[1]}) does not match mapping layout size ({len(mapping)}).")
+
+ self.out = out
+ self.input_channels = channels
+ self.input_dtype = dtype
+ self.input_mapping = mapping
+
+ return out, frames
+
+ def callback_enter(self, status, data):
+ """
+ Trigger entry procedures upon receiving stream callback events.
+
+ Args:
+ status (CallbackFlags): Hardware status flags.
+ data (buffer): The raw buffer payload context.
+ """
+
+ self.status |= status
+ # Prevent accessing more index blocks than available remaining sizes
+ self.blocksize = min(self.frames - self.frame, len(data))
+
+ def read_indata(self, indata):
+ """
+ Map incoming hardware recording frames directly into the target output array.
+
+ Args:
+ indata (numpy.ndarray): Audio matrix captured from the hardware.
+ """
+
+ for target, source in enumerate(self.input_mapping):
+ self.out[self.frame:self.frame + self.blocksize, target] = indata[:self.blocksize, source]
+
+ def write_outdata(self, outdata):
+ """
+ Write user audio context structures into physical hardware output data frames.
+
+ Args:
+ outdata (numpy.ndarray): Target output sound array to fill.
+ """
+
+ outdata[:self.blocksize, self.output_mapping] = self.data[self.frame:self.frame + self.blocksize]
+ outdata[:self.blocksize, self.silent_channels] = 0
+
+ # Recursively loop back the play block pointer if file looping is toggled
+ if self.loop and self.blocksize < len(outdata):
+ self.frame = 0
+ outdata = outdata[self.blocksize:]
+
+ self.blocksize = min(self.frames, len(outdata))
+ self.write_outdata(outdata)
+ else:
+ outdata[self.blocksize:] = 0
+
+ def callback_exit(self):
+ """Manage loop state iterations on exit; terminates execution on zero blocks."""
+
+ if not self.blocksize: raise CallbackAbort("Stream processing loop complete; no further frames remaining.")
+ self.frame += self.blocksize
+
+ def finished_callback(self):
+ """Release streaming lock contexts safely upon execution termination."""
+
+ self.event.set()
+
+ # Safely clean up local references to clear out memory scopes
+ with contextlib.suppress(AttributeError):
+ del self.data
+
+ with contextlib.suppress(AttributeError):
+ del self.out
+
+ self.stream._callback = None
+ self.stream._finished_callback = None
+
+ def start_stream(self, StreamClass, samplerate, channels, dtype, callback, blocking, **kwargs):
+ """
+ Instantiate and execute a target hardware streaming class pipeline.
+
+ Args:
+ StreamClass (type): Class instance type used for stream instantiation.
+ samplerate (float): System sampling rate target.
+ channels (int): Channel count constraints.
+ dtype (str or numpy.dtype): Target audio structure type.
+ callback (callable): Runtime event processing block function.
+ blocking (bool): Blocks thread operations until playback has finished.
+ """
+
+ stop()
+ self.stream = StreamClass(samplerate=samplerate, channels=channels, dtype=dtype, callback=callback, finished_callback=self.finished_callback, **kwargs)
+ self.stream.start()
+
+ global last_callback
+ last_callback = self
+ if blocking: self.wait()
+
+ def wait(self, ignore_errors=True):
+ """
+ Block thread operations waiting for the signal notification lock.
+
+ Args:
+ ignore_errors (bool): Prevents raising errors on stream teardown.
+
+ Returns:
+ CallbackFlags or None: Hardware status log updates or None.
+ """
+ try:
+ self.event.wait()
+ finally:
+ self.stream.close(ignore_errors)
+
+ return self.status if self.status else None
+
+def ffi_string(cdata):
+ """Safely cast raw C data strings via FFI translation scopes."""
+
+ return ffi.string(cdata)
+
+def remove_self(d):
+ """
+ Generate a clean shallow copy dictionary without local 'self' reference keys.
+
+ Args:
+ d (dict): Local variable dictionaries via `locals()`.
+
+ Returns:
+ dict: A mutated variable block structure map.
+ """
+
+ d = d.copy()
+ del d['self']
+
+ return d
+
+def check_mapping(mapping, channels):
+ """
+ Validate and align target device routing map index arrays.
+
+ Args:
+ mapping (list, array or None): Input/Output channel layout structure arrays.
+ channels (int): System base reference channel context size.
+
+ Returns:
+ tuple: A tuple containing the parsed mapping array (numpy.ndarray) and channel count (int).
+ """
+
+ if mapping is None: mapping = np.arange(channels)
+ else:
+ mapping = np.array(mapping, copy=True)
+ mapping = np.atleast_1d(mapping)
+
+ # 1-based index validation check for safety
+ if mapping.min() < 1: raise ValueError("Channel mapping indices must be 1-based (greater than or equal to 1).")
+
+ channels = mapping.max()
+ mapping -= 1 # Convert to 0-based index for internal numpy access
+
+ return mapping, channels
+
+def check_dtype(dtype):
+ """
+ Verify system-specific formatting target matches supported sound device ranges.
+
+ Args:
+ dtype (str or numpy.dtype): Data type to check.
+
+ Returns:
+ str: Validated numpy data type name string.
+ """
+
+ dtype = np.dtype(dtype).name
+
+ if dtype in sampleformats: pass
+ elif dtype == 'float64': dtype = np.dtype(np.float32).name # Cast double-precision to standard floats
+ else: raise TypeError(f"Unsupported audio data type structure: {dtype}")
+
+ return dtype
+
+def get_stream_parameters(kind, device, channels, dtype, latency, extra_settings, samplerate):
+ """Generate structured CFFI parameter pointers matching specific hardware pipeline options.
+
+ Args:
+ kind (str): Stream type direction ('input' or 'output').
+ device (int or str or None): Audio target index identifier.
+ channels (int or None): Channel allocation maps.
+ dtype (str or None): Targeted layout types.
+ latency (float or str or None): System hardware speed presets.
+ extra_settings (object or None): Custom API specific stream contexts.
+ samplerate (float or None): Baseline clock speed targets.
+
+ Returns:
+ tuple: (PaStreamParameters pointer, datatype name, sample size, sample rate)
+ """
+
+ assert kind in ('input', 'output')
+ # Resolve default fallbacks if properties match None state parameters
+ if device is None: device = default.device
+ device = get_device_id(device, kind, raise_on_error=True)
+
+ if channels is None: channels = default.channels
+ channels = select_input_or_output(channels, kind)
+
+ if dtype is None: dtype = default.dtype
+ dtype = select_input_or_output(dtype, kind)
+
+ if latency is None: latency = default.latency
+ latency = select_input_or_output(latency, kind)
+
+ if extra_settings is None: extra_settings = default.extra_settings
+ extra_settings = select_input_or_output(extra_settings, kind)
+
+ if samplerate is None: samplerate = default.samplerate
+ info = query_devices(device)
+
+ if channels is None: channels = info['max_' + kind + '_channels']
+
+ try:
+ dtype = sys.modules['numpy'].dtype(dtype).name
+ except Exception:
+ pass
+
+ try:
+ sampleformat = sampleformats[dtype]
+ except KeyError as e:
+ raise ValueError(f"The specified data type format '{dtype}' is invalid or unmapped.")
+
+ samplesize = check(portaudiolib.Pa_GetSampleSize(sampleformat))
+
+ if latency in ('low', 'high'): latency = info['default_' + latency + '_' + kind + '_latency']
+ if samplerate is None: samplerate = info['default_samplerate']
+
+ parameters = ffi.new('PaStreamParameters*', (device, channels, sampleformat, latency, extra_settings._streaminfo if extra_settings else ffi.NULL))
+ return parameters, dtype, samplesize, samplerate
+
+def _wrap_callback(callback, *args):
+ """Internal proxy callback to process wrapper conversions and map Python returns into C codes."""
+
+ args = args[:-1] + (CallbackFlags(args[-1]),)
+
+ try:
+ callback(*args)
+ except CallbackStop:
+ return portaudiolib.paComplete
+ except CallbackAbort:
+ return portaudiolib.paAbort
+
+ return portaudiolib.paContinue
+
+def mbuffer(ptr, frames, channels, samplesize):
+ """Directly wrap explicit pointers safely into an exposed memory viewing panel."""
+
+ return ffi.buffer(ptr, frames * channels * samplesize)
+
+def array(buffer, channels, dtype):
+ """Convert flat raw buffer data structures back into formatted 2D multidimensional matrices."""
+
+ data = np.frombuffer(buffer, dtype=dtype)
+ data.shape = -1, channels
+ return data
+
+def split(value):
+ """
+ Split unified pair parameters down evenly into directional component parameters.
+
+ Args:
+ value (object): Single target configuration entry or a sequence list pair block.
+
+ Returns:
+ tuple: Formatted pair values representing (input_value, output_value).
+ """
+
+ if isinstance(value, (str, bytes)): return value, value
+
+ try:
+ invalue, outvalue = value
+ except TypeError:
+ invalue = outvalue = value
+ except ValueError:
+ raise ValueError("Sequence unpacking failed. Target value must contain exactly 2 components.")
+
+ return invalue, outvalue
+
+def check(err, msg=''):
+ """Assert PortAudio result code statuses, tracking errors dynamically to throw custom Exceptions.
+
+ Args:
+ err (int): Error return code flag generated by the dynamic C library pipeline.
+ msg (str): Append error context traces.
+
+ Returns:
+ int: The validated error code if greater than or equal to zero.
+ """
+
+ if err >= 0: return err
+
+ errormsg = ffi_string(portaudiolib.Pa_GetErrorText(err)).decode()
+ if msg: errormsg = f'{msg}: {errormsg}'
+
+ # Check for underlying unanticipated driver or OS layer exceptions
+ if err == portaudiolib.paUnanticipatedHostError:
+ info = portaudiolib.Pa_GetLastHostErrorInfo()
+ host_api = portaudiolib.Pa_HostApiTypeIdToHostApiIndex(info.hostApiType)
+
+ hosterror_text = ffi_string(info.errorText).decode()
+ hosterror_info = host_api, info.errorCode, hosterror_text
+
+ raise PortAudioError(errormsg, err, hosterror_info)
+ raise PortAudioError(errormsg, err)
+
+def select_input_or_output(value_or_pair, kind):
+ """Pick correct routing fields from combined value/pair sets based on device path directions."""
+
+ ivalue, ovalue = split(value_or_pair)
+
+ if kind == 'input': return ivalue
+ elif kind == 'output': return ovalue
+
+ assert False
+
+def get_device_id(id_or_query_string, kind, raise_on_error=False):
+ """
+ Locate unique hardware target IDs dynamically parsing indices or string search query patterns.
+
+ Args:
+ id_or_query_string (int or str): Device index identifier or partial search string name match.
+ kind (str or None): Targeted pathway validation format filter ('input' or 'output').
+ raise_on_error (bool): Flag forcing explicit lookup tracking to crash on failure states.
+
+ Returns:
+ int: The resolved integer identifier index for the targeted physical hardware device.
+ """
+
+ assert kind in ('input', 'output', None)
+
+ if id_or_query_string is None: id_or_query_string = default.device
+ idev, odev = split(id_or_query_string)
+
+ if kind == 'input': id_or_query_string = idev
+ elif kind == 'output': id_or_query_string = odev
+ else:
+ if idev == odev: id_or_query_string = idev
+ else: raise ValueError(id_or_query_string)
+
+ if isinstance(id_or_query_string, int): return id_or_query_string
+ device_list, matches, exact_device_matches = [], [], []
+
+ # Map down a clean list of candidate sound card systems running on the host OS
+ for id, info in enumerate(query_devices()):
+ if not kind or info['max_' + kind + '_channels'] > 0:
+ hostapi_info = query_hostapis(info['hostapi'])
+ device_list.append((id, info['name'], hostapi_info['name']))
+
+ query_string = id_or_query_string.lower()
+ substrings = query_string.split()
+
+ # Search for matching text blocks across device and host API names
+ for id, device_string, hostapi_string in device_list:
+ full_string = device_string + ', ' + hostapi_string
+ pos = 0
+
+ for substring in substrings:
+ pos = full_string.lower().find(substring, pos)
+ if pos < 0: break
+ pos += len(substring)
+ else:
+ matches.append((id, full_string))
+ if query_string in [device_string.lower(), full_string.lower()]: exact_device_matches.append(id)
+
+ if kind is None: kind = 'input/output'
+ if not matches:
+ if raise_on_error: raise ValueError(f"No matching {kind} device found for query string: {repr(id_or_query_string)}")
+ else: return -1
+
+ if len(matches) > 1:
+ if len(exact_device_matches) == 1: return exact_device_matches[0]
+
+ if raise_on_error: raise ValueError(f"Multiple {kind} device matches found for '{id_or_query_string}':\n" +'\n'.join(f'[{id}] {name}' for id, name in matches))
+ else: return -1
+
+ return matches[0][0]
+
+def initialize():
+ """Boot standard runtime dependencies inside PortAudio environments safely silencing error tracks."""
+
+ old_stderr = None
+
+ try:
+ # Duplicate standard error file descriptors to silence noisy default hardware terminal warning texts
+ old_stderr = os.dup(2)
+ devnull = os.open(os.devnull, os.O_WRONLY)
+ os.dup2(devnull, 2)
+ os.close(devnull)
+ except OSError:
+ pass
+
+ try:
+ check(portaudiolib.Pa_Initialize())
+ global initialized
+ initialized += 1
+ finally:
+ if old_stderr is not None:
+ os.dup2(old_stderr, 2)
+ os.close(old_stderr)
+
+def terminate():
+ """Unload internal runtime modules shutting down backend audio driver hooks cleanly."""
+
+ global initialized
+
+ check(portaudiolib.Pa_Terminate())
+ initialized -= 1
+
+
+def exit_handler():
+ """Global system process callback handling script exit hooks to close active streaming tracks."""
+
+ assert initialized >= 0
+
+ if last_callback:
+ last_callback.stream.stop()
+ last_callback.stream.close()
+
+ while initialized:
+ terminate()
+
+# Register cleanup routines to run at program termination
+atexit.register(exit_handler)
+initialize()
\ No newline at end of file
diff --git a/main/library/audio/stftpitchshift.py b/main/library/audio/stftpitchshift.py
new file mode 100644
index 0000000000000000000000000000000000000000..850efb739e3718eda5d5d9c9bb9800490bafe88d
--- /dev/null
+++ b/main/library/audio/stftpitchshift.py
@@ -0,0 +1,487 @@
+import numpy as np
+
+from numpy.lib.stride_tricks import sliding_window_view
+
+def istft(frames, framesize, hopsize):
+ """
+ Inverse Short-Time Fourier Transform (ISTFT) with overlap-add method.
+
+ Parameters:
+ frames (ndarray): The STFT frames of shape (num_frames, num_bins).
+ framesize (int or list/tuple): The size of the analysis and synthesis windows.
+ hopsize (int): The number of samples between successive frames.
+
+ Returns:
+ ndarray: The reconstructed 1D time-domain signal.
+ """
+
+ # Ensure the input frames are a 2D numpy array
+ frames = np.atleast_2d(frames)
+ assert frames.ndim == 2
+
+ # Extract analysis and synthesis window sizes (handles single integer or array-like inputs)
+ analysis_window_size = np.ravel(framesize)[0]
+ synthesis_window_size = np.ravel(framesize)[-1]
+ # Analysis window must be larger than or equal to synthesis window size
+ assert analysis_window_size >= synthesis_window_size
+
+ # Select appropriate windows based on whether they are symmetric or asymmetric
+ A = asymmetric_analysis_window(analysis_window_size, synthesis_window_size) if analysis_window_size != synthesis_window_size else symmetric_window(analysis_window_size)
+ S = asymmetric_synthesis_window(analysis_window_size, synthesis_window_size) if analysis_window_size != synthesis_window_size else symmetric_window(synthesis_window_size)
+ # Compute synthesis window scale factor for amplitude normalization
+ W = S * hopsize / np.sum(A * S)
+ # Calculate the total length of the target output signal
+ N = frames.shape[0] * hopsize + analysis_window_size
+
+ # Initialize the output time-domain signal with zeros
+ y = np.zeros((N), float)
+
+ # Zero out the first and last frequency bins (often required for constraints)
+ frames[:, 0] = 0
+ frames[:, -1] = 0
+ # Create a writable sliding window view overlapping the output array
+ frames0 = sliding_window_view(y, analysis_window_size, writeable=True)[::hopsize]
+ # Perform Inverse Real FFT and scale with the window factor
+ frames1 = np.fft.irfft(frames, axis=-1, norm='forward') * W
+
+ # Perform overlap-add reconstruction
+ for i in range(min(len(frames0), len(frames1))):
+ frames0[i] += frames1[i]
+
+ return y
+
+def asymmetric_synthesis_window(analysis_window_size, synthesis_window_size):
+ """
+ Generates an asymmetric synthesis window based on specified window sizes.
+
+ Parameters:
+ analysis_window_size (int): Size of the analysis window.
+ synthesis_window_size (int): Size of the synthesis window.
+
+ Returns:
+ ndarray: The generated asymmetric synthesis window.
+ """
+
+ n = analysis_window_size
+ m = synthesis_window_size // 2
+
+ # Get a base symmetric window for the right half
+ right = symmetric_window(2 * m)
+ window = np.zeros(n)
+ # Construct the asymmetric parts using squared symmetric windows normalized by overlapping segments
+ window[n-m-m:n-m] = np.square(right[:m]) / symmetric_window(2 * n - 2 * m)[n-m-m:n-m]
+ window[-m:] = right[-m:]
+
+ return window
+
+def asymmetric_analysis_window(analysis_window_size, synthesis_window_size):
+ """
+ Generates an asymmetric analysis window based on specified window sizes.
+
+ Parameters:
+ analysis_window_size (int): Size of the analysis window.
+ synthesis_window_size (int): Size of the synthesis window.
+
+ Returns:
+ ndarray: The generated asymmetric analysis window.
+ """
+
+ n = analysis_window_size
+ m = synthesis_window_size // 2
+
+ window = np.zeros(n)
+ # Merge portions of symmetric windows of different lengths
+ window[:n-m] = symmetric_window(2 * n - 2 * m)[:n-m]
+ window[-m:] = symmetric_window(2 * m)[-m:]
+
+ return window
+
+def symmetric_window(symmetric_window_size):
+ """
+ Generates a standard symmetric Hann window.
+
+ Parameters:
+ symmetric_window_size (int): Total size of the window.
+
+ Returns:
+ ndarray: The symmetric Hann window.
+ """
+ n = symmetric_window_size
+ # Compute the Hann window mathematical equation
+ window = 0.5 - 0.5 * np.cos(2 * np.pi * np.arange(n) / n)
+
+ return window
+
+def stft(x, framesize, hopsize):
+ """
+ Short-Time Fourier Transform (STFT) using a sliding window view.
+
+ Parameters:
+ x (ndarray): The 1D input time-domain signal.
+ framesize (int or list/tuple): The size of the analysis and synthesis windows.
+ hopsize (int): The number of samples between successive frames.
+
+ Returns:
+ ndarray: The complex STFT spectrogram of shape (num_frames, num_bins).
+ """
+
+ # Ensure input signal is 1D array
+ x = np.atleast_1d(x)
+ assert x.ndim == 1
+
+ # Extract window sizes
+ analysis_window_size = np.ravel(framesize)[0]
+ synthesis_window_size = np.ravel(framesize)[-1]
+
+ assert analysis_window_size >= synthesis_window_size
+ # Choose analysis window function
+ W = asymmetric_analysis_window(analysis_window_size, synthesis_window_size) if analysis_window_size != synthesis_window_size else symmetric_window(analysis_window_size)
+
+ # Create overlapping temporal frames from the input signal
+ frames0 = sliding_window_view(x, analysis_window_size, writeable=False)[::hopsize]
+ # Multiply by the window function and compute Real FFT along the last axis
+ frames1 = np.fft.rfft(frames0 * W, axis=-1, norm='forward')
+
+ return frames1
+
+def normalize(frames, frames0):
+ """
+ Normalizes the energy of frames to match the original frames0 energy.
+
+ Parameters:
+ frames (ndarray): Modified frames.
+ frames0 (ndarray): Original reference frames.
+
+ Returns:
+ ndarray: Energy-normalized frames.
+ """
+
+ for i in range(len(frames)):
+ a = np.real(frames0[i])
+ b = np.real(frames[i])
+ # Compute dot product (total energy/squared norm)
+ a = np.dot(a, a)
+ b = np.dot(b, b)
+
+ # Avoid division by zero if target frame energy is 0
+ if b == 0: continue
+ # Normalize real components based on energy ratio while preserving imaginary components
+ frames[i] = np.real(frames[i]) * np.sqrt(a / b) + 1j * np.imag(frames[i])
+
+ return frames
+
+def lowpass(cepstrum, quefrency):
+ """
+ Applies a basic lowpass filter on a liftered/cepstral sequence.
+
+ Parameters:
+ cepstrum (ndarray): The input cepstrum sequence.
+ quefrency (int): The cutoff quefrency index.
+
+ Returns:
+ ndarray: The filtered cepstrum.
+ """
+
+ # Boost lower quefrencies and discard frequencies above the cutoff
+ cepstrum[1:quefrency] *= 2
+ cepstrum[quefrency+1:] = 0
+
+ return cepstrum
+
+def lifter(frames, quefrency):
+ """
+ Extracts the spectral envelope using liftering (cepstral smoothing).
+
+ Parameters:
+ frames (ndarray): Input complex spectrogram frames.
+ quefrency (int): Quefrency cutoff for spectral smoothing.
+
+ Returns:
+ ndarray: Spectral envelopes matching the shape of input frames.
+ """
+
+ envelopes = np.zeros(frames.shape)
+
+ for i, frame in enumerate(frames):
+ # Mute divide/invalid warnings when computing log10 of 0 or negative real components
+ with np.errstate(divide='ignore', invalid='ignore'):
+ spectrum = np.log10(np.real(frame))
+
+ # Transform to cepstral domain (IFFT), apply lowpass filter, transform back (FFT), and undo log
+ envelopes[i] = np.power(10, np.real(np.fft.rfft(lowpass(np.fft.irfft(spectrum, norm='forward'), quefrency), norm='forward')))
+
+ return envelopes
+
+def resample(x, factor):
+ """
+ Resamples a 1D array using basic linear interpolation.
+
+ Parameters:
+ x (ndarray): 1D input array.
+ factor (float): Resampling scale factor.
+
+ Returns:
+ ndarray: Resampled array.
+ """
+
+ if factor == 1: return x.copy()
+ y = np.zeros(x.shape, dtype=x.dtype)
+
+ n = len(x)
+ m = int(n * factor)
+ # Compute new index mappings
+ i = np.arange(min(n, m))
+ k = i * (n / m)
+ # Truncate to find lower index bounding
+ j = np.trunc(k).astype(int)
+ k = k - j
+ # Keep indices strictly inside bounds
+ ok = (0 <= j) & (j < n - 1)
+ # Perform linear interpolation
+ y[i[ok]] = k[ok] * x[j[ok] + 1] + (1 - k[ok]) * x[j[ok]]
+
+ return y
+
+def shiftpitch(frames, factors, samplerate):
+ """
+ Shifts the pitch of encoded phase-vocoder frames.
+
+ Parameters:
+ frames (ndarray): Encoded phase-vocoder frames (Magnitudes + 1j * Frequencies).
+ factors (ndarray/list): Pitch scaling factors.
+ samplerate (int): The sample rate of the audio.
+
+ Returns:
+ ndarray: Pitch-shifted frames.
+ """
+
+ for i in range(len(frames)):
+ # Resample magnitude spectrum according to factors
+ magnitudes = np.vstack([resample(np.real(frames[i]), factor) for factor in factors])
+ # Resample and scale instantaneous frequencies
+ frequencies = np.vstack([resample(np.imag(frames[i]), factor) * factor for factor in factors])
+ # Zero out frequencies that fall out of the valid Nyquist range
+ magnitudes[(frequencies <= 0) | (frequencies >= samplerate / 2)] = 0
+ # Determine peak dominant tracking pitch indices
+ mask = np.argmax(magnitudes, axis=0)
+ # Pick the values matching peak tracking
+ magnitudes = np.take_along_axis(magnitudes, mask[None,:], axis=0)
+ frequencies = np.take_along_axis(frequencies, mask[None,:], axis=0)
+ # Recombine into magnitude and frequency pairs
+ frames[i] = magnitudes + 1j * frequencies
+
+ return frames
+
+def wrap(x):
+ """
+ Wraps phase angles to the range [-pi, pi].
+
+ Parameters:
+ x (ndarray): Phase angles in radians.
+
+ Returns:
+ ndarray: Wrapped phase angles.
+ """
+
+ return (x + np.pi) % (2 * np.pi) - np.pi
+
+def encode(frames, framesize, hopsize, samplerate):
+ """
+ Encodes STFT spectrogram frames into magnitude and instantaneous frequency pairs.
+
+ Parameters:
+ frames (ndarray): Complex spectrogram frames.
+ framesize (int or list/tuple): The size of the analysis window.
+ hopsize (int): Hop size in samples.
+ samplerate (int): Sample rate in Hz.
+
+ Returns:
+ ndarray: Encoded phase-vocoder frames (Magnitude + 1j * Instantaneous Frequency).
+ """
+
+ M, N = frames.shape
+ analysis_framesize = np.ravel(framesize)[0]
+
+ # Calculate frequency bin step and theoretical phase advancement step
+ freqinc = samplerate / analysis_framesize
+ phaseinc = 2 * np.pi * hopsize / analysis_framesize
+
+ buffer = np.zeros(N)
+ data = np.zeros((M, N), complex)
+
+ for m, frame in enumerate(frames):
+ arg = np.angle(frame)
+ delta = arg - buffer # Phase difference between successive frames
+ buffer = arg # Store current phase for the next frame iteration
+
+ i = np.arange(N)
+ # Convert phase deviations to absolute instantaneous frequencies
+ data[m] = np.abs(frame) + 1j * ((i + (wrap(delta - i * phaseinc) / phaseinc)) * freqinc)
+
+ return data
+
+def decode(frames, framesize, hopsize, samplerate):
+ """
+ Decodes magnitude and instantaneous frequency pairs back into complex STFT frames.
+
+ Parameters:
+ frames (ndarray): Encoded frames (Magnitude + 1j * Instantaneous Frequency).
+ framesize (int or list/tuple): Windows size settings.
+ hopsize (int): Hop size in samples.
+ samplerate (int): Sample rate in Hz.
+
+ Returns:
+ ndarray: Decoded complex STFT frames.
+ """
+
+ M, N = frames.shape
+ analysis_framesize = np.ravel(framesize)[0]
+ synthesis_framesize = np.ravel(framesize)[-1]
+ # Compute frequency bin step and phase advancement step
+ freqinc = samplerate / analysis_framesize
+ phaseinc = 2 * np.pi * hopsize / analysis_framesize
+ # Set time shift compensation factor if analysis and synthesis windows differ
+ timeshift = 2 * np.pi * synthesis_framesize * np.arange(N) / N if synthesis_framesize != analysis_framesize else 0
+
+ buffer = np.zeros(N)
+ data = np.zeros((M, N), complex)
+
+ for m, frame in enumerate(frames):
+ i = np.arange(N)
+ # Extract phase difference from instantaneous frequencies
+ delta = (i + ((np.imag(frame) - i * freqinc) / freqinc)) * phaseinc
+ buffer += delta # Accumulate phase over frames
+ arg = buffer.copy()
+ arg -= timeshift
+ # Reconstruct complex values from magnitudes and accumulated phases
+ data[m] = np.real(frame) * np.exp(1j * arg)
+
+ return data
+
+class StftPitchShift:
+ """
+ A class wrapper to orchestrate high-quality pitch-shifting using STFT,
+ phase-vocoder encoding/decoding, and formant preservation via liftering.
+ """
+
+ def __init__(self, framesize, hopsize, samplerate):
+ """
+ Initializes the StftPitchShift pipeline context.
+
+ Parameters:
+ framesize (int or list/tuple): Resolution/size configuration of audio windowing.
+ hopsize (int): Hop interval step between sliding segments.
+ samplerate (int): Processing sampling rate indicator.
+ """
+
+ self.framesize = framesize
+ self.hopsize = hopsize
+ self.samplerate = samplerate
+
+ def shiftpitch(self, input, factors = 1, quefrency = 0, distortion = 1, normalization = False):
+ """
+ Applies pitch-shifting on a given 1D audio signal array.
+
+ Parameters:
+ input (ndarray): Input time-domain signal.
+ factors (float or list/ndarray): Pitch scaling ratios (e.g., 2.0 shifts up one octave).
+ quefrency (float): Formant preservation threshold coefficient (in seconds).
+ distortion (float): Spectral envelope/formant stretching distortion factor.
+ normalization (bool): True to enable energy normalization after processing.
+
+ Returns:
+ ndarray: The pitch-shifted time-domain audio signal.
+ """
+
+ # Ensure array properties match processing structure
+ input = np.atleast_1d(input)
+ dtype = input.dtype
+ shape = input.shape
+
+ input = np.squeeze(input)
+ if input.ndim != 1: raise ValueError(f'Invalid input shape {shape}, expected a one-dimensional array!')
+
+ # If data is in integer form, normalize range scale to floats [-1.0, 1.0]
+ if np.issubdtype(dtype, np.integer):
+ a, b = np.iinfo(dtype).min, np.iinfo(dtype).max
+
+ input = ((input.astype(float) - a) / (b - a)) * 2 - 1
+ elif not np.issubdtype(dtype, np.floating): raise TypeError(f'Invalid input data type {dtype}, expected {np.floating} or {np.integer}!')
+
+ # Inline utility helper to flag bad matrix numbers (Inf, NaN, or underflow subnormals)
+ def isnotnormal(x):
+ return (np.isinf(x)) | (np.isnan(x)) | (abs(x) < np.finfo(x.dtype).tiny)
+
+ framesize = self.framesize
+ hopsize = self.hopsize
+ samplerate = self.samplerate
+ # Format parsing parameters
+ factors = np.asarray(factors).flatten()
+ quefrency = int(quefrency * samplerate)
+ # Step 1: Compute STFT and encode phases into magnitudes and frequencies
+ frames = encode(
+ stft(
+ input,
+ framesize,
+ hopsize
+ ),
+ framesize,
+ hopsize,
+ samplerate
+ )
+
+ # Cache reference frames if normalization is requested
+ if normalization: frames0 = frames.copy()
+
+ # Step 2: Extract, modify, and process formants if a quefrency is set
+ if quefrency:
+ envelopes = lifter(frames, quefrency)
+ mask = isnotnormal(envelopes)
+ # Flatten/whiten the spectrum magnitude by stripping the spectral envelope
+ frames.real /= envelopes
+ frames.real[mask] = 0
+
+ if distortion != 1: # Distort formants separately if distortion factor is requested
+ envelopes[mask] = 0
+
+ for i in range(len(envelopes)):
+ envelopes[i] = resample(envelopes[i], distortion)
+
+ mask = isnotnormal(envelopes)
+
+ # Perform the core pitch shifting process
+ frames = shiftpitch(
+ frames,
+ factors,
+ samplerate
+ )
+ # Re-apply the spectral envelope to restore formants
+ frames.real *= envelopes
+ frames.real[mask] = 0
+ else:
+ # Perform basic pitch shifting without formant tracking
+ frames = shiftpitch(frames, factors, samplerate)
+
+ # Step 3: Run optional frame energy restoration normalization
+ if normalization:
+ frames = normalize(
+ frames,
+ frames0
+ )
+
+ # Step 4: Decode tracking phases and reconstruct time domain signal using ISTFT
+ output = istft(decode(frames, framesize, hopsize, samplerate), framesize, hopsize)
+ output.resize(shape, refcheck=False)
+
+ # Step 5: Convert back to original data type if input was integer-based
+ if np.issubdtype(dtype, np.integer):
+ a, b = np.iinfo(dtype).min, np.iinfo(dtype).max
+
+ output = (((output + 1) / 2) * (b - a) + a).clip(a, b).astype(dtype)
+ elif output.dtype != dtype: output = output.astype(dtype)
+
+ # Final structural validity assurance safety assertions
+ assert output.dtype == dtype
+ assert output.shape == shape
+
+ return output
\ No newline at end of file
diff --git a/main/library/audio/upscaler.py b/main/library/audio/upscaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0d2bc28877c856cf39b22eb805e1cc3a2f0a39c
--- /dev/null
+++ b/main/library/audio/upscaler.py
@@ -0,0 +1,1840 @@
+import os
+import sys
+import math
+import torch
+import einops
+import librosa
+
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.utils.parametrize as parametrize
+
+from inspect import isfunction
+from abc import abstractmethod
+from torch.nn.utils import remove_weight_norm
+from torch.nn.utils.parametrizations import weight_norm
+from scipy.signal import butter, cheby1, cheby2, ellip, bessel, sosfiltfilt, resample_poly
+
+sys.path.append(os.getcwd())
+
+from main.library.audio.features import mel
+from main.library.generators.bigvgan import UpSample1d, DownSample1d
+from main.library.algorithm.commons import init_weights, get_padding
+
+def get_window(window_size, fade_size):
+ """
+ Generates a 1D window tensor with cross-fading linear slopes at boundaries.
+
+ Args:
+ window_size (int): Total length of the window.
+ fade_size (int): Size of the fade-in and fade-out regions.
+
+ Returns:
+ Tensor: A 1D tensor containing the window weights.
+ """
+
+ # Initialize a window with ones
+ window = torch.ones(window_size)
+ # Apply a linear decay slope to the trailing fade region
+ window[-fade_size:] *= torch.linspace(1, 0, fade_size)
+ # Apply a linear attack slope to the leading fade region
+ window[:fade_size] *= torch.linspace(0, 1, fade_size)
+ return window
+
+def align_length(x, y):
+ """
+ Aligns the length of array y to match array x via padding or truncation.
+
+ Args:
+ x (ndarray): Reference sequence determining target length.
+ y (ndarray): Input sequence to be aligned.
+
+ Returns:
+ ndarray: Aligned version of sequence y.
+ """
+
+ Lx, Ly = len(x), len(y)
+
+ if Lx == Ly: return y # Return as-is if lengths already match
+ elif Lx > Ly: return np.pad(y, (0, Lx - Ly), mode="constant") # Right-pad with zeros if sequence y is shorter than reference x
+ else: return y[:Lx] # Truncate sequence y if it is longer than reference x
+
+def subsampling(data, lowpass_ratio, fs_ori=44100, upsample_to_original=True):
+ """
+ Simulates sample rate degradation via downsampling, with optional reconstruction.
+
+ Args:
+ data (ndarray): 1D time-domain audio signal array.
+ lowpass_ratio (float): Ratio determining downsampling target speed.
+ fs_ori (int, optional): Original sampling rate. Defaults to 44100.
+ upsample_to_original (bool, optional): Whether to resample back to original rate. Defaults to True.
+
+ Returns:
+ ndarray: Resampled audio signal data.
+ """
+
+ # Enforce 1D input constraints
+ assert len(data.shape) == 1
+
+ # Calculate target downsampled frequency
+ fs_down = int(lowpass_ratio * fs_ori)
+ # Perform downsampling via polyphase filtering
+ y = resample_poly(data, fs_down, fs_ori)
+
+ if upsample_to_original: # Optionally upsample back to match the original sample rate grid
+ y = resample_poly(y, fs_ori, fs_down)
+ # Handle shape mismatches caused by rounding differences
+ if len(y) != len(data): y = align_length(data, y)
+
+ return y
+
+def lowpass_filter(x, highcutoff_freq, fs, order, ftype, upsample_to_original = True):
+ """
+ Applies a specific scipy-based lowpass filter using Second-Order Sections (SOS).
+
+ Args:
+ x (ndarray): 1D target audio signal sequence.
+ highcutoff_freq (float): Cutoff frequency limit in Hz.
+ fs (int): Sample rate of the signal.
+ order (int): Filter order design steepness metric.
+ ftype (str): Type of filter engine ('butter', 'cheby1', 'cheby2', 'ellip', 'bessel').
+ upsample_to_original (bool, optional): Resample back to original grid. Defaults to True.
+
+ Returns:
+ ndarray: Filtered audio signal.
+ """
+
+ # Compute the Nyquist frequency limit
+ nyq = 0.5 * fs
+ # Calculate normalized cutoff frequency coefficient
+ hi = highcutoff_freq / nyq
+
+ # Instantiate selected digital filter structure in SOS format
+ if ftype == "butter": sos = butter(order, hi, btype="low", output="sos")
+ elif ftype == "cheby1": sos = cheby1(order, 0.1, hi, btype="low", output="sos")
+ elif ftype == "cheby2": sos = cheby2(order, 60, hi, btype="low", output="sos")
+ elif ftype == "ellip": sos = ellip(order, 0.1, 60, hi, btype="low", output="sos")
+ elif ftype == "bessel": sos = bessel(order, hi, btype="low", output="sos")
+ else: raise ValueError(f"Unknown filter type: {ftype}")
+
+ # Apply zero-phase forward-backward digital filter
+ y = sosfiltfilt(sos, x)
+ # Re-verify sequence length constraints
+ if len(y) != len(x): y = align_length(x, y)
+
+ # Simulate corresponding resolution downsampling effects
+ y = subsampling(y, lowpass_ratio=highcutoff_freq / int(fs / 2), fs_ori=fs, upsample_to_original=upsample_to_original)
+ return y
+
+def lowpass(audio, sr, filter_name, filter_order, cutoff_freq, upsample_to_original = True):
+ """
+ Wrapper to apply lowpass filtering on 1D mono or 2D multi-channel audio buffers.
+
+ Args:
+ audio (ndarray): Input audio array (can be 1D, or 2D with channels as first dim).
+ sr (int): Audio sample rate.
+ filter_name (str): Design type selector name.
+ filter_order (int): Target order complexity bounds [2, 10].
+ cutoff_freq (float): Processing cutoff threshold.
+ upsample_to_original (bool, optional): Keep original scale size. Defaults to True.
+
+ Returns:
+ ndarray: Filtered copy of the input audio.
+ """
+
+ # Enforce dimensions restriction (supports 1D or 2D up to 2 channels)
+ assert len(audio.shape) == 1 or (len(audio.shape) == 2 and (audio.shape[0] == 1 or audio.shape[0] == 2))
+ # Standardize name string alias
+ if filter_name == "cheby": filter_name = "cheby1"
+ # Ensure standard order constraints are respected
+ assert filter_order >= 2 and filter_order <= 10
+ # Guard against invalid boundaries equalling Nyquist limits
+ if cutoff_freq == sr: cutoff_freq -= 1
+
+ # Route multi-channel arrays through iterative single-channel processing loops
+ if len(audio.shape) == 2:
+ lowpassed_audio = np.zeros_like(audio)
+
+ for i in range(audio.shape[0]):
+ lowpassed_audio[i] = lowpass_filter(
+ x=audio[i],
+ highcutoff_freq=int(cutoff_freq),
+ fs=sr,
+ order=filter_order,
+ ftype=filter_name,
+ upsample_to_original=upsample_to_original
+ )
+ else:
+ # Fallback to direct execution for simple 1D signals
+ lowpassed_audio = lowpass_filter(
+ x=audio,
+ highcutoff_freq=int(cutoff_freq),
+ fs=sr,
+ order=filter_order,
+ ftype=filter_name,
+ upsample_to_original=upsample_to_original
+ )
+
+ # Confirm consistency properties are correctly preserved
+ if upsample_to_original: assert lowpassed_audio.shape == audio.shape
+ return lowpassed_audio.copy()
+
+def find_cutoff(x, percentile=0.95):
+ """
+ Finds the frequency index where the cumulative sum passes a given percentile threshold.
+
+ Args:
+ x (Tensor): Cumulative sum sequence of spectral magnitudes.
+ percentile (float, optional): Target threshold ratio. Defaults to 0.95.
+
+ Returns:
+ int: Index matching cutoff position parameters.
+ """
+
+ # Determine the target cumulative energy threshold
+ percentile = x[-1] * percentile
+ # Search backwards from higher to lower indices
+ for i in range(1, x.shape[0]):
+ if x[-i] < percentile: return x.shape[0] - i
+
+ return 0
+
+def locate_cutoff_freq(stft, percentile=0.985):
+ """
+ Interface wrapper around find_cutoff helper logic blocks.
+
+ Args:
+ stft (Tensor): Cumulative spectral magnitude vector.
+ percentile (float, optional): Extraction target indicator. Defaults to 0.985.
+
+ Returns:
+ int: Threshold index representation.
+ """
+
+ return find_cutoff(stft, percentile)
+
+def find_cutoff_freq(audio):
+ """
+ Calculates the effective high-frequency cutoff of an audio signal using STFT energy tracking.
+
+ Args:
+ audio (Tensor): Time-domain raw sound signal.
+
+ Returns:
+ float: Estimated cutoff frequency value.
+ """
+
+ device = audio.device
+ # Fallback to CPU execution if the processing device type is unrecognizable
+ if not audio.device.type.startswith(("cpu", "cuda", "xpu")): audio = audio.cpu()
+
+ # Compute magnitude distribution via STFT
+ stft_spec = torch.stft(audio, n_fft=2048, hop_length=480, win_length=2048, window=torch.hann_window(2048).to(audio.device), center=False, pad_mode="reflect", normalized=False, onesided=True, return_complex=True)
+ # Collapse frames, compute absolute sums, and track via cumulative summation
+ stft_spec = stft_spec[0].T.abs().sum(dim=0).cumsum(dim=0).float()
+
+ # Locate sample limit threshold index and convert back to absolute frequency bounds
+ cutoff_freq = (locate_cutoff_freq(stft_spec.to(device), percentile=0.983) / 1024) * 24000
+ # Fallback to default high target limits if results are unreasonably low
+ if cutoff_freq < 1000: cutoff_freq = 24000
+
+ return cutoff_freq
+
+def get_diffusers_output_type_name(ddpm_module):
+ """
+ Maps an internal model parameter string to diffusers-compatible prediction terminology.
+
+ Args:
+ ddpm_module (nn.Module): target DDPM instance containing model configuration properties.
+
+ Returns:
+ str: Standard output type name.
+ """
+
+ output_type_dict = {"v_prediction": "v_prediction", "noise": "epsilon", "x_start": "sample"}
+ return output_type_dict[ddpm_module.model_output_type]
+
+def get_diffusers_scheduler_config(ddpm_module, scheduler_args):
+ """
+ Builds a dictionary configuration schema for HuggingFace diffusers schedulers.
+
+ Args:
+ ddpm_module (nn.Module): Reference DDPM context.
+ scheduler_args (dict): Extra parameters to combine with the configuration.
+
+ Returns:
+ dict: Populated config settings package.
+ """
+
+ config = {"num_train_timesteps": ddpm_module.timesteps, "trained_betas": ddpm_module.betas.to("cpu"), "prediction_type": get_diffusers_output_type_name(ddpm_module)}
+ config.update(scheduler_args)
+
+ return config
+
+def freeze_param(model):
+ """
+ Freezes model gradients and forces evaluation behavior configuration permanently.
+
+ Args:
+ model (nn.Module): Target network topology instance.
+
+ Returns:
+ nn.Module: Configured frozen network model structure.
+ """
+
+ model = model.eval()
+ # Override train mode calls to keep the model locked in evaluation mode
+ model.train = lambda self: self
+ # Disable gradient tracking across all parameters
+ for param in model.parameters():
+ param.requires_grad = False
+
+ return model
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+ """
+ Computes sinusoidal or repetitive positional embedding vectors for diffusion steps.
+
+ Args:
+ timesteps (Tensor): 1D batch steps indices.
+ dim (int): Dimensional length scale of projection space.
+ max_period (int, optional): Scale boundaries frequency factor. Defaults to 10000.
+ repeat_only (bool, optional): Skip sinusoidal maths and tile values instead. Defaults to False.
+
+ Returns:
+ Tensor: Processed step embeddings.
+ """
+ if not repeat_only:
+ half = dim // 2
+ # Compute exponential log-scaled frequency distributions
+ freqs = (-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).exp().to(device=timesteps.device)
+
+ # Generate outer product mapping matrix arguments
+ args = timesteps[:, None].float() * freqs[None]
+ # Concat paired sine and cosine coordinates projections
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+
+ # Pad with zeros along boundaries if dimension counts are odd numbers
+ if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+ else:
+ # Tile basic broadcast configurations directly instead of computing cosines
+ embedding = einops.repeat(timesteps, "b -> b d", d=dim)
+
+ return embedding
+
+def zero_module(module):
+ """
+ Forces all parameters inside a module to be initialized with zeros, isolating weights.
+
+ Args:
+ module (nn.Module): Target layer block.
+
+ Returns:
+ nn.Module: Modified block layer reference.
+ """
+
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+def register_buffer(model, variable_name, value, dtype = torch.float32):
+ """
+ Registers an array as a persistent, non-trainable state buffer within a PyTorch module.
+
+ Args:
+ model (nn.Module): Target module parent framework.
+ variable_name (str): Identifier key string naming properties.
+ value (ndarray/Tensor): Target matrix values to save.
+ dtype (type, optional): Targeted numerical storage definition format. Defaults to torch.float32.
+
+ Returns:
+ Tensor: Registered persistent buffer reference attribute.
+ """
+
+ if type(value) != torch.Tensor: value = torch.tensor(value, dtype=dtype)
+ model.register_buffer(variable_name, value)
+ return getattr(model, variable_name)
+
+def checkpoint(func, inputs, params, flag):
+ """
+ Conditional activation wrapper around standard gradient memory checkpointing methods.
+
+ Args:
+ func (callable): The forward execution pipeline method logic.
+ inputs (tuple): Forward input parameters arguments structure.
+ params (tuple/list): Tracked variable weights parameter set list.
+ flag (bool): Checkpoint toggle activation state.
+
+ Returns:
+ Any: Executed method outputs sequence data.
+ """
+
+ return CheckpointFunction.apply(func, len(inputs), *(tuple(inputs) + tuple(params))) if flag else func(*inputs)
+
+def exists(x):
+ """
+ Checks if a variable is not None.
+
+ Args:
+ x (Any): Input object.
+
+ Returns:
+ bool: True if not None, else False.
+ """
+
+ return x is not None
+
+def default(val, d):
+ """
+ Returns a default value if the given value is None.
+
+ Args:
+ val (Any): Input variable.
+ d (Any/callable): Fallback default value or lazy generator function.
+
+ Returns:
+ Any: Resolved valid value.
+ """
+
+ if exists(val): return val
+ return d() if isfunction(d) else d
+
+class GroupNorm32(nn.GroupNorm):
+ """
+ Custom Group Normalization subclass that explicitly calls the functional normalization routine.
+ """
+
+ def forward(self, x):
+ return F.group_norm(
+ x,
+ self.num_groups,
+ self.weight,
+ self.bias,
+ self.eps
+ )
+
+class CheckpointFunction(torch.autograd.Function):
+ """
+ Custom autograd Function to handle memory preservation checkpoints manually.
+ """
+
+ @staticmethod
+ def forward(ctx, run_function, length, *args):
+ # Save custom function and inputs context
+ ctx.run_function = run_function
+ ctx.input_tensors = list(args[:length])
+ ctx.input_params = list(args[length:])
+
+ with torch.no_grad(): # Execute target model sub-components under zero-gradient modes
+ output_tensors = ctx.run_function(*ctx.input_tensors)
+
+ return output_tensors
+
+ @staticmethod
+ def backward(ctx, *output_grads):
+ # Re-enable variable tracking gradients features to rerun operations
+ ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+ with torch.enable_grad():
+ shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+ output_tensors = ctx.run_function(*shallow_copies)
+
+ # Compute exact differentiation passes using intermediate graphs
+ input_grads = torch.autograd.grad(output_tensors, ctx.input_tensors + ctx.input_params, output_grads, allow_unused=True)
+ del ctx.input_tensors, ctx.input_params, output_tensors
+
+ return (None, None) + input_grads
+
+class UtilAudioMelSpec:
+ """
+ Audio feature utility block handling multi-channel STFT and log-Mel spectrogram conversion.
+ """
+
+ def __init__(self, nfft, hop_size, sample_rate, mel_size, frequency_min, frequency_max, device):
+ self.nfft = nfft
+ self.hop_size = hop_size
+ self.sample_rate = sample_rate
+ self.mel_size = mel_size
+ self.frequency_min = frequency_min
+ # Default to standard Nyquist frequency limit boundaries if maximum parameter is unspecified
+ self.frequency_max = frequency_max if frequency_max is not None else (sample_rate // 2)
+ self.hann_window = torch.hann_window(self.nfft)
+ self.mel_frequncies = librosa.mel_frequencies(n_mels=self.mel_size, fmin=self.frequency_min, fmax=self.frequency_max)
+ # Instantiate mel filterbank basis tensor structure mapping STFT coordinates
+ self.mel_basis_tensor = mel(sr=self.sample_rate, n_fft=self.nfft, n_mels=self.mel_size, fmin=self.frequency_min, fmax=self.frequency_max, device=device, dtype=torch.float32)
+
+ def stft_torch(self, audio_torch):
+ """
+ Computes the STFT, extracting magnitude and phase angle from a time-domain tensor.
+ """
+
+ assert(len(audio_torch.shape) <= 3)
+ if (len(audio_torch.shape) == 1): audio_torch = audio_torch.unsqueeze(0)
+ shape_is_three = len(audio_torch.shape) == 3
+ # Flatten multi-channel batches into 2D configurations to uniform parallel execution steps
+ if shape_is_three:
+ batch_size, channels_num, segment_samples = audio_torch.shape
+ audio_torch = audio_torch.reshape(batch_size * channels_num, segment_samples)
+
+ spec_dict = dict()
+ # Pad bounds symmetrically to center spectral analysis windows correctly
+ audio_torch = F.pad(audio_torch.unsqueeze(1), (int((self.nfft - self.hop_size) / 2), int((self.nfft - self.hop_size) / 2)), mode='reflect').squeeze(1)
+
+ device = audio_torch.device
+ if not audio_torch.device.type.startswith(("cpu", "cuda", "xpu")): audio_torch = audio_torch.cpu()
+
+ # Compute one-sided Short-Time Fourier Transform
+ spec_dict['stft'] = torch.stft(audio_torch, self.nfft, hop_length=self.hop_size, window=self.hann_window.to(audio_torch.device), center=False, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
+ # Extract magnitudes and polar phase components
+ spec_dict['mag'] = spec_dict['stft'].abs().to(device)
+ spec_dict['angle'] = spec_dict['stft'].angle().to(device)
+
+ # Unflatten temporary flattened arrays back into their multi-channel batch formats
+ if shape_is_three:
+ _, time_steps, freq_bins = spec_dict['stft'].shape
+ for feature_name in spec_dict:
+ spec_dict[feature_name] = spec_dict[feature_name].reshape(batch_size, channels_num, time_steps, freq_bins)
+
+ return spec_dict
+
+ def get_mel_spec(self, audio):
+ """
+ Converts raw 1D time audio sequences directly into log-Mel scaling representations.
+ """
+
+ while len(audio.shape) < 2: audio = audio.unsqueeze(0)
+ # Apply matrix multiplication to match Mel frequencies, floor minimum limits, and execute log operations
+ return ((self.mel_basis_tensor @ self.stft_torch(audio)["mag"]).clamp(min=1e-5) * 1.0).log()
+
+class ResnetBlock(nn.Module):
+ """
+ A 2D ResNet block augmented with optional FiLM/timestep condition projections.
+ """
+
+ def __init__(self, in_channels, out_channels=None, conv_shortcut=False, dropout=0.0, temb_channels=512):
+ super().__init__()
+ self.in_channels = in_channels
+ out_channels = in_channels if out_channels is None else out_channels
+ self.out_channels = out_channels
+ self.use_conv_shortcut = conv_shortcut
+ # Setup primary convolution processing layer paths
+ self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+ self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+ # Optional conditioning layer mapping linear embedding attributes
+ if temb_channels > 0: self.temb_proj = nn.Linear(temb_channels, out_channels)
+ # Setup secondary convolution processing layer paths
+ self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
+ self.dropout = nn.Dropout(dropout)
+ self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+ # Build shortcut connections to bridge dimensional transitions if channel sizes differ
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut: self.conv_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+ else: self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+ def forward(self, x, temb):
+ h = x
+ # Layer 1 normalization and Swish-like activation execution
+ h = self.norm1(h)
+ h = self.conv1(h * h.sigmoid())
+
+ # Inject auxiliary timestep context signals via addition across channel spaces
+ if temb is not None: h = h + self.temb_proj(temb * temb.sigmoid())[:, :, None, None]
+
+ # Layer 2 processing steps
+ h = self.norm2(h)
+ h = self.conv2(self.dropout(h * h.sigmoid()))
+
+ # Direct channel matching routing across skip connection pathways
+ if self.in_channels != self.out_channels: x = self.conv_shortcut(x) if self.use_conv_shortcut else self.nin_shortcut(x)
+ return x + h
+
+class AttnBlock(nn.Module):
+ """
+ Standard spatial 2D Self-Attention layer using scaled dot-products.
+ """
+
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+ self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+ self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+ self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+ self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+ self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape
+
+ # Flatten dimensions, calculate batched matrix multiplication weights, apply Softmax, and merge profiles back
+ return x + self.proj_out(
+ torch.bmm(
+ v.reshape(b, c, h * w).contiguous(),
+ F.softmax(
+ torch.bmm(
+ q.reshape(b, c, h * w).contiguous().permute(0, 2, 1).contiguous(),
+ k.reshape(b, c, h * w).contiguous()
+ ).contiguous() * (int(c) ** (-0.5)),
+ dim=2
+ ).permute(0, 2, 1).contiguous()
+ ).contiguous().reshape(b, c, h, w).contiguous()
+ )
+
+class _Downsample(nn.Module):
+ """
+ Downsampling layer using either strided 2D convolution or average pooling.
+ """
+
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ if self.with_conv: self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+
+ def forward(self, x):
+ # Apply asymmetric zero-padding before strided convolution if enabled
+ return self.conv(F.pad(x, (0, 1, 0, 1), mode="constant", value=0)) if self.with_conv else F.avg_pool2d(x, kernel_size=2, stride=2)
+
+class _Upsample(nn.Module):
+ """
+ Upsampling layer using nearest-neighbor interpolation followed by optional 2D convolution.
+ """
+
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ if self.with_conv: self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+ def forward(self, x):
+ # Stretch grid coordinates factor 2x via nearest interpolations
+ x = F.interpolate(x, scale_factor=2.0, mode="nearest")
+ if self.with_conv: x = self.conv(x)
+ return x
+
+class Encoder(nn.Module):
+ """
+ Hierarchical convolutional VAE encoder network architecture mapping inputs to latent dimensions.
+ """
+
+ def __init__(self, ch, ch_mult=(1, 2, 4, 8), num_res_blocks=2, attn_resolutions=[], dropout=0.0, resamp_with_conv=True, in_channels=1, resolution=256, z_channels=16, double_z=True, attn_type="vanilla"):
+ super().__init__()
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ curr_res = resolution
+ in_ch_mult = (1,) + tuple(ch_mult)
+ self.in_ch_mult = in_ch_mult
+ # Primary input processing entry convolution block
+ self.conv_in = nn.Conv2d(in_channels, ch, kernel_size=3, stride=1, padding=1)
+ self.down = nn.ModuleList()
+ # Build multilevel downscaling block pathways
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+
+ block_in = ch * in_ch_mult[i_level]
+ block_out = ch * ch_mult[i_level]
+
+ for _ in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions: attn.append(AttnBlock(block_in))
+
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+
+ if i_level != self.num_resolutions - 1:
+ down.downsample = _Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+
+ self.down.append(down)
+ # Assemble bottleneck layers
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout)
+ self.mid.attn_1 = AttnBlock(block_in)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout)
+ # Normalization and final projection layers targeting codebook distribution metrics
+ self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+ self.conv_out = nn.Conv2d(block_in, 2 * z_channels if double_z else z_channels, kernel_size=3, stride=1, padding=1)
+
+ def forward(self, x):
+ temb = None
+ hs = [self.conv_in(x)]
+
+ # Traverse down scaling paths progressively
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](hs[-1], temb)
+ if len(self.down[i_level].attn) > 0: h = self.down[i_level].attn[i_block](h)
+ hs.append(h)
+
+ if i_level != self.num_resolutions - 1: hs.append(self.down[i_level].downsample(hs[-1]))
+
+ # Run bottleneck calculations and compute output distributions feature maps
+ h = self.norm_out(self.mid.block_2(self.mid.attn_1(self.mid.block_1(hs[-1], temb)), temb))
+ return self.conv_out(h * h.sigmoid())
+
+class Decoder(nn.Module):
+ """
+ Hierarchical convolutional VAE decoder reconstruction network mirroring encoder pathways.
+ """
+
+ def __init__(self, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks=2, attn_resolutions=[], dropout=0.0, resamp_with_conv=True, resolution=256, z_channels=16, attn_type="vanilla"):
+ super().__init__()
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ block_in = ch * ch_mult[self.num_resolutions - 1]
+ curr_res = resolution // 2 ** (self.num_resolutions - 1)
+
+ self.up = nn.ModuleList()
+ # Initial latent mapping convolution channel projection
+ self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
+ # Assemble bottleneck decoding blocks
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout)
+ self.mid.attn_1 = AttnBlock(block_in)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout)
+
+ # Build multilevel upscaling block pathways
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+
+ block_out = ch * ch_mult[i_level]
+
+ for _ in range(self.num_res_blocks + 1):
+ block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, temb_channels=self.temb_ch, dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions: attn.append(AttnBlock(block_in))
+
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+
+ if i_level != 0:
+ up.upsample = _Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+
+ self.up.insert(0, up)
+
+ # Reconstruct output channel profiles
+ self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+ self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
+
+ def forward(self, z):
+ self.last_z_shape = z.shape
+ temb = None
+
+ # Execute intermediate processing paths over latent variable blocks
+ h = self.mid.block_2(self.mid.attn_1(self.mid.block_1(self.conv_in(z), temb)), temb)
+ # Traverse up scaling block tracks sequentially
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks + 1):
+ h = self.up[i_level].block[i_block](h, temb)
+ if len(self.up[i_level].attn) > 0: h = self.up[i_level].attn[i_block](h)
+
+ if i_level != 0: h = self.up[i_level].upsample(h)
+
+ h = self.norm_out(h)
+ return self.conv_out(h * h.sigmoid())
+
+class DiagonalGaussianDistribution(object):
+ """
+ Represents a diagonal parameter Gaussian distribution to manage VAE reparameterization tricks.
+ """
+
+ def __init__(self, parameters):
+ """
+ Splits parameter matrices into explicit mean and log-variance tracking sets.
+ """
+
+ self.parameters = parameters
+ # Split parameters into mean and log-variance chunks along channel dimension
+ self.mean, self.logvar = parameters.chunk(2, dim=1)
+ # Clamp log variance bounds to prevent numerical underflow/overflow instabilities
+ self.logvar = (self.logvar).clamp(-30.0, 20.0)
+ self.std = (0.5 * self.logvar).exp()
+
+ def sample(self):
+ """
+ Samples from the Gaussian distribution using the reparameterization trick.
+ """
+
+ return self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+
+class AutoencoderKL(nn.Module):
+ """
+ Complete Kullback-Leibler symmetric Autoencoder module context framework.
+ """
+
+ def __init__(self, embed_dim=None):
+ super().__init__()
+ self.encoder = Encoder(ch=128, ch_mult=(1, 2, 4, 8), num_res_blocks=2, attn_resolutions=[], dropout=0.1, resamp_with_conv=True, in_channels=1, resolution=256, z_channels=16, double_z=True, attn_type="vanilla")
+ self.decoder = Decoder(ch=128, out_ch=1, ch_mult=(1, 2, 4, 8), num_res_blocks=2, attn_resolutions=[], dropout=0.1, resamp_with_conv=True, resolution=256, z_channels=16, attn_type="vanilla")
+ # Convolution blocks mapping hidden states dimensions to latent embeddings configurations
+ self.quant_conv = nn.Conv2d(2 * 16, 2 * embed_dim, 1)
+ self.post_quant_conv = nn.Conv2d(embed_dim, 16, 1)
+ self.embed_dim = embed_dim
+
+ def encode(self, x):
+ """Maps input spatial representations to Diagonal Gaussian models."""
+ return DiagonalGaussianDistribution(self.quant_conv(self.encoder(x)))
+
+ def decode(self, z):
+ """Decodes latent variables back into output reconstructions maps."""
+ return self.decoder(self.post_quant_conv(z))
+
+class TimestepBlock(nn.Module):
+ """
+ Abstract interface defining classes that receive auxiliary timestep/FiLM embedding tensors.
+ """
+
+ @abstractmethod
+ def forward(self, x, emb):
+ return None
+
+class GEGLU(nn.Module):
+ """
+ Gated Linear Unit activation block utilizing the Gaussian Error Linear Activation (GELU) function.
+ """
+
+ def __init__(self, dim_in, dim_out):
+ super().__init__()
+ self.proj = nn.Linear(dim_in, dim_out * 2)
+
+ def forward(self, x):
+ # Split features into primary projection data and gate tracks
+ x, gate = self.proj(x).chunk(2, dim=-1)
+ return x * F.gelu(gate)
+
+class FeedForward(nn.Module):
+ """
+ Position-wise feed-forward neural layer block supporting gated execution options.
+ """
+
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ dim_out = default(dim_out, dim)
+ # Select input mapping layers based on whether GLU gating mechanics are enabled
+ project_in = nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) if not glu else GEGLU(dim, inner_dim)
+ self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
+
+ def forward(self, x):
+ return self.net(x)
+
+class CrossAttention(nn.Module):
+ """
+ Multi-head Self/Cross Attention module mapping sequences across hidden condition fields.
+ """
+
+ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
+ super().__init__()
+ inner_dim = dim_head * heads
+ context_dim = default(context_dim, query_dim)
+ self.scale = dim_head ** -0.5
+ self.heads = heads
+ self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+ self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+
+ def forward(self, x, context=None, mask=None):
+ h = self.heads
+ q = self.to_q(x)
+ # Default context to match input sequence if cross-attention targets are undefined
+ context = default(context, x)
+
+ k = self.to_k(context)
+ v = self.to_v(context)
+ # Rearrange tensors to split channels across isolated multi-head contexts
+ q, k, v = map(lambda t: einops.rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
+ # Compute multi-head attention similarity weight matrices
+ sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
+
+ if exists(mask): # Mask attention paths if conditioning vectors contain sequence constraints
+ mask = einops.repeat(einops.rearrange(mask, "b ... -> b (...)"), "b j -> (b h) () j", h=h)
+ sim.masked_fill_(~(mask == 1), -torch.finfo(sim.dtype).max)
+
+ # Accumulate context values and apply final linear out-projections
+ return self.to_out(einops.rearrange(torch.einsum("b i j, b j d -> b i d", sim.softmax(dim=-1), v), "(b h) n d -> b n (h d)", h=h))
+
+class BasicTransformerBlock(nn.Module):
+ """
+ Standard Transformer block containing self-attention, cross-attention, and feed-forward tracks.
+ """
+
+ def __init__(self, dim, n_heads, d_head, dropout=0.0, context_dim=None, gated_ff=True, checkpoint=True):
+ super().__init__()
+ self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)
+ self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+ self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout)
+ self.norm1 = nn.LayerNorm(dim)
+ self.norm2 = nn.LayerNorm(dim)
+ self.norm3 = nn.LayerNorm(dim)
+ self.checkpoint = checkpoint
+
+ def forward(self, x, context=None, mask=None):
+ # Route processing through memory-optimized gradient checkpoints based on context configurations
+ return checkpoint(self._forward, (x,), self.parameters(), self.checkpoint) if context is None else checkpoint(self._forward, (x, context, mask), self.parameters(), self.checkpoint)
+
+ def _forward(self, x, context=None, mask=None):
+ # Run standard LayerNorm, Attention, and residual connection execution paths
+ x = self.attn1(self.norm1(x)) + x
+ x = self.attn2(self.norm2(x), context=context, mask=mask) + x
+ return self.ff(self.norm3(x)) + x
+
+class SpatialTransformer(nn.Module):
+ """
+ Bridges 2D feature maps to 1D sequence-oriented attention transformers.
+ """
+
+ def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None):
+ super().__init__()
+ context_dim = context_dim
+ self.in_channels = in_channels
+ inner_dim = n_heads * d_head
+
+ self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+ self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+ self.transformer_blocks = nn.ModuleList([BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim) for _ in range(depth)])
+ self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
+
+ def forward(self, x, context=None, mask=None):
+ _, _, h, w = x.shape
+
+ x_in = x
+ # Flatten 2D space fields into 1D sequences before attention passes
+ x = einops.rearrange(self.proj_in(self.norm(x)), "b c h w -> b (h w) c")
+
+ for block in self.transformer_blocks:
+ x = block(x, context=context, mask=mask)
+
+ # Restore 1D sequences back into standard 2D spatial channel representations
+ x = self.proj_out(einops.rearrange(x, "b (h w) c -> b c h w", h=h, w=w))
+ return x + x_in
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+ """
+ Custom sequential container tracking layer identities to route timestep or conditioning contexts.
+ """
+
+ def forward(self, x, emb, context_list=None, mask_list=None):
+ spatial_transformer_id = 0
+ # Initialize conditioning lists with safety padding markers
+ context_list = [None] + context_list
+ mask_list = [None] + mask_list
+
+ for layer in self:
+ # Route diffusion step index embeddings to compatible blocks
+ if isinstance(layer, TimestepBlock): x = layer(x, emb)
+ elif isinstance(layer, SpatialTransformer): # Inject attention context items selectively into SpatialTransformers
+ context, mask = (None, None) if spatial_transformer_id >= len(context_list) else (context_list[spatial_transformer_id], mask_list[spatial_transformer_id])
+ x = layer(x, context, mask=mask)
+ spatial_transformer_id += 1
+ else: x = layer(x)
+
+ return x
+
+class Downsample(nn.Module):
+ """
+ Flexible pooling downsample module supporting 2D and 3D tensors.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ stride = 2 if dims != 3 else (1, 2, 2)
+
+ if use_conv:
+ conv_fn = nn.Conv2d if dims == 2 else nn.Conv3d
+ self.op = conv_fn(self.channels, self.out_channels, 3, stride=stride, padding=padding)
+ else:
+ assert self.channels == self.out_channels
+ avg_fn = nn.AvgPool2d if dims == 2 else nn.AvgPool3d
+ self.op = avg_fn(kernel_size=stride, stride=stride)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ return self.op(x)
+
+class Upsample(nn.Module):
+ """
+ Flexible interpolation upsample module supporting 2D and 3D spatial grids.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ if use_conv:
+ conv_fn = nn.Conv2d if dims == 2 else nn.Conv3d
+ self.conv = conv_fn(self.channels, self.out_channels, 3, padding=padding)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ # Apply dimensional matching rules based on tensor structures
+ x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest") if self.dims == 3 else F.interpolate(x, scale_factor=2, mode="nearest")
+ if self.use_conv: x = self.conv(x)
+
+ return x
+
+class ResBlock(TimestepBlock):
+ """
+ Advanced ResNet residual block managing timestep embedding additions or scale-shift norm paths.
+ """
+
+ def __init__(self, channels, emb_channels, dropout, out_channels=None, use_conv=False, use_scale_shift_norm=False, dims=2, use_checkpoint=False, up=False, down=False):
+ super().__init__()
+ self.channels = channels
+ self.emb_channels = emb_channels
+ self.dropout = dropout
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.use_checkpoint = use_checkpoint
+ self.use_scale_shift_norm = use_scale_shift_norm
+
+ self.in_layers = nn.Sequential(GroupNorm32(32, channels), nn.SiLU(), nn.Conv2d(channels, self.out_channels, 3, padding=1))
+ self.updown = up or down
+
+ if up: self.h_upd, self.x_upd = Upsample(channels, False, dims), Upsample(channels, False, dims)
+ elif down: self.h_upd, self.x_upd = Downsample(channels, False, dims), Downsample(channels, False, dims)
+ else: self.h_upd = self.x_upd = nn.Identity()
+
+ self.emb_layers = nn.Sequential(nn.SiLU(), nn.Linear(emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels))
+ self.out_layers = nn.Sequential(GroupNorm32(32, self.out_channels), nn.SiLU(), nn.Dropout(p=dropout), zero_module(nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)))
+
+ if self.out_channels == channels: self.skip_connection = nn.Identity()
+ elif use_conv: self.skip_connection = nn.Conv2d(channels, self.out_channels, 3, padding=1)
+ else: self.skip_connection = nn.Conv2d(channels, self.out_channels, 1)
+
+ def forward(self, x, emb):
+ return checkpoint(self._forward, (x, emb), self.parameters(), self.use_checkpoint)
+
+ def _forward(self, x, emb):
+ # Manage scale adjustments if block requires dimension transformations
+
+ if self.updown:
+ in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+
+ h = self.h_upd(in_rest(x))
+ x = self.x_upd(x)
+ h = in_conv(h)
+ else: h = self.in_layers(x)
+
+ # Unpack embedding projections to match target dimension lengths
+ emb_out = self.emb_layers(emb).type(h.dtype)
+ while len(emb_out.shape) < len(h.shape):
+ emb_out = emb_out[..., None]
+
+ # Apply standard shift scaling parameters vs simple feature additions
+ if self.use_scale_shift_norm:
+ out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+ scale, shift = torch.chunk(emb_out, 2, dim=1)
+ h = out_rest(out_norm(h) * (1 + scale) + shift)
+ else: h = self.out_layers(h + emb_out)
+
+ return self.skip_connection(x) + h
+
+class AudioSRUnet(nn.Module):
+ """
+ U-Net backbone architecture tailored for audio super-resolution diffusion workflows.
+ """
+
+ def __init__(self, image_size = 64, in_channels = 32, model_channels = 128, out_channels = 16, num_res_blocks = 2, attention_resolutions = [8, 4, 2], dropout=0, channel_mult=[1, 2, 3, 5], conv_resample=True, num_classes=None, extra_film_condition_dim=None, use_checkpoint=False, num_heads=-1, num_head_channels=32, num_heads_upsample=-1, use_scale_shift_norm=False, transformer_depth=1, context_dim=None, n_embed=None):
+ super().__init__()
+
+ if num_heads_upsample == -1: num_heads_upsample = num_heads
+ if num_heads == -1: assert num_head_channels != -1
+ if num_head_channels == -1: assert num_heads != -1
+
+ self.image_size = image_size
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ self.out_channels = out_channels
+ self.num_res_blocks = num_res_blocks
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.num_classes = num_classes
+ self.extra_film_condition_dim = extra_film_condition_dim
+ self.use_checkpoint = use_checkpoint
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.predict_codebook_ids = n_embed is not None
+ time_embed_dim = model_channels * 4
+ self.use_extra_film_by_concat = self.extra_film_condition_dim is not None
+ self.time_embed = nn.Sequential(nn.Linear(model_channels, time_embed_dim), nn.SiLU(), nn.Linear(time_embed_dim, time_embed_dim))
+
+ # Setup configuration mapping properties conditional contexts
+ if self.num_classes is not None: self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+ if self.extra_film_condition_dim is not None: self.film_emb = nn.Linear(self.extra_film_condition_dim, time_embed_dim)
+ if context_dim is not None and not isinstance(context_dim, list): context_dim = [context_dim]
+ elif context_dim is None: context_dim = [None]
+
+ self.input_blocks = nn.ModuleList([TimestepEmbedSequential(nn.Conv2d(in_channels, model_channels, 3, padding=1))])
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+
+ # Populate entry scaling paths block sets
+ for level, mult in enumerate(channel_mult):
+ for _ in range(num_res_blocks):
+ layers = [ResBlock(ch, time_embed_dim if not self.use_extra_film_by_concat else (time_embed_dim * 2), dropout, out_channels=mult * model_channels, dims=2, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm)]
+ ch = mult * model_channels
+
+ if ds in attention_resolutions:
+ if num_head_channels == -1: dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+
+ dim_head = ch // num_heads
+ layers.append(SpatialTransformer(ch, num_heads, dim_head, depth=transformer_depth, context_dim=None))
+
+ for context_dim_id in range(len(context_dim)):
+ layers.append(SpatialTransformer(ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim[context_dim_id]))
+
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(TimestepEmbedSequential(Downsample(ch, conv_resample, dims=2, out_channels=out_ch)))
+ ch = out_ch
+
+ input_block_chans.append(ch)
+ ds *= 2
+ self._feature_size += ch
+
+ if num_head_channels == -1: dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+
+ # Setup core middle bottleneck modules layer structures
+ dim_head = ch // num_heads
+ middle_layers = [ResBlock(ch, time_embed_dim if not self.use_extra_film_by_concat else (time_embed_dim * 2), dropout, dims=2, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm,)]
+ middle_layers.append(SpatialTransformer(ch, num_heads, dim_head, depth=transformer_depth, context_dim=None))
+
+ for context_dim_id in range(len(context_dim)):
+ middle_layers.append(SpatialTransformer(ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim[context_dim_id]))
+
+ middle_layers.append(ResBlock(ch, time_embed_dim if not self.use_extra_film_by_concat else (time_embed_dim * 2), dropout, dims=2, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm))
+ self.middle_block = TimestepEmbedSequential(*middle_layers)
+
+ self._feature_size += ch
+ self.output_blocks = nn.ModuleList([])
+
+ # Populate decoding scaling outputs blocks using skip connections
+ for level, mult in list(enumerate(channel_mult))[::-1]:
+ for i in range(num_res_blocks + 1):
+ ich = input_block_chans.pop()
+ layers = [ResBlock(ch + ich, time_embed_dim if (not self.use_extra_film_by_concat) else time_embed_dim * 2, dropout, out_channels=model_channels * mult, dims=2, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm)]
+
+ ch = model_channels * mult
+ if ds in attention_resolutions:
+ if num_head_channels == -1: dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+
+ dim_head = ch // num_heads
+ layers.append(SpatialTransformer(ch, num_heads, dim_head, depth=transformer_depth, context_dim=None))
+
+ for context_dim_id in range(len(context_dim)):
+ layers.append(SpatialTransformer(ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim[context_dim_id]))
+
+ if level and i == num_res_blocks:
+ out_ch = ch
+ layers.append(Upsample(ch, conv_resample, dims=2, out_channels=out_ch))
+ ds //= 2
+
+ self.output_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+
+ # Set up standard output projections layers maps
+ self.out = nn.Sequential(GroupNorm32(32, ch), nn.SiLU(), zero_module(nn.Conv2d(model_channels, out_channels, 3, padding=1)))
+ if self.predict_codebook_ids: self.id_predictor = nn.Sequential(GroupNorm32(32, ch), nn.Conv2d(model_channels, n_embed, 1))
+
+ def forward(self, x, timesteps=None, y=None, context_list=list(), context_attn_mask_list=list(), **kwargs):
+ """Executes full U-Net resolution forward passes mapping features."""
+
+ # Concat standard model feature views and conditioning fields together
+ x = torch.concat([x, y], dim=1)
+ y = None
+
+ assert (y is not None) == (
+ self.num_classes is not None or
+ self.extra_film_condition_dim is not None
+ )
+
+ hs = []
+ # Generate sinusoidal embedding trackers tracking diffusion timesteps indices
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.to(next(self.parameters()).dtype))
+
+ emb = self.time_embed(t_emb)
+ if self.use_extra_film_by_concat: emb = torch.cat([emb, self.film_emb(y)], dim=-1)
+
+ h = x.to(next(self.parameters()).dtype)
+ # Traverse encoder levels, tracking intermediate maps inside a cache stack
+ for module in self.input_blocks:
+ h = module(h, emb, context_list, context_attn_mask_list)
+ hs.append(h)
+
+ # Process bottleneck layers
+ h = self.middle_block(h, emb, context_list, context_attn_mask_list)
+ # Traverse decoder layers, popping encoder map links to stitch skip-connections
+ for module in self.output_blocks:
+ h = module(torch.cat([h, hs.pop()], dim=1), emb, context_list, context_attn_mask_list)
+
+ h = h.type(x.dtype)
+ return self.id_predictor(h) if self.predict_codebook_ids else self.out(h)
+
+class Activation1d(nn.Module):
+ """
+ Applies non-linear activation functions on 1D audio data with anti-aliasing up/downsampling.
+ """
+
+ def __init__(self, activation, up_ratio = 2, down_ratio = 2, up_kernel_size = 12, down_kernel_size = 12):
+ super().__init__()
+ self.up_ratio = up_ratio
+ self.down_ratio = down_ratio
+ self.act = activation
+ self.upsample = UpSample1d(up_ratio, up_kernel_size)
+ self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+ def forward(self, x):
+ # Prevent aliasing artifacts by filtering/stretching signals before applying activations
+ return self.downsample(self.act(self.upsample(x)))
+
+class SnakeBeta(nn.Module):
+ """
+ SnakeBeta periodic activation block tracking learnable parameter frequencies for periodic tasks.
+ """
+
+ def __init__(self, in_features, alpha=1.0, alpha_trainable=True):
+ super(SnakeBeta, self).__init__()
+ self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)
+ self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
+ self.alpha.requires_grad = alpha_trainable
+ self.beta.requires_grad = alpha_trainable
+
+ def forward(self, x):
+ # Evaluate standard Snake Beta harmonic mapping equations
+ return x + (1.0 / (self.beta.unsqueeze(0).unsqueeze(-1).exp() + 1e-9)) * pow((x * self.alpha.unsqueeze(0).unsqueeze(-1).exp()).sin(), 2)
+
+class AMPBlock1(nn.Module):
+ """
+ Anti-Aliased Multi-Period (AMP) ResNet Block using snake activations.
+ """
+
+ def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+ super(AMPBlock1, self).__init__()
+ self.convs1 = nn.ModuleList([weight_norm(nn.Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(nn.Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))), weight_norm(nn.Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2])))])
+ self.convs1.apply(init_weights)
+ self.convs2 = nn.ModuleList([weight_norm(nn.Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(nn.Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(nn.Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1)))])
+ self.convs2.apply(init_weights)
+
+ self.num_layers = len(self.convs1) + len(self.convs2)
+ self.activations = nn.ModuleList([Activation1d(activation=SnakeBeta(channels)) for _ in range(self.num_layers)])
+
+ def forward(self, x):
+ # Unpack layer groups, applying multi-scale residual processing updates
+ for c1, c2, a1, a2 in zip(self.convs1, self.convs2, self.activations[::2], self.activations[1::2]):
+ x = c2(a2(c1(a1(x)))) + x
+
+ return x
+
+ def remove_weight_norm(self):
+ """Detaches weight normalization Hooks from child convolution layers parameters."""
+
+ for l in self.convs1:
+ if hasattr(l, "parametrizations") and "weight" in l.parametrizations: parametrize.remove_parametrizations(l, "weight", leave_parametrized=True)
+ else: remove_weight_norm(l)
+ for l in self.convs2:
+ if hasattr(l, "parametrizations") and "weight" in l.parametrizations: parametrize.remove_parametrizations(l, "weight", leave_parametrized=True)
+ else: remove_weight_norm(l)
+
+class SRVocoder(nn.Module):
+ """
+ BigVGAN-based Super-Resolution Neural Audio Vocoder architecture structure model.
+ """
+
+ def __init__(self, num_mels = 256, upsample_initial_channel = 1536, resblock_kernel_sizes = [3, 7, 11], resblock_dilation_sizes = [[1, 3, 5], [1, 3, 5], [1, 3, 5]], upsample_rates = [10, 6, 2, 2, 2], upsample_kernel_sizes = None, snake_logscale = True):
+ super(SRVocoder, self).__init__()
+ if upsample_kernel_sizes is None: upsample_kernel_sizes = [upsample_rate * 2 for upsample_rate in upsample_rates]
+
+ self.audio_block = nn.ModuleDict()
+ self.audio_block["downsamples"] = nn.ModuleList()
+ self.audio_block["emb"] = nn.Conv1d(1, upsample_initial_channel // (2 ** len(upsample_rates)), 7, bias=True, padding=(7 - 1) // 2)
+
+ # Assemble downsampling blocks to process reference low-resolution audio waveforms
+ for i in reversed(range(len(upsample_kernel_sizes))):
+ self.audio_block["downsamples"] += [nn.Sequential(nn.Conv1d(upsample_initial_channel // (2 ** (i + 1)), upsample_initial_channel // (2 ** i), upsample_kernel_sizes[i], upsample_rates[i], padding=upsample_rates[i] - (upsample_kernel_sizes[i] % 2 == 0), bias=True), nn.LeakyReLU(negative_slope = 0.1))]
+
+ self.num_kernels = len(resblock_kernel_sizes)
+ self.num_upsamples = len(upsample_rates)
+ self.conv_pre = weight_norm(nn.Conv1d(num_mels, upsample_initial_channel, 7, 1, padding=3))
+
+ self.ups = nn.ModuleList()
+ # Assemble transposed 1D convolutions layers mapping mel features dimensions
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+ self.ups.append(nn.ModuleList([weight_norm(nn.ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)), k, u, padding=(k - u) // 2))]))
+
+ self.resblocks = nn.ModuleList()
+ # Initialize specialized residual multi-period AMP blocks sets
+ for i in range(len(self.ups)):
+ ch = upsample_initial_channel // (2 ** (i + 1))
+ for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+ self.resblocks.append(AMPBlock1(ch, k, d))
+
+ activation_post = SnakeBeta(ch)
+ self.activation_post = Activation1d(activation=activation_post)
+ self.conv_post = weight_norm(nn.Conv1d(ch, 1, 7, 1, padding=3))
+
+ self.conv_post.apply(init_weights)
+ for i in range(len(self.ups)):
+ self.ups[i].apply(init_weights)
+
+ def forward(self, mel_spec, lr_audio):
+ """Synthesizes high-resolution waveforms from Mel spectrograms and conditioning signals."""
+
+ audio_emb = self.audio_block["emb"](lr_audio.unsqueeze(1))
+ audio_emb_list = [audio_emb]
+ # Extract downsampled conditioning embedding blocks hierarchical layers list
+ for i in range(self.num_upsamples - 1):
+ audio_emb = self.audio_block["downsamples"][i](audio_emb)
+ audio_emb_list += [audio_emb]
+
+ x = self.conv_pre(mel_spec)
+ # Reconstruct waveforms progressively, adding low-res details across levels
+ for i in range(self.num_upsamples):
+ for i_up in range(len(self.ups[i])):
+ x = self.ups[i][i_up](x) + audio_emb_list[-1-i]
+
+ xs = None
+ for j in range(self.num_kernels):
+ if xs is None: xs = self.resblocks[i * self.num_kernels + j](x)
+ else: xs += self.resblocks[i * self.num_kernels + j](x)
+
+ x = xs / self.num_kernels
+
+ # Return final predictions scaled within safe tanh boundaries
+ return {'pred_hr_audio': self.conv_post(self.activation_post(x)).tanh().squeeze(1)}
+
+ def remove_weight_norm(self):
+ """Detaches all weight normalization hooks across child dependencies layers."""
+
+ for l in self.ups:
+ for l_i in l:
+ if hasattr(l_i, "parametrizations") and "weight" in l_i.parametrizations: parametrize.remove_parametrizations(l_i, "weight", leave_parametrized=True)
+ else: remove_weight_norm(l_i)
+
+ for l in self.resblocks:
+ l.remove_weight_norm()
+
+ if hasattr(self.conv_pre, "parametrizations") and "weight" in self.conv_pre.parametrizations: parametrize.remove_parametrizations(self.conv_pre, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv_pre)
+ if hasattr(self.conv_post, "parametrizations") and "weight" in self.conv_post.parametrizations: parametrize.remove_parametrizations(self.conv_post, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv_post)
+
+class BetaSchedule:
+ """
+ Static noise scheduling generators helper package to manage diffusion tracks steps.
+ """
+
+ @staticmethod
+ def linear(timesteps, start=1e-4, end=2e-2):
+ """Generates a standard linear schedule for variance bounding."""
+
+ return np.linspace(start, end, timesteps)
+
+ @staticmethod
+ def cosine(timesteps, s=0.008):
+ """Generates an advanced cosine variance scheduling pattern sequence."""
+
+ steps = timesteps + 1
+ x = np.linspace(0, steps, steps)
+
+ # Evaluate standard continuous cosine alpha cumprod variance formulas
+ alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
+ alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
+
+ # Convert cumulative profiles back into localized sequential beta coefficients
+ return np.clip(1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]), a_min=0, a_max=0.999)
+
+class DDPM(nn.Module):
+ """
+ Denoising Diffusion Probabilistic Model (DDPM) pipeline tracking variance scheduling coefficients.
+ """
+
+ def __init__(self, model_output_type = 'noise', timesteps = 1000, loss_func = F.mse_loss, betas = None, beta_schedule_type = 'cosine', beta_arg_dict = dict(), unconditional_prob = 0, cfg_scale = None):
+ super().__init__()
+ self.model = AudioSRUnet()
+ self.model_output_type = model_output_type
+ self.loss_func = loss_func
+ self.timesteps = timesteps
+ self.set_noise_schedule(betas=betas, beta_schedule_type=beta_schedule_type, beta_arg_dict=beta_arg_dict, timesteps=timesteps)
+ self.unconditional_prob = unconditional_prob
+ self.cfg_scale = cfg_scale
+
+ def set_noise_schedule(self, betas = None, beta_schedule_type = 'linear', beta_arg_dict = dict(), timesteps = 1000):
+ """Precomputes diffusion schedule coefficients to accelerate forward passes."""
+
+ if betas is None:
+ beta_arg_dict.update({'timesteps':timesteps})
+ betas = getattr(BetaSchedule, beta_schedule_type)(**beta_arg_dict)
+
+ # Derived algebraic expressions equations mapping diffusion probabilities coefficients
+ alphas = 1.0 - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+ self.betas = register_buffer(model = self, variable_name = 'betas', value = betas)
+ self.alphas_cumprod = register_buffer(model = self, variable_name = 'alphas_cumprod', value = alphas_cumprod)
+ self.alphas_cumprod_prev = register_buffer(model = self, variable_name = 'alphas_cumprod_prev', value = alphas_cumprod_prev)
+ self.sqrt_alphas_cumprod = register_buffer(model = self, variable_name = 'sqrt_alphas_cumprod', value = np.sqrt(alphas_cumprod))
+ self.sqrt_one_minus_alphas_cumprod = register_buffer(model = self, variable_name = 'sqrt_one_minus_alphas_cumprod', value = np.sqrt(1. - alphas_cumprod))
+ self.log_one_minus_alphas_cumprod = register_buffer(model = self, variable_name = 'log_one_minus_alphas_cumprod', value = np.log(1. - alphas_cumprod))
+ self.sqrt_recip_alphas_cumprod = register_buffer(model = self, variable_name = 'sqrt_recip_alphas_cumprod', value = np.sqrt(1. / alphas_cumprod))
+ self.sqrt_recipm1_alphas_cumprod = register_buffer(model = self, variable_name = 'sqrt_recipm1_alphas_cumprod', value = np.sqrt(1. / alphas_cumprod - 1))
+ posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+ self.posterior_variance = register_buffer(model = self, variable_name = 'posterior_variance', value = posterior_variance)
+ self.posterior_log_variance_clipped = register_buffer(model = self, variable_name = 'posterior_log_variance_clipped', value = np.log(np.maximum(posterior_variance, 1e-20)))
+ self.posterior_mean_coef1 = register_buffer(model = self, variable_name = 'posterior_mean_coef1', value = betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
+ self.posterior_mean_coef2 = register_buffer(model = self, variable_name = 'posterior_mean_coef2', value = (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))
+
+ def apply_model(self, x, t, cond, is_cond_unpack, cfg_scale = None):
+ """Routes forward evaluation updates through models, supporting classifier-free guidance scaling."""
+
+ if cfg_scale is None or cfg_scale == 1.0:
+ if cond is None: return self.model(x, t)
+ elif is_cond_unpack: return self.model(x, t, **cond)
+ else: return self.model(x, t, cond)
+ else:
+ # Evaluate paths with and without conditioning to apply classifier-free guidance steps
+ model_conditioned_output = self.model(x, t, **cond) if is_cond_unpack else self.model(x, t, cond)
+ unconditional_conditioning = self.get_unconditional_condition(cond=cond)
+ model_unconditioned_output = self.model(x, t, **unconditional_conditioning) if is_cond_unpack else self.model(x, t, unconditional_conditioning)
+ # Extrapolate outputs based on guidance scales factors
+ return model_unconditioned_output + cfg_scale * (model_conditioned_output - model_unconditioned_output)
+
+ def get_unconditional_condition(self, cond = None, cond_shape = None, condition_device = None):
+ """Generates dummy unconditional tensor representations for classifier-free guidance pipelines."""
+
+ if cond_shape is None: cond_shape = cond.shape
+ if cond is not None and isinstance(cond, torch.Tensor): condition_device = cond.device
+ # Use an empirical constant negative offset value to populate mask profiles
+ return (-11.4981 + torch.zeros(cond_shape)).to(condition_device)
+
+class DPMSolverMultistepScheduler:
+ """
+ Advanced Multistep DPM-Solver inference scheduler optimizing generation steps.
+ """
+
+ def __init__(
+ self,
+ num_train_timesteps,
+ trained_betas,
+ prediction_type="epsilon",
+ solver_order=2,
+ **kwargs
+ ):
+ self.num_train_timesteps = num_train_timesteps
+ self.prediction_type = prediction_type
+ self.solver_order = solver_order
+
+ betas = trained_betas.float()
+
+ alphas = 1.0 - betas
+ alphas_cumprod = torch.cumprod(alphas, dim=0)
+
+ self.alphas_cumprod = alphas_cumprod
+ self.alpha_t = alphas_cumprod.sqrt()
+ self.sigma_t = (1 - alphas_cumprod).sqrt()
+
+ self.model_outputs = []
+ self.timesteps = None
+
+ def set_timesteps(self, num_inference_steps, device=None):
+ """Generates sampling index sequences spaced evenly across training parameters."""
+
+ timesteps = torch.linspace(
+ self.num_train_timesteps - 1,
+ 0,
+ num_inference_steps,
+ dtype=torch.long,
+ device=device
+ )
+
+ self.timesteps = timesteps
+ self.model_outputs = []
+
+ def scale_model_input(self, sample, timestep):
+ """Input scaling interface stub matching diffusers design patterns."""
+
+ return sample
+
+ def convert_model_output(self, model_output, sample, timestep):
+ """Transforms raw network outputs into original sample space estimates."""
+
+ alpha_t = self.alpha_t[timestep].to(sample.device)
+ sigma_t = self.sigma_t[timestep].to(sample.device)
+
+ while alpha_t.ndim < sample.ndim:
+ alpha_t = alpha_t.unsqueeze(-1)
+ sigma_t = sigma_t.unsqueeze(-1)
+
+ # Convert predictions based on active scheduling types ('epsilon', 'sample', 'v_prediction')
+ if self.prediction_type == "epsilon": x0 = (sample - sigma_t * model_output) / alpha_t
+ elif self.prediction_type == "sample": x0 = model_output
+ elif self.prediction_type == "v_prediction": x0 = alpha_t * sample - sigma_t * model_output
+ else: raise ValueError("Invalid `prediction_type`. Please select a type from ('epsilon', 'sample', 'v_prediction')")
+
+ return x0
+
+ def dpm_solver_first_order_update(
+ self,
+ model_output,
+ sample,
+ timestep,
+ prev_timestep
+ ):
+ """Computes basic analytical first-order diffusion step tracking updates."""
+
+ alpha_t = self.alpha_t[timestep].to(sample.device)
+ sigma_t = self.sigma_t[timestep].to(sample.device)
+
+ alpha_s = self.alpha_t[prev_timestep].to(sample.device)
+ sigma_s = self.sigma_t[prev_timestep].to(sample.device)
+
+ while alpha_t.ndim < sample.ndim:
+ alpha_t = alpha_t.unsqueeze(-1)
+ sigma_t = sigma_t.unsqueeze(-1)
+ alpha_s = alpha_s.unsqueeze(-1)
+ sigma_s = sigma_s.unsqueeze(-1)
+
+ return (
+ alpha_s * model_output +
+ sigma_s * (
+ sample - alpha_t * model_output
+ ) / sigma_t
+ )
+
+ def multistep_dpm_solver_second_order_update(
+ self,
+ model_output_list,
+ sample,
+ timestep,
+ prev_timestep
+ ):
+ """Computes advanced second-order precision updates using multi-step history trackers."""
+
+ m0 = model_output_list[-1]
+ m1 = model_output_list[-2]
+
+ alpha_t = self.alpha_t[timestep]
+ sigma_t = self.sigma_t[timestep]
+
+ alpha_s = self.alpha_t[prev_timestep]
+ sigma_s = self.sigma_t[prev_timestep]
+
+ while alpha_t.ndim < sample.ndim:
+ alpha_t = alpha_t.unsqueeze(-1)
+ sigma_t = sigma_t.unsqueeze(-1)
+ alpha_s = alpha_s.unsqueeze(-1)
+ sigma_s = sigma_s.unsqueeze(-1)
+
+ D0 = m0
+ D1 = m0 - m1
+ # Interpolate coordinates values using finite difference estimation adjustments
+ x0 = (
+ alpha_s * D0 +
+ sigma_s * (
+ sample - alpha_t * D0
+ ) / sigma_t
+ )
+
+ return x0 + 0.5 * D1
+
+ def step(
+ self,
+ model_output,
+ timestep,
+ sample,
+ return_dict=False
+ ):
+ """Advances inference trajectories by one scheduler generation step."""
+
+ step_index = (self.timesteps == timestep).nonzero()[0].item()
+ prev_timestep = 0 if (step_index == len(self.timesteps) - 1) else self.timesteps[step_index + 1]
+ # Convert predictions to clean target coordinate models
+ model_output = self.convert_model_output(
+ model_output,
+ sample,
+ timestep
+ )
+ # Update historical queues context structures arrays
+ self.model_outputs.append(model_output)
+ if len(self.model_outputs) > self.solver_order: self.model_outputs.pop(0)
+ # Fallback to simple first order steps if queue history is insufficient
+ if len(self.model_outputs) == 1:
+ prev_sample = self.dpm_solver_first_order_update(
+ model_output,
+ sample,
+ timestep,
+ prev_timestep
+ )
+ else:
+ prev_sample = self.multistep_dpm_solver_second_order_update(
+ self.model_outputs,
+ sample,
+ timestep,
+ prev_timestep
+ )
+
+ if return_dict:
+ return {"prev_sample": prev_sample}
+
+ return (prev_sample,)
+
+class FlashSR:
+ """
+ High-level orchestrator class to handle rapid audio super-resolution generation pipelines.
+ """
+
+ def __init__(self, model_path, scale_factor_z = 0.3342, model_output_type = 'v_prediction', beta_schedule_type = 'cosine', device = None, is_half = False, **kwargs):
+ super().__init__()
+ self.scale_factor_z = scale_factor_z
+ self.device = device
+ self.is_half = is_half
+ # Initialize network sub-components
+ self.sr_vocoder = SRVocoder()
+ self.autoencoder = AutoencoderKL(embed_dim=16)
+ self.ddpm = DDPM(model_output_type=model_output_type, beta_schedule_type=beta_schedule_type, **kwargs)
+ self.util_mel_spec = UtilAudioMelSpec(nfft=2048, hop_size=480, sample_rate=48000, mel_size=256, frequency_min=20, frequency_max=24000, device=device)
+ # Load parameter state weights packages from storage safely
+ model = torch.load(model_path, map_location="cpu", weights_only=True)
+
+ self.sr_vocoder.load_state_dict(model["vocoder"])
+ self.autoencoder.load_state_dict(model["vae"])
+ self.ddpm.load_state_dict(model["ldm"])
+ # Remove weight normalization to increase processing speed; retaining it slows down inference, as it is only useful during training.
+ self.sr_vocoder.remove_weight_norm()
+ # Permanently lock autoencoder weight adjustments tracking fields
+ self.autoencoder = freeze_param(self.autoencoder)
+ # Move modules to target compute devices and enable inference modes
+ self.ddpm.to(device).eval()
+ self.sr_vocoder.to(device).eval()
+ self.autoencoder.to(device).eval()
+ # Enforce target precision type representations (fp16 vs fp32)
+ dtype = torch.float16 if is_half else torch.float32
+ self.ddpm.to(dtype)
+ self.sr_vocoder.to(dtype)
+ self.autoencoder.to(dtype)
+
+ def _upscaler(self, lr_audio, num_steps = 1, lowpass_input = True, lowpass_cutoff_freq = None):
+ """Internal worker core executing single window upscaling passes."""
+
+ if lowpass_input:
+ # Find lowpass filter cutoff frequency automatically if unprovided
+ if lowpass_cutoff_freq is None: lowpass_cutoff_freq = find_cutoff_freq(lr_audio)
+ # Apply conditioning filters to prepare incoming wave sequences
+ lr_audio = torch.from_numpy(
+ lowpass(
+ lr_audio.cpu().numpy(),
+ 48000,
+ filter_name='cheby',
+ filter_order=8,
+ cutoff_freq=lowpass_cutoff_freq
+ )
+ ).to(lr_audio.device)
+
+ with torch.no_grad():
+ # Run diffusion denoiser pipeline sequences
+ pred_hr_audio = self.infer(
+ lr_audio,
+ is_cond_unpack=False,
+ num_steps=num_steps,
+ device=lr_audio.device
+ )
+
+ # Squeeze output shapes to match entry bounds profiles
+ return pred_hr_audio[...,:lr_audio.shape[-1]]
+
+ def infer(self, cond, is_cond_unpack = False, num_steps = 20, scheduler_args = {'timestep_spacing': 'trailing'}, cfg_scale = None, device = None):
+ """Runs the iterative reverse-diffusion loop to denoise latent representations."""
+ noise_scheduler = DPMSolverMultistepScheduler(**get_diffusers_scheduler_config(self.ddpm, scheduler_args))
+ # Preprocess signals, projecting entries into latent codebook spaces
+ _, cond, additional_data_dict = self.preprocess(x_start=None, cond=cond)
+
+ x_shape = cond.shape
+ noise_scheduler.set_timesteps(num_steps)
+ # Sample initial raw Gaussian noise arrays fields
+ x = torch.randn(x_shape, device=device) * 1.0
+ # Progress through reverse denoiser scheduler path intervals
+ for t in noise_scheduler.timesteps:
+ denoiser_input = noise_scheduler.scale_model_input(x, t)
+ denoiser_input, cond = denoiser_input.to(torch.float16 if self.is_half else torch.float32), cond.to(torch.float16 if self.is_half else torch.float32)
+ # Evaluate score estimates models updates channels profiles
+ model_output = self.ddpm.apply_model(denoiser_input, torch.full((x_shape[0],), t, device=device, dtype=torch.long), cond, is_cond_unpack, cfg_scale=self.ddpm.cfg_scale if cfg_scale is None else cfg_scale)
+ # Advance trajectories tracking step indexes variables
+ x = noise_scheduler.step(model_output, t, x, return_dict=False)[0]
+
+ return self.postprocess(x, additional_data_dict)
+
+ def preprocess(self, x_start, cond = None):
+ """Preprocesses input signals by normalizing waveforms and encoding to latent z-space."""
+
+ x_dict = dict()
+ cond_dict = self.encode_to_z(cond)
+
+ # Sync normalization coefficients if running parallel targets evaluation validation
+ if x_start is not None:
+ x_dict = self.encode_to_z(
+ x_start,
+ scale_dict={
+ 'mean_scale_factor': cond_dict['mean_scale_factor'],
+ 'var_scale_factor': cond_dict['var_scale_factor']
+ }
+ )
+
+ return x_dict.get('z', None), cond_dict['z'], cond_dict
+
+ def postprocess(self, x, additional_data_dict):
+ """Converts latent features back into high-resolution waveforms via the vocoder."""
+
+ return self.denormalize_wav(
+ self.sr_vocoder(
+ self.z_to_mel(x).squeeze(1).transpose(1, 2),
+ additional_data_dict['norm_wav']
+ )['pred_hr_audio'],
+ additional_data_dict
+ )
+
+ def denormalize_wav(self, waveform, scale_dict):
+ """Inverts standard scaling transformations using cached reference coefficients."""
+
+ return ((waveform * 2.0) * (scale_dict['var_scale_factor'] + 1e-8)) + scale_dict['mean_scale_factor']
+
+ @torch.no_grad()
+ def encode_to_z(self, audio, normalize = True, scale_dict = None):
+ """Encodes raw audio data into scaled latent variables (z) via Mel spectrogram features."""
+
+ assert len(audio.shape) == 2
+ result_dict = {'wav': audio}
+ # Handle structural waveform amplitude normalizations steps
+ if normalize:
+ audio, scale_dict = self.normalize_wav(audio, scale_dict=scale_dict)
+ result_dict['norm_wav'] = audio
+ result_dict.update(scale_dict)
+
+ # Extract log Mel-spectrogram features mappings
+ mel_spec = self.audio_to_mel(audio).to(torch.float16 if self.is_half else torch.float32)
+ result_dict['mel_spec'] = mel_spec
+ # Route spectral images through VAE encoder distribution blocks
+ result_dict['z'] = self.autoencoder.encode(mel_spec).sample() * self.scale_factor_z
+
+ return result_dict
+
+ def normalize_wav(self, waveform, scale_dict):
+ """Normalizes waveform amplitudes to a standardized variance range."""
+ mean_scale_factor = waveform.mean(dim=1, keepdim=True) if scale_dict is None else scale_dict['mean_scale_factor']
+ waveform -= mean_scale_factor
+
+ var_scale_factor = waveform.abs().max(dim=1, keepdim=True)[0] if scale_dict is None else scale_dict['var_scale_factor']
+ waveform /= (var_scale_factor + 1e-8)
+
+ return waveform * 0.5, {'mean_scale_factor': mean_scale_factor, 'var_scale_factor': var_scale_factor}
+
+ @torch.no_grad()
+ def audio_to_mel(self, audio):
+ """Extracts aligned Mel-spectrogram images from raw waveform tensors."""
+
+ mel_spec = self.util_mel_spec.get_mel_spec(audio).to(self.device)
+ if len(mel_spec.shape) == 3: mel_spec = mel_spec.unsqueeze(1)
+ return mel_spec.permute(0, 1, 3, 2)
+
+ def z_to_mel(self, z):
+ """Decodes latent variables (z) back into standard Mel-spectrogram profiles."""
+
+ with torch.no_grad():
+ x = (1.0 / self.scale_factor_z) * z
+ return self.autoencoder.decode(x.to(torch.float16 if self.is_half else torch.float32))
+
+ def upscaler(self, audio, sample_rate = 48000, pbar = None, C = 245760, step = 122880, fade_size = 24576, border = 122880):
+ """
+ Applies super-resolution processing over long audio sequences using a sliding-window view.
+
+ Args:
+ audio (ndarray): Input time-domain audio array sequence buffer.
+ sample_rate (int, optional): Source sample rate frequency. Defaults to 48000.
+ pbar (tqdm, optional): Progress bar tracking execution iterations. Defaults to None.
+ C (int, optional): Processing segment chunk duration width limits. Defaults to 245760.
+ step (int, optional): Sliding stride step size increment interval. Defaults to 122880.
+ fade_size (int, optional): Cross-fading transition envelope region. Defaults to 24576.
+ border (int, optional): Margin width used for reflection padding. Defaults to 122880.
+
+ Returns:
+ ndarray: Up-scaled time-domain audio signal sequence array.
+ """
+
+ # Resample incoming streams to a high-fidelity 192kHz processing grid pattern base
+ audio = librosa.resample(audio, orig_sr=sample_rate, target_sr=192000, res_type="soxr_vhq")
+ audio = torch.from_numpy(audio.flatten()).to(self.device).float()
+
+ if len(audio.shape) == 1: audio = audio.unsqueeze(0)
+ # Apply padding configurations to isolate boundary reflection artifacts
+ if audio.shape[1] > 2 * border and (border > 0): audio = F.pad(audio, (border, border), mode="reflect")
+
+ # Initialize accumulation buffers tracking output values weights sum indices
+ result = torch.zeros((1,) + tuple(audio.shape), device=audio.device, dtype=torch.float32)
+ counter = torch.zeros((1,) + tuple(audio.shape), device=audio.device, dtype=torch.float32)
+
+ i = 0
+ # Calibrate progress parameters total indices target limits step counts
+ pbar.total = pbar.total + math.ceil(audio.size(1) / step)
+ pbar.refresh()
+
+ # Step along sequences progressively using windows slices
+ while i < audio.shape[1]:
+ part = audio[:, i : i + C]
+ length = part.shape[-1]
+
+ # Handle boundary padding anomalies when segments are shorter than target size C
+ if length < C: part = F.pad(part, pad=(0, C - length), mode="reflect") if length > C // 2 + 1 else F.pad(part, pad=(0, C - length, 0, 0), mode="constant", value=0.0)
+ # Run single block upscaler evaluation paths
+ out = self._upscaler(part, lowpass_input=True)
+ window = get_window(C, fade_size).to(audio.device)
+
+ # Suppress linear fade blending equations at outer edge boundaries
+ if i == 0: window[:fade_size] = 1
+ elif i + C >= audio.shape[1]: window[-fade_size:] = 1
+
+ # Accumulate window-weighted segment outputs into the global buffer
+ result[..., i : i + length] += out[..., :length] * window[..., :length]
+ counter[..., i : i + length] += window[..., :length]
+
+ i += step
+ pbar.update(1)
+
+ # Normalize overlapping regions by dividing by total accumulated weights
+ final_output = (result / counter).squeeze(0)
+ # Strip padding margins to restore native audio dimensions
+ if audio.shape[1] > 2 * border and (border > 0): final_output = final_output[..., border:-border]
+
+ return final_output.float().flatten().cpu().detach().numpy()
\ No newline at end of file
diff --git a/main/library/backends/directml.py b/main/library/backends/directml.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfc71bab1c709420c7d1d44d34f1ef33d46d22c8
--- /dev/null
+++ b/main/library/backends/directml.py
@@ -0,0 +1,498 @@
+import os
+import gc
+import sys
+import torch
+import struct
+import ctypes
+
+from ctypes import wintypes
+
+sys.path.append(os.getcwd())
+
+from main.library.embedders import fairseq
+from main.library.backends.utils import GRU, DeviceProperties
+
+# Use these backends only if the library is installed.
+try:
+ import torch_directml
+except:
+ torch_directml = None
+
+# Global hardware configurations and hooks
+devices = []
+torch_available = torch_directml != None
+setupapi, advapi32, empty_cache_ctypes = None, None, None
+orig_conv_transpose1d = torch.nn.functional.conv_transpose1d
+empty_cache_dll = os.path.join("assets", "binary", "empty_cache.dll")
+
+class GUID_BYTE(ctypes.Structure):
+ """
+ Represents a Windows Globally Unique Identifier (GUID) structure.
+ Uses standard byte array format for compatibility with SetupAPI calls.
+ """
+
+ _fields_ = [("Data1", wintypes.DWORD), ("Data2", wintypes.WORD), ("Data3", wintypes.WORD), ("Data4", ctypes.c_byte * 8)]
+
+class GUID_UBYTPE(ctypes.Structure):
+ """
+ Represents a Windows GUID structure using unsigned byte values.
+ Typically required for COM and DXGI component registration interfaces.
+ """
+
+ _fields_ = [("Data1", wintypes.DWORD), ("Data2", wintypes.WORD), ("Data3", wintypes.WORD), ("Data4", ctypes.c_ubyte * 8)]
+
+class SP_DEVINFO_DATA(ctypes.Structure):
+ """
+ Defines a Windows Device Information structure (`SP_DEVINFO_DATA`).
+ Contains metadata referencing a specific device instance within a device information set.
+ """
+
+ _fields_ = [("cbSize", wintypes.DWORD), ("ClassGuid", GUID_BYTE), ("DevInst", wintypes.DWORD), ("Reserved", ctypes.c_void_p)]
+
+class DXGI_ADAPTER_DESC1(ctypes.Structure):
+ """
+ Describes a Direct3D 11 / DXGI graphics adapter interface descriptor block.
+ Extracts explicit VRAM capacities and low-level device descriptors.
+ """
+
+ _fields_ = [("Description", ctypes.c_wchar * 128), ("VendorId", wintypes.UINT), ("DeviceId", wintypes.UINT), ("SubSysId", wintypes.UINT), ("Revision", wintypes.UINT), ("DedicatedVideoMemory", ctypes.c_size_t), ("DedicatedSystemMemory", ctypes.c_size_t), ("SharedSystemMemory", ctypes.c_size_t), ("AdapterLuid", ctypes.c_uint64), ("Flags", wintypes.UINT)]
+
+def unpack_empty_cache():
+ """
+ Unpacks compressed binary dependency payloads (`.dll`, `.exp`, `.lib`, `.obj`)
+ from a pickled backup file (`empty_cache.bin`) and binds the target WinDLL interface.
+
+ Raises:
+ OSError: If binary assets cannot be created or loaded via ctypes.
+ """
+
+ global empty_cache_ctypes
+ # Extract binaries if missing
+ if not os.path.exists(empty_cache_dll):
+ with open(os.path.join("assets", "binary", "empty_cache.bin"), "rb") as f:
+ for ext in ["dll", "exp", "lib", "obj"]:
+ size_bytes = f.read(4)
+ if not size_bytes: break
+
+ content = f.read(struct.unpack("I", size_bytes)[0])
+ with open(empty_cache_dll.replace(".dll", "." + ext), "wb") as f2:
+ f2.write(content)
+
+ # Load DLL and map parameter signatures
+ try:
+ empty_cache_ctypes = ctypes.WinDLL(empty_cache_dll)
+ empty_cache_ctypes.empty_cache.argtypes = [ctypes.c_uint]
+ empty_cache_ctypes.empty_cache.restype = ctypes.c_bool
+ except Exception as e:
+ raise OSError(f"Failed to load dynamic library interface from '{empty_cache_dll}': {e}")
+
+def setup_api():
+ """
+ Initializes system-level ctypes mappings for Windows SetupAPI and Advapi32 DLLs.
+ Configures strict static type declarations for external C functions.
+ """
+
+ global setupapi, advapi32
+
+ try:
+ setupapi = ctypes.windll.setupapi
+ advapi32 = ctypes.windll.advapi32
+ except Exception as e:
+ raise OSError(f"Failed to initialize target Windows environment subsystem libraries: {e}")
+
+ # SetupDiGetClassDevsW: Query device information sets based on GUID class
+ setupapi.SetupDiGetClassDevsW.argtypes = [ctypes.POINTER(GUID_BYTE), ctypes.c_wchar_p, wintypes.HWND, wintypes.DWORD]
+ setupapi.SetupDiGetClassDevsW.restype = ctypes.c_void_p
+
+ # SetupDiEnumDeviceInfo: Enumerate elements within a device information set
+ setupapi.SetupDiEnumDeviceInfo.argtypes = [ctypes.c_void_p, wintypes.DWORD, ctypes.POINTER(SP_DEVINFO_DATA)]
+ setupapi.SetupDiEnumDeviceInfo.restype = wintypes.BOOL
+
+ # SetupDiGetDeviceRegistryPropertyW: Extract explicit registry keys representing hardware traits
+ setupapi.SetupDiGetDeviceRegistryPropertyW.argtypes = [ctypes.c_void_p, ctypes.POINTER(SP_DEVINFO_DATA), wintypes.DWORD, ctypes.POINTER(wintypes.DWORD), ctypes.c_void_p, wintypes.DWORD, ctypes.POINTER(wintypes.DWORD)]
+ setupapi.SetupDiGetDeviceRegistryPropertyW.restype = wintypes.BOOL
+
+ # SetupDiOpenDevRegKey: Open configuration registry scopes belonging to device drivers
+ setupapi.SetupDiOpenDevRegKey.argtypes = [ctypes.c_void_p, ctypes.POINTER(SP_DEVINFO_DATA), wintypes.DWORD, wintypes.DWORD, wintypes.DWORD, wintypes.DWORD]
+ setupapi.SetupDiOpenDevRegKey.restype = wintypes.HKEY
+
+ # Bind Advapi32 functions to evaluate properties directly out of registry pipelines
+ advapi32.RegQueryValueExW.argtypes = [wintypes.HKEY, ctypes.c_wchar_p, ctypes.POINTER(wintypes.DWORD), ctypes.POINTER(wintypes.DWORD), ctypes.c_void_p, ctypes.POINTER(wintypes.DWORD)]
+ advapi32.RegQueryValueExW.restype = wintypes.LONG
+ advapi32.RegCloseKey.argtypes = [wintypes.HKEY]
+
+ # Dynamically extract and assign internal system clearing binaries
+ unpack_empty_cache()
+
+def get_directml_device():
+ """
+ Scans the local operating system configuration space to discover hardware units matching
+ DirectML structural categories, parsing absolute VRAM values from physical system nodes.
+ """
+
+ global devices
+
+ if len(devices) >= 1: return
+ if not torch_available: return
+ if setupapi is None or advapi32 is None: setup_api()
+
+ # GUID matching system Display Adapter profiles
+ display_guid = GUID_BYTE(0x4D36E968, 0xE325, 0x11CE, (ctypes.c_byte * 8)(0xBF, 0xC1, 0x08, 0x00, 0x2B, 0xE1, 0x03, 0x18))
+ h_dev_info = ctypes.c_void_p(setupapi.SetupDiGetClassDevsW(ctypes.byref(display_guid), None, None, 0x00000002))
+ if h_dev_info.value is None or h_dev_info.value == int(0xFFFFFFFFFFFFFFFF): return
+
+ dev_info_data = SP_DEVINFO_DATA()
+ dev_info_data.cbSize = ctypes.sizeof(SP_DEVINFO_DATA)
+ idx, gpu_idx = 0, 0
+
+ # Iterative discovery loops across system hardware trees
+ while setupapi.SetupDiEnumDeviceInfo(h_dev_info, idx, ctypes.byref(dev_info_data)):
+ buf = ctypes.create_unicode_buffer(256)
+ # Retrieve structural name descriptions
+ if setupapi.SetupDiGetDeviceRegistryPropertyW(h_dev_info, ctypes.byref(dev_info_data), 0x00000000, None, ctypes.byref(buf), ctypes.sizeof(buf), ctypes.byref(wintypes.DWORD())):
+ gpu_name = buf.value.replace("\x00", "").strip()
+ name_lower = gpu_name.lower()
+
+ # Exclude virtual visualization interfaces and mirror rendering targets
+ if gpu_name and "virtual" not in name_lower and "mirror" not in name_lower:
+ # Omit underpowered integrated legacy Intel lines unless they belong to modern high-performance series
+ if "intel" in name_lower and "graphics" in name_lower:
+ if not ("arc" in name_lower or "iris" in name_lower or "ultra" in name_lower):
+ idx += 1
+ continue
+
+ vram_bytes = 0.0
+ h_key = setupapi.SetupDiOpenDevRegKey(h_dev_info, ctypes.byref(dev_info_data), 0x00000001, 0, 0x00000002, 0x20019)
+ # Extract allocation sizes directly out of active device hardware registry entries
+ if h_key and h_key != int(0xFFFFFFFFFFFFFFFF):
+ size_buf_64 = ctypes.c_ulonglong()
+ size_len = wintypes.DWORD(ctypes.sizeof(size_buf_64))
+ type_buf = wintypes.DWORD()
+ # Attempt reading 64-bit size values
+ ret = advapi32.RegQueryValueExW(h_key, "HardwareInformation.qwMemorySize", None, ctypes.byref(type_buf), ctypes.byref(size_buf_64), ctypes.byref(size_len))
+ if ret == 0 and size_buf_64.value > 0: vram_bytes = size_buf_64.value
+ else:
+ # Fallback to standard 32-bit values if 64-bit property doesn't exist
+ size_buf_32 = wintypes.DWORD()
+ size_len = wintypes.DWORD(ctypes.sizeof(size_buf_32))
+
+ ret = advapi32.RegQueryValueExW(h_key, "HardwareInformation.MemorySize", None, ctypes.byref(type_buf), ctypes.byref(size_buf_32), ctypes.byref(size_len))
+ if ret == 0 and size_buf_32.value > 0: vram_bytes = size_buf_32.value
+
+ advapi32.RegCloseKey(h_key)
+
+ # Add the GPU descriptor to the device list.
+ devices.append(DeviceProperties(gpu_idx, gpu_name, float(vram_bytes)))
+ gpu_idx += 1
+ idx += 1
+
+def mapping_directml(device_id = 0):
+ """
+ Basically, torch_directml prioritizes device 0 as the most powerful GPU available on the system, whereas ONNX Runtime uses the system's actual device indices.
+ This function remaps the ONNX Runtime device indices to match the device ordering used by torch_directml.
+
+ Args:
+ device_id (int): Logical tracking identifier for execution. Defaults to 0.
+
+ Returns:
+ int: Real hardware hardware registry execution target context runtime ID offset index.
+
+ Raises:
+ IndexError: If device_id is completely out of bounds of scanned targets.
+ OSError: If DXGI component initialization fails or factory instantiation falls through.
+ """
+
+ if device_id < 0 or device_id >= len(devices):
+ raise IndexError(f"Provided device index '{device_id}' maps outside bounds of discovered system components.")
+
+ IID_IDXGIFactory1 = GUID_UBYTPE(0x770AAE78, 0xF26F, 0x4DBA, (ctypes.c_ubyte * 8)(0xA8, 0x29, 0x25, 0x3C, 0x83, 0xD1, 0xB3, 0x87))
+
+ try:
+ CreateDXGIFactory1 = ctypes.WinDLL("dxgi.dll").CreateDXGIFactory1
+ except Exception as e:
+ raise OSError(f"Failed to access backend dxgi.dll drivers: {e}")
+
+ CreateDXGIFactory1.argtypes = [ctypes.POINTER(GUID_UBYTPE), ctypes.POINTER(ctypes.c_void_p)]
+ CreateDXGIFactory1.restype = ctypes.c_long
+
+ factory = ctypes.c_void_p()
+ hr = CreateDXGIFactory1(ctypes.byref(IID_IDXGIFactory1), ctypes.byref(factory))
+ if hr != 0: raise OSError(f"Failed to create DXGI factory instance block. ComHRESULT error token: {hex(hr & 0xffffffff)}")
+
+ # Navigate virtual tables manually to call standard DXGI interfaces
+ factory_vtbl = ctypes.cast(factory, ctypes.POINTER(ctypes.POINTER(ctypes.c_void_p))).contents
+ EnumAdapters1 = ctypes.WINFUNCTYPE(ctypes.c_long, ctypes.c_void_p, wintypes.UINT, ctypes.POINTER(ctypes.c_void_p))(factory_vtbl[12])
+ idx = 0
+
+ while 1:
+ adapter = ctypes.c_void_p()
+ hr = EnumAdapters1(factory, idx, ctypes.byref(adapter))
+ if (hr & 0xffffffff) == 0x887A0002: break # DXGI_ERROR_NOT_FOUND signals completion
+
+ desc = DXGI_ADAPTER_DESC1()
+ ctypes.WINFUNCTYPE(ctypes.c_long, ctypes.c_void_p, ctypes.POINTER(DXGI_ADAPTER_DESC1))(ctypes.cast(adapter, ctypes.POINTER(ctypes.POINTER(ctypes.c_void_p))).contents[10])(adapter, ctypes.byref(desc))
+ # Confirm exact text matching to bind accurate identity references
+ if desc.Description.lower() == devices[device_id].name.lower(): return idx
+ idx += 1
+
+ return device_id
+
+def device_count():
+ """
+ Obtains the count of valid acceleration targets registered inside active execution environments.
+
+ Returns:
+ int: Number of hardware units discovered.
+ """
+
+ if (setupapi is None or advapi32 is None) and len(devices) == 0: get_directml_device()
+
+ gpus = len(devices)
+ return (torch_directml.device_count() if gpus == 0 else gpus) if torch_available else 0
+
+def get_device_name(device_id = 0):
+ """
+ Queries descriptive naming configurations representing a given system index target.
+
+ Args:
+ device_id (int): Hardware reference ID. Defaults to 0.
+
+ Returns:
+ str: Discovered string representation of the targeted GPU device model name.
+
+ Raises:
+ RuntimeError: If execution context is triggered but no active targets were detected.
+ ValueError: If requested device offset outranges total hardware inventory.
+ """
+
+ if (setupapi is None or advapi32 is None) and len(devices) == 0: get_directml_device()
+
+ if len(devices) == 0:
+ raise RuntimeError("No hardware acceleration devices available in the current execution environment.")
+
+ if device_id >= 0 and device_id < device_count():
+ try:
+ return devices[device_id].name
+ except:
+ return torch_directml.device_name(device_id)
+ else:
+ raise ValueError(f"Requested hardware reference target index ({device_id}) out of bounds. Found total devices: {device_count()}")
+
+def get_device_properties(device_id = 0):
+ """
+ Extracts the custom property profiles detailing capacities belonging to standard acceleration IDs.
+
+ Args:
+ device_id (int): Hardware target identifier tracking context references. Defaults to 0.
+
+ Returns:
+ DeviceProperties: Configuration object detailing tracking specifications.
+
+ Raises:
+ RuntimeError: If properties are requested but no hardware layer is tracked.
+ ValueError: If requested index falls out of registered hardware profile ranges.
+ """
+
+ if (setupapi is None or advapi32 is None) and len(devices) == 0: get_directml_device()
+
+ if len(devices) == 0:
+ raise RuntimeError("No hardware acceleration devices available in the current execution environment.")
+
+ if device_id >= 0 and device_id < device_count():
+ try:
+ return devices[device_id]
+ except:
+ return DeviceProperties(device_id, torch_directml.device_name(device_id), 0.0)
+ else:
+ raise ValueError(f"Requested hardware properties target index ({device_id}) out of bounds. Found total profiles: {device_count()}")
+
+def is_available():
+ """
+ Checks if DirectML runtime dependencies are valid and accessible.
+
+ Returns:
+ bool: True if acceleration structures pass setup conditions successfully.
+ """
+
+ return torch_directml.is_available() if torch_available else False
+
+def empty_cache():
+ """
+ Invokes external unmanaged native code logic to release unused VRAM/cache buffers,
+ then triggers Python's built-in garbage collector to reclaim system resources.
+
+ C++ source code: ```
+ #include
+
+ #include
+ #include
+ #include
+
+ #include
+
+ using Microsoft::WRL::ComPtr;
+
+ extern "C" __declspec(dllexport)
+ bool empty_cache(UINT adapterIndex)
+ {
+ HRESULT hr;
+
+ ComPtr factory;
+ hr = CreateDXGIFactory1(IID_PPV_ARGS(&factory));
+ if (FAILED(hr)) return false;
+
+ // std::wcout << L"========== GPU List ==========\n";
+
+ // UINT i = 0;
+ // ComPtr adapter;
+
+ // while (factory->EnumAdapters1(i, &adapter) != DXGI_ERROR_NOT_FOUND)
+ // {
+ // DXGI_ADAPTER_DESC1 desc;
+ // adapter->GetDesc1(&desc);
+
+ // std::wcout
+ // << L"[" << i << L"] "
+ // << desc.Description
+ // << L" | Dedicated VRAM: "
+ // << desc.DedicatedVideoMemory / 1024 / 1024
+ // << L" MB"
+ // << std::endl;
+
+ // adapter.Reset();
+ // ++i;
+ // }
+
+ // std::wcout << L"==============================\n";
+
+ ComPtr adapter1;
+
+ hr = factory->EnumAdapters1(adapterIndex, &adapter1);
+ if (FAILED(hr)) return false;
+
+ DXGI_ADAPTER_DESC1 desc;
+ adapter1->GetDesc1(&desc);
+
+ // std::wcout << L"Using GPU: " << desc.Description << std::endl;
+
+ ComPtr adapter3;
+ adapter1.As(&adapter3);
+
+ DXGI_QUERY_VIDEO_MEMORY_INFO before = {};
+ DXGI_QUERY_VIDEO_MEMORY_INFO after = {};
+
+ adapter3->QueryVideoMemoryInfo(
+ 0,
+ DXGI_MEMORY_SEGMENT_GROUP_LOCAL,
+ &before
+ );
+
+ ComPtr device;
+
+ hr = D3D12CreateDevice(
+ adapter1.Get(),
+ D3D_FEATURE_LEVEL_11_0,
+ IID_PPV_ARGS(&device)
+ );
+
+ if (FAILED(hr)) return false;
+ D3D12_COMMAND_QUEUE_DESC queueDesc = {};
+ queueDesc.Type = D3D12_COMMAND_LIST_TYPE_DIRECT;
+
+ ComPtr queue;
+
+ hr = device->CreateCommandQueue(
+ &queueDesc,
+ IID_PPV_ARGS(&queue)
+ );
+
+ if (FAILED(hr)) return false;
+ ComPtr fence;
+
+ hr = device->CreateFence(
+ 0,
+ D3D12_FENCE_FLAG_NONE,
+ IID_PPV_ARGS(&fence));
+
+ if (FAILED(hr))
+ return false;
+
+ HANDLE eventHandle = CreateEvent(nullptr, FALSE, FALSE, nullptr);
+ UINT64 value = 1;
+
+ queue->Signal(fence.Get(), value);
+
+ if (fence->GetCompletedValue() < value)
+ {
+ fence->SetEventOnCompletion(value, eventHandle);
+ WaitForSingleObject(eventHandle, INFINITE);
+ }
+
+ CloseHandle(eventHandle);
+ ComPtr dxgiDevice;
+
+ if (SUCCEEDED(device.As(&dxgiDevice))) dxgiDevice->Trim();
+
+ queue.Reset();
+ fence.Reset();
+ device.Reset();
+
+ adapter3->QueryVideoMemoryInfo(
+ 0,
+ DXGI_MEMORY_SEGMENT_GROUP_LOCAL,
+ &after
+ );
+
+ double beforeMB = before.CurrentUsage / 1024.0 / 1024.0;
+ double afterMB = after.CurrentUsage / 1024.0 / 1024.0;
+
+ // std::cout << "VRAM Before : " << beforeMB << " MB\n";
+ // std::cout << "VRAM After : " << afterMB << " MB\n";
+ // std::cout << "Delta : " << (afterMB - beforeMB) << " MB\n";
+
+ return true;
+ }
+ ```
+ """
+
+ idx = 0
+
+ if torch_available and empty_cache_ctypes is not None and os.path.exists(empty_cache_dll):
+ while 1:
+ results = empty_cache_ctypes.empty_cache(idx)
+ idx += 1
+
+ if not bool(results): break
+
+ gc.collect()
+
+def forward_dml(ctx, x, scale):
+ """Newer versions of DirectML can function well without this component; it was developed based on the original RVC."""
+
+ ctx.scale = scale
+ res = x.clone().detach()
+ return res
+
+def cpu_conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
+ """
+ Wrapper function routing specific structural 1D convolution calculations through the CPU.
+ Fixes compatibility errors since certain DirectML frameworks.
+ """
+
+ return orig_conv_transpose1d(
+ input.cpu(),
+ weight.cpu(),
+ bias.cpu() if bias is not None else bias,
+ stride,
+ padding,
+ output_padding,
+ groups,
+ dilation
+ ).to(input.device)
+
+# Patch classes and functions to ensure everything operates correctly
+if torch_available:
+ torch.nn.GRU = GRU # The GRU layer does not work on this backend, so I switched to a GRU Wrapper layer
+ torch.inference_mode = torch.no_grad # Using `inference_mode` causes errors in some cases; therefore, I switched to using `no_grad`
+ fairseq.GradMultiply.forward = forward_dml # Patch specialized forward properties
+ torch.nn.functional.conv_transpose1d = cpu_conv_transpose1d # This function might not work or may not function correctly, so it should be moved to the CPU
\ No newline at end of file
diff --git a/main/library/backends/opencl.py b/main/library/backends/opencl.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b497bfda873b47c0bf06bae433329fd7d589c24
--- /dev/null
+++ b/main/library/backends/opencl.py
@@ -0,0 +1,278 @@
+import os
+import sys
+import torch
+import ctypes
+
+# Use these backends only if the library is installed.
+try:
+ import pytorch_ocl
+except:
+ pytorch_ocl = None
+
+sys.path.append(os.getcwd())
+
+from main.library.backends.utils import GRU, DeviceProperties
+
+# Global variables tracking OpenCL state and devices
+devices = []
+torch_available = pytorch_ocl != None
+adaptive_orig, softmax_orig, opencl = None, None, None
+
+# Backup original PyTorch operations for future reference or fallbacks
+if torch_available:
+ adaptive_orig = torch.nn.AdaptiveAvgPool2d
+ softmax_orig = torch.nn.functional.softmax
+
+def get_opencl_lib():
+ """
+ Loads the system-specific OpenCL dynamic link library (.dll/.so).
+
+ This function updates the global `opencl` variable. It handles cross-platform
+ loading for Windows and Linux architectures.
+
+ Raises:
+ OSError: If the OpenCL runtime library cannot be located or loaded on the host system.
+ """
+
+ global opencl
+
+ if sys.platform == "win32":
+ try:
+ opencl = ctypes.CDLL("OpenCL.dll")
+ except:
+ raise OSError("Failed to load OpenCL.dll. Ensure OpenCL drivers are installed.")
+ elif sys.platform == "linux":
+ for path in ["libOpenCL.so", "libOpenCL.so.1", "/usr/lib/x86_64-linux-gnu/libOpenCL.so.1"]:
+ try:
+ opencl = ctypes.CDLL(path)
+ break
+ except OSError:
+ continue
+
+ if not opencl: raise OSError("OpenCL shared library (.so) not found in standard Linux paths.")
+ else: return
+
+def get_opencl_device():
+ """
+ Queries and populates the list of available OpenCL compute devices.
+
+ Uses ctypes to interface directly with the OpenCL runtime API, extracting
+ vendor metadata, device names, board names (for AMD devices), and available VRAM.
+ """
+
+ global devices
+
+ # Short-circuit if devices are already cached, torch is missing, or library fails to load
+ if len(devices) >= 1: return
+ if not torch_available: return
+ if opencl is None: get_opencl_lib()
+
+ # Define strict argument signatures for C-functions to prevent memory corruption
+ opencl.clGetPlatformIDs.argtypes = [ctypes.c_uint32, ctypes.POINTER(ctypes.c_void_p), ctypes.POINTER(ctypes.c_uint32)]
+ opencl.clGetPlatformInfo.argtypes = [ctypes.c_void_p, ctypes.c_uint32, ctypes.c_size_t, ctypes.c_void_p, ctypes.POINTER(ctypes.c_size_t)]
+ opencl.clGetDeviceIDs.argtypes = [ctypes.c_void_p, ctypes.c_uint64, ctypes.c_uint32, ctypes.POINTER(ctypes.c_void_p), ctypes.POINTER(ctypes.c_uint32)]
+ opencl.clGetDeviceInfo.argtypes = [ctypes.c_void_p, ctypes.c_uint32, ctypes.c_size_t, ctypes.c_void_p, ctypes.POINTER(ctypes.c_size_t)]
+
+ # Fetch total number of platforms available
+ num_platforms = ctypes.c_uint32(0)
+ opencl.clGetPlatformIDs(0, None, ctypes.byref(num_platforms))
+ if num_platforms.value == 0: return
+
+ idx = 0
+ platforms = (ctypes.c_void_p * num_platforms.value)()
+ opencl.clGetPlatformIDs(num_platforms.value, platforms, None)
+
+ # Loop through each discovered platform
+ for p in platforms:
+ if p is None: continue
+
+ # Query platform Vendor Name (Parameter ID: 0x0903)
+ param_size = ctypes.c_size_t(0)
+ opencl.clGetPlatformInfo(p, 0x0903, 0, None, ctypes.byref(param_size))
+ v_name = ctypes.create_string_buffer(param_size.value)
+ opencl.clGetPlatformInfo(p, 0x0903, param_size.value, v_name, None)
+ vendor_str = v_name.value.decode('utf-8', errors='ignore').lower()
+
+ # Query devices belonging to this specific platform (0xFFFFFFFF handles CL_DEVICE_TYPE_ALL)
+ num_devices = ctypes.c_uint32(0)
+ res = opencl.clGetDeviceIDs(p, 0xFFFFFFFF, 0, None, ctypes.byref(num_devices))
+ if res != 0 or num_devices.value == 0: continue
+
+ ocl_devices = (ctypes.c_void_p * num_devices.value)()
+ opencl.clGetDeviceIDs(p, 0xFFFFFFFF, num_devices.value, ocl_devices, None)
+
+ # Loop through each device on the platform
+ for d in ocl_devices:
+ if d is None: continue
+
+ # Query individual Device Name (Parameter ID: 0x102B)
+ opencl.clGetDeviceInfo(d, 0x102B, 0, None, ctypes.byref(param_size))
+ d_name = ctypes.create_string_buffer(param_size.value)
+ opencl.clGetDeviceInfo(d, 0x102B, param_size.value, d_name, None)
+ d_name = d_name.value.decode('utf-8', errors='ignore').strip()
+
+ # AMD-specific extension to fetch the precise Board Name (Parameter ID: 0x4038)
+ if "advanced micro devices" in vendor_str or "amd" in vendor_str:
+ try:
+ opencl.clGetDeviceInfo(d, 0x4038, 0, None, ctypes.byref(param_size))
+
+ if param_size.value > 0:
+ board = ctypes.create_string_buffer(param_size.value)
+ opencl.clGetDeviceInfo(d, 0x4038, param_size.value, board, None)
+ board = board.value.decode('utf-8', errors='ignore').strip()
+ if board: d_name = f"{board} ({d_name})"
+ except:
+ pass # Non-critical error fallback if extensions fail
+
+ # Query global device VRAM size in bytes (Parameter ID: 0x101F)
+ vram_bytes = ctypes.c_uint64(0)
+ opencl.clGetDeviceInfo(d, 0x101F, ctypes.sizeof(vram_bytes), ctypes.byref(vram_bytes), None)
+
+ # Add the GPU descriptor to the device list.
+ devices.append(DeviceProperties(idx, d_name, float(vram_bytes.value)))
+ idx += 1
+
+def device_count():
+ """
+ Returns the total number of detected OpenCL devices.
+
+ Returns:
+ int: The number of tracked OpenCL devices if PyTorch backend is available, otherwise 0.
+ """
+
+ if opencl is None and len(devices) == 0: get_opencl_device()
+
+ return len(devices) if torch_available else 0
+
+def get_device_name(device_id = 0):
+ """
+ Retrieves the human-readable string name of a specific OpenCL device.
+
+ Args:
+ device_id (int, optional): Zero-indexed identifier of the target device. Defaults to 0.
+
+ Returns:
+ str: The designated name of the requested compute device.
+
+ Raises:
+ RuntimeError: If no hardware devices are registered in the current context.
+ ValueError: If the `device_id` falls outside the bounds of discovered devices.
+ """
+
+ if opencl is None and len(devices) == 0: get_opencl_device()
+
+ if len(devices) == 0:
+ raise RuntimeError("No OpenCL devices found on the system.")
+
+ if device_id >= 0 and device_id < device_count():
+ return devices[device_id].name
+ else:
+ raise ValueError(f"Device ID {device_id} is out of bounds. Available count: {device_count()}.")
+
+def get_device_properties(device_id = 0):
+ if opencl is None and len(devices) == 0: get_opencl_device()
+
+ if len(devices) == 0:
+ raise RuntimeError("No OpenCL devices found on the system.")
+
+ if device_id >= 0 and device_id < device_count():
+ return devices[device_id]
+ else:
+ raise ValueError(f"Device ID {device_id} is out of bounds. Available count: {device_count()}.")
+
+def is_available():
+ """
+ Checks whether OpenCL acceleration is active and usable.
+
+ Returns:
+ bool: True if at least one device is found and the backend is ready; otherwise False.
+ """
+
+ return (device_count() > 0) if torch_available else False
+
+def empty_cache():
+ """
+ Triggers the underlying PyTorch OpenCL backend memory cache garbage collection.
+ Flushes unused allocations to reclaim hardware memory segments.
+ """
+
+ if opencl is None and len(devices) == 0: get_opencl_device()
+
+ if torch_available:
+ pytorch_ocl.empty_cache()
+ else:
+ return
+
+def group_norm(x, num_groups, weight=None, bias=None, eps=1e-5):
+ """
+ Applies Group Normalization over a mini-batch of inputs.
+
+ Args:
+ x (torch.Tensor): Input tensor of shape (N, C, *).
+ num_groups (int): Number of separate groups to split the channels into.
+ weight (torch.Tensor, optional): Scale parameter tensor of shape (C). Defaults to None.
+ bias (torch.Tensor, optional): Offset parameter tensor of shape (C). Defaults to None.
+ eps (float, optional): Value added to the denominator for numerical stability. Defaults to 1e-5.
+
+ Returns:
+ torch.Tensor: The normalized tensor with identical dimensions to the input `x`.
+ """
+
+ N, C = x.shape[:2]
+ assert C % num_groups == 0
+
+ shape = (N, num_groups, C // num_groups) + x.shape[2:]
+ x_reshaped = x.view(shape)
+ # Calculate mean and variance over the spatial and intra-group channel axis dimensions
+ dims = (2,) + tuple(range(3, x_reshaped.dim()))
+ mean = x_reshaped.mean(dim=dims, keepdim=True)
+ var = x_reshaped.var(dim=dims, keepdim=True, unbiased=False)
+
+ x_norm = (x_reshaped - mean) / (var + eps).sqrt()
+ x_norm = x_norm.view_as(x)
+
+ if weight is not None: # Apply scaling transformation weights if supplied
+ weight = weight.view(1, C, *([1] * (x.dim() - 2)))
+ x_norm = x_norm * weight
+
+ if bias is not None: # Apply positional translation bias adjustments if supplied
+ bias = bias.view(1, C, *([1] * (x.dim() - 2)))
+ x_norm = x_norm + bias
+
+ return x_norm
+
+def script(f, *_, **__):
+ """
+ Mock decorator implementation replacing standard JIT compiling scripts.
+ """
+
+ f.graph = pytorch_ocl.torch._C.Graph()
+ return f
+
+def AdaptiveAvgPool2d(input):
+ """
+ Wrapper for ``torch.nn.AdaptiveAvgPool2d`` that accepts tuple inputs.
+
+ Args:
+ input (Any | tuple): Output size or a tuple containing the output size as its first element.
+
+ Returns:
+ torch.nn.AdaptiveAvgPool2d: Configured adaptive average pooling module.
+ """
+
+ input = input[0] if isinstance(input, tuple) else input
+ return adaptive_orig(input)
+
+def softmax_cpu(input, dim = None, _stacklevel = 3, dtype = None):
+ """softmax is executed on the CPU because the OpenCL implementation may produce incorrect results on NVIDIA GPUs and potentially other GPUs as well."""
+
+ return softmax_orig(input.cpu(), dim, _stacklevel, dtype).to(input.device)
+
+# Patch classes and functions to ensure everything operates correctly
+if torch_available:
+ torch.nn.GRU = GRU # The GRU layer does not work on this backend, so I switched to a GRU Wrapper layer.
+ torch.jit.script = script
+ torch.inference_mode = torch.no_grad # Using `inference_mode` causes errors in some cases; therefore, I switched to using `no_grad`
+ torch.nn.functional.softmax = softmax_cpu # The softmax function was not working correctly on NVIDIA hardware, so it has been replaced.
+ torch.nn.functional.group_norm = group_norm # GroupNorm was not working correctly, so I replaced it.
+ torch.nn.AdaptiveAvgPool2d = AdaptiveAvgPool2d
\ No newline at end of file
diff --git a/main/library/backends/utils.py b/main/library/backends/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f45626def63a6414a2c744c8214309c2b461ed3b
--- /dev/null
+++ b/main/library/backends/utils.py
@@ -0,0 +1,392 @@
+import torch
+
+import numpy as np
+import torch.nn.functional as F
+
+from librosa.util import pad_center
+from scipy.signal import get_window
+
+class STFT(torch.nn.Module):
+ """
+ Short-Time Fourier Transform (STFT) module implemented as a PyTorch Module.
+ This class handles both the forward transform (STFT) and inverse transform (iSTFT)
+ using 1D convolutions and linear matrix operations.
+ """
+
+ def __init__(
+ self,
+ filter_length=1024,
+ hop_length=512,
+ win_length=None,
+ window="hann",
+ pad_mode="reflect"
+ ):
+ """
+ Initializes the STFT module and pre-computes the Fourier basis filters.
+
+ Args:
+ filter_length (int): Length of the FFT window. Default: 1024.
+ hop_length (int): Number of audio samples between adjacent STFT columns. Default: 512.
+ win_length (int, optional): Window size. If None, it defaults to filter_length.
+ window (str): Type of window function supported by scipy.signal.get_window. Default: "hann".
+ pad_mode (str): Padding mode for signal boundaries. Default: "reflect".
+ """
+
+ super(STFT, self).__init__()
+ self.filter_length = filter_length
+ self.hop_length = hop_length
+ self.pad_amount = int(self.filter_length / 2)
+ self.cutoff = int(self.filter_length / 2 + 1) # Nyquist frequency bin index
+ self.win_length = win_length
+ self.pad_mode = pad_mode
+
+ # Generate standard Discrete Fourier Transform (DFT) matrix using NumPy
+ fourier_basis = np.fft.fft(
+ np.eye(self.filter_length)
+ )
+ # Split into real and imaginary parts up to the Nyquist frequency (cutoff)
+ fourier_basis = np.vstack([
+ np.real(fourier_basis[:self.cutoff, :]),
+ np.imag(fourier_basis[:self.cutoff, :])
+ ])
+
+ # Convert the basis matrices into PyTorch FloatTensors
+ forward_basis = torch.FloatTensor(fourier_basis)
+ # Compute the Moore-Penrose pseudo-inverse for the inverse transform
+ inverse_basis = torch.FloatTensor(np.linalg.pinv(fourier_basis))
+
+ # Handle fallback for window length
+ if win_length is None or not win_length: win_length = filter_length
+ assert filter_length >= win_length
+
+ # Generate the window function and pad it to match filter_length
+ fft_window = torch.from_numpy(
+ pad_center(
+ get_window(
+ window,
+ win_length,
+ fftbins=True
+ ),
+ size=filter_length
+ )
+ ).float()
+
+ # Apply the windowing function to both forward and inverse bases
+ forward_basis *= fft_window
+ inverse_basis = (inverse_basis.T * fft_window).T
+ # Register bases as buffers so they move automatically to GPU alongside the module
+ self.register_buffer("forward_basis", forward_basis.float().unsqueeze(1))
+ self.register_buffer("inverse_basis", inverse_basis.float())
+ self.register_buffer("fft_window", fft_window.float())
+
+ def transform(
+ self,
+ input_data,
+ eps=1e-9,
+ return_phase=False,
+ center=True
+ ):
+ """
+ Transforms a 1D time-domain signal into its frequency-domain magnitude (and optional phase).
+
+ Args:
+ input_data (Tensor): Input signal of shape (Batch, Time).
+ eps (float): Small constant to avoid square root of zero or log issues. Default: 1e-9.
+ return_phase (bool): If True, returns both magnitude and phase. Default: False.
+ center (bool): If True, pads the input signal symmetrically. Default: True.
+
+ Returns:
+ Tensor or Tuple[Tensor, Tensor]: Magnitude spectrogram, or (magnitude, phase).
+ """
+
+ # Symmetrically pad the input signal if center is enabled
+ if center:
+ input_data = F.pad(
+ input_data,
+ (self.pad_amount, self.pad_amount),
+ mode=self.pad_mode
+ )
+
+ # Compute STFT using 1D Convolution (acting as a bank of filters)
+ forward_transform = F.conv1d(
+ input_data.unsqueeze(1),
+ self.forward_basis,
+ stride=self.hop_length
+ )
+
+ # Separate the real and imaginary components from the convolution channels
+ real_part = forward_transform[:, :self.cutoff, :]
+ imag_part = forward_transform[:, self.cutoff:, :]
+
+ # Calculate the magnitude spectrogram
+ magnitude = (real_part**2 + imag_part**2 + eps).sqrt()
+
+ # Compute phase if requested using atan2
+ if return_phase:
+ phase = imag_part.data.atan2(real_part.data)
+ return magnitude, phase
+
+ return magnitude
+
+ def inverse(
+ self,
+ magnitude,
+ phase
+ ):
+ """
+ Reconstructs the 1D time-domain signal from its magnitude and phase spectrograms (iSTFT).
+
+ Args:
+ magnitude (Tensor): Magnitude spectrogram of shape (Batch, Freq, Time).
+ phase (Tensor): Phase spectrogram of shape (Batch, Freq, Time).
+
+ Returns:
+ Tensor: Reconstructed 1D time-domain signal.
+ """
+
+ # Reconstruct real and imaginary parts, then concatenate along the channel dimension
+ cat = torch.cat([
+ magnitude * phase.cos(),
+ magnitude * phase.sin()
+ ], dim=1)
+
+ # Initialize the Overlap-Add operator (Fold) to reconstruct the original timeline
+ fold = torch.nn.Fold(
+ output_size=(1, (cat.size(-1) - 1) * self.hop_length + self.filter_length),
+ kernel_size=(1, self.filter_length),
+ stride=(1, self.hop_length)
+ )
+
+ # Apply inverse basis matrix and project overlapping frames back to 1D signal space
+ inverse_transform = fold(
+ self.inverse_basis @ cat
+ )[:, 0, 0, self.pad_amount : -self.pad_amount]
+
+ # Calculate the sum of squared windows to compensate for windowing gain artifacts
+ window_square_sum = (
+ fold(
+ self.fft_window.cpu().pow(2).repeat(cat.size(-1), 1).T.unsqueeze(0) # Workaround for OpenCL backend devices if applicable
+ )
+ ) if str(cat.device).startswith("ocl") else (
+ fold(
+ self.fft_window.pow(2).repeat(cat.size(-1), 1).T.unsqueeze(0) # Standard routine for DML/Other devices
+ )
+ )[:, 0, 0, self.pad_amount : -self.pad_amount].to(cat.device)
+
+ # Normalize the inverse signal by dividing by the window square sum
+ return inverse_transform / window_square_sum
+
+class GRU(torch.nn.RNNBase):
+ """
+ A custom implementation of the Gated Recurrent Unit (GRU) extending torch.nn.RNNBase.
+ Provides customizable cell-level step looping over time sequences.
+ """
+
+ def __init__(
+ self,
+ input_size,
+ hidden_size,
+ num_layers=1,
+ bias=True,
+ batch_first=True,
+ dropout=0.0,
+ bidirectional=False,
+ device=None,
+ dtype=None
+ ):
+ """
+ Initializes the custom GRU layer configurations.
+ """
+
+ super().__init__(
+ "GRU",
+ input_size=input_size,
+ hidden_size=hidden_size,
+ num_layers=num_layers,
+ bias=bias,
+ batch_first=batch_first,
+ dropout=dropout,
+ bidirectional=bidirectional,
+ device=device,
+ dtype=dtype
+ )
+
+ def _gru_layer(self, x, hx, weights):
+ """
+ Processes a single directional GRU layer across all time steps.
+
+ Args:
+ x (Tensor): Input tensor of shape (Batch, Time, Input_Dim).
+ hx (Tensor): Initial hidden state of shape (Batch, Hidden_Dim).
+ weights (tuple): Contains weight matrices and bias tensors (weight_ih, weight_hh, bias_ih, bias_hh).
+
+ Returns:
+ Tuple[Tensor, Tensor]: All hidden state outputs across time steps and the final hidden state.
+ """
+
+ weight_ih, weight_hh, bias_ih, bias_hh = weights
+ B, T, _ = x.shape
+ # Precompute the input-to-hidden linear projection for all time steps at once
+ gate_x = F.linear(x, weight_ih, bias_ih)
+ outputs = torch.empty(B, T, self.hidden_size, device=x.device, dtype=x.dtype)
+
+ # Segment hidden-to-hidden biases into update, reset, and new gates
+ if bias_hh is not None: b_hr, b_hz, b_hn = bias_hh.chunk(3)
+ else: b_hr = b_hz = b_hn = None
+
+ # Segment hidden-to-hidden weights into update, reset, and new gates
+ w_hr, w_hz, w_hn = weight_hh.chunk(3, dim=0)
+
+ # Explicit loop over each chronological time-step
+ for t in range(T):
+ # Split the input-to-hidden projections into reset (r), update (z), and new (n) chunks
+ x_r, x_z, x_n = gate_x[:, t].chunk(3, dim=1)
+ # Compute Reset Gate and Update Gate activations
+ r = (x_r + F.linear(hx, w_hr, b_hr)).sigmoid()
+ z = (x_z + F.linear(hx, w_hz, b_hz)).sigmoid()
+ # Compute Candidate/New Gate state activation
+ n = (x_n + r * F.linear(hx, w_hn, b_hn)).tanh()
+ # Linear interpolation for the final hidden state output of this step
+ hx = n + z * (hx - n)
+ outputs[:, t] = hx
+
+ return outputs, hx
+
+ def _gru(self, x, hx):
+ """
+ Iterates and processes the multi-layer (and optionally bidirectional) GRU architecture.
+
+ Args:
+ x (Tensor): Input feature sequences.
+ hx (Tensor): Hidden state collections.
+
+ Returns:
+ Tuple[Tensor, Tensor]: Output sequences and aggregated stacked final hidden states.
+ """
+
+ # Permute if data structure is not configured as batch-first
+ if not self.batch_first: x = x.permute(1, 0, 2)
+ num_directions = 2 if self.bidirectional else 1
+
+ h_n = []
+ output_fwd, output_bwd = x, x
+
+ for layer in range(self.num_layers):
+ fwd_idx = layer * num_directions
+ bwd_idx = fwd_idx + 1 if self.bidirectional else None
+ # Fetch parameters for the forward layer direction
+ weights_fwd = self._get_weights(fwd_idx)
+ h_fwd = hx[fwd_idx]
+
+ # Execute forward sequence processing
+ out_fwd, h_out_fwd = self._gru_layer(
+ output_fwd,
+ h_fwd,
+ weights_fwd
+ )
+
+ h_n.append(h_out_fwd)
+
+ # Conditional check and execution path for bidirectional layering
+ if self.bidirectional:
+ weights_bwd = self._get_weights(bwd_idx)
+ h_bwd = hx[bwd_idx]
+
+ # Flip time dimension for backward pass execution
+ reversed_input = output_bwd.flip(dims=[1])
+ out_bwd, h_out_bwd = self._gru_layer(
+ reversed_input,
+ h_bwd,
+ weights_bwd
+ )
+
+ # Re-reverse output back to original chronological alignment
+ out_bwd = out_bwd.flip(dims=[1])
+ h_n.append(h_out_bwd)
+
+ # Concatenate forward and backward channel outputs together
+ output_fwd = torch.cat([out_fwd, out_bwd], dim=2)
+ output_bwd = output_fwd
+ else: output_fwd = out_fwd
+
+ # Apply dropout normalization on inner stacked hidden layers
+ if layer < self.num_layers - 1 and self.dropout > 0:
+ output_fwd = F.dropout(
+ output_fwd,
+ p=self.dropout,
+ training=self.training
+ )
+
+ if self.bidirectional: output_bwd = output_fwd
+
+ output = output_fwd
+ h_n = torch.stack(h_n, dim=0)
+
+ # Restore original non batch-first sequence ordering if applicable
+ if not self.batch_first: output = output.permute(1, 0, 2)
+ return output, h_n
+
+ def _get_weights(self, layer_idx):
+ """
+ Helper method to look up weight and bias parameters from internal structures.
+ """
+
+ weights = self._all_weights[layer_idx]
+
+ weight_ih = getattr(self, weights[0])
+ weight_hh = getattr(self, weights[1])
+
+ bias_ih = getattr(self, weights[2]) if self.bias else None
+ bias_hh = getattr(self, weights[3]) if self.bias else None
+
+ return weight_ih, weight_hh, bias_ih, bias_hh
+
+ def forward(self, input, hx=None):
+ """
+ Defines the computation performed at every call.
+
+ Args:
+ input (Tensor): Input tensor.
+ hx (Tensor, optional): Initial hidden state.
+
+ Raises:
+ ValueError: If input tensor does not have exactly 3 dimensions.
+ """
+
+ if input.dim() != 3: raise ValueError(f"Expected 3D input tensor, but got {input.dim()}D tensor instead.")
+
+ batch_size = input.size(0) if self.batch_first else input.size(1)
+ num_directions = 2 if self.bidirectional else 1
+
+ # Initialize zero states default tensor if hx isn't explicitly supplied
+ if hx is None:
+ hx = torch.zeros(
+ self.num_layers * num_directions,
+ batch_size,
+ self.hidden_size,
+ dtype=input.dtype,
+ device=input.device
+ )
+
+ # Built-in PyTorch sanity check tool verifying valid parameter alignments
+ self.check_forward_args(input, hx, batch_sizes=None)
+ return self._gru(input, hx)
+
+class DeviceProperties:
+ """
+ A simple data structure class representing backend compute hardware specifications.
+ """
+
+ def __init__(self, index, name, total_memory):
+ """
+ Initializes DeviceProperties configuration properties.
+
+ Args:
+ index (int): Hardware identifier index assigned to the device.
+ name (str): Device model naming moniker.
+ total_memory (int or float): Total hardware runtime memory limit availability.
+ """
+
+ self.index = index
+ self.name = name
+ self.total_memory = total_memory
\ No newline at end of file
diff --git a/main/library/backends/xpu.py b/main/library/backends/xpu.py
new file mode 100644
index 0000000000000000000000000000000000000000..eccd06b9e3710006e494b8a69b198af832f76595
--- /dev/null
+++ b/main/library/backends/xpu.py
@@ -0,0 +1,214 @@
+import torch
+
+from collections import defaultdict
+from torch.amp.grad_scaler import _MultiDeviceReplicator, OptState, _refresh_per_optimizer_state
+
+def _amp_update_scale_(input, growth_tracker, found_inf, growth_factor=2.0, backoff_factor=0.5, growth_interval=2000):
+ """
+ Updates the scaling factor based on whether infs/NaNs were found in the gradients.
+
+ If infs are found, the scale is multiplied by backoff_factor and the growth tracker resets.
+ If no infs are found, the growth tracker increments, and the scale is multiplied by
+ growth_factor once the tracker reaches the growth_interval.
+
+ Args:
+ input (Tensor): The current scale tensor to be updated in-place.
+ growth_tracker (Tensor): Tensor tracking consecutive steps without infs/NaNs.
+ found_inf (Tensor): Scalar tensor indicating if infs/NaNs were found (> 0).
+ growth_factor (float): Multiplier used to increase the scale. Default: 2.0.
+ backoff_factor (float): Multiplier used to decrease the scale. Default: 0.5.
+ growth_interval (int): Number of inf-free steps required to grow the scale. Default: 2000.
+
+ Returns:
+ Tensor: The updated scale tensor.
+ """
+
+ # If infs/NaNs were detected in gradients, decrease the scale immediately
+ if found_inf > 0:
+ input.mul_(backoff_factor)
+ growth_tracker.zero_() # Reset the consecutive successful steps tracker
+ else:
+ # Increment successful step counter
+ growth_tracker.add_(1)
+ # Grow the scale factor if interval milestone is reached
+ if growth_tracker >= growth_interval:
+ input.mul_(growth_factor)
+ growth_tracker.zero_() # Reset tracker after growing scale
+
+ return input
+
+def _unscale_grads_(self, optimizer, inv_scale, found_inf, allow_fp16):
+ """
+ Unscales the gradients of the optimizer's parameters using the inverse scale.
+
+ Also checks for infs/NaNs across multiple devices and data types.
+
+ Args:
+ optimizer (torch.optim.Optimizer): The optimizer holding parameters to unscale.
+ inv_scale (Tensor): Reciprocal of the current scale factor.
+ found_inf (Tensor): Scalar tensor to store whether an inf/NaN was found.
+ allow_fp16 (bool): If False, raises an error if float16 gradients are encountered.
+ """
+
+ # Replicate scales and inf trackers across target devices automatically
+ per_device_inv_scale = _MultiDeviceReplicator(inv_scale)
+ per_device_found_inf = _MultiDeviceReplicator(found_inf)
+
+ # Structure to pool gradients dynamically by device and data type
+ per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))
+
+ with torch.no_grad():
+ for group in optimizer.param_groups:
+ for param in group["params"]:
+ if not isinstance(param, torch.Tensor): raise AssertionError(f"expected param to be torch.Tensor, got {type(param).__name__}")
+ if param.grad is None: continue
+ if (not allow_fp16) and param.grad.dtype == torch.float16: raise ValueError("Attempting to unscale FP16 gradients.")
+
+ # Handle sparse matrix conversions cleanly
+ if param.grad.is_sparse:
+ if param.grad.dtype is torch.float16: param.grad = param.grad.coalesce()
+ to_unscale = param.grad._values()
+ else: to_unscale = param.grad
+
+ # Bucketize reference tensor configurations
+ per_device_and_dtype_grads[to_unscale.device][to_unscale.dtype].append(to_unscale)
+
+ # Batch execute in-place unscaling routine using PyTorch specialized internal C++ extensions
+ for device, per_dtype_grads in per_device_and_dtype_grads.items():
+ for grads in per_dtype_grads.values():
+ torch._amp_foreach_non_finite_check_and_unscale_(grads, per_device_found_inf.get(device), per_device_inv_scale.get(device))
+
+ return per_device_found_inf._per_device_tensors
+
+def unscale_(self, optimizer):
+ """
+ Divides ("unscales") the optimizer's gradient tensors by the scale factor.
+
+ :meth:`unscale_` is optional, serving cases where you need to
+ :ref:`modify or inspect gradients`
+ between the backward pass(es) and :meth:`step`.
+ If :meth:`unscale_` is not called explicitly, gradients will be unscaled automatically during :meth:`step`.
+
+ Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients::
+
+ ...
+ scaler.scale(loss).backward()
+ scaler.unscale_(optimizer)
+ torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
+ scaler.step(optimizer)
+ scaler.update()
+
+ Args:
+ optimizer (torch.optim.Optimizer): Optimizer that owns the gradients to be unscaled.
+
+ .. note::
+ :meth:`unscale_` does not incur a CPU-GPU sync.
+
+ .. warning::
+ :meth:`unscale_` should only be called once per optimizer per :meth:`step` call,
+ and only after all gradients for that optimizer's assigned parameters have been accumulated.
+ Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError.
+
+ .. warning::
+ :meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute.
+ """
+
+ if not self._enabled: return
+ # Check internal scale and growth tracker state validity
+ self._check_scale_growth_tracker("unscale_")
+ optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+ if optimizer_state["stage"] is OptState.UNSCALED: raise RuntimeError("unscale_() has already been called on this optimizer since the last update().")
+ elif optimizer_state["stage"] is OptState.STEPPED: raise RuntimeError("unscale_() is being called after step().")
+
+ if self._scale is None: raise AssertionError("_scale is None in unscale_")
+
+ # Calculate multiplier factor inverses
+ inv_scale = self._scale.reciprocal()
+ found_inf = torch.full((), 0.0, dtype=torch.float32, device=self._scale.device)
+
+ # Process back-end math executions
+ optimizer_state["found_inf_per_device"] = self._unscale_grads_(optimizer, inv_scale, found_inf, False)
+ optimizer_state["stage"] = OptState.UNSCALED
+
+def update(self, new_scale = None):
+ """
+ Update the scale factor.
+
+ If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
+ to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
+ the scale is multiplied by ``growth_factor`` to increase it.
+
+ Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
+ used directly, it's used to fill GradScaler's internal scale tensor. So if
+ ``new_scale`` was a tensor, later in-place changes to that tensor will not further
+ affect the scale GradScaler uses internally.)
+
+ Args:
+ new_scale (float or :class:`torch.Tensor`, optional, default=None): New scale factor.
+
+ .. warning::
+ :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
+ been invoked for all optimizers used this iteration.
+
+ .. warning::
+ For performance reasons, we do not check the scale factor value to avoid synchronizations,
+ so the scale factor is not guaranteed to be above 1. If the scale falls below 1 and/or
+ you are seeing NaNs in your gradients or loss, something is likely wrong. For example,
+ bf16-pretrained models are often incompatible with AMP/fp16 due to differing dynamic ranges.
+ """
+
+ if not self._enabled: return
+
+ _scale, _growth_tracker = self._check_scale_growth_tracker("update")
+
+ if new_scale is not None:
+ if self._scale is None: raise AssertionError("_scale is None in update")
+
+ if isinstance(new_scale, float): self._scale.fill_(new_scale)
+ else:
+ reason = "new_scale should be a float or a 1-element torch.cuda.FloatTensor or torch.FloatTensor with requires_grad=False."
+
+ if new_scale.device.type != self._device: raise AssertionError(reason)
+ if new_scale.numel() != 1: raise AssertionError(reason)
+ if new_scale.requires_grad is True: raise AssertionError(reason)
+
+ self._scale.copy_(new_scale)
+ else:
+ # Pull across distributed device tensors to central hub for examination
+ found_infs = [found_inf.to(device=_scale.device, non_blocking=True) for state in self._per_optimizer_states.values() for found_inf in state["found_inf_per_device"].values()]
+ if len(found_infs) == 0: raise AssertionError("No inf checks were recorded prior to update.")
+
+ # Combine all sub-tensors together to isolate failures
+ found_inf_combined = found_infs[0]
+ if len(found_infs) > 1:
+ for i in range(1, len(found_infs)):
+ found_inf_combined += found_infs[i]
+
+ # Trigger analytical mathematical adjustments
+ _amp_update_scale_(_scale, _growth_tracker, found_inf_combined, self._growth_factor, self._backoff_factor, self._growth_interval)
+
+ # Flush current memory maps, refreshing structures ahead of subsequent backprop passes
+ self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
+
+def setup_gradscaler():
+ """
+ Monkey-patches the custom internal scaling methods directly onto the core
+ native PyTorch `GradScaler` architecture classes.
+ """
+
+ torch.amp.grad_scaler.GradScaler._unscale_grads_ = _unscale_grads_
+ torch.amp.grad_scaler.GradScaler.unscale_ = unscale_
+ torch.amp.grad_scaler.GradScaler.update = update
+
+def setup_onnxruntime_xpu():
+ """
+ Attempts to inject OpenVINO Windows path libraries dynamically into the system
+ environment, safeguarding against failures via fallback catch blocks.
+ """
+
+ try:
+ import onnxruntime.tools.add_openvino_win_libs as utils
+ utils.add_openvino_libs_to_path()
+ except:
+ pass
\ No newline at end of file
diff --git a/main/library/backends/zluda.py b/main/library/backends/zluda.py
new file mode 100644
index 0000000000000000000000000000000000000000..370af94c634f3264de25cc45415f014bcbe389af
--- /dev/null
+++ b/main/library/backends/zluda.py
@@ -0,0 +1,59 @@
+import torch
+
+def init_zluda():
+ """
+ Initializes a compatibility environment patch for ZLUDA (CUDA on AMD GPUs).
+
+ This function monkey-patches PyTorch's STFT/iSTFT operations to execute on the CPU
+ as a workaround for hardware backend driver limitations, and configures specific
+ accelerator backend flags for stable execution.
+ """
+
+ # Store references to the original native PyTorch STFT and iSTFT operations
+ _torch_stft = torch.stft
+ _torch_istft = torch.istft
+
+ def z_stft(input, window, *args, **kwargs):
+ """
+ ZLUDA wrapper for Short-Time Fourier Transform (STFT).
+
+ Forces computation onto the CPU to avoid backend driver crashes,
+ then projects the final tensor back onto the original target device.
+ """
+
+ # Offload tensors to CPU, execute STFT, and cast the output back to the original device
+ return _torch_stft(
+ input=input.cpu(), window=window.cpu(), *args, **kwargs
+ ).to(input.device)
+
+ def z_istft(input, window, *args, **kwargs):
+ """
+ ZLUDA wrapper for Inverse Short-Time Fourier Transform (iSTFT).
+
+ Forces computation onto the CPU to avoid backend driver crashes,
+ then projects the final tensor back onto the original target device.
+ """
+
+ # Offload tensors to CPU, execute iSTFT, and cast the output back to the original device
+ return _torch_istft(
+ input=input.cpu(), window=window.cpu(), *args, **kwargs
+ ).to(input.device)
+
+ def z_jit(f, *_, **__):
+ """
+ Mock decorator implementation replacing standard JIT compiling scripts.
+ """
+
+ f.graph = torch._C.Graph()
+ return f
+
+ # Override standard PyTorch namespace hooks with custom ZLUDA-compatible patches
+ torch.stft = z_stft
+ torch.istft = z_istft
+ torch.jit.script = z_jit
+ # Disable cuDNN since ZLUDA environments might lack complete deep learning runtime parity
+ torch.backends.cudnn.enabled = False
+ # Configure Scaled Dot Product Attention (SDPA) math backends
+ torch.backends.cuda.enable_math_sdp(True) # Enable stable, high-precision native math fallback path
+ torch.backends.cuda.enable_flash_sdp(False) # Disable hardware-specific FlashAttention kernels
+ torch.backends.cuda.enable_mem_efficient_sdp(False) # Disable proprietary memory-efficient attention kernels
\ No newline at end of file
diff --git a/main/library/embedders/fairseq.py b/main/library/embedders/fairseq.py
new file mode 100644
index 0000000000000000000000000000000000000000..ace9c305fc4ebc8af28c528f623f9858d5aa192a
--- /dev/null
+++ b/main/library/embedders/fairseq.py
@@ -0,0 +1,2152 @@
+import os
+import re
+import sys
+import math
+import uuid
+import torch
+import types
+import contextlib
+
+import numpy as np
+import torch.nn.functional as F
+import torch.nn.utils.parametrize as parametrize
+
+from torch import nn
+from omegaconf import DictConfig, open_dict
+
+sys.path.append(os.getcwd())
+
+from main.library.algorithm.normalization import Fp32GroupNorm, Fp32LayerNorm
+
+def load_model(filename):
+ """
+ Loads a Hubert model checkpoint from a specified file path.
+
+ It attempts a safe load first. If it fails, it checks configuration
+ fallbacks to allow unsafe fairseq dictionary loading before initializing
+ the architecture.
+
+ Args:
+ filename (str): Path to the model checkpoint file.
+
+ Returns:
+ HubertModel: The initialized HuBERT model with loaded weights.
+ """
+
+ try:
+ # Attempt secure loading of weights using restricted unpickling (weights_only=True)
+ # to block arbitrary code execution risks embedded within malicious pickles.
+ state = torch.load(filename, map_location="cpu", weights_only=True)
+ except:
+ # Fallback tracking if the secure loading fails, typically because fairseq structures require full module definitions to unpickle.
+ from main.app.variables import logger, configs, translations
+
+ logger.warning(translations["hubert_warn_1"])
+ logger.warning(translations["hubert_warn_2"])
+
+ # Check application runtime configurations to see if the user explicitly allowed unsafe loads
+ if configs.get("allow_unsafe_fairseq", False):
+ # Define an empty mock class to step in for the missing fairseq Dictionary structure
+ class Dictionary:
+ def __init__(self, *args, **kwargs):
+ pass
+
+ # Dynamically instantiate mock modules and hook them directly into sys.modules.
+ # This fools the pickle unpickler into believing fairseq is fully installed.
+ fairseq = types.ModuleType("fairseq")
+ fairseq_data = types.ModuleType("fairseq.data")
+ fairseq_data_dictionary = types.ModuleType("fairseq.data.dictionary")
+ # Map the mock Dictionary class onto our dynamically spawned namespace structure
+ fairseq_data_dictionary.Dictionary = Dictionary
+ fairseq.data = fairseq_data
+ fairseq_data.dictionary = fairseq_data_dictionary
+ # Register the dynamic modules in global system cache so internal torch.load calls match them
+ sys.modules["fairseq"] = fairseq
+ sys.modules["fairseq.data"] = fairseq_data
+ sys.modules["fairseq.data.dictionary"] = fairseq_data_dictionary
+
+ # Re-attempt the load with weights_only=False, allowing standard (potentially unsafe) unpickling
+ state = torch.load(filename, map_location="cpu", weights_only=False)
+ else: sys.exit(0)
+
+ # Instantiate the target model architecture using configurations parsed directly out of the checkpoint dictionary
+ # The number of target output classes is determined dynamically by viewing the shape of the label embeddings.
+ model = HubertModel(
+ HubertConfig(
+ **state['cfg']['model']
+ ),
+ num_classes=int(state['model']['label_embs_concat'].shape[0])
+ )
+ # Inject state dictionary weights into model parameters; strict=False ignores missing/extra non-critical keys
+ model.load_state_dict(state['model'], strict=False)
+ del state
+
+ return model
+
+def softmax(x, dim, onnx_trace = False):
+ """Computes softmax activation over a specified dimension.
+
+ Args:
+ x (torch.Tensor): Input tensor.
+ dim (int): The dimension along which softmax will be computed.
+ onnx_trace (bool, optional): If True, casts tensor to float before softmax
+ to ensure ONNX compatibility. Defaults to False.
+
+ Returns:
+ torch.Tensor: Normalized probability distribution.
+ """
+
+ # ONNX tracking has strict constraints regarding data type inference tracking.
+ # If tracing is active, explicitly cast to float, otherwise execute native float32-enforced activation.
+ return F.softmax(x.float(), dim=dim) if onnx_trace else F.softmax(x, dim=dim, dtype=torch.float32)
+
+def log_softmax(x, dim, onnx_trace = False):
+ """Computes log-softmax activation over a specified dimension.
+
+ Args:
+ x (torch.Tensor): Input tensor.
+ dim (int): The dimension along which log-softmax will be computed.
+ onnx_trace (bool, optional): If True, casts tensor to float before log-softmax
+ to ensure ONNX compatibility. Defaults to False.
+
+ Returns:
+ torch.Tensor: Log-probabilities tensor.
+ """
+
+ # Follows identical pattern as softmax helper: handles ONNX graph export limitations securely.
+ return F.log_softmax(x.float(), dim=dim) if onnx_trace else F.log_softmax(x, dim=dim, dtype=torch.float32)
+
+def with_incremental_state(cls):
+ """Class decorator to seamlessly inject FairseqIncrementalState capabilities into a class hierarchy.
+
+ Args:
+ cls (type): The target class to be decorated.
+
+ Returns:
+ type: Modified class with FairseqIncrementalState as its primary base class.
+ """
+
+ # Manipulate Python MRO (Method Resolution Order) tuple dynamically by prefixing the target class base list
+ # with the FairseqIncrementalState class object, eliminating redundant inheritances along the way.
+ cls.__bases__ = (FairseqIncrementalState,) + tuple(b for b in cls.__bases__ if b != FairseqIncrementalState)
+ return cls
+
+def quant_noise(module, p, block_size):
+ """Applies quantization noise regularization to a module's weights during training.
+
+ Args:
+ module (nn.Module): Linear, Embedding, or Conv2d layer to apply noise to.
+ p (float): Probability of introducing quantization noise (dropout rate for blocks).
+ block_size (int): Size of structural blocks to apply noise quantization to.
+
+ Returns:
+ nn.Module: The modified module attached with a forward pre-hook for quantization noise.
+ """
+
+ # Short-circuit out immediately if the quantization execution probability is zero or negative
+ if p <= 0: return module
+ assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d))
+ is_conv = module.weight.ndim == 4
+
+ if not is_conv:
+ # Linear/Embedding check: Input dimensions must be cleanly divisible by the block clustering rate
+ assert (module.weight.size(1) % block_size == 0)
+ else:
+ if module.kernel_size == (1, 1):
+ # 1x1 Convolution check: Total input channels must be divisible by block groups
+ assert (module.in_channels % block_size == 0)
+ else:
+ # Spatial Conv check: The product of kernel height and width parameters must align with block sizes
+ k = module.kernel_size[0] * module.kernel_size[1]
+ assert k % block_size == 0
+
+ def _forward_pre_hook(mod, input):
+ """Applies random block-based zeroing-out noise to model parameters during training."""
+
+ # Quantization noise injection is exclusively active during standard model training mode iterations
+ if mod.training:
+ if not is_conv:
+ weight = mod.weight
+
+ in_features = weight.size(1)
+ out_features = weight.size(0)
+ # Initialize flat zeros array corresponding to target scaled feature blocks
+ mask = torch.zeros(in_features // block_size * out_features, device=weight.device)
+ # Populate values randomly using a Bernoulli trials distribution set at execution probability 'p'
+ mask.bernoulli_(p)
+ # Expand mask back out across block windows and reshape to match structural weight rank configurations
+ mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)
+ else:
+ weight = mod.weight
+
+ in_channels = mod.in_channels
+ out_channels = mod.out_channels
+
+ if mod.kernel_size == (1, 1):
+ # Construct and map block masks specific to flat 1x1 convolutional tensors
+ mask = torch.zeros(int(in_channels // block_size * out_channels), device=weight.device)
+ mask.bernoulli_(p)
+ mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels)
+ else:
+ # Construct structural spatial masks spanning cross-channel dimension planes entirely
+ mask = torch.zeros(weight.size(0), weight.size(1), device=weight.device)
+ mask.bernoulli_(p)
+ # Extend matrix ranks via unsqueeze operators to align seamlessly with spatial
+ mask = (mask.unsqueeze(2).unsqueeze(3).repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1]))
+
+ # Convert mask elements to boolean tensors for efficient masking masking behaviors
+ mask = mask.to(torch.bool)
+ # Compute inverted scaling coefficient factor to keep overall parameter magnitude distributions consistent
+ s = 1 / (1 - p)
+ # Mutate weight data in place, filling selected masked regions with zero and scaling active values
+ mod.weight.data = s * weight.masked_fill(mask, 0)
+
+ # Attach our defined hook to run immediately prior to executing standard forward layers processing passes
+ module.register_forward_pre_hook(_forward_pre_hook)
+ return module
+
+class FairseqDropout(nn.Module):
+ """Dropout wrapper designed to allow configuration of dropout rules during inference mode."""
+
+ def __init__(self, p, module_name=None):
+ super().__init__()
+ self.p = p # Target probability field configuration parameter
+ self.module_name = module_name # Internal descriptor key tag used to isolate modules for targeted configuration changes
+ self.apply_during_inference = False # State flag allowing active dropping evaluations during test loops
+
+ def forward(self, x, inplace = False):
+ # Apply dropping function if execution rate is positive AND module status matches active generation metrics
+ return F.dropout(x, p=self.p, training=True, inplace=inplace) if self.p > 0 and (self.training or self.apply_during_inference) else x
+
+ def make_generation_fast_(self, name, retain_dropout = False, retain_dropout_modules = None, **kwargs):
+ """Enables specific dropout layers during inference tracking to support variational generation."""
+
+ # If tracking state overrides are globally called, check if our local layer identifier matches explicit targeted lists
+ if retain_dropout and (retain_dropout_modules is None or self.module_name in retain_dropout_modules): self.apply_during_inference = True
+
+class FairseqIncrementalState(object):
+ """Abstract interface class providing standard handling for incremental decoding buffers."""
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.init_incremental_state()
+
+ def init_incremental_state(self):
+ # Generate an isolated completely distinct uuid sequence string to securely index local module storage items
+ self._incremental_state_id = str(uuid.uuid4())
+
+ def _get_full_incremental_state_key(self, key):
+ # Constructs fully qualified compound unique mapping keys to safeguard against dictionary key collision risks
+ return "{}.{}".format(self._incremental_state_id, key)
+
+ def get_incremental_state(self, incremental_state, key):
+ """Extracts cached decoder states based on specific unique context keys."""
+
+ full_key = self._get_full_incremental_state_key(key)
+ # Check dictionary records safely; if tracking object isn't constructed, gracefully output default None values
+ if incremental_state is None or full_key not in incremental_state: return None
+ return incremental_state[full_key]
+
+ def set_incremental_state(self, incremental_state, key, value):
+ """Caches structural sequence tensors into the active global incremental state storage."""
+
+ # Mutate the parent shared inference context track matrix in-place by writing current state records
+ if incremental_state is not None: incremental_state[self._get_full_incremental_state_key(key)] = value
+ return incremental_state
+
+class FairseqDecoder(nn.Module):
+ """Base abstract decoder structure utilized within sequence translation architectures."""
+
+ def __init__(self, dictionary):
+ super().__init__()
+ self.dictionary = dictionary
+ self.onnx_trace = False # Operational flag handling active standard deployment parsing graphs
+ self.adaptive_softmax = None # Placeholder attribute mapping hierarchical projection functions
+
+ def forward(self, prev_output_tokens, encoder_out=None, **kwargs):
+ """Processes target sequences through features layer mapping up to final logit evaluations."""
+ # Route historical tracking input structures down through primary mapping layer pathways
+ x, extra = self.extract_features(prev_output_tokens, encoder_out=encoder_out, **kwargs)
+ # Push abstract sequence maps through final projection blocks to map vocabulary dimensions
+ return self.output_layer(x), extra
+
+ def extract_features(self, prev_output_tokens, encoder_out=None, **kwargs):
+ # Abstract placeholder design; must be declared inside inheriting sub-architectures
+ pass
+
+ def output_layer(self, features, **kwargs):
+ # Abstract placeholder design; must be declared inside inheriting sub-architectures
+ pass
+
+ def get_normalized_probs(self, net_output, log_probs, sample = None):
+ """Retrieves normalized predictive distribution tensor outputs."""
+ return self.get_normalized_probs_scriptable(net_output, log_probs, sample)
+
+ def get_normalized_probs_scriptable(self, net_output, log_probs, sample = None):
+ """Computes softmax probabilities or log-probabilities from model outputs in a scriptable format."""
+
+ # Verify if an adaptive projection calculation alternative is explicitly deployed in place
+ if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None:
+ if sample is not None:
+ assert "target" in sample
+ target = sample["target"]
+ else: target = None
+
+ # Route tokens down specialized adaptive tracking pipelines to compute regional target distributions
+ out = self.adaptive_softmax.get_log_prob(net_output[0], target=target)
+ # If log distribution metrics are rejected by call parameters, unpack exponentials to get standard probabilities
+ return out.exp_() if not log_probs else out
+
+ # Fallback tracking sequence route: compute standard calculations using standard full vocabulary structures
+ logits = net_output[0]
+ return log_softmax(logits, dim=-1, onnx_trace=self.onnx_trace) if log_probs else softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
+
+ def max_positions(self):
+ """Returns the maximum absolute sequence length threshold."""
+
+ return 1e6
+
+ def upgrade_state_dict_named(self, state_dict, name):
+ """Hook method to adjust parameter naming compatibility across checkpoint versions."""
+
+ return state_dict
+
+ def prepare_for_onnx_export_(self):
+ """Flips processing flags to conform to standard tracking limitations under ONNX compilation."""
+
+ self.onnx_trace = True
+
+@with_incremental_state
+class FairseqIncrementalDecoder(FairseqDecoder):
+ """Extension of FairseqDecoder explicitly supporting sequential state caching (incremental decoding)."""
+
+ def __init__(self, dictionary):
+ super().__init__(dictionary)
+
+ def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs):
+ pass
+
+ def extract_features(self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs):
+ pass
+
+ def reorder_incremental_state(self, incremental_state, new_order):
+ pass
+
+ def reorder_incremental_state_scripting(self, incremental_state, new_order):
+ """Reorders cached hidden states to sync up with changed step exploration paths during beam search."""
+
+ # Traverse recursively down through sub-modules to update index arrangements within caching tracking buffers
+ for module in self.modules():
+ if hasattr(module, "reorder_incremental_state"):
+ result = module.reorder_incremental_state(incremental_state, new_order)
+ if result is not None: incremental_state = result
+
+ def set_beam_size(self, beam_size):
+ """Sets internal search beam tracking attributes dynamically across sub-modules."""
+
+ if getattr(self, "_beam_size", -1) != beam_size:
+ seen = set()
+ # Local helper mapping utility tracking structural execution history paths
+ def apply_set_beam_size(module):
+ if (module != self and hasattr(module, "set_beam_size") and module not in seen):
+ seen.add(module)
+ module.set_beam_size(beam_size)
+
+ # Route functional application mappings down full nested network trees completely
+ self.apply(apply_set_beam_size)
+ self._beam_size = beam_size
+
+class MultiheadAttention(FairseqIncrementalDecoder):
+ """Multi-Head Attention module supporting self-attention, cross-attention, and incremental caching."""
+
+ def __init__(
+ self,
+ embed_dim,
+ num_heads,
+ kdim=None,
+ vdim=None,
+ dropout=0.0,
+ bias=True,
+ add_bias_kv=False,
+ add_zero_attn=False,
+ self_attention=False,
+ encoder_decoder_attention=False,
+ dictionary=None,
+ q_noise=0.0,
+ qn_block_size=8,
+ xformers_att_config=None,
+ xformers_blocksparse_layout=None,
+ xformers_blocksparse_blocksize=16
+ ):
+ super().__init__(dictionary)
+ if xformers_att_config is None: xformers_att_config = None
+ # Evaluate configuration script strings dynamically if configurations are encapsulated inside flat text format blocks
+ if isinstance(xformers_att_config, str): xformers_att_config = eval(xformers_att_config)
+ # Parse boolean metrics indicating if highly optimized external xformers execution libraries are demanded
+ self.use_xformers = xformers_att_config is not None
+ if self.use_xformers: raise ImportError("Configuration mismatch: xformers acceleration backend called but unsupported in current architecture environments.")
+ self.embed_dim = embed_dim
+ self.kdim = kdim if kdim is not None else embed_dim
+ self.vdim = vdim if vdim is not None else embed_dim
+ # Check if projection shapes map symmetrically to determine parameter initialization options later
+ self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
+ self.num_heads = num_heads
+ # Initialize isolated structural dropout tracking blocks
+ self.dropout_module = FairseqDropout(dropout, module_name=self.__class__.__name__)
+ # Compute specific inner hidden dimension metrics assigned uniquely per core attention head calculation channel
+ self.head_dim = embed_dim // num_heads
+ # Assert head divisibility compatibility directly to avoid bad matrix mapping shape states during forward actions
+ assert (self.head_dim * num_heads == self.embed_dim)
+ # Calculate scaled dot-product attention stabilization multiplier factor
+ self.scaling = self.head_dim**-0.5
+ self.self_attention = self_attention
+ self.encoder_decoder_attention = encoder_decoder_attention
+ assert not self.self_attention or self.qkv_same_dim
+ # Initialize linear weight projection components wrapped with quantization noise simulation masks
+ self.k_proj = quant_noise(nn.Linear(self.kdim, embed_dim, bias=bias), q_noise, qn_block_size)
+ self.v_proj = quant_noise(nn.Linear(self.vdim, embed_dim, bias=bias), q_noise, qn_block_size)
+ self.q_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size)
+ self.out_proj = quant_noise(nn.Linear(embed_dim, embed_dim, bias=bias), q_noise, qn_block_size)
+ # Handle custom permanent context parameter embeddings configurations if spatial biases are requested
+ if add_bias_kv: self.bias_k, self.bias_v = nn.Parameter(torch.Tensor(1, 1, embed_dim)), nn.Parameter(torch.Tensor(1, 1, embed_dim))
+ else: self.bias_k = self.bias_v = None
+ self.add_zero_attn = add_zero_attn
+ self.beam_size = 1
+ self.reset_parameters()
+ self.onnx_trace = False
+ self.skip_embed_dim_check = False
+ self.init_incremental_state()
+
+ def prepare_for_onnx_export_(self):
+ self.onnx_trace = True
+
+ def reset_parameters(self):
+ """Initializes model weights using Xavier uniform initialization."""
+
+ if self.qkv_same_dim:
+ # Scale gain factors effectively down to compensate for overlapping tracking channels variances
+ nn.init.xavier_uniform_(self.k_proj.weight, gain=1 / math.sqrt(2))
+ nn.init.xavier_uniform_(self.v_proj.weight, gain=1 / math.sqrt(2))
+ nn.init.xavier_uniform_(self.q_proj.weight, gain=1 / math.sqrt(2))
+ else:
+ nn.init.xavier_uniform_(self.k_proj.weight)
+ nn.init.xavier_uniform_(self.v_proj.weight)
+ nn.init.xavier_uniform_(self.q_proj.weight)
+
+ nn.init.xavier_uniform_(self.out_proj.weight)
+ # Set default zero parameters for bias maps safely
+ if self.out_proj.bias is not None: nn.init.constant_(self.out_proj.bias, 0.0)
+ if self.bias_k is not None: nn.init.xavier_normal_(self.bias_k)
+ if self.bias_v is not None: nn.init.xavier_normal_(self.bias_v)
+
+ def _get_reserve_head_index(self, num_heads_to_keep: int):
+ """Identifies and retains index pointers for attention heads exhibiting highest weight norms."""
+
+ k_proj_heads_norm, q_proj_heads_norm, v_proj_heads_norm = [], [], []
+ # Iterate over separate heads tracking blocks to extract aggregate internal matrix norm summaries
+ for i in range(self.num_heads):
+ start_idx = i * self.head_dim
+ end_idx = (i + 1) * self.head_dim
+
+ # Sum up absolute weights and bias records belonging uniquely inside the active head segment slice
+ k_proj_heads_norm.append(
+ (self.k_proj.weight[start_idx:end_idx]).abs().sum().tolist() +
+ (self.k_proj.bias[start_idx:end_idx]).abs().sum().tolist()
+ )
+ q_proj_heads_norm.append(
+ (self.q_proj.weight[start_idx:end_idx]).abs().sum().tolist() +
+ (self.q_proj.bias[start_idx:end_idx]).abs().sum().tolist()
+ )
+ v_proj_heads_norm.append(
+ (self.v_proj.weight[start_idx:end_idx]).abs().sum().tolist() +
+ (self.v_proj.bias[start_idx:end_idx]).abs().sum().tolist()
+ )
+
+ # Combine computed projection statistics across matrix families per individual head indices
+ heads_norm = []
+ for i in range(self.num_heads):
+ heads_norm.append(k_proj_heads_norm[i] + q_proj_heads_norm[i] + v_proj_heads_norm[i])
+ # Sort head reference index markers from maximum to minimum depending on importance scores
+ sorted_head_index = sorted(range(self.num_heads), key=lambda k: heads_norm[k], reverse=True)
+ # Isolate boundary mapping coordinate pairs for the top-N retained structural attention tracks
+ reserve_head_index = []
+ for i in range(num_heads_to_keep):
+ reserve_head_index.append((sorted_head_index[i] * self.head_dim, (sorted_head_index[i] + 1) * self.head_dim))
+
+ return reserve_head_index
+
+ def _adaptive_prune_heads(self, reserve_head_index):
+ """Prunes less informative attention heads structurally from projection weights."""
+
+ new_q_weight, new_q_bias, new_k_weight, new_k_bias, new_v_weight, new_v_bias, new_out_proj_weight = [], [], [], [], [], [], []
+ # Slice relevant sections out of weight blocks using coordinates collected from the validation index maps
+ for ele in reserve_head_index:
+ start_idx, end_idx = ele
+
+ new_q_weight.append(self.q_proj.weight[start_idx:end_idx])
+ new_q_bias.append(self.q_proj.bias[start_idx:end_idx])
+
+ new_k_weight.append(self.k_proj.weight[start_idx:end_idx])
+ new_k_bias.append(self.k_proj.bias[start_idx:end_idx])
+
+ new_v_weight.append(self.v_proj.weight[start_idx:end_idx])
+ new_v_bias.append(self.v_proj.bias[start_idx:end_idx])
+
+ # For output projections, slice columns instead of rows because heads compile horizontally
+ new_out_proj_weight.append(self.out_proj.weight[:, start_idx:end_idx])
+
+ # Concatenate structural segments back together to synthesize leaner parameter matrices
+ new_q_weight = torch.cat(new_q_weight).detach()
+ new_k_weight = torch.cat(new_k_weight).detach()
+ new_v_weight = torch.cat(new_v_weight).detach()
+ new_out_proj_weight = torch.cat(new_out_proj_weight, dim=-1).detach()
+
+ # Re-enable backpropagation gradient tracking paths for optimized parameter components
+ new_q_weight.requires_grad = True
+ new_k_weight.requires_grad = True
+ new_v_weight.requires_grad = True
+ new_out_proj_weight.requires_grad = True
+
+ new_q_bias = torch.cat(new_q_bias).detach()
+ new_q_bias.requires_grad = True
+ new_k_bias = torch.cat(new_k_bias).detach()
+ new_k_bias.requires_grad = True
+ new_v_bias = torch.cat(new_v_bias).detach()
+ new_v_bias.requires_grad = True
+
+ # Re-bind modified parameters back into module parameters fields
+ self.q_proj.weight = nn.Parameter(new_q_weight)
+ self.q_proj.bias = nn.Parameter(new_q_bias)
+ self.k_proj.weight = nn.Parameter(new_k_weight)
+ self.k_proj.bias = nn.Parameter(new_k_bias)
+ self.v_proj.weight = nn.Parameter(new_v_weight)
+ self.v_proj.bias = nn.Parameter(new_v_bias)
+ self.out_proj.weight = nn.Parameter(new_out_proj_weight)
+ # Update dimensional attribute values inside the class object records to accurately match current configurations
+ self.num_heads = len(reserve_head_index)
+ self.embed_dim = self.head_dim * self.num_heads
+ self.q_proj.out_features = self.embed_dim
+ self.k_proj.out_features = self.embed_dim
+ self.v_proj.out_features = self.embed_dim
+
+ def _set_skip_embed_dim_check(self):
+ self.skip_embed_dim_check = True
+
+ def _pad_masks(self, key_padding_mask, attn_mask):
+ """Pads masks to match targets when explicit key/value biases are concatenated."""
+
+ # When bias vectors add custom static token tracks onto the sequence len, masks must be scaled out to prevent errors
+ if attn_mask is not None:
+ shape = attn_mask.size()[:-1] + torch.Size([1])
+ attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(shape)], dim=-1)
+
+ if key_padding_mask is not None:
+ shape = key_padding_mask.size()[:-1] + torch.Size([1])
+ key_padding_mask = torch.cat([key_padding_mask, key_padding_mask.new_zeros(shape)], dim=-1)
+
+ return key_padding_mask, attn_mask
+
+ def _add_bias(self, k, v, key_padding_mask, attn_mask, bsz):
+ """Appends trained positional bias tokens directly onto key and value tracking."""
+
+ assert self.bias_k is not None or self.bias_v is not None
+ key_padding_mask, attn_mask = self._pad_masks(key_padding_mask=key_padding_mask, attn_mask=attn_mask)
+ # Concatenate structural bias frames directly along the historical sequence dimension row bounds
+ return torch.cat([k, self.bias_k.repeat(1, bsz, 1)]), torch.cat([v, self.bias_v.repeat(1, bsz, 1)]), key_padding_mask, attn_mask
+
+ def _append_zero_attn(self, k, v, key_padding_mask, attn_mask):
+ """Concatenates an explicit padding zero-channel sequence element to key-value variables."""
+
+ # Establish target dimension sizes tracking spatial metrics matching current key states
+ zero_attn_shape = k.size()[:-2] + torch.Size([1]) + k.size()[-1:]
+ key_padding_mask, attn_mask = self._pad_masks(key_padding_mask=key_padding_mask, attn_mask=attn_mask)
+ # Concatenate hard zeros directly to sequences; this provides attention vectors a fallback option to distribute weights onto
+ return torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=-2), torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=-2), key_padding_mask, attn_mask
+
+ def forward(self, query, key, value, key_padding_mask = None, incremental_state = None, need_weights = True, static_kv = False, attn_mask = None, before_softmax = False, need_head_weights = False):
+ # Head tracking configurations parameter adjustment rules check
+ if need_head_weights: need_weights = True
+ # Unpack input tensor structural dimensions
+ tgt_len, bsz, embed_dim = query.size()
+ src_len = tgt_len
+
+ # Run shape checks to guarantee operational calculations match up safely
+ if not self.skip_embed_dim_check: assert (embed_dim == self.embed_dim)
+ assert list(query.size()) == [tgt_len, bsz, embed_dim]
+
+ # If localized key inputs are defined, check internal dimension metrics and shapes match values
+ if key is not None:
+ src_len, key_bsz, _ = key.size()
+ if not torch.jit.is_scripting():
+ assert value is not None
+ assert src_len, key_bsz == value.shape[:2]
+
+ if (not self.onnx_trace and incremental_state is None and not static_kv and not torch.jit.is_scripting() and not self.skip_embed_dim_check):
+ assert key is not None and value is not None
+
+ return F.multi_head_attention_forward(
+ query,
+ key,
+ value,
+ self.embed_dim,
+ self.num_heads,
+ torch.empty([0]),
+ torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
+ self.bias_k,
+ self.bias_v,
+ self.add_zero_attn,
+ self.dropout_module.p,
+ self.out_proj.weight,
+ self.out_proj.bias,
+ self.training or self.dropout_module.apply_during_inference,
+ key_padding_mask.bool() if key_padding_mask is not None else None,
+ need_weights,
+ attn_mask,
+ use_separate_proj_weight=True,
+ q_proj_weight=self.q_proj.weight,
+ k_proj_weight=self.k_proj.weight,
+ v_proj_weight=self.v_proj.weight
+ )
+
+ if incremental_state is not None:
+ # Extract step context out of the persistent data objects dictionary
+ saved_state = self._get_input_buffer(incremental_state)
+
+ if saved_state is not None and "prev_key" in saved_state:
+ if static_kv:
+ assert self.encoder_decoder_attention and not self.self_attention
+ key = value = None
+ else: saved_state = None
+
+ # Execute structural forward matrix projections based on current operational attention styles
+ if self.self_attention:
+ q = self.q_proj(query)
+ k = self.k_proj(query)
+ v = self.v_proj(query)
+ elif self.encoder_decoder_attention:
+ q = self.q_proj(query)
+ if key is None:
+ assert value is None
+ k = v = None
+ else:
+ # Handle tracking adjustments when search beams span width dimensions completely
+ if self.beam_size > 1 and bsz == key.size(1):
+ key = key.view(key.size(0), -1, self.beam_size, key.size(2))[:, :, 0, :]
+ if key_padding_mask is not None: key_padding_mask = key_padding_mask.view(-1, self.beam_size, key_padding_mask.size(1))[:, 0, :]
+
+ k = self.k_proj(key)
+ v = self.v_proj(key)
+ else:
+ assert key is not None and value is not None
+ q = self.q_proj(query)
+ k = self.k_proj(key)
+ v = self.v_proj(value)
+
+ # Apply spatial scaling corrections directly onto query distributions
+ q *= self.scaling
+ if self.bias_k is not None:
+ assert self.bias_v is not None
+ k, v, attn_mask, key_padding_mask = self._add_bias(k, v, attn_mask, key_padding_mask, bsz)
+
+ # Reshape data structures to run simultaneous batch parallel computations on individual heads:
+ q = (q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1))
+ kv_bsz = bsz
+
+ if k is not None:
+ kv_bsz = k.size(1)
+ k = (k.contiguous().view(-1, kv_bsz * self.num_heads, self.head_dim).transpose(0, 1))
+
+ if v is not None: v = (v.contiguous().view(-1, kv_bsz * self.num_heads, self.head_dim).transpose(0, 1))
+ # Merge historical tensor into current tracking variables if sequential logs are found
+ if saved_state is not None:
+ if "prev_key" in saved_state:
+ _prev_key = saved_state["prev_key"]
+ assert _prev_key is not None
+
+ kv_bsz = _prev_key.size(0)
+ # Reshape tracked matrix patterns to align with operational shapes
+ prev_key = _prev_key.view(kv_bsz * self.num_heads, -1, self.head_dim)
+
+ if static_kv: k = prev_key
+ else:
+ assert k is not None
+ k = torch.cat([prev_key, k], dim=1)
+
+ src_len = k.size(1)
+
+ if "prev_value" in saved_state:
+ _prev_value = saved_state["prev_value"]
+ assert _prev_value is not None or kv_bsz == _prev_value.size(0)
+ prev_value = _prev_value.view(kv_bsz * self.num_heads, -1, self.head_dim)
+ if static_kv: v = prev_value
+ else:
+ assert v is not None
+ v = torch.cat([prev_value, v], dim=1)
+
+ prev_key_padding_mask = None
+ if "prev_key_padding_mask" in saved_state: prev_key_padding_mask = saved_state["prev_key_padding_mask"]
+ assert k is not None and v is not None
+
+ # Append padding tracking masks safely
+ key_padding_mask = MultiheadAttention._append_prev_key_padding_mask(
+ key_padding_mask=key_padding_mask,
+ prev_key_padding_mask=prev_key_padding_mask,
+ batch_size=kv_bsz,
+ src_len=k.size(1),
+ static_kv=static_kv
+ )
+
+ # Store updated sequence updates back inside memory storage for subsequent step tracking iterations
+ saved_state["prev_key"] = k.view(kv_bsz, self.num_heads, -1, self.head_dim)
+ saved_state["prev_value"] = v.view(kv_bsz, self.num_heads, -1, self.head_dim)
+ saved_state["prev_key_padding_mask"] = key_padding_mask
+
+ assert incremental_state is not None
+ incremental_state = self._set_input_buffer(incremental_state, saved_state)
+
+ assert k is not None
+ assert k.size(1) == src_len
+
+ if key_padding_mask is not None and key_padding_mask.dim() == 0: key_padding_mask = None
+
+ if key_padding_mask is not None:
+ assert key_padding_mask.size(0) == kv_bsz
+ assert key_padding_mask.size(1) == src_len
+
+ if self.add_zero_attn:
+ assert v is not None
+ src_len += 1
+ k, v, key_padding_mask, attn_mask = self._append_zero_attn(k=k, v=v, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
+
+ if self.encoder_decoder_attention and bsz != kv_bsz:
+ # Deploy customized Einstein summation configurations to bridge dimension gaps safely if batch dimensions vary
+ attn_weights = torch.einsum(
+ "bxhtd,bhsd->bxhts",
+ q.view((kv_bsz, -1, self.num_heads) + q.size()[1:]),
+ k.view((kv_bsz, self.num_heads) + k.size()[1:])
+ )
+
+ attn_weights = attn_weights.reshape((-1,) + attn_weights.size()[-2:])
+ else: attn_weights = q.bmm(k.transpose(1, 2)) # Standard batch matrix multiplication across matching rank
+
+ assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
+
+ # Apply structural masks by injecting hard negative infinity to suppress selected attention pathways completely
+ if attn_mask is not None:
+ attn_mask = attn_mask.unsqueeze(0)
+ if self.onnx_trace: attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
+ attn_weights += attn_mask
+
+ if key_padding_mask is not None:
+ attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+ attn_weights = attn_weights.view(kv_bsz, -1, self.num_heads, tgt_len, src_len)
+ # Replace active padding tracking paths with extreme negative floor limits to nullify activation probability scores
+ attn_weights = attn_weights.masked_fill(key_padding_mask.unsqueeze(1).unsqueeze(2).unsqueeze(3).to(torch.bool), float("-inf"))
+ attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+ # Break out early if calculations call for raw pre-softmax values
+ if before_softmax: return attn_weights, v
+
+ # Normalize attention scores into standard valid probability distributions
+ attn_weights_float = softmax(attn_weights, dim=-1, onnx_trace=self.onnx_trace)
+ attn_weights = attn_weights_float.type_as(attn_weights)
+ # Apply structured drop out regularizations directly over probability maps
+ attn_probs = self.dropout_module(attn_weights)
+
+ assert v is not None
+ attn = None
+ # Gather context feature representations by mapping attention probabilities against value frames
+ if self.encoder_decoder_attention and bsz != kv_bsz:
+ attn = torch.einsum(
+ "bxhts,bhsd->bxhtd",
+ attn_probs.view((kv_bsz, -1, self.num_heads) + attn_probs.size()[1:]),
+ v.view((kv_bsz, self.num_heads) + v.size()[1:])
+ )
+
+ attn = attn.reshape((-1,) + attn.size()[-2:])
+ else: attn = attn_probs.bmm(v)
+ assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
+
+ # Transpose values back to standard formats: [Batch * Heads, Length, HeadDim] -> [Length, Batch, EmbedDim]
+ attn = attn.contiguous().view(tgt_len, bsz, self.embed_dim) if self.onnx_trace and attn.size(1) == 1 else attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)
+ # Route through final linear output mapping layer
+ attn = self.out_proj(attn)
+ attn_weights = None
+
+ # If requested, format attention weight summary matrices to return alongside context
+ if need_weights:
+ attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
+ if not need_head_weights:
+ # Average scores across individual head channels to synthesize global visualization charts
+ attn_weights = attn_weights.mean(dim=0)
+
+ return attn, attn_weights
+
+ @staticmethod
+ def _append_prev_key_padding_mask(key_padding_mask, prev_key_padding_mask, batch_size, src_len, static_kv):
+ """Merges historical step padding specifications with incoming step structural mask lists."""
+
+ if prev_key_padding_mask is not None and static_kv:
+ new_key_padding_mask = prev_key_padding_mask
+ elif prev_key_padding_mask is not None and key_padding_mask is not None:
+ new_key_padding_mask = torch.cat([prev_key_padding_mask.float(), key_padding_mask.float()], dim=1)
+ elif prev_key_padding_mask is not None:
+ if src_len > prev_key_padding_mask.size(1):
+ filler = torch.zeros((batch_size, src_len - prev_key_padding_mask.size(1)), device=prev_key_padding_mask.device)
+ new_key_padding_mask = torch.cat([prev_key_padding_mask.float(), filler.float()], dim=1)
+ else:
+ new_key_padding_mask = prev_key_padding_mask.float()
+ elif key_padding_mask is not None:
+ if src_len > key_padding_mask.size(1):
+ filler = torch.zeros((batch_size, src_len - key_padding_mask.size(1)), device=key_padding_mask.device)
+ new_key_padding_mask = torch.cat([filler.float(), key_padding_mask.float()], dim=1)
+ else:
+ new_key_padding_mask = key_padding_mask.float()
+ else:
+ new_key_padding_mask = prev_key_padding_mask
+
+ return new_key_padding_mask
+
+ @torch.jit.export
+ def reorder_incremental_state(self, incremental_state, new_order):
+ """Rearranges memory caching channels relative to active branch shifts during candidate generation."""
+
+ input_buffer = self._get_input_buffer(incremental_state)
+
+ if input_buffer is not None:
+ for k in input_buffer.keys():
+ input_buffer_k = input_buffer[k]
+
+ if input_buffer_k is not None:
+ if self.encoder_decoder_attention:
+ if input_buffer_k.size(0) * self.beam_size == new_order.size(0):
+ return incremental_state
+ elif self.beam_size > 1:
+ # Slice out specific entries matching active operational routes chosen by search logs
+ input_buffer[k] = input_buffer_k.index_select(0, new_order.reshape(-1, self.beam_size)[:, 0] // self.beam_size)
+ else:
+ input_buffer[k] = input_buffer_k.index_select(0, new_order)
+ else:
+ input_buffer[k] = input_buffer_k.index_select(0, new_order)
+
+ # Re-save organized buffer modifications back down into step execution environment histories
+ incremental_state = self._set_input_buffer(incremental_state, input_buffer)
+
+ return incremental_state
+
+ def set_beam_size(self, beam_size):
+ self.beam_size = beam_size
+
+ def _get_input_buffer(self, incremental_state):
+ result = self.get_incremental_state(incremental_state, "attn_state")
+ return result if result is not None else {}
+
+ def _set_input_buffer(self, incremental_state, buffer):
+ return self.set_incremental_state(incremental_state, "attn_state", buffer)
+
+ def upgrade_state_dict_named(self, state_dict, name):
+ """Splits unified `in_proj_weight` vectors into distinct Q, K, V sub-projection weights."""
+
+ prefix = name + "." if name != "" else ""
+ items_to_add, keys_to_remove = {}, []
+ for k in state_dict.keys():
+ if k.endswith(prefix + "in_proj_weight"):
+ dim = int(state_dict[k].shape[0] / 3)
+ # Segment unified tensors into three isolated linear parameters maps
+ items_to_add[prefix + "q_proj.weight"] = state_dict[k][:dim]
+ items_to_add[prefix + "k_proj.weight"] = state_dict[k][dim : 2 * dim]
+ items_to_add[prefix + "v_proj.weight"] = state_dict[k][2 * dim :]
+
+ keys_to_remove.append(k)
+ k_bias = prefix + "in_proj_bias"
+ # Check if bias parameters follow identical formatting layouts and process them if present
+ if k_bias in state_dict.keys():
+ dim = int(state_dict[k].shape[0] / 3)
+ items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim]
+ items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][dim : 2 * dim]
+ items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :]
+ keys_to_remove.append(prefix + "in_proj_bias")
+
+ # Delete deprecated outdated state key references out of the configuration map dictionary
+ for k in keys_to_remove:
+ del state_dict[k]
+
+ # Inject newly structured parameter updates into the active runtime initialization configurations list
+ for key, value in items_to_add.items():
+ state_dict[key] = value
+
+def init_bert_params(module):
+ """Initializes sub-module layers using a BERT normal distribution setup."""
+
+ def normal_(data):
+ # In-place copy normal values sampled at mean 0.0 and standard deviation 0.02
+ data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device))
+
+ # Apply specialized initialization rules across various structural layer components
+ if isinstance(module, nn.Linear):
+ normal_(module.weight.data)
+ if module.bias is not None: module.bias.data.zero_()
+
+ if isinstance(module, nn.Embedding):
+ normal_(module.weight.data)
+ if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
+
+ if isinstance(module, MultiheadAttention):
+ normal_(module.q_proj.weight.data)
+ normal_(module.k_proj.weight.data)
+ normal_(module.v_proj.weight.data)
+
+def make_conv_pos(e, k, g):
+ """Creates a 1D convolutional positional embedding sequence layer with weight normalization applied."""
+
+ pos_conv = nn.Conv1d(e, e, kernel_size=k, padding=k // 2, groups=g)
+ dropout = 0
+ # Initialize weights normally leveraging mathematical equations matching specialized feature scales
+ nn.init.normal_(pos_conv.weight, mean=0, std=math.sqrt((4 * (1.0 - dropout)) / (k * e)))
+ nn.init.constant_(pos_conv.bias, 0)
+ # Compile layers within Sequential wrapping structures, injecting Weight Normalization parametrizations
+ return nn.Sequential(nn.utils.parametrizations.weight_norm(pos_conv, name="weight", dim=2), SamePad(k), nn.GELU())
+
+def index_put(tensor, indices, value):
+ """Performs an in-place tensor modification by putting values into specified indices."""
+
+ # Write incoming value tracking fields into selected locations matching index parameters
+ tensor[indices] = value
+ return tensor
+
+def pad_to_multiple(x, multiple, dim=-1, value=0):
+ """
+ Pads a tensor on a given dimension to make its size a multiple of a specified value.
+
+ Args:
+ x (torch.Tensor or None): The input tensor to pad.
+ multiple (int): The target multiple value for padding.
+ dim (int, optional): The dimension along which to pad. Defaults to -1.
+ value (float, optional): The padding value. Defaults to 0.
+
+ Returns:
+ tuple: (padded_tensor, remainder)
+ - padded_tensor (torch.Tensor or None): The padded tensor or None if input is None.
+ - remainder (int): The number of padded elements added.
+ """
+
+ if x is None: return None, 0
+
+ tsz = x.size(dim)
+ m = tsz / multiple
+ # Calculate how many elements are missing to reach the next multiple
+ remainder = math.ceil(m) * multiple - tsz
+ # If already a multiple, return the original tensor
+ if m.is_integer(): return x, 0
+ # Create the correct padding tuple based on the target dimension
+ return F.pad(x, (*((0,) * (-1 - dim) * 2), 0, remainder), value=value), remainder
+
+def prune_state_dict(state_dict, model_cfg):
+ """
+ Prunes layers from a state dictionary based on configuration constraints.
+
+ Args:
+ state_dict (dict): The original state dictionary containing model weights.
+ model_cfg (object): The model configuration object (can be a DictConfig or standard object).
+
+ Returns:
+ dict: The pruned state dictionary with mapped layer indices.
+ """
+
+ arch = None
+ if model_cfg is not None: arch = (model_cfg._name if isinstance(model_cfg, DictConfig) else getattr(model_cfg, "arch", None))
+ # Return early if configuration is empty or architecture does not require pruning
+ if not model_cfg or arch is None or arch == "ptt_transformer": return state_dict
+ encoder_layers_to_keep = getattr(model_cfg, "encoder_layers_to_keep", None)
+ decoder_layers_to_keep = getattr(model_cfg, "decoder_layers_to_keep", None)
+ if not encoder_layers_to_keep and not decoder_layers_to_keep: return state_dict
+
+ def create_pruning_pass(layers_to_keep, layer_name):
+ # Parse comma-separated string of layer indices to keep and sort them
+ keep_layers = sorted(int(layer_string) for layer_string in layers_to_keep.split(","))
+ mapping_dict = {}
+ # Create a mapping from old indices to new contiguous indices
+ for i in range(len(keep_layers)):
+ mapping_dict[str(keep_layers[i])] = str(i)
+
+ return {"substitution_regex": re.compile(r"^{layer}.*\.layers\.(\d+)".format(layer=layer_name)), "mapping_dict": mapping_dict}
+
+ pruning_passes, new_state_dict = [], {}
+ if encoder_layers_to_keep: pruning_passes.append(create_pruning_pass(encoder_layers_to_keep, "encoder"))
+ if decoder_layers_to_keep: pruning_passes.append(create_pruning_pass(decoder_layers_to_keep, "decoder"))
+
+ for layer_name in state_dict.keys():
+ match = re.search(r"\.layers\.(\d+)\.", layer_name)
+ # If the parameter does not belong to a numbered layer, keep it as is
+ if not match:
+ new_state_dict[layer_name] = state_dict[layer_name]
+ continue
+
+ original_layer_number = match.group(1)
+ for pruning_pass in pruning_passes:
+ # Check if this layer is marked to be kept and matches the current pass's regex
+ if original_layer_number in pruning_pass["mapping_dict"] and pruning_pass["substitution_regex"].search(layer_name):
+ substitution_match = pruning_pass["substitution_regex"].search(layer_name)
+ # Reconstruct the layer name with the new pruned index mapping
+ new_state_dict[
+ (
+ layer_name[: substitution_match.start(1)] +
+ pruning_pass["mapping_dict"][original_layer_number] +
+ layer_name[substitution_match.end(1) :]
+ )
+ ] = state_dict[layer_name]
+
+ # Clean up the configuration properties after pruning is done
+ with open_dict(model_cfg) if isinstance(model_cfg, DictConfig) else contextlib.ExitStack():
+ if hasattr(model_cfg, "encoder_layers_to_keep"): model_cfg.encoder_layers_to_keep = None
+ if hasattr(model_cfg, "decoder_layers_to_keep"): model_cfg.decoder_layers_to_keep = None
+
+ return new_state_dict
+
+def get_activation_fn(activation):
+ """
+ Factory function to retrieve an activation function callable based on a string identifier.
+
+ Args:
+ activation (str): The name of the activation function.
+
+ Returns:
+ callable: The requested activation function or module reference.
+
+ Raises:
+ ValueError: If an unsupported activation function name is requested.
+ """
+
+ def relu_squared(x):
+ return F.relu(x).pow(2)
+
+ def gelu(x):
+ # Internal gelu casting safely handles fp16 precision stability
+ return nn.functional.gelu(x.float()).type_as(x)
+
+ def gelu_accurate(x):
+ if not hasattr(gelu_accurate, "_a"):
+ gelu_accurate._a = math.sqrt(2 / math.pi)
+ # Approximation formula for accurate/fast GeLU computation
+ return (0.5 * x * (1 + (gelu_accurate._a * (x + 0.044715 * x.pow(3))).tanh()))
+
+ if activation == "relu": return F.relu
+ elif activation == "relu_squared": return relu_squared
+ elif activation == "gelu": return gelu
+ elif activation == "gelu_fast": return gelu_accurate
+ elif activation == "gelu_accurate": return gelu_accurate
+ elif activation == "tanh": return torch.tanh
+ elif activation == "linear": return lambda x: x
+ elif activation == "swish": return nn.SiLU
+ else: raise ValueError(f"Activation function '{activation}' is not supported.")
+
+class SamePad(nn.Module):
+ """
+ Applies right-side cropping to match standard 'SAME' padding behavior in convolutions.
+ """
+
+ def __init__(
+ self,
+ kernel_size,
+ causal=False
+ ):
+ super().__init__()
+ if causal: self.remove = kernel_size - 1
+ else: self.remove = int(kernel_size % 2 == 0)
+
+ def forward(self, x):
+ if self.remove > 0: x = x[:, :, : -self.remove]
+ return x
+
+class TransformerSentenceEncoderLayer(nn.Module):
+ """
+ Standard Transformer Encoder Layer supporting post-LN and pre-LN styles.
+ """
+
+ def __init__(
+ self,
+ embedding_dim = 768,
+ ffn_embedding_dim = 3072,
+ num_attention_heads = 8,
+ dropout = 0.1,
+ attention_dropout = 0.1,
+ activation_dropout = 0.1,
+ activation_fn = "relu",
+ layer_norm_first = False
+ ):
+ super().__init__()
+ self.embedding_dim = embedding_dim
+ self.dropout = dropout
+ self.activation_dropout = activation_dropout
+ self.activation_fn = get_activation_fn(activation_fn)
+ # Self-attention submodule
+ self.self_attn = MultiheadAttention(self.embedding_dim, num_attention_heads, dropout=attention_dropout, self_attention=True)
+ # Regularization dropouts
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(self.activation_dropout)
+ self.dropout3 = nn.Dropout(dropout)
+ # Normalization layers
+ self.self_attn_layer_norm = nn.LayerNorm(self.embedding_dim, eps=1e-5, elementwise_affine=True)
+ self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
+ self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
+ self.final_layer_norm = nn.LayerNorm(self.embedding_dim, eps=1e-5, elementwise_affine=True)
+ # Bind the correct forward routing style dynamically
+ self.forward = self.forward_layer_norm_first if layer_norm_first else self.forward_non_layer_norm_first
+
+ def forward_layer_norm_first(self, x, self_attn_mask=None, self_attn_padding_mask=None, need_weights=False, att_args=None):
+ residual = x
+
+ x = self.self_attn_layer_norm(x)
+ x, attn = self.self_attn(query=x, key=x, value=x, key_padding_mask=self_attn_padding_mask, attn_mask=self_attn_mask, need_weights=False)
+ x = residual + self.dropout1(x)
+
+ residual = x
+ x = self.fc2(self.dropout2(self.activation_fn(self.fc1(self.final_layer_norm(x)))))
+
+ layer_result = x
+ x = residual + self.dropout3(x)
+
+ return x, (attn, layer_result)
+
+ def forward_non_layer_norm_first(self, x, self_attn_mask=None, self_attn_padding_mask=None, need_weights=False, att_args=None):
+ residual = x
+
+ x, attn = self.self_attn(query=x, key=x, value=x, key_padding_mask=self_attn_padding_mask, need_weights=False)
+ x = self.self_attn_layer_norm(residual + self.dropout1(x))
+
+ residual = x
+ x = self.fc2(self.dropout2(self.activation_fn(self.fc1(x))))
+
+ layer_result = x
+ x = self.final_layer_norm(residual + self.dropout3(x))
+
+ return x, (attn, layer_result)
+
+class AdapterFast(nn.Module):
+ """
+ Fast Parallel/Batched Multi-head Adapter Module for Parameter Efficient Fine Tuning.
+ """
+
+ def __init__(
+ self,
+ adapter_num,
+ input_dim,
+ hidden_dim,
+ act_fn
+ ):
+ super().__init__()
+ self.adapter_num = adapter_num
+ self.input_dim = input_dim
+ self.hidden_dim = hidden_dim
+ # Batched parameters for processing unique adapter weights in a single tensor operation
+ self.W_a = nn.Parameter(torch.empty(adapter_num, hidden_dim, input_dim))
+ self.W_b = nn.Parameter(torch.empty(adapter_num, input_dim, hidden_dim))
+ self.b_a = nn.Parameter(torch.empty(adapter_num, hidden_dim))
+ self.b_b = nn.Parameter(torch.empty(adapter_num, input_dim))
+ self.ln_W = nn.Parameter(torch.empty(adapter_num, input_dim))
+ self.ln_b = nn.Parameter(torch.empty(adapter_num, input_dim))
+ self.act_fn = nn.Identity()
+ if act_fn == "relu": self.act_fn = nn.ReLU()
+ elif act_fn == "gelu": self.act_fn = nn.GELU()
+ elif act_fn == "selu": self.act_fn = nn.SELU()
+ else: raise ValueError(f"Activation function '{act_fn}' is not supported in AdapterFast module.")
+ self.input_dim = input_dim
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ # Uniform Kaiming Initialization across batch components
+ for ii in range(self.adapter_num):
+ nn.init.kaiming_uniform_(self.W_a[ii], a=math.sqrt(5))
+ nn.init.kaiming_uniform_(self.W_b[ii], a=math.sqrt(5))
+ # Bound handling for consistent linear initialization
+ fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.W_a[ii])
+ bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
+ nn.init.uniform_(self.b_a[ii], -bound, bound)
+ fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.W_b[ii])
+ bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
+ nn.init.uniform_(self.b_b[ii], -bound, bound)
+
+ nn.init.ones_(self.ln_W)
+ nn.init.zeros_(self.ln_b)
+
+ def forward(self, x, adapter_id):
+ ii = adapter_id
+ # Evaluates specialized LayerNorm and Linear transforms tied to adapter index `ii`
+ return F.linear(
+ self.act_fn(
+ F.linear(
+ F.layer_norm(
+ x,
+ (self.input_dim, ),
+ self.ln_W[ii],
+ self.ln_b[ii]
+ ),
+ self.W_a[ii],
+ self.b_a[ii]
+ )
+ ),
+ self.W_b[ii],
+ self.b_b[ii]
+ )
+
+ def extra_repr(self):
+ return (
+ 'adapter={}, input_dim={}, hidden_dim={}'.format(self.adapter_num, self.input_dim, self.hidden_dim)
+ )
+
+class FeedForwardModule(nn.Module):
+ """
+ Conformer Feed Forward module processing sequential inputs.
+ """
+
+ def __init__(
+ self,
+ input_feat,
+ hidden_units,
+ dropout1,
+ dropout2,
+ activation_fn="swish",
+ bias=True
+ ):
+ super(FeedForwardModule, self).__init__()
+ self.layer_norm = nn.LayerNorm(input_feat, eps=1e-5, elementwise_affine=True)
+ self.w_1 = nn.Linear(input_feat, hidden_units, bias=bias)
+ self.w_2 = nn.Linear(hidden_units, input_feat, bias=bias)
+ self.dropout1 = nn.Dropout(dropout1)
+ self.dropout2 = nn.Dropout(dropout2)
+ # Instantiate the functional/callable wrapper via the helper factory
+ self.activation = get_activation_fn(activation_fn)(hidden_units)
+
+ def forward(self, x):
+ return self.dropout2(
+ self.w_2(
+ self.dropout1(
+ self.activation(
+ self.w_1(
+ self.layer_norm(x)
+ )
+ )
+ )
+ )
+ )
+
+class ConvolutionModule(nn.Module):
+ def __init__(
+ self,
+ embed_dim,
+ channels,
+ depthwise_kernel_size,
+ dropout,
+ activation_fn="swish",
+ bias=False
+ ):
+ super(ConvolutionModule, self).__init__()
+ # Enforce symmetrical padding compatibility requirements
+ assert (depthwise_kernel_size - 1) % 2 == 0
+ self.layer_norm = nn.LayerNorm(embed_dim, eps=1e-5, elementwise_affine=True)
+ # Pointwise convolution expands the channel dimension for Gated Linear Unit processing
+ self.pointwise_conv1 = nn.Conv1d(embed_dim, 2 * channels, kernel_size=1, stride=1, padding=0, bias=bias)
+ self.glu = nn.GLU(dim=1)
+ self.depthwise_conv = nn.Conv1d(channels, channels, depthwise_kernel_size, stride=1, padding=(depthwise_kernel_size - 1) // 2, groups=channels, bias=bias)
+ self.batch_norm = nn.BatchNorm1d(channels)
+ self.activation = get_activation_fn(activation_fn)(channels)
+ self.pointwise_conv2 = nn.Conv1d(channels, embed_dim, kernel_size=1, stride=1, padding=0, bias=bias)
+ self.dropout = nn.Dropout(dropout)
+
+ def forward(self, x):
+ # Rearrange dimensionality format to adapt to PyTorch's native 1D Conv structure
+ return self.dropout(self.pointwise_conv2(self.activation(self.batch_norm(self.depthwise_conv(self.glu(self.pointwise_conv1(self.layer_norm(x).transpose(1, 2)))))))).transpose(1, 2)
+
+def rotate_half(x):
+ """
+ Splits the final embedding dimension into halves and rotates their coordinates.
+ """
+
+ x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
+
+ return torch.cat((-x2, x1), dim=x1.ndim - 1)
+
+def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0):
+ """
+ Applies Rotary Position Embedding (RoPE) matrices to query and key tensors.
+ """
+
+ cos, sin = (cos[offset : q.shape[0] + offset, ...], sin[offset : q.shape[0] + offset, ...])
+
+ return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
+
+class RotaryPositionalEmbedding(nn.Module):
+ """
+ Computes sin/cos dynamic frequency caches for RoPE positional embeddings.
+ """
+
+ def __init__(
+ self,
+ dim,
+ base=10000,
+ precision=torch.half
+ ):
+ super().__init__()
+ inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+ self.register_buffer("inv_freq", inv_freq)
+ self.seq_len_cached = 0
+ self.cos_cached = torch.empty(self.seq_len_cached, 1, 1, dim)
+ self.sin_cached = torch.empty(self.seq_len_cached, 1, 1, dim)
+ self.precision = precision
+
+ def forward(self, x, seq_len = 0):
+ # Regenerate caches when the maximum execution sequence boundary expands
+ if seq_len > self.seq_len_cached:
+ self.seq_len_cached = seq_len
+
+ freqs = torch.einsum("i,j->ij", torch.arange(seq_len, device=x.device).type_as(self.inv_freq), self.inv_freq)
+ emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+
+ self.cos_cached = emb.cos().view(emb.size(0), 1, 1, emb.size(1))
+ self.sin_cached = emb.sin().view(emb.size(0), 1, 1, emb.size(1))
+
+ return self.cos_cached, self.sin_cached
+
+class ESPNETMultiHeadedAttention(nn.Module):
+ """
+ Multi-Head Attention component following ESPNET structural standards.
+ """
+
+ def __init__(
+ self,
+ n_feat,
+ n_head,
+ dropout
+ ):
+ super(ESPNETMultiHeadedAttention, self).__init__()
+ assert n_feat % n_head == 0
+ self.d_k = n_feat // n_head
+ self.h = n_head
+ self.linear_q = nn.Linear(n_feat, n_feat)
+ self.linear_k = nn.Linear(n_feat, n_feat)
+ self.linear_v = nn.Linear(n_feat, n_feat)
+ self.linear_out = nn.Linear(n_feat, n_feat)
+ self.attn = None
+ self.dropout = nn.Dropout(p=dropout)
+
+ def forward_qkv(self, query, key, value, **kwargs):
+ n_batch = query.size(0)
+ # Returns mapped projections formatted as (Batch, Head, SeqLen, HeadDim)
+ return (
+ self.linear_q(query).view(n_batch, -1, self.h, self.d_k).transpose(1, 2),
+ self.linear_k(key).view(n_batch, -1, self.h, self.d_k).transpose(1, 2),
+ self.linear_v(value).view(n_batch, -1, self.h, self.d_k).transpose(1, 2)
+ )
+
+ def forward_attention(self, value, scores, mask):
+ n_batch = value.size(0)
+ if mask is not None:
+ scores = scores.masked_fill(mask.unsqueeze(1).unsqueeze(2).to(bool), float("-inf"))
+
+ self.attn = scores.softmax(dim=-1)
+ return self.linear_out(((self.dropout(self.attn) @ value).transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)))
+
+ def forward(self, query, key, value, key_padding_mask=None, **kwargs):
+ # Transpose input shapes from (SeqLen, Batch, Dim) to (Batch, SeqLen, Dim)
+ q, k, v = self.forward_qkv(query.transpose(0, 1), key.transpose(0, 1), value.transpose(0, 1))
+ return self.forward_attention(v, (q @ k.transpose(-2, -1)) / math.sqrt(self.d_k), key_padding_mask).transpose(0, 1), None
+
+class RelPositionMultiHeadedAttention(ESPNETMultiHeadedAttention):
+ """
+ Multi-Head Attention featuring Relative Positional Encoding structures.
+ """
+
+ def __init__(self, n_feat, n_head, dropout):
+ super().__init__(n_feat, n_head, dropout)
+ self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
+ self.pos_bias_u = nn.Parameter(torch.zeros(self.h, self.d_k))
+ self.pos_bias_v = nn.Parameter(torch.zeros(self.h, self.d_k))
+ nn.init.xavier_uniform_(self.pos_bias_u)
+ nn.init.xavier_uniform_(self.pos_bias_v)
+
+ def rel_shift(self, x):
+ # Shifts attention matrix results cleanly to represent accurate relative index offsets
+ x = torch.cat(
+ [
+ torch.zeros(
+ (*x.size()[:3], 1),
+ device=x.device,
+ dtype=x.dtype
+ ),
+ x
+ ],
+ dim=-1
+ ).view(*x.size()[:2], x.size(3) + 1, x.size(2))[:, :, 1:].view_as(x)[:, :, :, : x.size(-1) // 2 + 1]
+
+ return x
+
+ def forward(self, query, key, value, pos_emb, key_padding_mask=None, **kwargs):
+ pos_emb = pos_emb.transpose(0, 1)
+ q, k, v = self.forward_qkv(query.transpose(0, 1), key.transpose(0, 1), value.transpose(0, 1))
+ q = q.transpose(1, 2)
+
+ return self.forward_attention(
+ v, (
+ (
+ (q + self.pos_bias_u).transpose(1, 2) @ k.transpose(-2, -1)
+ ) + self.rel_shift(
+ ((q + self.pos_bias_v).transpose(1, 2) @ self.linear_pos(pos_emb).view(pos_emb.size(0), -1, self.h, self.d_k).transpose(1, 2).transpose(-2, -1))
+ )
+ ) / math.sqrt(self.d_k), key_padding_mask).transpose(0, 1), None
+
+class RotaryPositionMultiHeadedAttention(ESPNETMultiHeadedAttention):
+ """
+ Multi-Head Attention integrating Rotary Positional Embeddings.
+ """
+
+ def __init__(
+ self,
+ n_feat,
+ n_head,
+ dropout,
+ precision,
+ rotary_emd_base=10000
+ ):
+ super().__init__(n_feat, n_head, dropout)
+ precision = torch.float
+ self.rotary_ndims = self.d_k
+ if precision == "fp16": precision = torch.half
+ self.rotary_emb = RotaryPositionalEmbedding(self.rotary_ndims, base=rotary_emd_base, precision=precision)
+
+ def forward(self, query, key, value, key_padding_mask=None, **kwargs):
+ T, B, _ = value.size()
+
+ query = query.view(T, B, self.h, self.d_k)
+ key = key.view(T, B, self.h, self.d_k)
+
+ value = value.view(T, B, self.h, self.d_k)
+ cos, sin = self.rotary_emb(value, seq_len=T)
+
+ query, key = apply_rotary_pos_emb(query, key, cos, sin, offset=0)
+ query = query.view(T, B, self.h * self.d_k)
+
+ key = key.view(T, B, self.h * self.d_k)
+ value = value.view(T, B, self.h * self.d_k)
+ q, k, v = self.forward_qkv(query.transpose(0, 1), key.transpose(0, 1), value.transpose(0, 1))
+
+ return self.forward_attention(v, (q @ k.transpose(-2, -1)) / math.sqrt(self.d_k), key_padding_mask).transpose(0, 1), None
+
+class ConformerEncoderLayer(nn.Module):
+ """
+ Conformer block structure featuring interleaved feed-forward modules.
+ """
+
+ def __init__(self, embed_dim, ffn_embed_dim, attention_heads, dropout, use_fp16, depthwise_conv_kernel_size=31, activation_fn="swish", attn_type=None, pos_enc_type="abs"):
+ self.pos_enc_type = pos_enc_type
+ super(ConformerEncoderLayer, self).__init__()
+ # Macaron-style sandwich feed forward networks
+ self.ffn1 = FeedForwardModule(embed_dim, ffn_embed_dim, dropout, dropout)
+ self.self_attn_layer_norm = nn.LayerNorm(embed_dim, eps=1e-5, elementwise_affine=True)
+ self.self_attn_dropout = nn.Dropout(dropout)
+ # Configure self-attention mechanics matching requested types
+ if attn_type == "espnet":
+ if self.pos_enc_type == "rel_pos": self.self_attn = RelPositionMultiHeadedAttention(embed_dim, attention_heads, dropout=dropout)
+ elif self.pos_enc_type == "rope": self.self_attn = RotaryPositionMultiHeadedAttention(embed_dim, attention_heads, dropout=dropout, precision=use_fp16)
+ elif self.pos_enc_type == "abs": self.self_attn = ESPNETMultiHeadedAttention(embed_dim, attention_heads, dropout=dropout)
+ else: raise ValueError(f"Unsupported positional encoding type '{self.pos_enc_type}' for espnet attention module.")
+ else: self.self_attn = MultiheadAttention(embed_dim, attention_heads, dropout=dropout)
+ self.conv_module = ConvolutionModule(embed_dim=embed_dim, channels=embed_dim, depthwise_kernel_size=depthwise_conv_kernel_size, dropout=dropout, activation_fn=activation_fn)
+ self.ffn2 = FeedForwardModule(embed_dim, ffn_embed_dim, dropout, dropout, activation_fn=activation_fn)
+ self.final_layer_norm = nn.LayerNorm(embed_dim, eps=1e-5, elementwise_affine=True)
+
+ def forward(self, x, encoder_padding_mask, position_emb = None):
+ residual = x
+ # 0.5 scaling factor matches standard Conformer architecture design
+ x = self.ffn1(x) * 0.5 + residual
+ residual = x
+
+ x = self.self_attn_layer_norm(x)
+ if self.pos_enc_type == "rel_pos": x, attn = self.self_attn(query=x, key=x, value=x, key_padding_mask=encoder_padding_mask, pos_emb=position_emb, need_weights=False)
+ else: x, attn = self.self_attn(query=x, key=x, value=x, key_padding_mask=encoder_padding_mask, need_weights=False)
+
+ x = self.self_attn_dropout(x)
+ x = x + residual
+ residual = x
+
+ # Convolution processing pass (requires dimension swapping to adapt sequence orders)
+ x = residual + self.conv_module(x.transpose(0, 1)).transpose(0, 1)
+ residual = x
+ x = self.ffn2(x)
+
+ layer_result = x
+ x = self.final_layer_norm(x * 0.5 + residual)
+
+ return x, (attn, layer_result)
+
+class ConformerWav2Vec2EncoderLayer(ConformerEncoderLayer):
+ def forward(
+ self,
+ x,
+ self_attn_mask=None,
+ self_attn_padding_mask=None,
+ need_weights=False,
+ att_args=None,
+ position_emb=None
+ ):
+ """
+ Subclass wrapper adjusting ConformerEncoderLayer interface format for compatibility with Wav2Vec2 pipelines.
+ """
+
+ return super().forward(
+ x,
+ self_attn_padding_mask,
+ position_emb
+ )
+
+class TransformerSentenceEncoderWithAdapterLayer(TransformerSentenceEncoderLayer):
+ """
+ Transformer Sentence Encoder Layer extended with a fast multi-head Adapter variant.
+ """
+
+ def __init__(
+ self,
+ embedding_dim = 768,
+ ffn_embedding_dim = 3072,
+ num_attention_heads = 8,
+ dropout = 0.1,
+ attention_dropout = 0.1,
+ activation_dropout = 0.1,
+ activation_fn = "relu",
+ layer_norm_first = False,
+ adapter_num=201,
+ adapter_dim=64,
+ adapter_act_fn="relu"
+ ):
+ super().__init__(
+ embedding_dim=embedding_dim,
+ ffn_embedding_dim=ffn_embedding_dim,
+ num_attention_heads=num_attention_heads,
+ dropout=dropout,
+ attention_dropout=attention_dropout,
+ activation_dropout=activation_dropout,
+ activation_fn=activation_fn,
+ layer_norm_first=layer_norm_first
+ )
+ self.adapter_num = adapter_num
+ self.adapter_dim = adapter_dim
+ # Initialize parallel multi-component Adapter instance
+ self.adapter_layer = AdapterFast(adapter_num, self.embedding_dim, self.adapter_dim, adapter_act_fn)
+
+ def forward(self, x, self_attn_mask=None, self_attn_padding_mask=None, need_weights=False, att_args=None, corpus_key=None):
+ # Delegate core computations to the standard transformer structure base class
+ x, (attn, layer_result) = super().forward(x=x, self_attn_mask=self_attn_mask, self_attn_padding_mask=self_attn_padding_mask, need_weights=need_weights, att_args=att_args)
+ # Guard rails verifying presence and single-domain homogeneity of adapter indexing arrays
+ assert corpus_key is not None
+ assert len(set(corpus_key)) == 1
+
+ # Add residual-like pathway scaling through the selected target domain adapter index
+ return x + self.adapter_layer(x, corpus_key[0]), (attn, layer_result)
+
+class TransposeLast(nn.Module):
+ """
+ Utility module to selectively deconstruct input containers and flip the final dimensions.
+ """
+
+ def __init__(self, deconstruct_idx=None, tranpose_dim=-2):
+ super().__init__()
+ self.deconstruct_idx = deconstruct_idx
+ self.tranpose_dim = tranpose_dim
+
+ def forward(self, x):
+ # Extract specific elements if input is a structured list/tuple container
+ if self.deconstruct_idx is not None: x = x[self.deconstruct_idx]
+ return x.transpose(self.tranpose_dim, -1)
+
+class TransformerEncoder(nn.Module):
+ """
+ Flexible Backing Encoder Layer Router supporting standard Transformer, Conformer, or Adapter variations.
+ """
+
+ def build_encoder_layer(self, args, **kwargs):
+ if args.layer_type == "transformer":
+ layer = TransformerSentenceEncoderLayer(
+ embedding_dim=self.embedding_dim,
+ ffn_embedding_dim=args.encoder_ffn_embed_dim,
+ num_attention_heads=args.encoder_attention_heads,
+ dropout=self.dropout,
+ attention_dropout=args.attention_dropout,
+ activation_dropout=args.activation_dropout,
+ activation_fn=args.activation_fn,
+ layer_norm_first=args.layer_norm_first
+ )
+ elif args.layer_type == "conformer":
+ layer = ConformerWav2Vec2EncoderLayer(
+ embed_dim=self.embedding_dim,
+ ffn_embed_dim=args.encoder_ffn_embed_dim,
+ attention_heads=args.encoder_attention_heads,
+ dropout=args.dropout,
+ depthwise_conv_kernel_size=args.depthwise_conv_kernel_size,
+ activation_fn="swish",
+ attn_type=args.attn_type,
+ use_fp16=args.fp16,
+ pos_enc_type="abs"
+ )
+ elif args.layer_type == "trf_adp":
+ use_adp = False
+ # Decide whether to mount Adapter units uniformly or selectively by sliced ranges
+ if args.adp_trf_idx == "all": use_adp = True
+ else:
+ if kwargs.get("layer_idx", None) in list(range(*[int(g) for g in args.adp_trf_idx.split(":")])): use_adp = True
+
+ layer = (
+ TransformerSentenceEncoderWithAdapterLayer(
+ embedding_dim=self.embedding_dim,
+ ffn_embedding_dim=args.encoder_ffn_embed_dim,
+ num_attention_heads=args.encoder_attention_heads,
+ dropout=self.dropout,
+ attention_dropout=args.attention_dropout,
+ activation_dropout=args.activation_dropout,
+ activation_fn=args.activation_fn,
+ layer_norm_first=args.layer_norm_first,
+ adapter_num=args.adp_num,
+ adapter_dim=args.adp_dim,
+ adapter_act_fn=args.adp_act_fn
+ )
+ ) if use_adp else (
+ TransformerSentenceEncoderLayer(
+ embedding_dim=self.embedding_dim,
+ ffn_embedding_dim=args.encoder_ffn_embed_dim,
+ num_attention_heads=args.encoder_attention_heads,
+ dropout=self.dropout,
+ attention_dropout=args.attention_dropout,
+ activation_dropout=args.activation_dropout,
+ activation_fn=args.activation_fn,
+ layer_norm_first=args.layer_norm_first
+ )
+ )
+
+ return layer
+
+ def __init__(self, args):
+ super().__init__()
+ self.dropout = args.dropout
+ self.embedding_dim = args.encoder_embed_dim
+ self.required_seq_len_multiple = args.required_seq_len_multiple
+ pos_conv_depth = getattr(args, "pos_conv_depth", 1)
+ if pos_conv_depth > 1:
+ num_layers = args.pos_conv_depth
+ k = max(3, args.conv_pos // num_layers)
+
+ def make_conv_block(e, k, g, l):
+ # Generates repetitive blocks mapping sequence dimensions for deep position convolutions
+ return nn.Sequential(
+ *[
+ nn.Sequential(
+ nn.Conv1d(
+ e,
+ e,
+ kernel_size=k,
+ padding=k // 2,
+ groups=g
+ ),
+ SamePad(k),
+ TransposeLast(),
+ nn.LayerNorm(e, eps=1e-5, elementwise_affine=False),
+ TransposeLast(),
+ nn.GELU()
+ )
+ for _ in range(l)
+ ]
+ )
+
+ self.pos_conv = make_conv_block(
+ self.embedding_dim,
+ k,
+ args.conv_pos_groups,
+ num_layers
+ )
+ else:
+ self.pos_conv = make_conv_pos(
+ self.embedding_dim,
+ args.conv_pos,
+ args.conv_pos_groups
+ )
+
+ # Dynamic construction loop filling sub-layer lists
+ self.layers = nn.ModuleList([self.build_encoder_layer(args, layer_idx=ii) for ii in range(args.encoder_layers)])
+ self.layer_norm_first = args.layer_norm_first
+ self.layer_norm = nn.LayerNorm(self.embedding_dim, eps=1e-5, elementwise_affine=True)
+ self.layerdrop = args.encoder_layerdrop
+ self.apply(init_bert_params)
+
+ def forward(self, x, padding_mask=None, layer=None):
+ x, layer_results = self.extract_features(x, padding_mask, layer)
+ if self.layer_norm_first and layer is None: x = self.layer_norm(x)
+ return x, layer_results
+
+ def extract_features(self, x, padding_mask=None, tgt_layer=None):
+ # Mask out padded positions explicitly to clean the feature stream
+ x = index_put(x, padding_mask, 0)
+ # Apply 1D positional convolution (shape transpositions needed for dimension compatibility)
+ x += self.pos_conv(x.transpose(1, 2)).transpose(1, 2)
+
+ if not self.layer_norm_first: x = self.layer_norm(x)
+
+ # Force structural sequence length adjustments matching target step multiples
+ x, pad_length = pad_to_multiple(x, self.required_seq_len_multiple, dim=-2, value=0)
+ padding_mask, _ = pad_to_multiple(padding_mask, self.required_seq_len_multiple, dim=-1, value=True)
+
+ x = F.dropout(x, p=self.dropout, training=self.training).transpose(0, 1)
+ layer_results = []
+ r = None
+
+ for i, layer in enumerate(self.layers):
+ dropout_probability = np.random.random() if self.layerdrop > 0 else 1
+ # LayerDrop implementation: drop random layers entirely during model training phases
+ if not self.training or (dropout_probability > self.layerdrop):
+ x, (z, lr) = layer(x, self_attn_padding_mask=padding_mask, need_weights=False)
+ if i >= 0: layer_results.append((x, z, lr))
+
+ if i == tgt_layer:
+ r = x
+ break
+
+ if r is not None: x = r
+ x = x.transpose(0, 1)
+ # Reverse structural adjustments, slicing off dummy tokens used during step alignments
+ if pad_length > 0:
+ x = x[:, :-pad_length]
+
+ def undo_pad(a, b, c):
+ return (a[:-pad_length], b[:-pad_length] if b is not None else b, c[:-pad_length])
+
+ layer_results = [undo_pad(*u) for u in layer_results]
+
+ return x, layer_results
+
+ def max_positions(self):
+ return self.args.max_positions
+
+ def upgrade_state_dict_named(self, state_dict, name):
+ return state_dict
+
+class ConvFeatureExtractionModel(nn.Module):
+ """
+ Strided 1D Convolution Network mapping audio waveforms to hidden feature streams.
+ """
+
+ def __init__(
+ self,
+ conv_layers,
+ dropout = 0.0,
+ mode = "default",
+ conv_bias = False
+ ):
+ super().__init__()
+ assert mode in {"default", "layer_norm"}
+
+ def block(n_in, n_out, k, stride, is_layer_norm=False, is_group_norm=False, conv_bias=False):
+ def make_conv():
+ conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias)
+ nn.init.kaiming_normal_(conv.weight)
+ return conv
+
+ assert (is_layer_norm and is_group_norm) == False
+
+ if is_layer_norm:
+ return nn.Sequential(
+ make_conv(),
+ nn.Dropout(p=dropout),
+ nn.Sequential(
+ TransposeLast(),
+ Fp32LayerNorm(
+ dim,
+ elementwise_affine=True
+ ),
+ TransposeLast()
+ ),
+ nn.GELU()
+ )
+ elif is_group_norm:
+ return nn.Sequential(
+ make_conv(),
+ nn.Dropout(p=dropout),
+ Fp32GroupNorm(dim, dim, affine=True),
+ nn.GELU()
+ )
+ else:
+ return nn.Sequential(
+ make_conv(),
+ nn.Dropout(p=dropout),
+ nn.GELU()
+ )
+
+ in_d = 1
+ self.conv_layers = nn.ModuleList()
+ for i, cl in enumerate(conv_layers):
+ assert len(cl) == 3
+ (dim, k, stride) = cl
+
+ self.conv_layers.append(
+ block(
+ in_d,
+ dim,
+ k,
+ stride,
+ is_layer_norm=mode == "layer_norm",
+ is_group_norm=mode == "default" and i == 0,
+ conv_bias=conv_bias
+ )
+ )
+
+ in_d = dim
+
+ def forward(self, x):
+ # Unsqueeze creates the distinct target input channel dimension
+ x = x.unsqueeze(1)
+ for conv in self.conv_layers:
+ x = conv(x)
+
+ return x
+
+class GradMultiply(torch.autograd.Function):
+ """
+ Autograd Function that rescales passing gradient bounds without altering forward data outputs.
+ """
+
+ @staticmethod
+ def forward(ctx, x, scale):
+ ctx.scale = scale
+ res = x.new(x)
+ return res
+
+ @staticmethod
+ def backward(ctx, grad):
+ # Scale backprop currents seamlessly during reverse topology loops
+ return grad * ctx.scale, None
+
+class BaseFairseqModel(nn.Module):
+ """
+ Abstract Base class offering state dictionary pruning and quick decoding/eval configurations.
+ """
+
+ def __init__(self):
+ super().__init__()
+ self._is_generation_fast = False
+
+ def extract_features(self, *args, **kwargs):
+ return self(*args, **kwargs)
+
+ def load_state_dict(self, state_dict, strict=True, model_cfg = None, args = None):
+ self.upgrade_state_dict(state_dict)
+ # Execute global state pruning loops targeting structural matches
+ new_state_dict = prune_state_dict(state_dict, model_cfg)
+ return super().load_state_dict(new_state_dict, strict)
+
+ def upgrade_state_dict(self, state_dict):
+ self.upgrade_state_dict_named(state_dict, "")
+
+ def upgrade_state_dict_named(self, state_dict, name):
+ assert state_dict is not None
+
+ def do_upgrade(m, prefix):
+ if len(prefix) > 0: prefix += "."
+ for n, c in m.named_children():
+ name = prefix + n
+ if hasattr(c, "upgrade_state_dict_named"): c.upgrade_state_dict_named(state_dict, name)
+ elif hasattr(c, "upgrade_state_dict"): c.upgrade_state_dict(state_dict)
+ do_upgrade(c, name)
+
+ do_upgrade(self, name)
+
+ def make_generation_fast_(self, **kwargs):
+ if self._is_generation_fast: return
+ self._is_generation_fast = True
+
+ def apply_remove_weight_norm(module):
+ try:
+ # Remove parameter wraps safely if PyTorch's newer parametrization architecture or weight_norm is found
+ if hasattr(module, "parametrizations") and "weight" in module.parametrizations: parametrize.remove_parametrizations(module, "weight", leave_parametrized=True)
+ else: nn.utils.remove_weight_norm(module)
+ except (AttributeError, ValueError):
+ return
+
+ self.apply(apply_remove_weight_norm)
+ def apply_make_generation_fast_(module, prefix):
+ if len(prefix) > 0: prefix += "."
+
+ base_func = BaseFairseqModel.make_generation_fast_
+ for n, m in module.named_modules():
+ # Trigger custom sub-module quick optimization steps recursively where defined
+ if (m != self and hasattr(m, "make_generation_fast_") and m.make_generation_fast_.__func__ is not base_func): m.make_generation_fast_(name=prefix + n, **kwargs)
+
+ apply_make_generation_fast_(self, "")
+ self.eval()
+
+class HubertConfig:
+ """
+ Configuration dataclass storing hyperparameters for HuBERT model initialization,
+ feature extraction setup, masking schedules, and optimization variables.
+ """
+
+ def __init__(
+ self,
+ _name = None,
+ label_rate = 50,
+ encoder_layers_1 = 3,
+ logit_temp_ctr = 0.1,
+ num_negatives = 100,
+ cross_sample_negatives = 0,
+ ctr_layers = [-6],
+ crop_seq_to_multiple = 1,
+ extractor_mode = "default",
+ encoder_layers = 12,
+ encoder_embed_dim = 768,
+ encoder_ffn_embed_dim = 3072,
+ encoder_attention_heads = 12,
+ activation_fn = "gelu",
+ layer_type = "transformer",
+ dropout = 0.1,
+ attention_dropout = 0.1,
+ activation_dropout = 0.0,
+ encoder_layerdrop = 0.0,
+ dropout_input = 0.0,
+ dropout_features = 0.0,
+ final_dim = 0,
+ untie_final_proj = False,
+ layer_norm_first = False,
+ conv_feature_layers = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2",
+ conv_bias = False,
+ logit_temp = 0.1,
+ target_glu = False,
+ feature_grad_mult = 1.0,
+ mask_length = 10,
+ mask_prob = 0.65,
+ mask_selection = "static",
+ mask_other = 0.0,
+ no_mask_overlap = False,
+ mask_min_space = 1,
+ mask_channel_length = 10,
+ mask_channel_prob = 0.0,
+ mask_channel_selection = "static",
+ mask_channel_other = 0.0,
+ no_mask_channel_overlap = False,
+ mask_channel_min_space = 1,
+ conv_pos = 128,
+ conv_pos_groups = 16,
+ conv_pos_batch_norm = False,
+ latent_temp = (2, 0.5, 0.999995),
+ skip_masked = False,
+ skip_nomask = False,
+ checkpoint_activations = False,
+ required_seq_len_multiple = 2,
+ depthwise_conv_kernel_size = 31,
+ attn_type = "",
+ pos_enc_type = "abs",
+ fp16 = False
+ ):
+ self._name = _name
+ self.label_rate = label_rate
+ self.encoder_layers_1 = encoder_layers_1
+ self.logit_temp_ctr = logit_temp_ctr
+ self.num_negatives = num_negatives
+ self.cross_sample_negatives = cross_sample_negatives
+ self.ctr_layers = ctr_layers
+ self.crop_seq_to_multiple = crop_seq_to_multiple
+ self.extractor_mode = extractor_mode
+ self.encoder_layers = encoder_layers
+ self.encoder_embed_dim = encoder_embed_dim
+ self.encoder_ffn_embed_dim = encoder_ffn_embed_dim
+ self.encoder_attention_heads = encoder_attention_heads
+ self.activation_fn = activation_fn
+ self.layer_type = layer_type
+ self.dropout = dropout
+ self.attention_dropout = attention_dropout
+ self.activation_dropout = activation_dropout
+ self.encoder_layerdrop = encoder_layerdrop
+ self.dropout_input = dropout_input
+ self.dropout_features = dropout_features
+ self.final_dim = final_dim
+ self.untie_final_proj = untie_final_proj
+ self.layer_norm_first = layer_norm_first
+ self.conv_feature_layers = conv_feature_layers
+ self.conv_bias = conv_bias
+ self.logit_temp = logit_temp
+ self.target_glu = target_glu
+ self.feature_grad_mult = feature_grad_mult
+ self.mask_length = mask_length
+ self.mask_prob = mask_prob
+ self.mask_selection = mask_selection
+ self.mask_other = mask_other
+ self.no_mask_overlap = no_mask_overlap
+ self.mask_min_space = mask_min_space
+ self.mask_channel_length = mask_channel_length
+ self.mask_channel_prob = mask_channel_prob
+ self.mask_channel_selection = mask_channel_selection
+ self.mask_channel_other = mask_channel_other
+ self.no_mask_channel_overlap = no_mask_channel_overlap
+ self.mask_channel_min_space = mask_channel_min_space
+ self.conv_pos = conv_pos
+ self.conv_pos_groups = conv_pos_groups
+ self.conv_pos_batch_norm = conv_pos_batch_norm
+ self.latent_temp = latent_temp
+ self.skip_masked = skip_masked
+ self.skip_nomask = skip_nomask
+ self.checkpoint_activations = checkpoint_activations
+ self.required_seq_len_multiple = required_seq_len_multiple
+ self.depthwise_conv_kernel_size = depthwise_conv_kernel_size
+ self.attn_type = attn_type
+ self.pos_enc_type = pos_enc_type
+ self.fp16 = fp16
+
+class HubertModel(BaseFairseqModel):
+ """
+ HuBERT (Hidden-Unit BERT) Model implementation for self-supervised speech representation learning.
+
+ This model processes raw audio waveforms through a convolutional feature extractor,
+ applies masking, and feeds the representations into a Transformer encoder to learn
+ contextualized speech representations.
+
+ Args:
+ cfg (object): Configuration object containing model hyperparameters.
+ num_classes (int): Total number of target classes/labels for token generation.
+ """
+
+ def __init__(
+ self,
+ cfg,
+ num_classes
+ ):
+ super().__init__()
+ feature_enc_layers = eval(cfg.conv_feature_layers) # Parse the convolutional layer configurations from a string representation of a list
+ # Determine the embedding dimension output by the final convolutional layer
+ self.embed = feature_enc_layers[-1][0]
+ # Convolutional feature extractor to process raw audio input
+ self.feature_extractor = ConvFeatureExtractionModel(conv_layers=feature_enc_layers, dropout=0.0, mode=cfg.extractor_mode, conv_bias=cfg.conv_bias)
+ # Projection layer if the convolutional embedding dimension differs from the transformer dimension
+ self.post_extract_proj = nn.Linear(self.embed, cfg.encoder_embed_dim) if self.embed != cfg.encoder_embed_dim else None
+ # Dropout layers for regularization
+ self.dropout_input = nn.Dropout(cfg.dropout_input)
+ self.dropout_features = nn.Dropout(cfg.dropout_features)
+ # Gradient multiplier for the convolutional feature extractor (used to scale or block gradients)
+ self.feature_grad_mult = cfg.feature_grad_mult
+ # Determine the final projection dimension
+ final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim
+ # Learnable mask embedding token used to replace masked frames during pre-training
+ self.mask_emb = nn.Parameter(torch.FloatTensor(cfg.encoder_embed_dim).uniform_())
+ # The core Transformer Encoder network
+ self.encoder = TransformerEncoder(cfg)
+ # Layer normalization applied to the extracted convolutional features
+ self.layer_norm = nn.LayerNorm(self.embed, eps=1e-5, elementwise_affine=True)
+ # Optional Gated Linear Unit (GLU) for target projections
+ self.target_glu = None
+ if cfg.target_glu: self.target_glu = nn.Sequential(nn.Linear(final_dim, final_dim * 2), nn.GLU())
+ self.final_proj = nn.Linear(cfg.encoder_embed_dim, final_dim) # Final linear projection layer
+ # Learnable label embeddings concatenated for cluster/target classification
+ self.label_embs_concat = nn.Parameter(torch.FloatTensor(sum([num_classes]), final_dim))
+ nn.init.uniform_(self.label_embs_concat)
+
+ def upgrade_state_dict_named(self, state_dict, name):
+ """
+ Upgrades a given state dict named for backward compatibility.
+
+ Args:
+ state_dict (dict): The state dictionary to upgrade.
+ name (str): The name of the model component.
+
+ Returns:
+ dict: The updated state dictionary.
+ """
+
+ super().upgrade_state_dict_named(state_dict, name)
+ return state_dict
+
+ def forward_features(self, source):
+ """
+ Extracts features from the raw audio source using the convolutional feature extractor.
+
+ Args:
+ source (Tensor): Input raw audio waveform tensor of shape `(batch, samples)`.
+
+ Returns:
+ Tensor: Extracted features tensor.
+ """
+
+ if self.feature_grad_mult > 0:
+ features = self.feature_extractor(source)
+ # Scale gradients if feature_grad_mult is explicitly set and not equal to 1.0
+ if self.feature_grad_mult != 1.0: features = GradMultiply.apply(features, self.feature_grad_mult)
+ else:
+ # Disable gradient computation for the feature extractor if multiplier is 0 or negative
+ with torch.no_grad():
+ features = self.feature_extractor(source)
+
+ return features
+
+ def forward_padding_mask(self, features, padding_mask):
+ """
+ Aligns and downsamples the attention padding mask to match the feature extractor's output length.
+
+ Args:
+ features (Tensor): Features tensor of shape `(batch, frames, channels)`.
+ padding_mask (Tensor): Original boolean padding mask for the raw audio.
+
+ Returns:
+ Tensor: Downsampled boolean padding mask of shape `(batch, frames)`.
+ """
+
+ # Calculate the remainder when matching the raw mask length with the downsampled feature length
+ extra = padding_mask.size(1) % features.size(1)
+ if extra > 0: padding_mask = padding_mask[:, :-extra]
+
+ return padding_mask.view(padding_mask.size(0), features.size(1), -1).all(-1)
+
+ def forward(self, source, padding_mask = None, output_layer = None):
+ """
+ Forward pass of the HuBERT model.
+
+ Args:
+ source (Tensor): Input raw audio waveform tensor of shape `(batch, samples)`.
+ padding_mask (Tensor, optional): Boolean padding mask for input. Defaults to None.
+ output_layer (int, optional): Specific Transformer layer index to output from. Defaults to None.
+
+ Returns:
+ - "x" (Tensor): The encoder's contextualized output representations.
+ - "padding_mask" (Tensor): The downsampled padding mask applied.
+ - "features" (Tensor): The projected convolutional features before the encoder.
+ """
+
+ # 1. Feature Extraction & Normalization
+ features = self.forward_features(source)
+ # Transpose to (batch, frames, channels) before applying LayerNorm
+ features = self.layer_norm(features.transpose(1, 2))
+
+ # Keep a copy of unmasked features (often used for computing target losses)
+ unmasked_features = features.clone()
+ # 2. Process Padding Mask
+ padding_mask = self.forward_padding_mask(features, padding_mask)
+
+ # 3. Projection & Regularization
+ if self.post_extract_proj is not None: features = self.post_extract_proj(features)
+
+ features = self.dropout_input(features)
+ unmasked_features = self.dropout_features(unmasked_features)
+
+ # 4. Transformer Encoding
+ x, _ = self.encoder(features, padding_mask=padding_mask, layer=None if output_layer is None else output_layer - 1)
+ return {"x": x, "padding_mask": padding_mask, "features": features}
+
+ def extract_features(self, source, ret_conv = False, output_layer = None):
+ """
+ Utility method to extract either convolutional or contextualized Transformer features.
+
+ Args:
+ source (Tensor): Input raw audio waveform tensor.
+ ret_conv (bool, optional): If True, returns CNN features; otherwise returns Transformer features. Defaults to False.
+ output_layer (int, optional): Specific Transformer layer index to extract from. Defaults to None.
+
+ Returns:
+ tuple: (features_tensor, padding_mask)
+ """
+
+ # Create a default empty padding mask (all False) matching the source shape
+ padding_mask = torch.BoolTensor(source.shape).fill_(False).to(source.device)
+ # Execute standard forward pass
+ res = self.forward(source, padding_mask=padding_mask, output_layer=output_layer)
+ # Return either the CNN projected features or the contextualized Transformer representations
+ return res["features"] if ret_conv else res["x"], res["padding_mask"]
\ No newline at end of file
diff --git a/main/library/embedders/onnx.py b/main/library/embedders/onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..0aa0410e29ae0101702d88f7eea40dfbbf08405d
--- /dev/null
+++ b/main/library/embedders/onnx.py
@@ -0,0 +1,135 @@
+import torch
+import onnxruntime
+
+import numpy as np
+
+class HubertModelONNX:
+ """
+ A wrapper class for running Hubert audio embedding models using ONNX Runtime.
+
+ This class supports both standard NumPy-based inference and optimized
+ I/O binding for CUDA/CPU providers to minimize data transfer overhead
+ between PyTorch and ONNX Runtime.
+ """
+
+ def __init__(
+ self,
+ embedder_model_path,
+ providers,
+ device
+ ):
+ """
+ Initializes the HubertModelONNX instance.
+
+ Args:
+ embedder_model_path (str): Path to the ONNX model file (.onnx).
+ providers (List): List of execution providers for ONNX Runtime.
+ device (str or torch.device): PyTorch device to use for the output tensors.
+ """
+
+ # Configure session options to minimize logging verbosity (3 = ERROR level)
+ sess_options = onnxruntime.SessionOptions()
+ sess_options.log_severity_level = 3
+ # Initialize the ONNX Runtime Inference Session
+ self.model = onnxruntime.InferenceSession(
+ embedder_model_path,
+ sess_options=sess_options,
+ providers=providers
+ )
+
+ self.device = device
+ self._final_proj = False # Control which output to return (False: 'feats', True: 'feats_proj')
+ # Determine device type string based on the primary execution provider
+ self._device = "cuda" if providers[0][0].startswith("CUDA") else "cpu"
+ # Optimize performance: use I/O binding if running on CUDA or CPU, else fallback
+ self.extract_features = self.extract_features_io if providers[0][0].startswith(("CUDA", "CPU")) else self.extract_features_non_io
+ # Pre-allocate output_layer buffer based on the execution path (Torch Tensor for I/O binding, NumPy for standard)
+ self.output_layer = torch.zeros((), device=self.device, dtype=torch.int64) if providers[0][0].startswith(("CUDA", "CPU")) else np.empty((), dtype=np.int64)
+
+ def final_proj(self, source):
+ """
+ A placeholder identity function for final projection manipulation.
+
+ Args:
+ source (torch.Tensor): Input feature tensor.
+
+ Returns:
+ torch.Tensor: The unmodified source tensor.
+ """
+
+ return source
+
+ def extract_features_non_io(self, source, output_layer = None):
+ """
+ Extracts features using standard ONNX Runtime inference without I/O binding.
+ Data is copied from GPU to CPU to run in ONNX, then converted back.
+
+ Args:
+ source (torch.Tensor): Input audio tensor.
+ output_layer (int, optional): The target layer index to extract features from.
+
+ Returns:
+ List[torch.Tensor]: A list containing the requested output tensor.
+ """
+
+ # Fill the pre-allocated NumPy array with the target layer index
+ self.output_layer.fill(output_layer)
+ dtype = source.dtype
+
+ # Run inference via standard ONNX CPU/GPU data copying pipeline
+ logits = self.model.run(
+ # During the model export, I inadvertently gave the input and output the same name without realizing it, which is why it ended up as 'feats.1'.
+ ["feats", "feats_proj"], {"feats.1": source.float().detach().cpu().numpy(), "output_layer": self.output_layer}
+ )
+
+ # Convert the chosen output back to a PyTorch tensor on the destination device
+ return [
+ torch.as_tensor(
+ logits[int(self._final_proj)],
+ dtype=dtype,
+ device=self.device
+ )
+ ]
+
+ def extract_features_io(self, source, output_layer = None):
+ """
+ Extracts features efficiently using ONNX Runtime I/O Binding.
+ This avoids expensive data copying between PyTorch tensors and NumPy arrays.
+
+ Args:
+ source (torch.Tensor): Input audio tensor residing on CPU/GPU.
+ output_layer (int, optional): The target layer index to extract features from.
+
+ Returns:
+ List[torch.Tensor]: A list containing the requested output tensor.
+ """
+
+ # In-place fill for the PyTorch buffer tensor
+ self.output_layer.fill_(output_layer)
+ device_idx = source.device.index or 0
+
+ # The exported models are in Float format, so the input must also be Float.
+ # And since I/O Binding requires contiguous tensors, they need to be converted before being fed in.
+ dtype = source.dtype
+ source = source.float().contiguous()
+
+ # Instantiate I/O binding object
+ io_binding = self.model.io_binding()
+ # Bind inputs using direct memory pointers (buffer_ptr)
+ io_binding.bind_input(name="feats.1", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(source.shape), buffer_ptr=source.data_ptr())
+ io_binding.bind_input(name="output_layer", device_type=self._device, device_id=device_idx, element_type=np.int64, shape=tuple(self.output_layer.shape), buffer_ptr=self.output_layer.data_ptr())
+
+ # Bind outputs to device memory to prevent automatic fallback copying to CPU host
+ io_binding.bind_output(name="feats", device_type=self._device, device_id=device_idx)
+ io_binding.bind_output(name="feats_proj", device_type=self._device, device_id=device_idx)
+ # Execute model inference with bindings
+ self.model.run_with_iobinding(io_binding)
+
+ # Retrieve outputs and convert back to the desired PyTorch configuration
+ return [
+ torch.as_tensor(
+ io_binding.get_outputs()[int(self._final_proj)].numpy(),
+ dtype=dtype,
+ device=self.device
+ )
+ ]
\ No newline at end of file
diff --git a/main/library/embedders/transformers.py b/main/library/embedders/transformers.py
new file mode 100644
index 0000000000000000000000000000000000000000..36b6ac2acc16260b53a8d0fe565b249c2eb3d3e8
--- /dev/null
+++ b/main/library/embedders/transformers.py
@@ -0,0 +1,46 @@
+from torch import nn
+from transformers import HubertModel
+
+class HubertModelWithFinalProj(HubertModel):
+ """
+ A subclass of transformers.HubertModel that adds a final linear projection layer
+ and custom feature extraction capabilities.
+
+ Attributes:
+ final_proj (nn.Linear): A linear layer that projects features from the hidden size to the classifier projection size.
+ """
+
+ def __init__(self, config):
+ """
+ Initializes the HubertModelWithFinalProj instance.
+
+ Args:
+ config (HubertConfig): The configuration object defining model hyperparameters.
+ """
+
+ super().__init__(config)
+ # Define a linear projection layer mapping from hidden size to classifier projection size
+ self.final_proj = nn.Linear(
+ config.hidden_size,
+ config.classifier_proj_size
+ )
+
+ def extract_features(self, source, output_layer=None):
+ """
+ Extracts hidden states/features from a specific layer of the Hubert model.
+
+ Args:
+ source (torch.Tensor): Input audio waveform tensor of shape (batch_size, sequence_length).
+ output_layer (int, optional): The index of the specific transformer layer from which to extract features. If None, the last hidden state is returned.
+
+ Returns:
+ List[torch.Tensor]: A list containing a single tensor of the extracted features.
+ """
+
+ # Perform a forward pass, explicitly requesting all hidden states to be returned
+ outputs = self.forward(source, output_hidden_states=True, return_dict=True)
+ # Select the features based on the requested output layer
+ # If output_layer is not specified, default to the very last layer's hidden state
+ feats = outputs.last_hidden_state if output_layer is None else outputs.hidden_states[output_layer]
+ # Return the extracted features wrapped inside a list
+ return [feats]
\ No newline at end of file
diff --git a/main/library/generators/bigvgan.py b/main/library/generators/bigvgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..297c07081c98869727bdae7f66df17dd22e60a35
--- /dev/null
+++ b/main/library/generators/bigvgan.py
@@ -0,0 +1,719 @@
+import math
+import torch
+
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.utils.parametrize as parametrize
+
+from torch.nn.utils import remove_weight_norm
+from torch.nn.utils.parametrizations import weight_norm
+
+class AntiAliasActivation(nn.Module):
+ """
+ Anti-aliased activation layer using upsampling, SnakeBeta activation, and downsampling.
+ This helps prevent high-frequency aliasing artifacts when applying non-linearities.
+ """
+
+ def __init__(
+ self,
+ channels,
+ up=2,
+ down=2,
+ up_k=12,
+ down_k=12
+ ):
+ """
+ Args:
+ channels (int): Number of input/output channels.
+ up (int): Upsampling factor. Default: 2.
+ down (int): Downsampling factor. Default: 2.
+ up_k (int): Kernel size for the upsampling low-pass filter. Default: 12.
+ down_k (int): Kernel size for the downsampling low-pass filter. Default: 12.
+ """
+
+ super().__init__()
+ # Internal modules for anti-aliasing pipeline
+ self.up = UpSample1d(up, up_k)
+ self.act = SnakeBeta(channels)
+ self.down = DownSample1d(down, down_k)
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Input tensor of shape (B, C, T).
+ Returns:
+ Tensor: Activated and filtered tensor of shape (B, C, T).
+ """
+
+ # Run through upsample -> non-linear activation -> downsample
+ return self.down(
+ self.act(self.up(x))
+ )
+
+class SnakeBeta(nn.Module):
+ """
+ SnakeBeta activation function proposed in BigVGAN.
+ Introduces learnable periodic parameters (alpha and beta) to better model periodic signals like audio.
+ """
+
+ def __init__(
+ self,
+ channels
+ ):
+ """
+ Args:
+ channels (int): Number of channels for learnable parameters.
+ """
+
+ super().__init__()
+ # Initialize learnable parameters to zeros
+ self.alpha = nn.Parameter(torch.zeros(1, channels, 1))
+ self.beta = nn.Parameter(torch.zeros(1, channels, 1))
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Input tensor of shape (B, C, T).
+ Returns:
+ Tensor: SnakeBeta activated tensor of shape (B, C, T).
+ """
+
+ # Formula: x + (1 / (e^beta + 1e-9)) * sin^2(x * e^alpha)
+ return x + (1.0 / (self.beta.exp() + 1e-9)) * (x * self.alpha.exp()).sin().pow(2)
+
+def kaiser_sinc_filter1d(
+ cutoff,
+ half_width,
+ kernel_size
+):
+ """
+ Generates a 1D low-pass sinc filter windowed with a Kaiser window.
+
+ Args:
+ cutoff (float): Cutoff frequency relative to Nyquist (0.0 to 0.5).
+ half_width (float): Transition band half-width.
+ kernel_size (int): Total size of the filter kernel.
+ Returns:
+ Tensor: Generated filter tensor of shape (1, 1, kernel_size).
+ """
+
+ even = kernel_size % 2 == 0
+ half_size = kernel_size // 2
+ delta_f = 4 * half_width
+
+ # Compute empirical Kaiser beta parameter based on stopband attenuation formula
+ A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+
+ if A > 50.0:
+ beta = 0.1102 * (A - 8.7)
+ elif A >= 21.0:
+ beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)
+ else:
+ beta = 0.0
+
+ # Generate Kaiser window
+ window = torch.kaiser_window(
+ kernel_size,
+ beta=beta,
+ periodic=False
+ )
+
+ # Compute time grid depending on kernel parity
+ time = (
+ torch.arange(-half_size, half_size) + 0.5
+ ) if even else (
+ torch.arange(kernel_size) - half_size
+ )
+
+ # Compute windowed sinc function
+ if cutoff == 0:
+ filter = torch.zeros_like(time)
+ else:
+ filter = 2 * cutoff * window * torch.sinc(2 * cutoff * time)
+ filter /= filter.sum() # Normalize to ensure unity gain at DC
+
+ return filter.view(1, 1, kernel_size)
+
+class UpSample1d(nn.Module):
+ """
+ 1D Upsampling layer utilizing a Kaiser-windowed sinc filter for anti-aliasing during interpolation.
+ """
+
+ def __init__(
+ self,
+ ratio=2,
+ kernel_size=None
+ ):
+ """
+ Args:
+ ratio (int): Upsampling factor. Default: 2.
+ kernel_size (int, optional): Size of the low-pass filter kernel. Defaults to automatic calculation.
+ """
+
+ super().__init__()
+ self.ratio = ratio
+ self.stride = ratio
+ # Determine kernel size if not provided
+ kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+ # Calculate padding to ensure proper centering after transposed convolution
+ self.pad = kernel_size // ratio - 1
+ self.pad_left = self.pad * self.stride + (kernel_size - self.stride) // 2
+ self.pad_right = self.pad * self.stride + (kernel_size - self.stride + 1) // 2
+
+ # Design the anti-aliasing low-pass filter
+ filter = kaiser_sinc_filter1d(
+ cutoff=0.5 / ratio,
+ half_width=0.6 / ratio,
+ kernel_size=kernel_size
+ )
+ self.register_buffer("filter", filter)
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Input tensor.
+
+ Returns:
+ Tensor: Upsampled tensor.
+ """
+
+ # Apply replicated padding to reduce edge artifacts
+ x = self.ratio * F.conv_transpose1d(
+ F.pad(
+ x,
+ (self.pad, self.pad),
+ mode="replicate"
+ ),
+ self.filter.expand(x.size(1), -1, -1), # Expand filter to match input channel size
+ stride=self.stride,
+ groups=x.size(1)
+ )
+
+ # Slice the tensor to remove extra padding artifacts
+ return x[..., self.pad_left : -self.pad_right]
+
+class LowPassFilter1d(nn.Module):
+ """
+ Standard 1D Low-Pass Filter layer using Kaiser-windowed sinc weights.
+ """
+
+ def __init__(
+ self,
+ cutoff=0.5,
+ half_width=0.6,
+ stride=1,
+ kernel_size=12
+ ):
+ """
+ Args:
+ cutoff (float): Cutoff frequency. Must be between 0.0 and 0.5. Default: 0.5.
+ half_width (float): Transition width. Default: 0.6.
+ stride (int): Stride for downsampling. Default: 1.
+ kernel_size (int): Size of the filter kernel. Default: 12.
+ """
+
+ super().__init__()
+ if cutoff < -0.0 or cutoff > 0.5:
+ raise ValueError("Cutoff frequency must be in the range [0.0, 0.5]")
+
+ even = kernel_size % 2 == 0
+ self.pad_left = kernel_size // 2 - int(even)
+ self.pad_right = kernel_size // 2
+ self.stride = stride
+ # Create filter buffer
+ filter = kaiser_sinc_filter1d(
+ cutoff,
+ half_width,
+ kernel_size
+ )
+ self.register_buffer("filter", filter)
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Input tensor of shape (B, C, T).
+ Returns:
+ Tensor: Filtered (and potentially downsampled) tensor.
+ """
+
+ # Pad edges, expand filter across channels, and apply depthwise convolution
+ return F.conv1d(
+ F.pad(
+ x,
+ (self.pad_left, self.pad_right),
+ mode="replicate"
+ ),
+ self.filter.expand(x.size(1), -1, -1),
+ stride=self.stride,
+ groups=x.size(1)
+ )
+
+class DownSample1d(nn.Module):
+ """
+ 1D Downsampling layer that applies a low-pass filter before strided decimation to avoid aliasing.
+ """
+
+ def __init__(
+ self,
+ ratio=2,
+ kernel_size=None
+ ):
+ """
+ Args:
+ ratio (int): Downsampling factor. Default: 2.
+ kernel_size (int, optional): Filter kernel size.
+ """
+ super().__init__()
+ # Combine low-pass filtering and strided downsampling into a single LowPassFilter1d module
+ self.lowpass = LowPassFilter1d(
+ cutoff=0.5 / ratio,
+ half_width=0.6 / ratio,
+ stride=ratio,
+ kernel_size=int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size,
+ )
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Input tensor of shape (B, C, T).
+ Returns:
+ Tensor: Downsampled tensor.
+ """
+
+ return self.lowpass(x)
+
+class AMPLayer(nn.Module):
+ def __init__(
+ self,
+ channels,
+ kernel_size,
+ dilation
+ ):
+ """
+ Args:
+ channels (int): Number of feature channels.
+ kernel_size (int): Size of the convolution kernel.
+ dilation (int): Dilation rate for the first convolution layer.
+ """
+
+ super().__init__()
+ # Dilated convolution with weight normalization
+ self.conv1 = weight_norm(
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ padding=(kernel_size * dilation - dilation) // 2,
+ dilation=dilation,
+ )
+ )
+ # Regular convolution with weight normalization
+ self.conv2 = weight_norm(
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ padding=kernel_size // 2,
+ dilation=1
+ )
+ )
+
+ # Anti-aliased activations instead of standard LeakyReLU/ReLU
+ self.act1 = AntiAliasActivation(channels)
+ self.act2 = AntiAliasActivation(channels)
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Input tensor.
+
+ Returns:
+ Tensor: Residual output tensor.
+ """
+
+ # Forward pass with residual/skip connection
+ y = self.conv1(self.act1(x))
+ y = self.conv2(self.act2(y))
+
+ return x + y
+
+ def remove_weight_norm(self):
+ """
+ Removes weight normalization from both convolution layers.
+ Supports both modern torch parametrization and legacy weight_norm hooks.
+ """
+
+ if hasattr(self.conv1, "parametrizations") and "weight" in self.conv1.parametrizations: parametrize.remove_parametrizations(self.conv1, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv1)
+
+ if hasattr(self.conv2, "parametrizations") and "weight" in self.conv2.parametrizations: parametrize.remove_parametrizations(self.conv2, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv2)
+
+class AMPBlock(nn.Module):
+ """
+ AMP Block containing multiple AMPLayers with different dilation factors.
+ """
+
+ def __init__(
+ self,
+ channels,
+ kernel_size,
+ dilations
+ ):
+ """
+ Args:
+ channels (int): Number of feature channels.
+ kernel_size (int): Kernel size for convolutions.
+ dilations (list of int): List of dilations for each AMPLayer.
+ """
+
+ super().__init__()
+ # Accumulate multiple AMPLayers sequentially
+ self.layers = nn.ModuleList([
+ AMPLayer(channels, kernel_size, dilation)
+ for dilation in dilations
+ ])
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Input feature map.
+
+ Returns:
+ Tensor: Processed feature map.
+ """
+
+ for layer in self.layers:
+ x = layer(x)
+
+ return x
+
+ def remove_weight_norm(self):
+ """
+ Recursively removes weight normalization from all sub-layers.
+ """
+
+ for layer in self.layers:
+ layer.remove_weight_norm()
+
+class SineGen(nn.Module):
+ """
+ Sine wave generator for Neural Source Filter (NSF) models.
+ Generates excitation signals based on Fundamental Frequency (F0).
+ """
+
+ def __init__(
+ self,
+ sampling_rate,
+ harmonic_num=0,
+ sine_amp=0.1,
+ noise_std=0.003,
+ voiced_threshold=0,
+ flag_for_pulse=False,
+ ):
+ """
+ Args:
+ sampling_rate (int): Sampling rate of the target audio.
+ harmonic_num (int): Number of harmonics to generate. Default: 0.
+ sine_amp (float): Amplitude of sine waves. Default: 0.1.
+ noise_std (float): Standard deviation of unvoiced noise. Default: 0.003.
+ voiced_threshold (float): F0 threshold to determine voiced/unvoiced frames. Default: 0.
+ flag_for_pulse (bool): Unused flag preserved for compatibility. Default: False.
+ """
+
+ super(SineGen, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = noise_std
+ self.harmonic_num = harmonic_num
+ self.dim = self.harmonic_num + 1
+ self.sampling_rate = sampling_rate
+ self.voiced_threshold = voiced_threshold
+ self.flag_for_pulse = flag_for_pulse
+
+ def _f02uv(self, f0):
+ """Calculates Voiced/Unvoiced (U/V) mask based on threshold."""
+
+ return (f0 > self.voiced_threshold).float()
+
+ def _f02sine(self, f0_values):
+ """
+ Converts continuous F0 trajectories into phase-continuous sine waves.
+
+ Args:
+ f0_values (Tensor): F0 values for harmonics.
+
+ Returns:
+ Tensor: Generated sine wave matrix.
+ """
+
+ # Calculate phase increment per sample step
+ rad_values = (f0_values / self.sampling_rate) % 1
+ rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], dtype=f0_values.dtype, device=f0_values.device)
+
+ # Set first time-step initial phase
+ rand_ini[:, 0] = 0
+ rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+
+ # Handle phase wrapping logic over multiple periods
+ tmp_over_one = torch.cumsum(rad_values, 1) % 1
+ tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
+
+ cumsum_shift = torch.zeros_like(rad_values)
+ cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+
+ # Compute continuous sine trajectories
+ return (torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi).sin()
+
+ def forward(self, f0):
+ """
+ Args:
+ f0 (Tensor): F0 tensor.
+
+ Returns:
+ Tensor: Sine waves mixed with noise excitation.
+ """
+
+ with torch.no_grad():
+ # Initialize array to contain fundamental frequency and its harmonics
+ f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, dtype=f0.dtype, device=f0.device)
+ f0_buf[:, :, 0] = f0[:, :, 0]
+
+ # Compute harmonic frequencies (F0 * 2, F0 * 3, ...)
+ for idx in np.arange(self.harmonic_num):
+ f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
+
+ # Generate pure sine waves
+ # The input must be kept as a float; otherwise, the output is prone to becoming NaN.
+ sine_waves = self._f02sine(f0_buf.float()) * self.sine_amp
+ uv = self._f02uv(f0)
+
+ # Blend sine waves with Gaussian noise for voiced sections, or pure noise for unvoiced sections
+ sine_waves = sine_waves * uv + ((uv * self.noise_std + (1 - uv) * self.sine_amp / 3) * torch.randn_like(sine_waves))
+
+ return sine_waves
+
+class SourceModuleHnNSF(nn.Module):
+ """
+ Source Module for Harmonic-plus-Noise NSF architecture.
+ Refines raw excitation signals through linear mixing and non-linear squashing.
+ """
+
+ def __init__(
+ self,
+ sampling_rate,
+ harmonic_num=0,
+ sine_amp=0.1,
+ add_noise_std=0.003,
+ voiced_threshod=0.0,
+ ):
+ """
+ Args:
+ sampling_rate (int): Audio sampling rate.
+ harmonic_num (int): Number of harmonics. Default: 0.
+ sine_amp (float): Sine amplitude. Default: 0.1.
+ add_noise_std (float): Noise standard deviation. Default: 0.003.
+ voiced_threshod (float): Threshold for voiced frames. Default: 0.0.
+ """
+
+ super(SourceModuleHnNSF, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = add_noise_std
+ self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod)
+ # Merge multi-harmonic signals into a mono channel excitation
+ self.l_linear = nn.Linear(harmonic_num + 1, 1)
+ self.l_tanh = nn.Tanh()
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): F0 input tensor.
+
+ Returns:
+ Tensor: Blended mono source excitation tensor.
+ """
+
+ # Generate, project, and squash excitation through Tanh
+ sine_merge = self.l_tanh(
+ self.l_linear(
+ self.l_sin_gen(x).to(dtype=self.l_linear.weight.dtype)
+ )
+ )
+
+ return sine_merge
+
+class BigVGANGenerator(nn.Module):
+ """
+ BigVGAN Generator incorporating Anti-Aliased Multi-Period Blocks (AMP)
+ and Neural Source Filter (NSF) excitation signals for high-fidelity vocoding.
+ """
+
+ def __init__(
+ self,
+ in_channel,
+ upsample_initial_channel,
+ upsample_rates,
+ upsample_kernel_sizes,
+ resblock_kernel_sizes,
+ resblock_dilations,
+ gin_channels,
+ sample_rate,
+ harmonic_num,
+ ):
+ """
+ Args:
+ in_channel (int): Number of input channels (e.g., Mel-spectrogram bins).
+ upsample_initial_channel (int): Number of hidden channels for the initial convolution.
+ upsample_rates (list of int): Upsampling factor per layer.
+ upsample_kernel_sizes (list of int): Kernel sizes for transposed convolutions.
+ resblock_kernel_sizes (list of int): Kernel sizes for AMP Blocks.
+ resblock_dilations (list of list of int): Dilations for layers inside AMP Blocks.
+ gin_channels (int): Global conditioning channels (0 if disabled).
+ sample_rate (int): Sampling rate for NSF module.
+ harmonic_num (int): Harmonic number for NSF module.
+ """
+
+ super().__init__()
+ self.num_kernels = len(resblock_kernel_sizes)
+ self.num_upsamples = len (upsample_rates)
+
+ # Temporal upsampling for F0 sequence to match audio resolution
+ self.f0_upsample = nn.Upsample(scale_factor=np.prod(upsample_rates))
+ self.m_source = SourceModuleHnNSF(sample_rate, harmonic_num)
+
+ # Initial preprocessing convolution
+ self.conv_pre = weight_norm(
+ nn.Conv1d(
+ in_channel,
+ upsample_initial_channel,
+ kernel_size=7,
+ stride=1,
+ padding=3
+ )
+ )
+
+ self.amps = nn.ModuleList()
+ self.upsamples = nn.ModuleList()
+ self.noise_convs = nn.ModuleList()
+
+ # Compute internal downsampling strides for multi-scale source conditioning
+ stride_f0s = [
+ math.prod(upsample_rates[i + 1 :]) if i + 1 < self.num_upsamples else 1
+ for i in range(self.num_upsamples)
+ ]
+
+ # Construct upsampling layers and corresponding noise downsamplers
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+ padding = ((k - u) // 2) if u % 2 == 0 else (u // 2 + u % 2)
+
+ self.upsamples.append(
+ weight_norm(
+ nn.ConvTranspose1d(
+ upsample_initial_channel // (2**i),
+ upsample_initial_channel // (2 ** (i + 1)),
+ kernel_size=k,
+ stride=u,
+ padding=padding,
+ output_padding=u % 2,
+ )
+ )
+ )
+
+ # Match source excitation resolution down to the specific upsampling scale
+ stride = stride_f0s[i]
+ kernel = (1 if stride == 1 else stride * 2 - stride % 2)
+ padding = (0 if stride == 1 else (kernel - stride) // 2)
+
+ self.noise_convs.append(
+ nn.Conv1d(
+ 1,
+ upsample_initial_channel // (2 ** (i + 1)),
+ kernel_size=kernel,
+ stride=stride,
+ padding=padding,
+ )
+ )
+
+ # Build parallel AMP blocks for multi-scale feature refinement
+ for i in range(len(self.upsamples)):
+ channel = upsample_initial_channel // (2 ** (i + 1))
+
+ self.amps.append(
+ nn.ModuleList([
+ AMPBlock(channel, kernel_size=k, dilations=d)
+ for k, d in zip(resblock_kernel_sizes, resblock_dilations)
+ ])
+ )
+
+ # Final output layers
+ self.act_post = AntiAliasActivation(channel)
+ self.conv_post = weight_norm(
+ nn.Conv1d(
+ channel,
+ 1,
+ kernel_size=7,
+ stride=1,
+ padding=3
+ )
+ )
+
+ # Global conditioning layer (e.g. speaker embeddings)
+ if gin_channels != 0:
+ self.cond = nn.Conv1d(
+ gin_channels,
+ upsample_initial_channel,
+ 1
+ )
+
+ def forward(self, x, f0, g=None):
+ """
+ Args:
+ x (Tensor): Mel-spectrogram features of shape (B, in_channels, T_mel).
+ f0 (Tensor): Fundamental frequency trajectory of shape (B, T_mel).
+ g (Tensor, optional): Global conditioning feature of shape (B, gin_channels, 1).
+ Returns:
+ Tensor: Synthesized audio waveform of shape (B, 1, T_audio).
+ """
+
+ # Generate full-resolution harmonic excitation source
+ har_source = self.m_source(self.f0_upsample(f0[:, None, :]).transpose(-1, -2)).transpose(-1, -2)
+ # Initial transformation
+ x = self.conv_pre(x)
+ if g is not None: x += self.cond(g)
+
+ # Iterative upsampling combined with AMP block refinement
+ for up, amp, noise_conv in zip(self.upsamples, self.amps, self.noise_convs):
+ xs = 0
+
+ x = up(x) # Scale feature maps up
+ x += noise_conv(har_source) # Inject multi-scale source excitation
+
+ # Process through parallel multi-kernel AMP layers
+ for layer in amp:
+ xs += layer(x)
+
+ # Average parallel block streams
+ x = xs / self.num_kernels
+
+ # Final activation and mapping to continuous waveform domain [-1.0, 1.0]
+ return self.conv_post(self.act_post(x)).tanh()
+
+ def remove_weight_norm(self):
+ """
+ Removes weight normalization from all pre-convolutions, post-convolutions,
+ and internal AMP blocks for inference deployment.
+ """
+
+ if hasattr(self.conv_pre, "parametrizations") and "weight" in self.conv_pre.parametrizations: parametrize.remove_parametrizations(self.conv_pre, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv_pre)
+
+ for up in self.upsamples:
+ if hasattr(up, "parametrizations") and "weight" in up.parametrizations: parametrize.remove_parametrizations(up, "weight", leave_parametrized=True)
+ else: remove_weight_norm(up)
+
+ for amps in self.amps:
+ for amp in amps:
+ amp.remove_weight_norm()
+
+ if hasattr(self.conv_post, "parametrizations") and "weight" in self.conv_post.parametrizations: parametrize.remove_parametrizations(self.conv_post, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv_post)
\ No newline at end of file
diff --git a/main/library/generators/hifigan.py b/main/library/generators/hifigan.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b83135c7246198019bb3e6ca584f4781da06add
--- /dev/null
+++ b/main/library/generators/hifigan.py
@@ -0,0 +1,130 @@
+import os
+import sys
+import torch
+
+import torch.nn.utils.parametrize as parametrize
+
+from torch.nn.utils import remove_weight_norm
+from torch.nn.utils.parametrizations import weight_norm
+
+sys.path.append(os.getcwd())
+
+from main.library.algorithm.commons import init_weights
+from main.library.algorithm.residuals import ResBlock, LRELU_SLOPE
+
+class HiFiGANGenerator(torch.nn.Module):
+ """
+ HiFi-GAN generator for synthesizing high-fidelity audio waveforms.
+
+ The generator progressively upsamples frame-level acoustic representations
+ through a stack of transposed convolution layers followed by
+ Multi-Receptive Field Fusion (MRF) residual blocks. Outputs from multiple
+ residual blocks at each upsampling stage are averaged to capture features
+ across different receptive fields.
+ """
+
+ def __init__(
+ self,
+ initial_channel,
+ resblock_kernel_sizes,
+ resblock_dilation_sizes,
+ upsample_rates,
+ upsample_initial_channel,
+ upsample_kernel_sizes,
+ gin_channels=0
+ ):
+ """
+ Initialize the HiFi-GAN generator.
+
+ Args:
+ initial_channel (int): Number of input feature channels.
+ resblock_kernel_sizes (list of int): Kernel sizes used by each residual block.
+ resblock_dilation_sizes (Sequencelist of int): Dilation rates for each residual block.
+ upsample_rates (list of int): Upsampling factors applied at each stage.
+ upsample_initial_channel (int): Number of channels before the first upsampling stage.
+ upsample_kernel_sizes (list of int): Kernel sizes for transposed convolution layers.
+ gin_channels (int, optional): Number of global conditioning channels. If zero, conditioning is disabled.
+ """
+
+ super(HiFiGANGenerator, self).__init__()
+ self.num_kernels = len(resblock_kernel_sizes)
+ self.num_upsamples = len(upsample_rates)
+ # Pre-convolution to map input features to the initial upsampling channel dimension
+ self.conv_pre = torch.nn.Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+ # Lists to hold progressive upsampling layers and corresponding multi-scale residual blocks
+ self.ups = torch.nn.ModuleList()
+ self.resblocks = torch.nn.ModuleList()
+
+ # Build upsampling modules paired with parallel multi-receptive field ResBlocks
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+ # Transposed convolution to upscale temporal resolution and halve channel dimension
+ self.ups.append(
+ weight_norm(
+ torch.nn.ConvTranspose1d(
+ upsample_initial_channel // (2**i),
+ upsample_initial_channel // (2 ** (i + 1)),
+ k,
+ u,
+ padding=(k - u) // 2
+ )
+ )
+ )
+
+ # Construct parallel ResBlocks for the current scale to capture different temporal patterns
+ ch = upsample_initial_channel // (2 ** (i + 1))
+ for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes):
+ self.resblocks.append(ResBlock(ch, k, d))
+
+ # Final post-convolution to map hidden features to a 1-channel raw audio waveform
+ self.conv_post = torch.nn.Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+ # Initialize weights for all upsampling layers using custom initialization scheme
+ self.ups.apply(init_weights)
+ # Global conditioning projection layer if speaker/style embedding channels are provided
+ if gin_channels != 0: self.cond = torch.nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+ def forward(self, x, f0, g = None):
+ """
+ Args:
+ x (Tensor): Input Mel-spectrogram tensor.
+ f0 (Tensor): Unused fundamental frequency tensor (maintained for API compatibility).
+ g (Tensor, optional): Global conditioning tensor.
+
+ Returns:
+ Tensor: Synthesized 1D waveform tensor.
+ """
+ # Initial transformation from Mel-spectrogram to feature map
+ x = self.conv_pre(x)
+ # Inject global speaker/style conditioning if available
+ if g is not None: x += self.cond(g)
+
+ # Progressively upsample and refine features stage-by-stage
+ for i in range(self.num_upsamples):
+ # Apply LeakyReLU activation followed by ConvTranspose1d upsampling
+ x = self.ups[i](torch.nn.functional.leaky_relu(x, LRELU_SLOPE))
+ xs = None
+
+ # Accumulate and average outputs from parallel Multi-Receptive Field ResBlocks
+ for j in range(self.num_kernels):
+ if xs is None: xs = self.resblocks[i * self.num_kernels + j](x)
+ else: xs += self.resblocks[i * self.num_kernels + j](x)
+
+ # Normalize by the number of parallel kernels to prevent scale explosion
+ x = xs / self.num_kernels
+
+ # Apply final LeakyReLU activation, squeeze via post-convolution, and bound audio to [-1.0, 1.0]
+ return self.conv_post(torch.nn.functional.leaky_relu(x)).tanh()
+
+ def remove_weight_norm(self):
+ """
+ Removes weight normalization from all transposed convolutions and residual blocks.
+ This optimization reduces computation overhead and speeds up the model during inference.
+ """
+
+ # Remove weight norm from upsampling layers
+ for l in self.ups:
+ if hasattr(l, "parametrizations") and "weight" in l.parametrizations: parametrize.remove_parametrizations(l, "weight", leave_parametrized=True)
+ else: remove_weight_norm(l)
+
+ # Delegate weight norm removal to individual ResBlocks
+ for l in self.resblocks:
+ l.remove_weight_norm()
\ No newline at end of file
diff --git a/main/library/generators/mrf_hifigan.py b/main/library/generators/mrf_hifigan.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ee7689b7ed667850fbb953809b7c25d74f4cf10
--- /dev/null
+++ b/main/library/generators/mrf_hifigan.py
@@ -0,0 +1,395 @@
+import math
+import torch
+
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.utils.parametrize as parametrize
+
+from torch.nn.utils import remove_weight_norm
+from torch.utils.checkpoint import checkpoint
+from torch.nn.utils.parametrizations import weight_norm
+
+LRELU_SLOPE = 0.1
+
+class MRFLayer(nn.Module):
+ """
+ Multi-Receptive Field (MRF) Layer containing two dilated convolutions with a residual connection.
+ Processes the features at a specific kernel size and dilation rate.
+ """
+
+ def __init__(
+ self,
+ channels,
+ kernel_size,
+ dilation
+ ):
+ """
+ Args:
+ channels (int): Number of input/output feature channels.
+ kernel_size (int): Convolution kernel size.
+ dilation (int): Dilation rate for the first convolution layer.
+ """
+
+ super().__init__()
+ # Dilated convolution layer with weight normalization applied
+ self.conv1 = weight_norm(
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ padding=(kernel_size * dilation - dilation) // 2,
+ dilation=dilation
+ )
+ )
+ # Regular non-dilated convolution layer with weight normalization applied
+ self.conv2 = weight_norm(
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ padding=kernel_size // 2,
+ dilation=1
+ )
+ )
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Input feature maps.
+ Returns:
+ Tensor: Output feature maps with residual addition.
+ """
+
+ return x + self.conv2(F.leaky_relu(self.conv1(F.leaky_relu(x, LRELU_SLOPE)), LRELU_SLOPE))
+
+ def remove_weight_norm(self):
+ """Removes weight normalization parametrization hooks from both convolutions."""
+
+ if hasattr(self.conv1, "parametrizations") and "weight" in self.conv1.parametrizations: parametrize.remove_parametrizations(self.conv1, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv1)
+
+ if hasattr(self.conv2, "parametrizations") and "weight" in self.conv2.parametrizations: parametrize.remove_parametrizations(self.conv2, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv2)
+
+class MRFBlock(nn.Module):
+ """
+ MRF Block containing multiple MRFLayers arranged sequentially with different dilations.
+ """
+
+ def __init__(self, channels, kernel_size, dilations):
+ """
+ Args:
+ channels (int): Number of feature channels.
+ kernel_size (int): Common kernel size for internal layers.
+ dilations (list of int): List of dilation factors for each sequential layer.
+ """
+
+ super().__init__()
+ self.layers = nn.ModuleList()
+ # Stack multiple MRF layers sequentially
+ for dilation in dilations:
+ self.layers.append(MRFLayer(channels, kernel_size, dilation))
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Input feature maps.
+ Returns:
+ Tensor: Sequentially processed feature maps.
+ """
+
+ for layer in self.layers:
+ x = layer(x)
+
+ return x
+
+ def remove_weight_norm(self):
+ """Recursively removes weight normalization from all sub-layers."""
+ for layer in self.layers:
+ layer.remove_weight_norm()
+
+class SineGen(nn.Module):
+ """
+ Sine wave oscillator for Neural Source Filter (NSF) components.
+ Generates deterministic phase-continuous harmonic excitations based on F0 sequence.
+ """
+
+ def __init__(
+ self,
+ sampling_rate,
+ harmonic_num = 0,
+ sine_amp = 0.1,
+ noise_std = 0.003,
+ voiced_threshold = 0
+ ):
+ """
+ Args:
+ sampling_rate (int): Target audio sampling rate.
+ harmonic_num (int): Number of upper harmonics to generate. Default: 0.
+ sine_amp (float): Base amplitude of the sine waves. Default: 0.1.
+ noise_std (float): Standard deviation of random Gaussian noise. Default: 0.003.
+ voiced_threshold (float): F0 threshold determining voiced/unvoiced frame status. Default: 0.
+ """
+
+ super(SineGen, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = noise_std
+ self.harmonic_num = harmonic_num
+ self.dim = self.harmonic_num + 1
+ self.sampling_rate = sampling_rate
+ self.voiced_threshold = voiced_threshold
+
+ def _f02uv(self, f0):
+ """Binarizes F0 trajectory into Voiced/Unvoiced mask."""
+
+ return (f0 > self.voiced_threshold).float()
+
+ def _f02sine(self, f0_values):
+ """
+ Converts dynamic F0 sequences into continuous sine waves without phase phase-breaks.
+
+ Args:
+ f0_values (Tensor): F0 tracks for each harmonic.
+
+ Returns:
+ Tensor: Synthesized phase-continuous sine waves.
+ """
+
+ # Calculate phase steps per temporal step
+ rad_values = (f0_values / self.sampling_rate) % 1
+ rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], dtype=f0_values.dtype, device=f0_values.device)
+ # Ground initial phase for the first time step
+ rand_ini[:, 0] = 0
+ rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+ # Accumulate phases and wrap bounds within [0, 1]
+ tmp_over_one = torch.cumsum(rad_values, 1) % 1
+ tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
+ cumsum_shift = torch.zeros_like(rad_values)
+ cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+
+ # Run final mapping via sine function
+ return (torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi).sin()
+
+ def forward(self, f0):
+ """
+ Args:
+ f0 (Tensor): Input raw fundamental frequency trajectory.
+
+ Returns:
+ Tensor: Combined sine and noise excitation matrix.
+ """
+
+ with torch.no_grad():
+ # Setup fundamental buffer and seed the root F0 track
+ f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, dtype=f0.dtype, device=f0.device)
+ f0_buf[:, :, 0] = f0[:, :, 0]
+
+ # Populate harmonic components (F0 * 2, F0 * 3, ...)
+ for idx in np.arange(self.harmonic_num):
+ f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
+
+ # Generate structured sine streams
+ # The input must be kept as a float; otherwise, the output is prone to becoming NaN.
+ sine_waves = self._f02sine(f0_buf.float()) * self.sine_amp
+ uv = self._f02uv(f0)
+ # Add Gaussian noise into voiced streams and soft white noise into unvoiced sections
+ sine_waves = sine_waves * uv + ((uv * self.noise_std + (1 - uv) * self.sine_amp / 3) * torch.randn_like(sine_waves))
+
+ return sine_waves
+
+class SourceModuleHnNSF(nn.Module):
+ """
+ Source wrapper for the Harmonic-plus-Noise Neural Source Filter mechanism.
+ Collapses multi-harmonic tracks down into a single composite raw excitation source.
+ """
+
+ def __init__(
+ self,
+ sampling_rate,
+ harmonic_num = 0,
+ sine_amp = 0.1,
+ add_noise_std = 0.003,
+ voiced_threshold = 0
+ ):
+ """
+ Args:
+ sampling_rate (int): Target sampling rate.
+ harmonic_num (int): Harmonic counts. Default: 0.
+ sine_amp (float): Sine component scale. Default: 0.1.
+ add_noise_std (float): Noise standard deviation. Default: 0.003.
+ voiced_threshold (float): Voiced threshold frequency. Default: 0.
+ """
+
+ super(SourceModuleHnNSF, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = add_noise_std
+ self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshold)
+ self.l_linear = nn.Linear(harmonic_num + 1, 1)
+ self.l_tanh = nn.Tanh()
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Fundamental frequency tracking tensor.
+ Returns:
+ Tensor: Projective 1D excitation signal tensor.
+ """
+
+ # Compute harmonics, map down to 1D via linear layer, and clip peaks via Tanh
+ return self.l_tanh(
+ self.l_linear(
+ self.l_sin_gen(x).to(dtype=self.l_linear.weight.dtype)
+ )
+ )
+
+class HiFiGANMRFGenerator(nn.Module):
+ """
+ Modified HiFi-GAN Generator equipped with Multi-Receptive Field (MRF) fusion blocks,
+ NSF multi-scale excitation conditioning, and optional Gradient Checkpointing for VRAM savings.
+ """
+
+ def __init__(
+ self,
+ in_channel,
+ upsample_initial_channel,
+ upsample_rates,
+ upsample_kernel_sizes,
+ resblock_kernel_sizes,
+ resblock_dilations,
+ gin_channels,
+ sample_rate,
+ harmonic_num,
+ checkpointing = False
+ ):
+ """
+ Args:
+ in_channel (int): Dimensions of input acoustic features (Mel-spec bins).
+ upsample_initial_channel (int): Number of base hidden channels for pre-convolution.
+ upsample_rates (list of int): Temporal scaling ratios per upsampling layer.
+ upsample_kernel_sizes (list of int): Kernel shapes for transposed convolutions.
+ resblock_kernel_sizes (list of int): Core kernel sizes utilized for MRF Blocks.
+ resblock_dilations (list of list of int): Dilations grouped for sequential MRF layers.
+ gin_channels (int): Dimensionality for speaker or style conditioning.
+ sample_rate (int): Sampling frequency for source wave generator.
+ harmonic_num (int): Harmonics capacity for the source filter.
+ checkpointing (bool): Activates PyTorch gradient checkpointing during training. Default: False.
+ """
+
+ super().__init__()
+ self.num_kernels = len(resblock_kernel_sizes)
+ self.checkpointing = checkpointing
+ # Temporal upsampler to stretch sparse F0 vectors to high-resolution raw audio lengths
+ self.f0_upsample = nn.Upsample(scale_factor=np.prod(upsample_rates))
+ self.m_source = SourceModuleHnNSF(sample_rate, harmonic_num)
+ # Frontend embedding convolution
+ self.conv_pre = weight_norm(nn.Conv1d(in_channel, upsample_initial_channel, kernel_size=7, stride=1, padding=3))
+ self.upsamples = nn.ModuleList()
+ self.noise_convs = nn.ModuleList()
+
+ # Calculate downsampling ratios needed to align master excitation down to each sub-scale block
+ stride_f0s = [
+ math.prod(upsample_rates[i + 1 :]) if i + 1 < len(upsample_rates) else 1
+ for i in range(len(upsample_rates))
+ ]
+
+ # Allocate transposed conv blocks and secondary source alignment pipelines
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+ self.upsamples.append(
+ weight_norm(
+ nn.ConvTranspose1d(
+ upsample_initial_channel // (2**i),
+ upsample_initial_channel // (2 ** (i + 1)),
+ kernel_size=k,
+ stride=u,
+ padding=((k - u) // 2) if u % 2 == 0 else (u // 2 + u % 2),
+ output_padding=u % 2
+ )
+ )
+ )
+
+ # Build matching scale downsampling convolutional layers for source signal addition
+ stride = stride_f0s[i]
+ kernel = 1 if stride == 1 else stride * 2 - stride % 2
+
+ self.noise_convs.append(
+ nn.Conv1d(
+ 1,
+ upsample_initial_channel // (2 ** (i + 1)),
+ kernel_size=kernel,
+ stride=stride,
+ padding=0 if stride == 1 else (kernel - stride) // 2
+ )
+ )
+
+ # Assemble independent MRF Blocks for multi-scale parallel processing
+ self.mrfs = nn.ModuleList()
+ for i in range(len(self.upsamples)):
+ channel = upsample_initial_channel // (2 ** (i + 1))
+ self.mrfs.append(
+ nn.ModuleList([
+ MRFBlock(
+ channel,
+ kernel_size=k,
+ dilations=d
+ )
+ for k, d in zip(resblock_kernel_sizes, resblock_dilations)
+ ])
+ )
+
+ # Final projection block mapping down to audio mono track
+ self.conv_post = weight_norm(nn.Conv1d(channel, 1, kernel_size=7, stride=1, padding=3))
+ if gin_channels != 0: self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+ def forward(self, x, f0, g = None):
+ """
+ Args:
+ x (Tensor): Input Mel-spectrogram tensor.
+ f0 (Tensor): Pitch trajectory vector.
+ g (Tensor, optional): Conditioning embed input.
+ Returns:
+ Tensor: Regenerated continuous time audio waveform.
+ """
+
+ # Synthesize audio-rate continuous excitation template from pitch track
+ har_source = self.m_source(self.f0_upsample(f0[:, None, :]).transpose(-1, -2)).transpose(-1, -2)
+
+ # Preprocess features and mix global condition profiles if available
+ x = self.conv_pre(x)
+ if g is not None: x += self.cond(g)
+
+ # Iterate progressive scale blocks sequentially
+ for ups, mrf, noise_conv in zip(self.upsamples, self.mrfs, self.noise_convs):
+ x = F.leaky_relu(x, LRELU_SLOPE)
+ # Execute forward layers with gradient checkpointing to reduce VRAM if requested
+ if self.training and self.checkpointing:
+ x = checkpoint(ups, x, use_reentrant=False) + noise_conv(har_source)
+ xs = sum([checkpoint(layer, x, use_reentrant=False) for layer in mrf])
+ else:
+ x = ups(x) + noise_conv(har_source)
+ xs = sum([layer(x) for layer in mrf])
+
+ # Blend features together from all multi-kernel paths
+ x = xs / self.num_kernels
+
+ # Project representations back to audio amplitudes bounded within [-1.0, 1.0]
+ return self.conv_post(F.leaky_relu(x)).tanh()
+
+ def remove_weight_norm(self):
+ """
+ Fuses weight normalization parameters into raw tensor parameters for fast inference deployment.
+ """
+
+ if hasattr(self.conv_pre, "parametrizations") and "weight" in self.conv_pre.parametrizations: parametrize.remove_parametrizations(self.conv_pre, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv_pre)
+
+ for up in self.upsamples:
+ if hasattr(up, "parametrizations") and "weight" in up.parametrizations: parametrize.remove_parametrizations(up, "weight", leave_parametrized=True)
+ else: remove_weight_norm(up)
+
+ for mrf in self.mrfs:
+ for block in mrf:
+ block.remove_weight_norm()
+
+ if hasattr(self.conv_post, "parametrizations") and "weight" in self.conv_post.parametrizations: parametrize.remove_parametrizations(self.conv_post, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv_post)
\ No newline at end of file
diff --git a/main/library/generators/nsf_hifigan.py b/main/library/generators/nsf_hifigan.py
new file mode 100644
index 0000000000000000000000000000000000000000..31eb6e4a7e22bc8ec526d7d3ec690f81b67d95a2
--- /dev/null
+++ b/main/library/generators/nsf_hifigan.py
@@ -0,0 +1,405 @@
+import os
+import sys
+import math
+import torch
+
+import numpy as np
+import torch.nn.functional as F
+import torch.nn.utils.parametrize as parametrize
+
+from torch.nn.utils import remove_weight_norm
+from torch.utils.checkpoint import checkpoint
+from torch.nn.utils.parametrizations import weight_norm
+
+sys.path.append(os.getcwd())
+
+from main.library.algorithm.commons import init_weights
+from main.library.algorithm.residuals import ResBlock, LRELU_SLOPE
+
+class SineGen2(torch.nn.Module):
+ """
+ Alternative Sine Wave Generator (v2) for Neural Source Filter (NSF).
+ Generates multi-harmonic sine wave tracks by matrix multiplying F0 with a harmonic multiplier tensor.
+ """
+
+ def __init__(
+ self,
+ sampling_rate,
+ harmonic_num=0,
+ sine_amp=0.1,
+ noise_std=0.003,
+ voiced_threshold=0,
+ ):
+ """
+ Args:
+ sampling_rate (int): Target audio sampling rate.
+ harmonic_num (int): Number of overtones/harmonics to generate. Default: 0.
+ sine_amp (float): Base amplitude factor of the sine waves. Default: 0.1.
+ noise_std (float): Standard deviation for voiced random noise. Default: 0.003.
+ voiced_threshold (float): F0 threshold value to determine voiced frames. Default: 0.
+ """
+
+ super(SineGen2, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = noise_std
+ self.harmonic_num = harmonic_num
+ self.dim = self.harmonic_num + 1
+ self.sampling_rate = sampling_rate
+ self.voiced_threshold = voiced_threshold
+
+ def _f02uv(self, f0):
+ """Maps continuous F0 tracking values into a binary Voiced/Unvoiced mask."""
+
+ return (f0 > self.voiced_threshold).float()
+
+ def _f02sine(self, f0_values):
+ """
+ Converts matrix-scaled F0 values into continuous sine wave sequences.
+
+ Args:
+ f0_values (Tensor): Multi-harmonic fundamental frequencies.
+
+ Returns:
+ Tensor: Continuous phase-aligned sine matrix.
+ """
+
+ # Calculate radiant steps per step and wrap phases within [0, 1]
+ rad_values = (f0_values / self.sampling_rate) % 1
+ rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], dtype=f0_values.dtype, device=f0_values.device)
+ # Lock phase start boundary for the initial index
+ rand_ini[:, 0] = 0
+ rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+ # Accumulate wrapped steps over time
+ tmp_over_one = torch.cumsum(rad_values, 1) % 1
+ tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
+ cumsum_shift = torch.zeros_like(rad_values)
+ cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+
+ # Run phase execution using the standard sine function
+ return (torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi).sin()
+
+ def forward(self, f0, upp):
+ """
+ Args:
+ f0 (Tensor): Upsampled raw F0 trajectory matrix.
+ upp (int): Total upsampling scale factor factor (unused inside v2, kept for interface symmetry).
+
+ Returns:
+ Tensor: Combined sine excitation signal matrix.
+ """
+
+ with torch.no_grad():
+ # Multiply base pitch tracking vector by harmonic indices [1, 2, ..., harmonic_num + 1]
+ sine_waves = self._f02sine(
+ torch.multiply(
+ f0,
+ torch.FloatTensor([
+ [range(1, self.harmonic_num + 2)]
+ ]).to(f0.device)
+ )
+ ) * self.sine_amp
+
+ # Extract Voiced/Unvoiced binary profile mask
+ uv = self._f02uv(f0)
+ # Blend generated sine waves with noise distributions based on voiced state status
+ sine_waves = sine_waves * uv + ((uv * self.noise_std + (1 - uv) * self.sine_amp / 3) * torch.randn_like(sine_waves))
+
+ return sine_waves
+
+class SineGen(torch.nn.Module):
+ """
+ Standard Phase-Continuous Sine Wave Generator (v1) for Neural Source Filter (NSF).
+ Performs precise custom step phase accumulation on audio frame strides.
+ """
+
+ def __init__(
+ self,
+ sampling_rate,
+ harmonic_num=0,
+ sine_amp=0.1,
+ noise_std=0.003,
+ voiced_threshold=0
+ ):
+ """
+ Args:
+ sampling_rate (int): Target audio sampling rate.
+ harmonic_num (int): Number of overtones/harmonics to generate. Default: 0.
+ sine_amp (float): Base amplitude factor of the sine waves. Default: 0.1.
+ noise_std (float): Standard deviation for voiced random noise. Default: 0.003.
+ voiced_threshold (float): F0 threshold value to determine voiced frames. Default: 0.
+ """
+
+ super(SineGen, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = noise_std
+ self.harmonic_num = harmonic_num
+ self.dim = self.harmonic_num + 1
+ self.sampling_rate = sampling_rate
+ self.voiced_threshold = voiced_threshold
+
+ def _f02uv(self, f0):
+ """Maps continuous F0 tracking values into a binary Voiced/Unvoiced mask."""
+
+ return (f0 > self.voiced_threshold).float()
+
+ def _f02sine(self, f0, upp):
+ """
+ Generates phase-continuous sine waves matching the audio sample scale.
+
+ Args:
+ f0 (Tensor): frame-level F0 tracking.
+ upp (int): Upsampling factor per audio frame step.
+
+ Returns:
+ Tensor: High-resolution phase-continuous multi-harmonic sine waves.
+ """
+
+ # Step increment calculation across the frame timeline blocks
+ rad = f0 / self.sampling_rate * torch.arange(1, upp + 1, dtype=f0.dtype, device=f0.device)
+ # Compensate step offsets over adjacent windows to maintain smooth continuous phases
+ rad += F.pad((torch.fmod(rad[:, :-1, -1:].float() + 0.5, 1.0) - 0.5).cumsum(dim=1).fmod(1.0).to(f0), (0, 0, 1, 0), mode='constant')
+ # Reshape to high-resolution time matrix and scale up across individual harmonic channels
+ rad = rad.reshape(f0.shape[0], -1, 1)
+ rad *= torch.arange(1, self.dim + 1, dtype=f0.dtype, device=f0.device).reshape(1, 1, -1)
+ # Append initial randomized start phase bounds to prevent phase locking artifacts
+ rand_ini = torch.rand(1, 1, self.dim, device=f0.device)
+ rand_ini[..., 0] = 0
+ rad += rand_ini
+
+ return (2 * np.pi * rad).sin()
+
+ def forward(self, f0, upp):
+ """
+ Args:
+ f0 (Tensor): Base acoustic frame pitch sequence.
+ upp (int): Frame-to-sample timeline stretching ratio.
+
+ Returns:
+ Tensor: Waveform-rate noise-modulated harmonic matrix.
+ """
+
+ with torch.no_grad():
+ f0 = f0.unsqueeze(-1)
+ # Synthesize basic sine wave foundations
+ sine_waves = self._f02sine(f0, upp) * self.sine_amp
+ # Interpolate Voiced/Unvoiced mask up to high-resolution timeline via nearest neighbor
+ uv = F.interpolate(self._f02uv(f0).transpose(2, 1), scale_factor=float(upp), mode="nearest").transpose(2, 1)
+ # Superimpose multi-band background noise profiles
+ sine_waves = sine_waves * uv + ((uv * self.noise_std + (1 - uv) * self.sine_amp / 3) * torch.randn_like(sine_waves))
+
+ return sine_waves
+
+class SourceModuleHnNSF(torch.nn.Module):
+ """
+ Source excitation mixer wrapper routing inputs to either SineGen or SineGen2 pipelines.
+ Projects multi-channel tracking branches down into a consolidated 1D source track.
+ """
+
+ def __init__(
+ self,
+ sample_rate,
+ harmonic_num=0,
+ sine_amp=0.1,
+ add_noise_std=0.003,
+ voiced_threshod=0,
+ sinegen_version="v1"
+ ):
+ """
+ Args:
+ sample_rate (int): Sampling rate for sound synthesis.
+ harmonic_num (int): Upper bound capacity for generated harmonics.
+ sine_amp (float): Core output amplitude setting.
+ add_noise_std (float): Target gaussian variance.
+ voiced_threshod (float): Minimum voiced pitch floor frequency.
+ sinegen_version (str): Target variant tag ("v1" or "v2").
+ """
+
+ super(SourceModuleHnNSF, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = add_noise_std
+ # Select target generator engine module instance based on requested version string
+ sine_gen_fn = SineGen if sinegen_version == "v1" else SineGen2
+ self.l_sin_gen = sine_gen_fn(sample_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod)
+ self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+ self.l_tanh = torch.nn.Tanh()
+
+ def forward(self, x, upsample_factor = 1):
+ """
+ Args:
+ x (Tensor): Input fundamental frequency track.
+ upsample_factor (int): Audio rate timeline multiplier. Default: 1.
+
+ Returns:
+ Tensor: Projective 1D filtered excitation wave track.
+ """
+
+ # Generate raw waveforms, mix down dimensions via a linear projection, and bound peaks using Tanh
+ return self.l_tanh(
+ self.l_linear(
+ self.l_sin_gen(
+ x,
+ upsample_factor
+ ).to(dtype=self.l_linear.weight.dtype)
+ )
+ )
+
+class HiFiGANNSFGenerator(torch.nn.Module):
+ """
+ HiFi-GAN Generator network variant equipped with Neural Source Filter (NSF) features,
+ source-signal injection multi-scale paths, and Gradient Checkpointing support.
+ """
+
+ def __init__(
+ self,
+ initial_channel,
+ resblock_kernel_sizes,
+ resblock_dilation_sizes,
+ upsample_rates,
+ upsample_initial_channel,
+ upsample_kernel_sizes,
+ gin_channels,
+ sr,
+ checkpointing = False,
+ harmonic_num = 0
+ ):
+ """
+ Args:
+ initial_channel (int): Number of input Mel-spectrogram channels.
+ resblock_kernel_sizes (list of int): Receptive field kernel lengths for ResBlocks.
+ resblock_dilation_sizes (list of list of int): Dilations nested for internal layers.
+ upsample_rates (list of int): Temporal scaling ratios per upsampling layer.
+ upsample_initial_channel (int): Number of base hidden channels for pre-convolution.
+ upsample_kernel_sizes (list of int): Convolution shapes assigned for upsamplers.
+ gin_channels (int): Dimensionality for speaker/style conditioning features.
+ sr (int): Target execution sample rate for the sound model.
+ checkpointing (bool): Toggle flag to bypass active memory consumption using checkpoint wrappers.
+ harmonic_num (int): Target tracking limits for individual pitch branches.
+ """
+
+ super(HiFiGANNSFGenerator, self).__init__()
+ self.num_kernels = len(resblock_kernel_sizes)
+ self.num_upsamples = len(upsample_rates)
+ self.upp = math.prod(upsample_rates)
+ self.harmonic_num = harmonic_num
+ # Linear interpolator to stretch raw frame F0 data up to high-resolution sample timeline lengths
+ self.f0_upsamp = torch.nn.Upsample(scale_factor=self.upp)
+ # Instantiate Source Module with version selection depending on harmonic profile bounds
+ self.m_source = SourceModuleHnNSF(sample_rate=sr, harmonic_num=harmonic_num, sinegen_version="v1" if harmonic_num == 0 else "v2")
+ # Frontend feature map projection block
+ self.conv_pre = torch.nn.Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+ self.checkpointing = checkpointing
+
+ self.ups = torch.nn.ModuleList()
+ self.noise_convs = torch.nn.ModuleList()
+
+ # Compute layer-by-layer channel dimension changes
+ channels = [
+ upsample_initial_channel // (2 ** (i + 1))
+ for i in range(self.num_upsamples)
+ ]
+ # Calculate sequential scaling coefficients to downscale excitation signals to current block rates
+ stride_f0s = [
+ math.prod(upsample_rates[i + 1 :]) if i + 1 < self.num_upsamples else 1
+ for i in range(self.num_upsamples)
+ ]
+
+ # Construct upsampling structures paired with matching source conditioning filters
+ for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+ self.ups.append(
+ weight_norm(
+ torch.nn.ConvTranspose1d(
+ upsample_initial_channel // (2**i),
+ channels[i],
+ k,
+ u,
+ padding=((k - u) // 2) if u % 2 == 0 else (u // 2 + u % 2),
+ output_padding=u % 2
+ )
+ )
+ )
+
+ # Downsample the main high-resolution excitation source to match the current layer's temporal resolution
+ stride = stride_f0s[i]
+ kernel = 1 if stride == 1 else stride * 2 - stride % 2
+
+ self.noise_convs.append(
+ torch.nn.Conv1d(
+ 1,
+ channels[i],
+ kernel_size=kernel,
+ stride=stride,
+ padding=0 if stride == 1 else (kernel - stride) // 2
+ )
+ )
+
+ # Build parallel MRF ResBlock modules across the progressive scaling stages
+ self.resblocks = torch.nn.ModuleList([
+ ResBlock(channels[i], k, d)
+ for i in range(len(self.ups))
+ for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes)
+ ])
+
+ # Tail synthesis projection layer
+ self.conv_post = torch.nn.Conv1d(channels[-1], 1, 7, 1, padding=3, bias=bool(harmonic_num != 0))
+ # Apply weight normalization on input/output endpoints if multi-harmonic modules are activated
+ if harmonic_num != 0: self.conv_pre, self.conv_post = weight_norm(self.conv_pre), weight_norm(self.conv_post)
+
+ self.ups.apply(init_weights)
+ # Global style profile mapping layer
+ if gin_channels != 0: self.cond = torch.nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+ def forward(self, x, f0, g = None):
+ """
+ Args:
+ x (Tensor): Acoustic Mel-spectrogram tensor input.
+ f0 (Tensor): F0 tracking matrix.
+ g (Tensor, optional): Global features profile tensor.
+
+ Returns:
+ Tensor: Synthesized time-domain speech audio track waveform.
+ """
+
+ # If running v2 harmonic settings, stretch low-res frame tracks up to match master audio resolution
+ if self.harmonic_num != 0: f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)
+ # Synthesize the continuous master source wave excitation track
+ har_source = self.m_source(f0, self.upp).transpose(1, 2)
+
+ # Initial transformation from input features to feature maps
+ x = self.conv_pre(x)
+ if g is not None: x += self.cond(g)
+
+ # Progressively upsample features and fuse downsampled source excitation components
+ for i, (ups, noise_convs) in enumerate(zip(self.ups, self.noise_convs)):
+ x = F.leaky_relu(x, LRELU_SLOPE)
+
+ # Route through memory checkpoint wrappers during active training phases if toggled
+ if self.training and self.checkpointing:
+ x = checkpoint(ups, x, use_reentrant=False) + noise_convs(har_source)
+ xs = sum([checkpoint(resblock, x, use_reentrant=False) for j, resblock in enumerate(self.resblocks) if j in range(i * self.num_kernels, (i + 1) * self.num_kernels)])
+ else:
+ x = ups(x) + noise_convs(har_source)
+ xs = sum([resblock(x) for j, resblock in enumerate(self.resblocks) if j in range(i * self.num_kernels, (i + 1) * self.num_kernels)])
+
+ # Normalize across the multi-kernel residual channels
+ x = xs / self.num_kernels
+
+ # Project features to 1D waveform channel and normalize amplitude within [-1.0, 1.0]
+ return self.conv_post(F.leaky_relu(x)).tanh()
+
+ def remove_weight_norm(self):
+ """Fuses weight norm parametrizations into single weight matrices to accelerate inference speeds."""
+
+ for l in self.ups:
+ if hasattr(l, "parametrizations") and "weight" in l.parametrizations: parametrize.remove_parametrizations(l, "weight", leave_parametrized=True)
+ else: remove_weight_norm(l)
+
+ # Clear normalization pipelines from input and output endpoints if harmonic modes were active
+ if self.harmonic_num != 0:
+ if hasattr(self.conv_pre, "parametrizations") and "weight" in self.conv_pre.parametrizations: parametrize.remove_parametrizations(self.conv_pre, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv_pre)
+ if hasattr(self.conv_post, "parametrizations") and "weight" in self.conv_post.parametrizations: parametrize.remove_parametrizations(self.conv_post, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv_post)
+
+ # Clear remaining normalization bindings from the nested ResBlock lists
+ for l in self.resblocks:
+ l.remove_weight_norm()
\ No newline at end of file
diff --git a/main/library/generators/refinegan.py b/main/library/generators/refinegan.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d267a871ee1956085c09ac3fcd81a32dac42839
--- /dev/null
+++ b/main/library/generators/refinegan.py
@@ -0,0 +1,441 @@
+import os
+import sys
+import torch
+import torchaudio
+
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.utils.parametrize as parametrize
+
+from torch.utils.checkpoint import checkpoint
+from torch.nn.utils import remove_weight_norm
+from torch.nn.utils.parametrizations import weight_norm
+
+sys.path.append(os.getcwd())
+
+from main.library.algorithm.commons import init_weights, get_padding
+
+class ResBlock(nn.Module):
+ """
+ Standard Residual Block containing sequential dilated convolutions.
+ Refines features using multiple expansion rates to capture varied context windows.
+ """
+
+ def __init__(self, channels, kernel_size = 7, dilation = (1, 3, 5), leaky_relu_slope = 0.2):
+ """
+ Args:
+ channels (int): Number of input/output feature channels.
+ kernel_size (int): Convolution kernel width. Default: 7.
+ dilation (tuple of int): Dilations applied sequentially to the internal convs. Default: (1, 3, 5).
+ leaky_relu_slope (float): Negative slope coefficient for LeakyReLU. Default: 0.2.
+ """
+
+ super().__init__()
+ self.leaky_relu_slope = leaky_relu_slope
+ # Primary convolution group using scheduled dilations and automated padding matching
+ self.convs1 = nn.ModuleList([
+ weight_norm(
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ stride=1,
+ dilation=d,
+ padding=get_padding(kernel_size, d)
+ )
+ )
+ for d in dilation
+ ])
+ self.convs1.apply(init_weights)
+
+ # Secondary convolution group using fixed dilation of 1
+ self.convs2 = nn.ModuleList([
+ weight_norm(
+ nn.Conv1d(
+ channels,
+ channels,
+ kernel_size,
+ stride=1,
+ dilation=1,
+ padding=get_padding(kernel_size, 1)
+ )
+ )
+ for _ in dilation
+ ])
+ self.convs2.apply(init_weights)
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Feature maps.
+
+ Returns:
+ Tensor: Refined feature maps after sequential residual accumulations.
+ """
+
+ for c1, c2 in zip(self.convs1, self.convs2):
+ x = c2(F.leaky_relu(c1(F.leaky_relu(x, self.leaky_relu_slope)), self.leaky_relu_slope)) + x
+
+ return x
+
+ def remove_weight_norm(self):
+ """Removes weight normalization parametrizations across both inner conv pipelines."""
+
+ for c1, c2 in zip(self.convs1, self.convs2):
+ if hasattr(c1, "parametrizations") and "weight" in c1.parametrizations: parametrize.remove_parametrizations(c1, "weight", leave_parametrized=True)
+ else: remove_weight_norm(c1)
+
+ if hasattr(c2, "parametrizations") and "weight" in c2.parametrizations: parametrize.remove_parametrizations(c2, "weight", leave_parametrized=True)
+ else: remove_weight_norm(c2)
+
+class AdaIN(nn.Module):
+ """
+ Adaptive Instance Normalization variant.
+ Injects scaled Gaussian perturbations as a form of lightweight stochastic conditioning.
+ """
+
+ def __init__(
+ self,
+ *,
+ channels,
+ leaky_relu_slope = 0.2
+ ):
+ """
+ Args:
+ channels (int): Channel capacity for input feature vectors.
+ leaky_relu_slope (float): LeakyReLU activation slope factor. Default: 0.2.
+ """
+
+ super().__init__()
+ # Small learned weight scalar parameter initialized to a base floor scale
+ self.weight = nn.Parameter(torch.ones(channels) * 1e-4)
+ self.activation = nn.LeakyReLU(leaky_relu_slope)
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Target feature map.
+ Returns:
+ Tensor: Perturbed feature maps after activation.
+ """
+
+ # Apply structured noise multiplication matched across the tensor shape dimensions
+ return self.activation(x + (torch.randn_like(x) * self.weight[None, :, None]))
+
+class ParallelResBlock(nn.Module):
+ """
+ Parallel Residual Fusion Block.
+ Processes features across distinct multi-scale kernel pathways and fuses their outputs.
+ """
+
+ def __init__(
+ self,
+ *,
+ in_channels,
+ out_channels,
+ kernel_sizes = (3, 7, 11),
+ dilation = (1, 3, 5),
+ leaky_relu_slope = 0.2
+ ):
+ """
+ Args:
+ in_channels (int): Input channel counts.
+ out_channels (int): Target output channel counts.
+ kernel_sizes (tuple of int): Discrete sizes assigned to separate parallel paths. Default: (3, 7, 11).
+ dilation (tuple of int): Common dilations passed down into inner ResBlocks. Default: (1, 3, 5).
+ leaky_relu_slope (float): Negative activation gradient rate. Default: 0.2.
+ """
+
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ # Entrance projection mapping to unify disparate incoming channel splits
+ self.input_conv = nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=7, stride=1, padding=3)
+ self.input_conv.apply(init_weights)
+
+ # Parallel block streams consisting of AdaIN wrappers surrounding kernel-specific ResBlocks
+ self.blocks = nn.ModuleList([
+ nn.Sequential(
+ AdaIN(
+ channels=out_channels
+ ),
+ ResBlock(
+ out_channels,
+ kernel_size=kernel_size,
+ dilation=dilation,
+ leaky_relu_slope=leaky_relu_slope
+ ),
+ AdaIN(
+ channels=out_channels
+ )
+ )
+ for kernel_size in kernel_sizes
+ ])
+
+ def forward(self, x):
+ """
+ Args:
+ x (Tensor): Integrated context tensors.
+
+ Returns:
+ Tensor: Averaged multi-scale fusion results.
+ """
+
+ x = self.input_conv(x)
+ # Compute parallel paths separately, stack them along a new dimension, and extract the mean
+ return torch.stack([block(x) for block in self.blocks], dim=0).mean(dim=0)
+
+ def remove_weight_norm(self):
+ """Unwinds weight norm parametrizations embedded within sub-ResBlocks."""
+
+ for block in self.blocks:
+ block[1].remove_weight_norm()
+
+class SineGen(nn.Module):
+ """
+ Sine Wave Excitation Generator component for Neural Source Filter (NSF) architecture.
+ Produces harmonic sine wave excitations and noise models based on a primary F0 trajectory.
+ """
+
+ def __init__(
+ self,
+ sampling_rate,
+ harmonic_num=0,
+ sine_amp=0.1,
+ noise_std=0.003,
+ voiced_threshold=0
+ ):
+ """
+ Args:
+ sampling_rate (int): Audio processing target sample rate.
+ harmonic_num (int): Additional overtones to synthesize. Default: 0.
+ sine_amp (float): Output base amplitude. Default: 0.1.
+ noise_std (float): Target white noise variance. Default: 0.003.
+ voiced_threshold (float): Baseline floor frequency determining unvoiced sections. Default: 0.
+ """
+
+ super(SineGen, self).__init__()
+ self.sine_amp = sine_amp
+ self.noise_std = noise_std
+ self.harmonic_num = harmonic_num
+ self.dim = self.harmonic_num + 1
+ self.sampling_rate = sampling_rate
+ self.voiced_threshold = voiced_threshold
+ # Collapses multi-harmonic tracks into a unified 1D excitation track
+ self.merge = nn.Sequential(nn.Linear(self.dim, 1, bias=False), nn.Tanh())
+
+ def _f02uv(self, f0):
+ """Maps continuous F0 tracking values into a binary Voiced/Unvoiced mask."""
+
+ return (f0 > self.voiced_threshold).float()
+
+ def _f02sine(self, f0_values):
+ """
+ Maps pitch metrics to phase-continuous sine waves.
+
+ Args:
+ f0_values (Tensor): Harmonically shifted fundamental frequency trackers.
+
+ Returns:
+ Tensor: Generated phase-aligned continuous sine tracking array.
+ """
+
+ rad_values = (f0_values / self.sampling_rate) % 1
+ rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], dtype=f0_values.dtype, device=f0_values.device)
+
+ # Seed zero starting phase parameters on target frame zero bounds
+ rand_ini[:, 0] = 0
+ rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+
+ # Accumulate time sequences smoothly across period limits
+ tmp_over_one = torch.cumsum(rad_values, 1) % 1
+ tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
+
+ cumsum_shift = torch.zeros_like(rad_values)
+ cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+
+ return (torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi).sin()
+
+ def forward(self, f0):
+ """
+ Args:
+ f0 (Tensor): Low-res fundamental frequency tracking matrix.
+
+ Returns:
+ Tensor: Projective 1D excitation signal.
+ """
+
+ with torch.no_grad():
+ f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, dtype=f0.dtype, device=f0.device)
+ f0_buf[:, :, 0] = f0[:, :, 0]
+
+ # Form overtone channels using consecutive integer multiplications
+ for idx in np.arange(self.harmonic_num):
+ f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)
+
+ # Synthesize basic sine structures
+ # The input must be kept as a float; otherwise, the output is prone to becoming NaN.
+ sine_waves = self._f02sine(f0_buf.float()) * self.sine_amp
+ uv = self._f02uv(f0)
+ sine_waves = sine_waves * uv + ((uv * self.noise_std + (1 - uv) * self.sine_amp / 3) * torch.randn_like(sine_waves))
+
+ return self.merge(sine_waves.to(f0.dtype))
+
+class RefineGANGenerator(nn.Module):
+ """
+ RefineGAN Generator network.
+ Combines a U-Net style downsampling analyzer for the raw excitation signal
+ with a parallel multi-scale upsampling decoder conditioned on Mel-spectrograms.
+ """
+
+ def __init__(
+ self,
+ *,
+ sample_rate = 44100,
+ upsample_rates = (8, 8, 2, 2),
+ leaky_relu_slope = 0.2,
+ num_mels = 128,
+ start_channels = 16,
+ gin_channels = 256,
+ checkpointing = False,
+ upsample_initial_channel = 512
+ ):
+ """
+ Args:
+ sample_rate (int): Base operational frequency used inside sine oscillators. Default: 44100.
+ upsample_rates (tuple of int): Multi-stage stride upscale coefficients. Default: (8, 8, 2, 2).
+ leaky_relu_slope (float): LeakyReLU negative alpha parameter. Default: 0.2.
+ num_mels (int): Dimensions of source Mel bands. Default: 128.
+ start_channels (int): Initial filter count for the first raw source encoder stage. Default: 16.
+ gin_channels (int): Sizing parameters for speaker or style embeddings. Default: 256.
+ checkpointing (bool): Toggle to activate VRAM-saving gradient checkpoint wrappers. Default: False.
+ upsample_initial_channel (int): Hidden feature channel depth for decoding branches. Default: 512.
+ """
+
+ super().__init__()
+ self.upsample_rates = upsample_rates
+ self.checkpointing = checkpointing
+ self.leaky_relu_slope = leaky_relu_slope
+ self.upp = np.prod(upsample_rates)
+ self.m_source = SineGen(sample_rate)
+ # Front conv mapping 1D excitation track up to base filter space
+ self.pre_conv = weight_norm(nn.Conv1d(1, 16, 7, 1, padding=3))
+ channels = start_channels
+ size = self.upp
+ self.downsample_blocks = nn.ModuleList([])
+ self.df0 = []
+
+ # Map downscaling steps backwards from master rate down to localized mel steps
+ for i, _ in enumerate(upsample_rates):
+ new_size = int(size / upsample_rates[-i - 1])
+ self.df0.append([size, new_size])
+ size = new_size
+
+ new_channels = channels * 2
+ self.downsample_blocks.append(weight_norm(nn.Conv1d(channels, new_channels, 7, 1, padding=3)))
+ channels = new_channels
+
+ # Mel acoustic map preprocessing layers
+ channels = upsample_initial_channel
+ self.mel_conv = weight_norm(nn.Conv1d(num_mels, channels // 2, 7, 1, padding=3))
+ self.mel_conv.apply(init_weights)
+
+ # Global style conditioning map vector layer
+ if gin_channels != 0: self.cond = nn.Conv1d(256, channels // 2, 1)
+
+ self.upsample_blocks = nn.ModuleList([])
+ self.upsample_conv_blocks = nn.ModuleList([])
+ # Allocate matching upsamplers along with Parallel Fusion blocks
+ for rate in upsample_rates:
+ new_channels = channels // 2
+ self.upsample_blocks.append(
+ nn.Upsample(
+ scale_factor=rate,
+ mode="linear"
+ )
+ )
+ self.upsample_conv_blocks.append(
+ ParallelResBlock(
+ in_channels=channels + channels // 4,
+ out_channels=new_channels,
+ kernel_sizes=(3, 7, 11),
+ dilation=(1, 3, 5),
+ leaky_relu_slope=leaky_relu_slope
+ )
+ )
+ channels = new_channels
+
+ # Terminal projection flattening down into standard 1D raw waveform output space
+ self.conv_post = weight_norm(nn.Conv1d(channels, 1, 7, 1, padding=3, bias=False))
+ self.conv_post.apply(init_weights)
+
+ def forward(self, mel, f0, g = None):
+ """
+ Args:
+ mel (Tensor): Mel-spectrogram features.
+ f0 (Tensor): Fundamental frequency trajectory.
+ g (Tensor, optional): Speaker/style condition identity maps.
+
+ Returns:
+ Tensor: Synthesized high-fidelity target audio wave.
+ """
+
+ f0_size = mel.shape[-1]
+ # Interpolate pitch sequence to audio rate and generate raw multi-harmonic excitation wave
+ har_source = self.m_source(F.interpolate(f0.unsqueeze(1), size=f0_size * self.upp, mode="linear").transpose(1, 2)).transpose(1, 2)
+ # Analyze excitation via progressive downsampling steps and collect multi-scale features
+ x = self.pre_conv(har_source)
+ downs = []
+
+ for block, (old_size, new_size) in zip(self.downsample_blocks, self.df0):
+ x = F.leaky_relu(x, self.leaky_relu_slope)
+ downs.append(x)
+
+ # High-fidelity resampler using Kaiser-windowed Sinc interpolation
+ # Half-precison turns this resampler into a professional audio destroyer.
+ x = torchaudio.functional.resample(
+ x.float().contiguous(),
+ orig_freq=int(f0_size * old_size),
+ new_freq=int(f0_size * new_size),
+ lowpass_filter_width=64,
+ rolloff=0.9475937167399596,
+ resampling_method="sinc_interp_kaiser",
+ beta=14.769656459379492
+ ).to(x.dtype)
+ x = block(x)
+
+ # Preprocess acoustic feature blueprints and mix identity descriptors if available
+ mel = self.mel_conv(mel)
+ if g is not None: mel += self.cond(g)
+
+ # Combine mel features with the lowest-resolution excitation features to begin decoding
+ x = torch.cat([mel, x], dim=1)
+
+ # Progressively upscale feature resolution while concatenating corresponding encoder features (skip connections)
+ for ups, res, down in zip(self.upsample_blocks, self.upsample_conv_blocks, reversed(downs)):
+ x = F.leaky_relu(x, self.leaky_relu_slope)
+ # Execute blocks using memory-saving checkpoint steps if training modes request it
+ x = checkpoint(res, torch.cat([checkpoint(ups, x, use_reentrant=False), down], dim=1), use_reentrant=False) if self.training and self.checkpointing else res(torch.cat([ups(x), down], dim=1))
+
+ # Perform final mapping back down into amplitude wave steps bounded within [-1.0, 1.0]
+ return self.conv_post(F.leaky_relu(x, self.leaky_relu_slope)).tanh()
+
+ def remove_weight_norm(self):
+ """Fuses weight norm parametrizations into single weight matrices to accelerate inference speeds."""
+
+ if hasattr(self.pre_conv, "parametrizations") and "weight" in self.pre_conv.parametrizations: parametrize.remove_parametrizations(self.pre_conv, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.pre_conv)
+
+ if hasattr(self.mel_conv, "parametrizations") and "weight" in self.mel_conv.parametrizations: parametrize.remove_parametrizations(self.mel_conv, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.mel_conv)
+
+ if hasattr(self.conv_post, "parametrizations") and "weight" in self.conv_post.parametrizations: parametrize.remove_parametrizations(self.conv_post, "weight", leave_parametrized=True)
+ else: remove_weight_norm(self.conv_post)
+
+ for block in self.downsample_blocks:
+ if hasattr(block, "parametrizations") and "weight" in block.parametrizations: parametrize.remove_parametrizations(block, "weight", leave_parametrized=True)
+ else: remove_weight_norm(block)
+
+ for block in self.upsample_conv_blocks:
+ block.remove_weight_norm()
\ No newline at end of file
diff --git a/main/library/onnx/onnx_export.py b/main/library/onnx/onnx_export.py
new file mode 100644
index 0000000000000000000000000000000000000000..26ed1a39db89a74dc20ace9abd5c0e1c119f568f
--- /dev/null
+++ b/main/library/onnx/onnx_export.py
@@ -0,0 +1,232 @@
+import os
+import io
+import sys
+import onnx
+import json
+import torch
+import onnxslim
+import warnings
+import traceback
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import logger, config
+from main.library.algorithm.synthesizers import Synthesizer, SynthesizerSVC
+
+if not config.debug_mode: warnings.filterwarnings("ignore")
+
+FEATS_LENGTH = 200
+
+def autocast(model):
+ """
+ This function is designed to allow a model—after being converted to the ONNX format—to accept Float-type inputs even when the model is in Half-precision.
+
+ Args:
+ model (torch.nn.Module): The PyTorch model to apply autocasting to.
+
+ Returns:
+ torch.nn.Module: The modified model with wrapped forward logic.
+ """
+
+ orig_forward = model.forward
+
+ def _forward(*args, **kwargs):
+ # Fetch the model's internal data type (e.g., float16 or float32)
+ dtype = next(model.parameters()).dtype
+ # Cast floating-point tensors dynamically, keeping integer or non-tensor inputs untouched
+ return orig_forward(*[a.to(dtype) if isinstance(a, torch.Tensor) and a.dtype.is_floating_point else a for a in args], **kwargs)
+
+ model.forward = _forward
+ return model
+
+def quantization_int8(output_path):
+ """
+ Applies dynamic INT8 quantization to an existing ONNX model to reduce file size
+ and accelerate CPU inference.
+
+ Args:
+ output_path (str): File path to the original FP32/FP16 ONNX model.
+
+ Returns:
+ str: The path to the quantized INT8 ONNX model, or the original path if failed.
+ """
+
+ from onnxruntime.quantization import quantize_dynamic, QuantType
+ from onnxruntime.quantization.shape_inference import quant_pre_process
+
+ # Target path for the newly generated INT8 model
+ output_path_int8 = output_path.replace(".onnx", "-int8.onnx")
+
+ try:
+ # Pre-process the model by executing symbolic shape inference (required before quantization)
+ quant_pre_process(output_path, output_path, skip_symbolic_shape=True)
+ except Exception as e:
+ logger.debug(traceback.format_exc())
+ logger.debug(e)
+
+ # Perform dynamic weights quantization to signed 8-bit integers
+ quantize_dynamic(model_input=output_path, model_output=output_path_int8, weight_type=QuantType.QInt8)
+ # Return the INT8 path if successfully generated; otherwise fallback to the original path
+ return output_path_int8 if os.path.exists(output_path_int8) else output_path
+
+def onnx_exporter(input_path, output_path, is_half=False, int8_mode=False, device="cpu"):
+ """
+ Exports a trained RVC or SVC synthesizer
+ checkpoint to an optimized ONNX model structure, attaching custom metadata.
+
+ Args:
+ input_path (str): File path to the PyTorch checkpoint (.pth).
+ output_path (str): File path where the output .onnx model will be stored.
+ is_half (bool, optional): If True, converts the model weights to Float16. Defaults to False.
+ int8_mode (bool, optional): If True, post-processes the model into INT8. Defaults to False.
+ device (str, optional): Computation device ('cpu' or 'cuda'). Defaults to "cpu".
+ """
+
+ # Force CPU fallback if CUDA is not requested, or if the system utilizes ZLUDA
+ if not device.startswith("cuda") or config.is_zluda: device = "cpu"
+
+ # Load checkpoint safely avoiding arbitrary code execution hazards
+ cpt = (torch.load(input_path, map_location="cpu", weights_only=True) if os.path.isfile(input_path) else None)
+ # Adjust dynamic speaker/embedding layer configuration from the loaded weight matrix
+ cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
+
+ # Unpack model metadata fields from the checkpoint dictionary with default fallbacks
+ (
+ model_name,
+ model_author,
+ epochs, steps,
+ version,
+ f0,
+ model_hash,
+ vocoder,
+ creation_date,
+ speakers_id,
+ architecture
+ ) = (
+ cpt.get("model_name", None),
+ cpt.get("author", None),
+ cpt.get("epoch", None),
+ cpt.get("step", None),
+ cpt.get("version", "v1"),
+ cpt.get("f0", 1),
+ cpt.get("model_hash", None),
+ cpt.get("vocoder", "Default"),
+ cpt.get("creation_date", None),
+ cpt.get("speakers_id", 1),
+ cpt.get("architecture", "RVC")
+ )
+
+ # Set up basic parameters based on model generation version
+ text_enc_hidden_dim = 768 if version == "v2" else 256
+ tgt_sr = cpt["config"][-1]
+
+ # Instantiate the correct Model Architecture depending on the model specification
+ if architecture == "RVC":
+ net_g = Synthesizer(
+ *cpt["config"],
+ use_f0=f0,
+ text_enc_hidden_dim=text_enc_hidden_dim,
+ vocoder=vocoder,
+ checkpointing=False,
+ onnx=True
+ )
+ else:
+ net_g = SynthesizerSVC(
+ *cpt["config"],
+ text_enc_hidden_dim=text_enc_hidden_dim,
+ vocoder=vocoder,
+ checkpointing=False,
+ noise_scale=0.4,
+ onnx=True
+ )
+
+ # Re-route the forward pass to the dedicated ONNX inference method
+ net_g.forward = net_g.onnx_infer
+ net_g.load_state_dict(cpt["weight"], strict=False)
+ # Switch model to evaluation state, transfer to target device, and assign float precision
+ net_g.eval().to(device).to(torch.float16 if is_half else torch.float32)
+ net_g.remove_weight_norm()
+ # Apply global dynamic casting wrapper if working with float16 targets
+ if is_half: net_g = autocast(net_g)
+
+ # Initialize mock structural dummy input variables for PyTorch's tracing engine
+ phone = torch.rand(1, FEATS_LENGTH, text_enc_hidden_dim).to(device)
+ phone_length = torch.LongTensor([FEATS_LENGTH]).to(device)
+ sid = torch.LongTensor([0]).to(device)
+ rate = torch.FloatTensor([1.0]).to(device)
+
+ args = [phone, phone_length, sid, rate]
+ input_names = ["phone", "phone_lengths", "sid", "rate"]
+
+ # Set dynamic axes dimensions to allow flexible batch (B) and sequence (T) runtime evaluation
+ dynamic_axes = {
+ "phone": {0: "B", 1: "T"},
+ "phone_lengths": {0: "B"},
+ "sid": {0: "B"},
+ "rate": {0: "B"}
+ }
+
+ if f0: # Append pitch-related parameters if the model variant relies on F0 fundamental frequency
+ pitch = torch.randint(size=(1, FEATS_LENGTH), low=5, high=255).to(device)
+ nsff0 = torch.rand(1, FEATS_LENGTH).to(device)
+
+ args += [pitch, nsff0]
+ input_names += ["pitch", "nsff0"]
+ dynamic_axes.update({
+ "pitch": {0: "B", 1: "T"},
+ "nsff0": {0: "B", 1: "T"},
+ })
+
+ try:
+ # Export model graph via byte stream pipeline to perform memory-contained optimization
+ with io.BytesIO() as model:
+ torch.onnx.export(
+ net_g,
+ tuple(args),
+ model,
+ do_constant_folding=True, # Pre-calculate constant nodes at compile time
+ opset_version=17,
+ verbose=False,
+ input_names=input_names,
+ output_names=["audio"],
+ dynamic_axes=dynamic_axes,
+ dynamo=False # Use classic tracing engine
+ )
+
+ # Prune unused node structures, fuse blocks and squeeze overhead via onnxslim
+ model = onnxslim.slim(onnx.load_model_from_string(model.getvalue()))
+ # Embed structured architecture details back directly into the ONNX model metadata block
+ model.metadata_props.append(
+ onnx.StringStringEntryProto(
+ key="model_info",
+ value=json.dumps(
+ {
+ "model_name": model_name,
+ "author": model_author,
+ "epoch": epochs,
+ "step": steps,
+ "version": version,
+ "sr": tgt_sr,
+ "f0": f0,
+ "model_hash": model_hash,
+ "creation_date": creation_date,
+ "vocoder": vocoder,
+ "text_enc_hidden_dim": text_enc_hidden_dim,
+ "speakers_id": speakers_id,
+ "architecture": architecture
+ }
+ )
+ )
+ )
+
+ # Save optimized model graph structure out into persistent disk storage
+ onnx.save(model, output_path)
+ # Clear heavy model instances from RAM memory
+ del net_g, model
+
+ # Route down into dynamic INT8 post-quantization pipeline if selected
+ if int8_mode: output_path = quantization_int8(output_path)
+ return output_path
+ except:
+ logger.error(traceback.format_exc())
+ return None
\ No newline at end of file
diff --git a/main/library/onnx/wrapper.py b/main/library/onnx/wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..06fa1b4e4367f6b708b7e696fc114f4a9e5810e0
--- /dev/null
+++ b/main/library/onnx/wrapper.py
@@ -0,0 +1,170 @@
+import json
+import torch
+import onnxruntime
+
+import numpy as np
+
+class ONNXRVC:
+ """
+ An inference wrapper for Retrieval-based Voice Conversion (RVC) models exported to ONNX format.
+
+ This class supports runtime optimization via ONNX Runtime I/O Binding to achieve zero-copy memory
+ transfers between PyTorch tensors and the underlying ONNX Runtime execution providers (e.g., CUDA).
+ """
+
+ def __init__(
+ self,
+ model_path,
+ providers,
+ log_severity_level = 3
+ ):
+ """
+ Initializes the ONNXRVC instance, reads embed metadata, and setups execution routes.
+
+ Args:
+ model_path (str): File path to the target .onnx model.
+ providers (List): List of registered execution providers (e.g., ['CUDAExecutionProvider']).
+ log_severity_level (int, optional): Log level threshold (3 = ERROR). Defaults to 3.
+ """
+
+ # Configure the Session options to limit log pollution
+ sess_options = onnxruntime.SessionOptions()
+ sess_options.log_severity_level = log_severity_level
+ # Initialize the actual core ONNX computational runtime session
+ self.net_g = onnxruntime.InferenceSession(
+ model_path,
+ sess_options=sess_options,
+ providers=providers
+ )
+
+ # Retrieve and parse serialized architectural settings attached to the ONNX graph metadata block
+ metadata_dict = json.loads(self.net_g.get_modelmeta().custom_metadata_map["model_info"])
+ self.use_f0 = metadata_dict.get("f0", 1)
+ # Route logic according to whether the model requires F0 pitch parameters
+ self.get_argument = self.get_onnx_argument_pitch if self.use_f0 else self.get_onnx_argument
+ self.cpt = {"tgt_sr": metadata_dict.get("sr", 32000), "use_f0": self.use_f0, "version": metadata_dict.get("version", "v1")}
+
+ # Allocate reusable placeholder arrays for traditional NumPy CPU runtime fallback paths
+ self.sid = np.empty(1, dtype=np.int64)
+ self.rate = np.ones(1, dtype=np.float32)
+
+ # Identify execution pipeline (Optimize utilizing direct pointers if backed by CUDA or CPU)
+ self._device = "cuda" if providers[0][0].startswith("CUDA") else "cpu"
+ self.infer = self.infer_io if providers[0][0].startswith(("CUDA", "CPU")) else self.infer_non_io
+
+ def get_onnx_argument_pitch(self, feats, p_len, sid, pitch = None, pitchf = None, rate = None):
+ """
+ Constructs standard raw NumPy dictionary inputs including pitch values (F0 enabled).
+
+ Args:
+ feats (torch.Tensor): Audio features/phone tensor.
+ p_len (torch.Tensor): Length array of the features.
+ sid (torch.Tensor): Speaker identity token tensor.
+ pitch (torch.Tensor, optional): Integer pitch (F0) contour values.
+ pitchf (torch.Tensor, optional): Floating-point normalized pitch values.
+ rate (torch.Tensor, optional): Speed/stretching factor.
+ """
+
+ self.sid[0] = sid.item()
+ self.rate[0] = 1.0 if rate is None else rate.item()
+
+ return {"phone": feats.cpu().numpy().astype(np.float32), "phone_lengths": p_len.cpu().numpy(), "sid": self.sid, "rate": self.rate, "pitch": pitch.cpu().numpy().astype(np.int64), "nsff0": pitchf.cpu().numpy().astype(np.float32)}
+
+ def get_onnx_argument(self, feats, p_len, sid, pitch = None, pitchf = None, rate = None):
+ """
+ Constructs standard raw NumPy dictionary inputs excluding pitch values (F0 disabled).
+ """
+
+ self.sid[0] = sid.item()
+ self.rate[0] = 1.0 if rate is None else rate.item()
+ return {"phone": feats.cpu().numpy().astype(np.float32), "phone_lengths": p_len.cpu().numpy(), "sid": self.sid, "rate": self.rate}
+
+ def to(self, device = "cpu"):
+ """
+ Sets the target system device where output synthesized audio tensors will be stored.
+
+ Args:
+ device (str or torch.device): Target device path (e.g., 'cuda:0', 'cpu').
+
+ Returns:
+ ONNXRVC: The instance itself for method chaining.
+ """
+
+ self.device = device
+ return self
+
+ def infer_non_io(
+ self,
+ feats = None,
+ p_len = None,
+ pitch = None,
+ pitchf = None,
+ sid = None,
+ rate = None
+ ):
+ """
+ Runs classic inference by mapping device matrices back into CPU NumPy arrays.
+ High data copying overhead, acts as fallback.
+
+ Returns:
+ torch.Tensor: The resulting synthesized raw audio waveform tensor.
+ """
+
+ # Execute model using classic key-value Python dictionaries
+ output = self.net_g.run(
+ ["audio"], (
+ self.get_argument(
+ feats,
+ p_len,
+ sid,
+ pitch,
+ pitchf,
+ rate
+ )
+ )
+ )
+
+ # Wrap array response back into PyTorch and push to the assigned destination device
+ return torch.from_numpy(output[0]).to(self.device)
+
+ def infer_io(
+ self,
+ feats = None,
+ p_len = None,
+ pitch = None,
+ pitchf = None,
+ sid = None,
+ rate = None
+ ):
+ """
+ Runs highly efficient execution passing physical GPU/CPU memory addresses via I/O bindings.
+ This cuts out all conversion lags from crossing frameworks.
+
+ Returns:
+ torch.Tensor: The resulting synthesized raw audio waveform tensor.
+ """
+
+ # Construct and format parameters directly inside native tensor layers
+ rate = torch.FloatTensor([1.0]).to(self.device) if rate is None else rate.float()
+ feats, p_len = feats.float().contiguous(), p_len.contiguous()
+ device_idx = feats.device.index or 0
+
+ # Construct individual runtime binding instance
+ io_binding = self.net_g.io_binding()
+ io_binding.bind_input(name="phone", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(feats.shape), buffer_ptr=feats.data_ptr())
+ io_binding.bind_input(name="phone_lengths", device_type=self._device, device_id=device_idx, element_type=np.int64, shape=tuple(p_len.shape), buffer_ptr=p_len.data_ptr())
+ io_binding.bind_input(name="sid", device_type=self._device, device_id=device_idx, element_type=np.int64, shape=tuple(sid.shape), buffer_ptr=sid.data_ptr())
+ io_binding.bind_input(name="rate", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(rate.shape), buffer_ptr=rate.data_ptr())
+
+ if self.use_f0: # Include F0 frequency layers conditionally if flagged
+ pitch, pitchf = pitch.contiguous(), pitchf.float().contiguous()
+ io_binding.bind_input(name="pitch", device_type=self._device, device_id=device_idx, element_type=np.int64, shape=tuple(pitch.shape), buffer_ptr=pitch.data_ptr())
+ io_binding.bind_input(name="nsff0", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(pitchf.shape), buffer_ptr=pitchf.data_ptr())
+
+ # Bind output receiver allocation pointing directly to target execution space block
+ io_binding.bind_output(name="audio", device_type=self._device, device_id=device_idx)
+ # Direct zero-copy inference execution step
+ self.net_g.run_with_iobinding(io_binding)
+
+ # Collect target array and safely encapsulate inside PyTorch tensor structures
+ return torch.from_numpy(io_binding.get_outputs()[0].numpy()).to(self.device)
\ No newline at end of file
diff --git a/main/library/optimizers/adabelief.py b/main/library/optimizers/adabelief.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd04001fdc4f888383894a57a137d02e03e1d583
--- /dev/null
+++ b/main/library/optimizers/adabelief.py
@@ -0,0 +1,164 @@
+import math
+import torch
+
+from torch.optim.optimizer import Optimizer
+
+class AdaBelief(Optimizer):
+ """
+ Implements the AdaBelief Optimizer with enhanced PyTorch FOREACH performance.
+
+ AdaBelief adapts step sizes based on the "belief" in the current gradient direction.
+ Instead of scaling by the raw uncentered variance (like Adam), it scales steps by the
+ variance of the prediction residual (gradient minus exponential moving average).
+
+ Includes built-in support for:
+ - Gradient Centralization (GC) to stabilize training.
+ - Rectified learning rate scheduler mechanism (RAdam-style) to reduce warmup needs.
+ """
+
+ def __init__(
+ self,
+ params,
+ lr = 1e-4,
+ betas = (0.8, 0.99),
+ eps = 1e-10,
+ weight_decay = 0.0,
+ use_gc = False,
+ rectify = False
+ ):
+ """
+ Initializes configuration properties for the AdaBelief optimizer.
+
+ Args:
+ params (Any): Iterable of parameters to optimize or dicts defining parameter groups.
+ lr (float): Base target learning rate step factor. Defaults to 1e-4.
+ betas (Tuple[float, float]): Coefficients used for computing running averages of gradient and its variance. Defaults to (0.8, 0.99).
+ eps (float): Term added to the denominator to improve numerical stability. Defaults to 1e-10.
+ weight_decay (float): Decoupled weight decay (L2 penalty) coefficient. Defaults to 0.0.
+ use_gc (bool): Activates Gradient Centralization for Conv/Linear layers. Defaults to False.
+ rectify (bool): Applies RAdam-style momentum rectification. Defaults to False.
+
+ Raises:
+ ValueError: If any initialization parameters violate standard baseline ranges.
+ """
+
+ if lr <= 0.0: raise ValueError(f"Invalid learning rate: {lr}")
+ if eps < 0.0: raise ValueError(f"Invalid epsilon value: {eps}")
+ if not 0.0 <= betas[0] < 1.0: raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+ if not 0.0 <= betas[1] < 1.0: raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+ if weight_decay < 0: raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+ defaults = dict(
+ lr=lr,
+ betas=betas,
+ eps=eps,
+ weight_decay=weight_decay,
+ use_gc=use_gc,
+ rectify=rectify
+ )
+ super().__init__(params, defaults)
+
+ def __setstate__(self, state):
+ """Synchronizes internal structural tracking status dictionaries."""
+
+ super().__setstate__(state)
+ for group in self.param_groups:
+ group.setdefault("use_gc", False)
+ group.setdefault("rectify", False)
+
+ @torch.no_grad()
+ def step(self, closure=None):
+ """
+ Performs a single optimization parameter update iteration step.
+
+ Args:
+ closure (Callable, optional): A closure that re-evaluates the model and returns the loss metric. Defaults to None.
+ """
+
+ loss = None
+ if closure is not None:
+ with torch.enable_grad():
+ loss = closure()
+
+ for group in self.param_groups:
+ beta1, beta2 = group["betas"]
+ eps, lr, weight_decay, use_gc, rectify = group["eps"], group["lr"], group["weight_decay"], group["use_gc"], group["rectify"]
+ # Tracking list parameters used to trigger multi-tensor vectorized foreach actions
+ params_with_grad, grads, exp_avgs, exp_avg_vars = [], [], [], []
+
+ for p in group["params"]:
+ if p.grad is None: continue
+
+ params_with_grad.append(p)
+ grad = p.grad
+
+ # Optional: Apply Gradient Centralization (GC) to weights across non-scalar axes
+ if use_gc and grad.dim() > 1 and grad[0].numel() > 1: grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True))
+ grads.append(grad)
+
+ state = self.state[p]
+ # Initialize state variables if they are empty
+ if len(state) == 0:
+ state["step"] = 0
+ # Exponential moving average of gradient values
+ state["exp_avg"] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ # Exponential moving average of variance deviations
+ state["exp_avg_var"] = torch.zeros_like(p, memory_format=torch.preserve_format)
+
+ exp_avgs.append(state["exp_avg"])
+ exp_avg_vars.append(state["exp_avg_var"])
+
+ if not params_with_grad: continue
+
+ # Synchronize parameter step tracking state counters safely
+ state = self.state[params_with_grad[0]]
+ state["step"] += 1
+ step = state["step"]
+
+ for p in params_with_grad[1:]:
+ self.state[p]["step"] = step
+
+ # Calculate mathematical statistical tracking bias correction metrics
+ bias_correction1 = 1 - beta1 ** step
+ bias_correction2 = 1 - beta2 ** step
+ # Apply standard decoupled weight decay parameter updates
+ if weight_decay != 0: torch._foreach_mul_(params_with_grad, 1 - lr * weight_decay)
+
+ # Update first momentum term
+ torch._foreach_mul_(exp_avgs, beta1)
+ torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
+
+ # Core AdaBelief step: Calculate prediction residual
+ grad_residuals = torch._foreach_sub(grads, exp_avgs)
+
+ # Update second momentum variance matrix
+ torch._foreach_mul_(exp_avg_vars, beta2)
+ torch._foreach_addcmul_(exp_avg_vars, grad_residuals, grad_residuals, value=1 - beta2)
+
+ use_variance = True
+ r_t = 1.0 # Default scale factor when rectification is inactive
+
+ # Optional: Dynamic RAdam-style momentum rectification sequence calculations
+ if rectify:
+ rho_inf = 2.0 / (1.0 - beta2) - 1.0
+ beta2_t = beta2**step
+ rho_t = rho_inf - 2.0 * step * beta2_t / (1.0 - beta2_t)
+ # Check if the variance tractability is reliable
+ use_variance = rho_t >= 5.0
+ if use_variance:
+ # Compute rectification scale modifier
+ r_t = math.sqrt(((rho_t - 4) * (rho_t - 2) * rho_inf) / ((rho_inf - 4) * (rho_inf - 2) * rho_t))
+
+ # Apply final adaptive step modifiers to base network parameters
+ if use_variance:
+ # Calculate denominators using fast elementwise sqrt operators
+ denom = torch._foreach_sqrt(torch._foreach_add(exp_avg_vars, eps))
+
+ torch._foreach_div_(denom, math.sqrt(bias_correction2))
+ # Update weights
+ torch._foreach_add_(params_with_grad, torch._foreach_div(exp_avgs, denom), alpha=-(lr * r_t / bias_correction1))
+ else:
+ # Fallback path if variance tracker isn't stable yet: update via unscaled momentum steps
+ torch._foreach_add_(params_with_grad, exp_avgs, alpha=-(lr / bias_correction1))
+
+ return loss
\ No newline at end of file
diff --git a/main/library/optimizers/anyprecision_optimizer.py b/main/library/optimizers/anyprecision_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..05f8d7974138aa71406c3f122c4eb2053bc3e261
--- /dev/null
+++ b/main/library/optimizers/anyprecision_optimizer.py
@@ -0,0 +1,133 @@
+import torch
+
+from torch.optim.optimizer import Optimizer
+
+class AnyPrecisionAdamW(Optimizer):
+ """
+ Implements AnyPrecisionAdamW optimizer with built-in Kahan Summation.
+
+ This optimizer allows training with low-precision states (such as BF16/FP16
+ for momentum, variance, and compensation buffers) to significantly reduce
+ the optimizer's memory footprint, while maintaining high training stability
+ via Kahan Summation accumulation adjustments.
+ """
+
+ def __init__(
+ self,
+ params,
+ lr=1e-3,
+ betas=(0.9, 0.999),
+ eps=1e-8,
+ weight_decay=0.0,
+ use_kahan_summation=True,
+ momentum_dtype=torch.bfloat16,
+ variance_dtype=torch.bfloat16,
+ compensation_buffer_dtype=torch.bfloat16
+ ):
+ """
+ Initializes configuration properties for the AnyPrecisionAdamW optimizer.
+
+ Args:
+ params (Any): Iterable of parameters to optimize or dicts defining parameter groups.
+ lr (float): Learning rate step factor coefficient. Defaults to 1e-3.
+ betas (Tuple[float, float]): Coefficients used for computing running averages of gradient and its square. Defaults to (0.9, 0.999).
+ eps (float): Term added to the denominator to improve numerical stability. Defaults to 1e-8.
+ weight_decay (float): Decoupled weight decay (L2 penalty) coefficient. Defaults to 0.0.
+ use_kahan_summation (bool): Enables Kahan summation algorithm to mitigate low-precision rounding errors during weight updates. Defaults to True.
+ momentum_dtype (torch.dtype): Data type used for first momentum storage. Defaults to torch.bfloat16.
+ variance_dtype (torch.dtype): Data type used for second momentum storage. Defaults to torch.bfloat16.
+ compensation_buffer_dtype (torch.dtype): Data type used for Kahan errors tracking. Defaults to torch.bfloat16.
+ """
+
+ defaults = dict(
+ lr=lr,
+ betas=betas,
+ eps=eps,
+ weight_decay=weight_decay,
+ use_kahan_summation=use_kahan_summation,
+ momentum_dtype=momentum_dtype,
+ variance_dtype=variance_dtype,
+ compensation_buffer_dtype=compensation_buffer_dtype
+ )
+
+ super().__init__(
+ params,
+ defaults
+ )
+
+ @torch.no_grad()
+ def step(self, closure=None):
+ """
+ Performs a single optimization parameter update iteration step.
+
+ Args:
+ closure (Callable, optional): A closure that re-evaluates the model
+ and returns the loss metric. Defaults to None.
+ """
+
+ if closure is not None:
+ with torch.enable_grad():
+ closure()
+
+ for group in self.param_groups:
+ beta1, beta2 = group["betas"]
+ lr = group["lr"]
+ weight_decay = group["weight_decay"]
+ eps = group["eps"]
+ use_kahan_summation = group["use_kahan_summation"]
+ momentum_dtype = group["momentum_dtype"]
+ variance_dtype = group["variance_dtype"]
+ compensation_buffer_dtype = group["compensation_buffer_dtype"]
+
+ for p in group["params"]:
+ if p.grad is None: continue
+ if p.grad.is_sparse: raise RuntimeError("AnyPrecisionAdamW does not support sparse gradients.")
+
+ state = self.state[p]
+ # Initialize state variables on demand
+ if len(state) == 0:
+ state["step"] = torch.tensor(0.0)
+ # First momentum tracking tensor in targeted low precision
+ state["exp_avg"] = torch.zeros_like(p, dtype=momentum_dtype)
+ # Second momentum variance tracking tensor in targeted low precision
+ state["exp_avg_sq"] = torch.zeros_like(p, dtype=variance_dtype)
+
+ if use_kahan_summation:
+ # Residual rounding error compensation buffer tracker
+ state["compensation"] = torch.zeros_like(p, dtype=compensation_buffer_dtype)
+
+ state["step"] += 1
+ step = state["step"]
+ exp_avg = state["exp_avg"]
+ exp_avg_sq = state["exp_avg_sq"]
+
+ grad = p.grad
+ # Perform decoupled AdamW weight decay
+ if weight_decay: p.data.mul_(1 - lr * weight_decay)
+
+ # Update biased first momentum
+ exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+ # Update biased second raw momentum
+ exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+ # Compute statistical bias corrections coefficients
+ bias_correction1 = 1 - beta1 ** step
+ step_size = lr / bias_correction1
+
+ denom_correction = (1 - beta2**step) ** 0.5
+ # Calculate stabilized adaptive denominators
+ centered_variance = (exp_avg_sq.sqrt() / denom_correction).add_(eps, alpha=1)
+
+ if use_kahan_summation:
+ compensation = state["compensation"]
+ # Accumulate the calculated delta step tracking update directly onto the compensation buffer
+ compensation.addcdiv_(exp_avg, centered_variance, value=-step_size)
+ # Snapshot current weights state before introducing target adjustments
+ temp_buffer = p.detach().clone()
+ # Apply compensated updates onto core high-precision parameters
+ p.data.add_(compensation)
+ # Extract the residual error caused by low-precision truncation or quantization
+ compensation.add_(temp_buffer.sub_(p.data))
+ else:
+ # Standard weight update step path if Kahan buffer mechanism is disabled
+ p.data.addcdiv_(exp_avg, centered_variance, value=-step_size)
\ No newline at end of file
diff --git a/main/library/predictors/CREPE/CREPE.py b/main/library/predictors/CREPE/CREPE.py
new file mode 100644
index 0000000000000000000000000000000000000000..5647ddac6a9d88bf5924a0335749f44a22f3eadd
--- /dev/null
+++ b/main/library/predictors/CREPE/CREPE.py
@@ -0,0 +1,313 @@
+import os
+import sys
+import torch
+import librosa
+import scipy.stats
+
+import numpy as np
+
+sys.path.append(os.getcwd())
+
+from main.library.algorithm.viterbi import viterbi
+
+CENTS_PER_BIN, PITCH_BINS, SAMPLE_RATE, WINDOW_SIZE = 20, 360, 16000, 1024
+
+class CREPE:
+ """
+ CREPE (Convolutional Representation for Pitch Estimation) model wrapper.
+
+ This class handles preprocessing, inference (via PyTorch or ONNX Runtime),
+ Viterbi decoding, and postprocessing to estimate fundamental frequency (F0)
+ and optional periodicity from raw audio waveforms.
+ """
+
+ def __init__(
+ self,
+ model_path,
+ model_size="full",
+ hop_length=512,
+ batch_size=None,
+ f0_min=50,
+ f0_max=1100,
+ device=None,
+ sample_rate=16000,
+ providers=None,
+ onnx=False,
+ is_half=False,
+ return_periodicity=False,
+ compile_model = False,
+ compile_mode = None
+ ):
+ """Initializes the CREPE model instance with specified configurations.
+
+ Args:
+ model_path (str): Path to the model weight file (.pth or .onnx).
+ model_size (str): Size of the CREPE architecture ('full', 'tiny', etc.). Default is "full".
+ hop_length (int, optional): Number of samples between frames. Defaults to 512.
+ batch_size (int, optional): Number of frames processed at once. Defaults to None (all frames).
+ f0_min (float): Minimum frequency bound for pitch filtering in Hz. Default is 50.
+ f0_max (float): Maximum frequency bound for pitch filtering in Hz. Default is 1100.
+ device (str or torch.device): Device execution target (e.g., 'cuda', 'cpu').
+ sample_rate (int): Input audio sampling rate. Default is 16000.
+ providers (list, optional): ONNX Execution Providers list (e.g., ['CUDAExecutionProvider']).
+ onnx (bool): If True, uses ONNX runtime inference. Default is False.
+ is_half (bool): If True, runs PyTorch model in FP16 precision. Default is False.
+ return_periodicity (bool): If True, returns confidence scores along with F0. Default is False.
+ compile_model (bool): If True, compiles PyTorch model using torch.compile(). Default is False.
+ compile_mode (str, optional): Optimization mode for torch.compile().
+ """
+
+ self.device = device
+ self.hop_length = hop_length
+ self.batch_size = batch_size
+ self.sample_rate = sample_rate
+ self.return_periodicity = return_periodicity
+
+ # Map minimum and maximum frequency constraints from Hz to CREPE's cent-based bins
+ self.f0_min_bin = (((1200 * torch.tensor(f0_min / 10).log2()) - 1997.3794084376191) / CENTS_PER_BIN).floor().int()
+ self.f0_max_bin = (((1200 * torch.tensor(f0_max / 10).log2()) - 1997.3794084376191) / CENTS_PER_BIN).ceil().int()
+ # Small constant to avoid division-by-zero during normalization
+ self.eps = torch.tensor(1e-10, device=device)
+ self.transition = None
+
+ # Load and configure model based on backend (ONNX vs PyTorch)
+ if onnx:
+ import onnxruntime as ort
+
+ sess_options = ort.SessionOptions()
+ sess_options.log_severity_level = 3 # Suppress verbose warnings
+ model = ort.InferenceSession(model_path, sess_options=sess_options, providers=providers)
+ else:
+ from main.library.predictors.CREPE.model import CREPEE
+
+ model = CREPEE(model_size)
+ model.load_state_dict(torch.load(model_path, map_location="cpu", weights_only=True))
+ model.to(device).eval()
+ # Apply performance optimizations if requested
+ # Warning: Not recommended, as it degrades the model's quality and introduces noise during silent segments.
+ if is_half: model = model.half()
+ if compile_model: model = torch.compile(model, mode=compile_mode)
+
+ self.model = model
+ # Route execution to the correct private inference function based on configuration
+ self._device = "cuda" if providers[0][0].startswith("CUDA") else "cpu"
+ self.infer = (self._infer_onnx_io if providers[0][0].startswith(("CUDA", "CPU")) else self._infer_onnx_non_io) if onnx else (self._infer_torch_fp16 if is_half else self._infer_torch_fp32)
+
+ def bins_to_frequency(self, bins):
+ """
+ Converts model bin indices back to frequency in Hz.
+
+ Args:
+ bins (torch.Tensor): Tensor containing predicted bin indices.
+
+ Returns:
+ torch.Tensor: Predicted fundamental frequencies in Hz.
+ """
+
+ # Ensure compatibility with specialized hardware accelerators
+ if str(bins.device).startswith(("ocl", "privateuseone")): bins = bins.to(torch.float32)
+
+ # Reconstruct pitch in cents from bins and add triangular noise for quantization smoothing
+ cents = CENTS_PER_BIN * bins + 1997.3794084376191
+ cents = (
+ cents + cents.new_tensor(
+ scipy.stats.triang.rvs(
+ c=0.5,
+ loc=-CENTS_PER_BIN,
+ scale=2 * CENTS_PER_BIN,
+ size=cents.size()
+ )
+ )
+ ) / 1200
+
+ # Convert cents back to Hz frequency
+ return 10 * 2 ** cents
+
+ def viterbi(self, logits):
+ """
+ Applies Viterbi decoding algorithm on the activation logits to find the optimal path.
+
+ Args:
+ logits (torch.Tensor): Model activation outputs before softmax.
+
+ Returns:
+ tuple: (bins, frequency)
+ - bins (torch.Tensor): Decoded bin indices.
+ - frequency (torch.Tensor): Corresponding frequencies in Hz.
+ """
+
+ # Lazy initialization of the transition matrix for Viterbi decoding
+ if self.transition is None:
+ idx = torch.arange(360, device=logits.device, dtype=logits.dtype)
+ # Create a symmetric penalty matrix based on absolute distance between bins
+ transition = (12 - (idx[:, None] - idx[None, :]).abs()).clamp(min=0)
+ # Normalize to form valid probability distribution per row
+ self.transition = transition / transition.sum(dim=1, keepdim=True)
+
+ with torch.no_grad():
+ probs = torch.nn.functional.softmax(logits, dim=1)
+ bins = viterbi(probs, self.transition)
+
+ return bins, self.bins_to_frequency(bins)
+
+ def preprocess(self, audio, pad=True):
+ """Resamples, frames, and normalizes raw audio into model-ready chunks.
+
+ Args:
+ audio (torch.Tensor): Input audio tensor with shape (1, samples).
+ pad (bool): Whether to pad the input to preserve center-aligned frames. Default is True.
+
+ Yields:
+ torch.Tensor: Standardized, normalized audio frames batch by batch.
+ """
+
+ # Fallback to default hop length if not explicitly defined
+ hop_length = (self.sample_rate // 100) if self.hop_length is None else self.hop_length
+
+ # Perform High-Quality Resampling if the input sample rate doesn't match CREPE's 16kHz
+ if self.sample_rate != SAMPLE_RATE:
+ audio = torch.tensor(
+ librosa.resample(
+ audio.detach().cpu().numpy().squeeze(0),
+ orig_sr=self.sample_rate,
+ target_sr=SAMPLE_RATE,
+ res_type="soxr_vhq"
+ ),
+ device=audio.device
+ ).unsqueeze(0)
+ # Adjust hop length proportionally to the new sample rate
+ hop_length = int(hop_length * SAMPLE_RATE / self.sample_rate)
+
+ # Pad or compute total frames based on padding strategy
+ if pad:
+ total_frames = 1 + int(audio.size(1) // hop_length)
+ audio = torch.nn.functional.pad(audio, (WINDOW_SIZE // 2, WINDOW_SIZE // 2))
+ else: total_frames = 1 + int((audio.size(1) - WINDOW_SIZE) // hop_length)
+
+ # Batch slicing initialization
+ batch_size = total_frames if self.batch_size is None else self.batch_size
+
+ # Slide over audio array using `unfold` to slice out overlapping windows
+ for i in range(0, total_frames, batch_size):
+ frames = torch.nn.functional.unfold(
+ audio[:, None, None, max(0, i * hop_length):min(audio.size(1), (i + batch_size - 1) * hop_length + WINDOW_SIZE)],
+ kernel_size=(1, WINDOW_SIZE),
+ stride=(1, hop_length)
+ ).transpose(1, 2)
+
+ if self.device.startswith(("ocl", "privateuseone")): frames = frames.contiguous()
+ frames = frames.reshape(-1, WINDOW_SIZE).to(self.device)
+ # Z-score Standardization (Zero mean, unit variance normalization per frame)
+ frames -= frames.mean(dim=1, keepdim=True)
+ frames /= self.eps.max(frames.std(dim=1, keepdim=True))
+
+ yield frames
+
+ def periodicity(self, probabilities, bins):
+ """
+ Extracts model confidence (periodicity) scores from active pitch bins.
+
+ Args:
+ probabilities (torch.Tensor): Softmax probabilities from model.
+ bins (torch.Tensor): Decoded optimal bin tracks.
+
+ Returns:
+ torch.Tensor: Extracted periodicity mapping.
+ """
+ probs_stacked = probabilities.transpose(1, 2).reshape(-1, PITCH_BINS)
+ periodicity = probs_stacked.gather(1, bins.reshape(-1, 1).to(torch.int64))
+
+ return periodicity.reshape(probabilities.size(0), probabilities.size(2))
+
+ def postprocess(self, probabilities):
+ """
+ Filters logits by frequency constraints, triggers Viterbi decoding, and extracts periodicity.
+
+ Args:
+ probabilities (torch.Tensor): Output activation tensor from the model inference.
+
+ Returns:
+ torch.Tensor or tuple: Pitch trajectory vector, or (pitch, periodicity) tuple.
+ """
+
+ probabilities = probabilities.detach()
+ # Suppress predictions out of the configured valid F0 boundaries
+ probabilities[:, :self.f0_min_bin] = -float('inf')
+ probabilities[:, self.f0_max_bin:] = -float('inf')
+
+ bins, pitch = self.viterbi(probabilities)
+
+ if not self.return_periodicity: return pitch
+ return pitch, self.periodicity(probabilities, bins)
+
+ def compute_f0(self, audio, pad=True):
+ """
+ Main entry point to execute the complete pitch track extraction pipeline.
+
+ Args:
+ audio (torch.Tensor): Raw input audio signal tensor.
+ pad (bool): Toggles temporal center padding. Default is True.
+
+ Returns:
+ torch.Tensor or tuple: Combined pitch values or a tuple of (pitch, periodicity).
+ """
+
+ results = []
+ with torch.no_grad():
+ for frames in self.preprocess(audio, pad):
+ # Infer -> Reshape/Transpose -> Post-process
+ result = self.postprocess(self.infer(frames).reshape(audio.size(0), -1, PITCH_BINS).transpose(1, 2))
+ # Manage data transfers securely to match incoming tensor's home device
+ results.append((result[0].to(audio.device), result[1].to(audio.device)) if isinstance(result, tuple) else result.to(audio.device))
+
+ # Aggregate chunked sequence tracks into contiguous output
+ if self.return_periodicity:
+ pitch, periodicity = zip(*results)
+ return torch.cat(pitch, 1), torch.cat(periodicity, 1)
+
+ return torch.cat(results, 1)
+
+ def _infer_torch_fp32(self, frames):
+ """Performs PyTorch inference in full 32-bit floating point precision."""
+
+ return self.model(
+ frames,
+ embed=False
+ )
+
+ def _infer_torch_fp16(self, frames):
+ """Performs PyTorch inference in half 16-bit floating point precision."""
+
+ return self.model(
+ frames.half(),
+ embed=False
+ ).float() # It needs to be converted to Float32; otherwise, the quality will be worse.
+
+ def _infer_onnx_non_io(self, frames):
+ """Performs traditional ONNX runtime inference with CPU/GPU memory copying overhead."""
+
+ return torch.tensor(
+ self.model.run(
+ ["f0"], {"frames": frames.cpu().numpy()}
+ )[0],
+ device=self.device
+ )
+
+ def _infer_onnx_io(self, frames):
+ """Performs zero-copy optimized ONNX runtime inference using explicit I/O binding."""
+
+ frames = frames.float().contiguous()
+ device_idx = frames.device.index or 0
+
+ io_binding = self.model.io_binding()
+ # Bind input tensor memory pointer directly to ONNX runtime
+ io_binding.bind_input(name="frames", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(frames.shape), buffer_ptr=frames.data_ptr())
+
+ # Bind output allocation to prevent redundant host-to-device transfers
+ io_binding.bind_output(name="f0", device_type=self._device, device_id=device_idx)
+ self.model.run_with_iobinding(io_binding)
+
+ return torch.tensor(
+ io_binding.get_outputs()[0].numpy(),
+ device=self.device
+ )
\ No newline at end of file
diff --git a/main/library/predictors/CREPE/filter.py b/main/library/predictors/CREPE/filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..64696d3437caced7bb79f2c358e076558cfba841
--- /dev/null
+++ b/main/library/predictors/CREPE/filter.py
@@ -0,0 +1,114 @@
+import torch
+
+def mean(signals, win_length=9):
+ """
+ Computes the moving average of a 2D tensor signal while safely handling NaN values.
+
+ This function applies a 1D convolution acts as a uniform moving average filter.
+ It masks out `NaN` values dynamically so they do not contaminate the windowed average.
+
+ Args:
+ signals (torch.Tensor): Input 2D tensor of shape (batch_size, signal_length).
+ win_length (int, optional): Size of the sliding window. Defaults to 9.
+
+ Returns:
+ torch.Tensor: The mean-pooled signal tensor with the original shape (batch_size, signal_length).
+ """
+
+ # Verify input shape constraint
+ assert signals.dim() == 2
+
+ # Add a channel dimension for compatibility with conv1d
+ signals = signals.unsqueeze(1)
+ # Create a boolean mask where True indicates valid (non-NaN) data
+ mask = ~torch.isnan(signals)
+ padding = win_length // 2
+
+ # Initialize a uniform 1D convolution kernel filled with ones
+ ones_kernel = torch.ones(signals.size(1), 1, win_length, device=signals.device)
+ # Calculate the moving sum of valid elements and divide by the count of valid elements per window
+ avg_pooled = torch.nn.functional.conv1d(
+ torch.where(
+ mask,
+ signals,
+ torch.zeros_like(signals) # Replace NaN with 0 temporarily so they don't corrupt the sum
+ ),
+ ones_kernel,
+ stride=1,
+ padding=padding
+ ) / torch.nn.functional.conv1d(
+ mask.float(), # Convert boolean mask to float (1.0 for valid, 0.0 for NaN) to count valid items
+ ones_kernel,
+ stride=1,
+ padding=padding
+ ).clamp(min=1) # Prevent division-by-zero on windows entirely populated by NaNs
+
+ # Revert regions with zero sum back to NaN to preserve missing data context
+ avg_pooled[avg_pooled == 0] = float("nan")
+ # Remove the temporary channel dimension and return
+ return avg_pooled.squeeze(1)
+
+def median(signals, win_length):
+ """
+ Computes the moving median of a 2D tensor signal while safely handling NaN values.
+
+ This function extracts sliding windows using `unfold`, sorts the valid elements
+ within each window, and adaptively selects the true median based on the number
+ of non-NaN elements present in each specific window.
+
+ Args:
+ signals (torch.Tensor): Input 2D tensor of shape (batch_size, signal_length).
+ win_length (int): Size of the sliding window.
+
+ Returns:
+ torch.Tensor: The median-pooled signal tensor with the original shape (batch_size, signal_length).
+ """
+
+ # Verify input shape constraint
+ assert signals.dim() == 2
+
+ # Add a channel dimension for window processing: (batch, 1, signal_length)
+ signals = signals.unsqueeze(1)
+ # Identify non-NaN values
+ mask = ~torch.isnan(signals)
+ padding = win_length // 2
+
+ # Pad signals using reflection mode to avoid boundary artifacts
+ x = torch.nn.functional.pad(
+ torch.where(
+ mask,
+ signals,
+ torch.zeros_like(signals) # Temporarily neutralize NaNs with zeros before padding
+ ),
+ (padding, padding),
+ mode="reflect"
+ )
+
+ # Pad mask with zeros at boundaries so padded regions are treated as invalid
+ mask = torch.nn.functional.pad(
+ mask.float(),
+ (padding, padding),
+ mode="constant",
+ value=0
+ )
+
+ # Extract overlapping windows across the sequence length dimension
+ x = x.unfold(2, win_length, 1)
+ mask = mask.unfold(2, win_length, 1)
+
+ # Flatten the trailing window dimensions to make them a contiguous 1D array per window step
+ x = x.contiguous().view(x.size()[:3] + (-1,))
+ mask = mask.contiguous().view(mask.size()[:3] + (-1,))
+
+ # Push NaN elements to the end of the window during sorting by replacing them with positive infinity (+inf)
+ x_sorted, _ = torch.where(mask.bool(), x.float(), float("inf")).to(x).sort(dim=-1)
+ # Dynamically compute the median index based on the actual number of valid elements in each window
+ idx = ((mask.sum(dim=-1) - 1) // 2).clamp(min=0).unsqueeze(-1).long()
+
+ # Extract the median element using hardware-compatible gathering strategies
+ median_pooled = x_sorted.take_along_dim(idx, dim=-1).squeeze(-1) if x.device.type.startswith(("ocl", "privateuseone")) else x_sorted.gather(-1, idx).squeeze(-1)
+ # Restore windows that resulted in +inf (all elements were NaN) back to actual NaNs
+ median_pooled[torch.isinf(median_pooled)] = float("nan")
+
+ # Remove the temporary channel dimension and return
+ return median_pooled.squeeze(1)
\ No newline at end of file
diff --git a/main/library/predictors/CREPE/model.py b/main/library/predictors/CREPE/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..a991e40e54a960851b6a008d9ef8a080e0c0cdcf
--- /dev/null
+++ b/main/library/predictors/CREPE/model.py
@@ -0,0 +1,104 @@
+import torch
+
+PITCH_BINS = 360
+
+class CREPEE(torch.nn.Module):
+ """
+ CREPEE model architecture for pitch estimation using deep convolutional networks.
+
+ This class implements the core neural network of CREPE, supporting five model sizes:
+ 'full', 'large', 'medium', 'small', and 'tiny'. It processes 1D raw audio signals mapped
+ into 2D space, applying convolutional blocks, max-pooling, and a linear classifier.
+ """
+
+ def __init__(self, model='full'):
+ """
+ Initializes the CREPEE model architecture according to the specified size.
+
+ Args:
+ model (str): The configuration capacity size. Options are 'full', 'large', 'medium', 'small', or 'tiny'. Default is 'full'.
+ """
+
+ super().__init__()
+ # Define the layer configuration dictionaries mapped to model capacity
+ in_channels = {"full": [1, 1024, 128, 128, 128, 256], "large": [1, 768, 96, 96, 96, 192], "medium": [1, 512, 64, 64, 64, 128], "small": [1, 256, 32, 32, 32, 64], "tiny": [1, 128, 16, 16, 16, 32]}[model]
+ out_channels = {"full": [1024, 128, 128, 128, 256, 512], "large": [768, 96, 96, 96, 192, 384], "medium": [512, 64, 64, 64, 128, 256], "small": [256, 32, 32, 32, 64, 128], "tiny": [128, 16, 16, 16, 32, 64]}[model]
+ self.in_features = {"full": 2048, "large": 1536, "medium": 1024, "small": 512, "tiny": 256}[model]
+
+ # Convolutional parameters setup: first layer has a large receptive field (512, 1)
+ kernel_sizes = [(512, 1)] + 5 * [(64, 1)]
+ strides = [(4, 1)] + 5 * [(1, 1)]
+
+ # Layer 1 Block (Processes raw audio waveforms down-sampled temporally by stride 4)
+ self.conv1 = torch.nn.Conv2d(in_channels=in_channels[0], out_channels=out_channels[0], kernel_size=kernel_sizes[0], stride=strides[0])
+ self.conv1_BN = torch.nn.BatchNorm2d(num_features=out_channels[0], eps=0.0010000000474974513, momentum=0.0)
+ # Layer 2 Block
+ self.conv2 = torch.nn.Conv2d(in_channels=in_channels[1], out_channels=out_channels[1], kernel_size=kernel_sizes[1], stride=strides[1])
+ self.conv2_BN = torch.nn.BatchNorm2d(num_features=out_channels[1], eps=0.0010000000474974513, momentum=0.0)
+ # Layer 3 Block
+ self.conv3 = torch.nn.Conv2d(in_channels=in_channels[2], out_channels=out_channels[2], kernel_size=kernel_sizes[2], stride=strides[2])
+ self.conv3_BN = torch.nn.BatchNorm2d(num_features=out_channels[2], eps=0.0010000000474974513, momentum=0.0)
+ # Layer 4 Block
+ self.conv4 = torch.nn.Conv2d(in_channels=in_channels[3], out_channels=out_channels[3], kernel_size=kernel_sizes[3], stride=strides[3])
+ self.conv4_BN = torch.nn.BatchNorm2d(num_features=out_channels[3], eps=0.0010000000474974513, momentum=0.0)
+ # Layer 5 Block
+ self.conv5 = torch.nn.Conv2d(in_channels=in_channels[4], out_channels=out_channels[4], kernel_size=kernel_sizes[4], stride=strides[4])
+ self.conv5_BN = torch.nn.BatchNorm2d(num_features=out_channels[4], eps=0.0010000000474974513, momentum=0.0)
+ # Layer 6 Block
+ self.conv6 = torch.nn.Conv2d(in_channels=in_channels[5], out_channels=out_channels[5], kernel_size=kernel_sizes[5], stride=strides[5])
+ self.conv6_BN = torch.nn.BatchNorm2d(num_features=out_channels[5], eps=0.0010000000474974513, momentum=0.0)
+
+ # Classification linear head maps latent features to pitch bin activations
+ self.classifier = torch.nn.Linear(in_features=self.in_features, out_features=PITCH_BINS)
+
+ def forward(self, x, embed=False):
+ """Executes the forward pass of the network.
+
+ Args:
+ x (torch.Tensor): Input audio frames tensor.
+ embed (bool): If True, returns the latent embeddings from layer 5 instead of pitch bins.
+
+ Returns:
+ torch.Tensor: Normalized class probabilities (via Sigmoid) or latent representations.
+ """
+
+ # Extract features up to layer 5
+ x = self.embed(x)
+ if embed: return x
+
+ # Execute final conv layer, flatten spatial dimensions, and map to activation values
+ return self.classifier(self.layer(x, self.conv6, self.conv6_BN).permute(0, 2, 1, 3).reshape(-1, self.in_features)).sigmoid()
+
+ def embed(self, x):
+ """Processes the tensor through the first 5 convolutional layer blocks.
+
+ Args:
+ x (torch.Tensor): Raw 1D input tensor of shape (batch, width).
+
+ Returns:
+ torch.Tensor: Latent feature maps block tensor.
+ """
+
+ # Reshape 1D audio sequence into a 4D tensor
+ x = x[:, None, :, None]
+ # Chain consecutive block processing pipelines sequentially
+ return self.layer(self.layer(self.layer(self.layer(self.layer(x, self.conv1, self.conv1_BN, (0, 0, 254, 254)), self.conv2, self.conv2_BN), self.conv3, self.conv3_BN), self.conv4, self.conv4_BN), self.conv5, self.conv5_BN)
+
+ def layer(self, x, conv, batch_norm, padding=(0, 0, 31, 32)):
+ """A reusable helper function representing a single CREPE network layer block.
+
+ Applies asymmetrical manual padding, 2D convolution, ReLU activation,
+ Batch Normalization, and spatial (2, 1) down-sampling Max Pooling.
+
+ Args:
+ x (torch.Tensor): Input activation map tensor.
+ conv (torch.nn.Conv2d): Convolutional layer module to evaluate.
+ batch_norm (torch.nn.BatchNorm2d): Associated batch normalization module.
+ padding (tuple): Left, Right, Top, Bottom manual zero padding configuration.
+
+ Returns:
+ torch.Tensor: Block downsampled representation matrix.
+ """
+
+ # Pad -> Conv -> ReLU -> BatchNorm -> MaxPool along the temporal dimension
+ return torch.nn.functional.max_pool2d(batch_norm(torch.nn.functional.relu(conv(torch.nn.functional.pad(x, padding)))), (2, 1), (2, 1))
\ No newline at end of file
diff --git a/main/library/predictors/DJCM/DJCM.py b/main/library/predictors/DJCM/DJCM.py
new file mode 100644
index 0000000000000000000000000000000000000000..196fb9f37d8584bf2eb4eace9d3c9e1ecc93be20
--- /dev/null
+++ b/main/library/predictors/DJCM/DJCM.py
@@ -0,0 +1,267 @@
+import os
+import sys
+import torch
+
+import numpy as np
+
+sys.path.append(os.getcwd())
+
+from main.library.predictors.DJCM.spec import Spectrogram
+
+SAMPLE_RATE, N_CLASS = 16000, 360
+
+class DJCM:
+ """
+ DJCM (A Deep Joint Cascade Model for Singing Voice Separation and Vocal Pitch Estimation) wrapper.
+
+ This class handles feature extraction (Spectrogram), overlapped audio segmentation,
+ multi-backend inference (PyTorch/ONNX), and local-average decoding to convert
+ salience maps into absolute fundamental frequencies (F0).
+ """
+
+ def __init__(
+ self,
+ model_path,
+ device = "cpu",
+ is_half = False,
+ onnx = False,
+ svs = False,
+ providers = ["CPUExecutionProvider"],
+ batch_size = 1,
+ segment_len = 5.12,
+ compile_model = False,
+ compile_mode = None,
+ return_tensor = False,
+ f0_min=50,
+ f0_max=1100
+ ):
+ """
+ Initializes the DJCM prediction engine.
+
+ Args:
+ model_path (str): Path to model weights (.pth or .onnx).
+ device (str): Device hardware target ('cpu', 'cuda', etc.). Default is "cpu".
+ is_half (bool): Toggles FP16 precision for PyTorch models. Default is False.
+ onnx (bool): Toggles ONNX Runtime session usage. Default is False.
+ svs (bool): Singing Voice Separation/Synthesis flag adjustments. Default is False.
+ providers (list): Execution providers for ONNX Runtime. Default is ["CPUExecutionProvider"].
+ batch_size (int): Segment processing batch count. Default is 1.
+ segment_len (float): Duration in seconds for sliced segments. Default is 5.12.
+ compile_model (bool): Toggles torch.compile optimization. Default is False.
+ compile_mode (str, optional): Compilation mode setting for PyTorch.
+ return_tensor (bool): Determines if decoding returns PyTorch Tensors or NumPy arrays. Default is False.
+ f0_min (float): Frequency floor in Hz for filtering predictions. Default is 50.
+ f0_max (float): Frequency ceiling in Hz for filtering predictions. Default is 1100.
+ """
+
+ super(DJCM, self).__init__()
+ # Adjust analysis window size dynamically depending on the task environment
+ window_length = 2048 if svs else 1024
+
+ # Model Loading Architecture
+ if onnx:
+ import onnxruntime as ort
+
+ sess_options = ort.SessionOptions()
+ sess_options.log_severity_level = 3 # Disable verbose logs
+ model = ort.InferenceSession(model_path, sess_options=sess_options, providers=providers)
+ else:
+ from main.library.predictors.DJCM.model import DJCMM
+
+ model = DJCMM(1, 1, 1, svs=svs, window_length=window_length, n_class=N_CLASS)
+ model.load_state_dict(torch.load(model_path, map_location="cpu", weights_only=True))
+ model.to(device).eval()
+
+ if is_half: model = model.half()
+ if compile_model: model = torch.compile(model, mode=compile_mode)
+
+ # Instance Variable Configuration
+ self.model = model
+ self.f0_min = f0_min
+ self.f0_max = f0_max
+ self.device = device
+ self.batch_size = batch_size
+ self.seg_len = int(segment_len * SAMPLE_RATE)
+ self.dtype = torch.float16 if is_half else torch.float32
+ self.seg_frames = int(self.seg_len // int(SAMPLE_RATE // 100))
+
+ # Cents mapping vector definition along with safety pads for local group window boundaries
+ self.cents_mapping = np.pad(20 * np.arange(N_CLASS) + 1997.3794084376191, (4, 4))
+ self.spec_extractor = Spectrogram(int(SAMPLE_RATE // 100), window_length).to(device)
+ # Structure data types according to backend pipeline specifications (Tensor vs NumPy Array)
+ if return_tensor: self.cents_mapping = torch.as_tensor(self.cents_mapping, dtype=self.dtype, device=device)
+ self.offsets = torch.arange(-4, 5, device=device) if return_tensor else np.arange(-4, 5)
+
+ # Method routers map to corresponding efficient execution branches
+ self._device = "cuda" if providers[0][0].startswith("CUDA") else "cpu"
+ self.to_local_average_cents = self._to_local_average_cents_tensor if return_tensor else self._to_local_average_cents_array
+ self.infer = (self._infer_onnx_io if providers[0][0].startswith(("CUDA", "CPU")) else self._infer_onnx_non_io) if onnx else (self._infer_torch_fp16 if is_half else self._infer_torch_fp32)
+
+ def infer_from_audio(self, audio, thred=0.03):
+ """
+ Extracts pitch predictions directly from a raw audio waveform tensor/array.
+
+ Args:
+ audio (torch.Tensor or numpy.ndarray): Time-domain audio vector.
+ thred (float): Activation confidence filtering threshold. Default is 0.03.
+
+ Returns:
+ torch.Tensor or numpy.ndarray: Unfiltered F0 trajectory track.
+ """
+
+ if not torch.is_tensor(audio): audio = torch.from_numpy(audio).to(self.device)
+ if audio.ndim > 1: audio = audio.squeeze()
+
+ with torch.inference_mode():
+ # Generate overlapping chunks of specific durations
+ segments = self.pad_audio(audio)
+
+ # Batched spectrogram extraction, model forward pass, and center-frame stitching
+ hidden = torch.cat([
+ seg[self.seg_frames // 4: int(self.seg_frames * 0.75)]
+ for seg in torch.cat([
+ self.infer(self.spec_extractor(segments[i:i + self.batch_size].float()))
+ for i in range(0, len(segments), self.batch_size)
+ ], dim=0)
+ ], dim=0)[:(audio.shape[-1] // int(SAMPLE_RATE // 100) + 1)].squeeze(0)
+
+ return self.decode(hidden, thred)
+
+ def infer_from_audio_with_pitch(self, audio, thred=0.03):
+ """
+ Extracts F0 tracks from raw audio and automatically applies boundary filters.
+
+ Args:
+ audio (torch.Tensor or numpy.ndarray): Time-domain audio vector.
+ thred (float): Activation confidence threshold. Default is 0.03.
+
+ Returns:
+ torch.Tensor or numpy.ndarray: Bounded F0 track where unvoiced/out-of-bounds frames are 0.
+ """
+
+ f0 = self.infer_from_audio(audio, thred)
+ # Clamp out-of-bounds frequencies down to zero indicating unvoiced regions
+ f0[(f0 < self.f0_min) | (f0 > self.f0_max)] = 0
+
+ return f0
+
+ def decode(self, hidden, thred=0.03):
+ """
+ Converts model activation salience states into frequencies (Hz).
+
+ Args:
+ hidden (torch.Tensor): Raw matrix output from inference.
+ thred (float): Salience masking threshold. Default is 0.03.
+
+ Returns:
+ torch.Tensor or numpy.ndarray: Estimated F0 pitch matrix.
+ """
+
+ # Convert cents array estimates out to regular frequency values
+ f0 = 10 * (2 ** (self.to_local_average_cents(hidden, thred=thred) / 1200))
+ # Mask out trivial floor configurations representing silence or invalid bounds
+ f0[f0 == 10] = 0
+ return f0
+
+ def pad_audio(self, audio):
+ """
+ Applies symmetrical padding and unrolls audio array into overlapping 2D segment windows.
+
+ Args:
+ audio (torch.Tensor): Unprocessed 1D input audio vector.
+
+ Returns:
+ torch.Tensor: Windowed structured frame blocks.
+ """
+
+ seg_len = self.seg_len
+ hop = seg_len // 2
+
+ audio_len = audio.shape[-1]
+ left_pad = seg_len // 4
+
+ # Compute remaining overlap tail size to match perfect integer windows before flattening layout
+ return torch.nn.functional.pad(
+ audio,
+ (left_pad, (((audio_len + seg_len - 1) // seg_len + 1) * seg_len + hop) - audio_len - left_pad)
+ ).unfold(0, seg_len, hop).unsqueeze(1).contiguous()
+
+ def _infer_torch_fp32(self, spec):
+ """Performs PyTorch inference in standard 32-bit floating point precision."""
+
+ return self.model(spec)
+
+ def _infer_torch_fp16(self, spec):
+ """Performs PyTorch inference in half 16-bit floating point precision."""
+
+ return self.model(spec.half())
+
+ def _infer_onnx_non_io(self, spec):
+ """Performs legacy ONNX runtime execution with CPU conversion/copy overhead."""
+
+ spec = spec.cpu().numpy().astype(np.float32)
+
+ return torch.as_tensor(
+ self.model.run(
+ ["f0"], {"spec": spec}
+ )[0],
+ device=self.device,
+ dtype=self.dtype
+ )
+
+ def _infer_onnx_io(self, spec):
+ """Executes zero-copy optimized ONNX runtime inference via strict explicit I/O allocation."""
+
+ spec = spec.float().contiguous()
+ device_idx = spec.device.index or 0
+
+ io_binding = self.model.io_binding()
+ # Direct raw pointer bindings skip duplicate memory allocations across systems
+ io_binding.bind_input(name="spec", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(spec.shape), buffer_ptr=spec.data_ptr())
+
+ io_binding.bind_output(name="f0", device_type=self._device, device_id=device_idx)
+ self.model.run_with_iobinding(io_binding)
+
+ return torch.as_tensor(
+ io_binding.get_outputs()[0].numpy(),
+ device=self.device,
+ dtype=self.dtype
+ )
+
+ def _to_local_average_cents_array(self, salience, thred=0.05):
+ """Decodes pitch tracking salience into smooth cents via NumPy arrays."""
+
+ salience = salience.cpu().numpy().astype(np.float32)
+
+ # Extract argmax activation positions
+ center = np.argmax(salience, axis=1)
+ salience = np.pad(salience, ((0, 0), (4, 4)))
+ center += 4 # Offset position map adjustment due to padding shift
+
+ # Slice out a neighborhood window of 9 elements around the argmax peak position
+ idx = center[:, None] + self.offsets[None, :]
+ local_salience = salience[np.arange(salience.shape[0])[:, None], idx]
+
+ # Calculate localized center of mass (weighted average) to achieve sub-bin frequency interpolation
+ devided = np.sum(local_salience * self.cents_mapping[idx], axis=1) / np.sum(local_salience, axis=1)
+ # Suppress noisy frames falling below confidence threshold constraints
+ devided[np.max(salience, axis=1) <= thred] = 0
+
+ return devided
+
+ def _to_local_average_cents_tensor(self, salience, thred=0.05):
+ """Decodes pitch tracking salience into smooth cents via PyTorch Tensors on GPU/CPU."""
+
+ center = torch.argmax(salience, dim=1)
+ salience = torch.nn.functional.pad(salience, (4, 4))
+ center += 4
+
+ # Vectorized gathering operation across tensor indices
+ idx = center[:, None] + self.offsets[None, :]
+ local_salience = salience[torch.arange(salience.shape[0], device=salience.device)[:, None], idx]
+
+ # Parallel math matrix computation for weighted frequency centroid decoding
+ devided = (local_salience * self.cents_mapping[idx]).sum(dim=1) / local_salience.sum(dim=1)
+ devided = torch.where(salience.max(dim=1).values <= thred, torch.zeros_like(devided), devided)
+
+ return devided
\ No newline at end of file
diff --git a/main/library/predictors/DJCM/decoder.py b/main/library/predictors/DJCM/decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..cae3921ce9b59ca62376719a56f3387c1e39f9d1
--- /dev/null
+++ b/main/library/predictors/DJCM/decoder.py
@@ -0,0 +1,214 @@
+import os
+import sys
+import torch
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+sys.path.append(os.getcwd())
+
+from main.library.predictors.DJCM.encoder import ResEncoderBlock
+from main.library.predictors.DJCM.utils import ResConvBlock, BiGRU, init_bn, init_layer
+
+class ResDecoderBlock(nn.Module):
+ """
+ Residual Decoder Block using Transposed Convolution for upsampling.
+
+ This block upsamples the feature maps, processes skip-connections (concatenation),
+ and applies a series of residual convolutional blocks to refine the features.
+ """
+
+ def __init__(self, in_channels, out_channels, n_blocks, stride):
+ """
+ Initializes the ResDecoderBlock.
+
+ Args:
+ in_channels (int): Number of channels in the input tensor.
+ out_channels (int): Number of channels after upsampling and processing.
+ n_blocks (int): Total number of residual convolutional blocks.
+ stride (tuple): Stride for the ConvTranspose2d layer (controls upsampling factors).
+ """
+
+ super(ResDecoderBlock, self).__init__()
+ # Transposed convolution to handle spatial upsampling (usually doubling the frequency dimension)
+ self.conv1 = nn.ConvTranspose2d(in_channels, out_channels, stride, stride, (0, 0), bias=False)
+ self.bn1 = nn.BatchNorm2d(in_channels, momentum=0.01)
+ # The first convolution handles concatenated features, hence input channels = out_channels * 2
+ self.conv = nn.ModuleList([ResConvBlock(out_channels * 2, out_channels)])
+
+ # Append remaining residual blocks keeping the channels constant
+ for _ in range(n_blocks - 1):
+ self.conv.append(ResConvBlock(out_channels, out_channels))
+
+ self.init_weights()
+
+ def init_weights(self):
+ """Initializes weights for Batch Normalization and Transposed Convolution layers."""
+
+ init_bn(self.bn1)
+ init_layer(self.conv1)
+
+ def forward(self, x, concat):
+ """
+ Executes the forward pass of the block.
+
+ Args:
+ x (torch.Tensor): Input tensor from the previous decoder layer.
+ concat (torch.Tensor): Skip-connection encoder tensor to concatenate.
+
+ Returns:
+ torch.Tensor: Upsampled and refined feature map tensor.
+ """
+
+ # Pre-activation standard pipeline: BN -> Inplace ReLU -> Transposed Conv Upsample
+ x = self.conv1(F.relu_(self.bn1(x)))
+ # Merge high-level abstract features with low-level spatial features from the encoder
+ x = torch.cat((x, concat), dim=1)
+
+ # Pass the concatenated tensor through the residual convolution sequence
+ for each_layer in self.conv:
+ x = each_layer(x)
+
+ return x
+
+class Decoder(nn.Module):
+ """
+ Core UNet-style Decoder trunk mapping latent features back to spatial resolutions.
+
+ Chains multiple ResDecoderBlock layers together, popping and merging historical
+ encoder feature mirrors sequentially via reverse indexing.
+ """
+
+ def __init__(self, n_blocks):
+ """
+ Initializes the main structural stack of Decoder components.
+
+ Args:
+ n_blocks (int): Number of inner residual blocks per decoder stage.
+ """
+
+ super(Decoder, self).__init__()
+ # Stacked configuration gradually expanding spatial dims while narrowing down feature channels
+ self.de_blocks = nn.ModuleList([
+ ResDecoderBlock(384, 384, n_blocks, (1, 2)),
+ ResDecoderBlock(384, 384, n_blocks, (1, 2)),
+ ResDecoderBlock(384, 256, n_blocks, (1, 2)),
+ ResDecoderBlock(256, 128, n_blocks, (1, 2)),
+ ResDecoderBlock(128, 64, n_blocks, (1, 2)),
+ ResDecoderBlock(64, 32, n_blocks, (1, 2))
+ ])
+
+ def forward(self, x, concat_tensors):
+ """
+ Sequentially upsamples the representation while feeding encoder mirrors.
+
+ Args:
+ x (torch.Tensor): Bottleneck latent tensor representation.
+ concat_tensors (list of torch.Tensor): List of accumulated encoder stage outputs.
+
+ Returns:
+ torch.Tensor: High-resolution reconstructed output block tensor.
+ """
+
+ # Iterate and match features back to corresponding encoder layers in reverse order
+ for i, layer in enumerate(self.de_blocks):
+ x = layer(x, concat_tensors[-1 - i])
+
+ return x
+
+class PE_Decoder(nn.Module):
+ """
+ Pitch Estimation (PE) Decoder module.
+
+ Processes the reconstructed decoder maps, applies standard convolutions, and maps
+ the spatial representation into frame-level pitch bin activations using a BiGRU and a Linear classifier.
+ """
+
+ def __init__(self, n_blocks, seq_layers=1, window_length = 1024, n_class = 360):
+ """
+ Initializes the Pitch Estimation Decoder module.
+
+ Args:
+ n_blocks (int): Number of residual blocks per decoder stage.
+ seq_layers (int): Number of recurrent layers within the BiGRU block. Default is 1.
+ window_length (int): Analysis FFT window size constraint. Default is 1024.
+ n_class (int): Total number of pitch output target classes. Default is 360.
+ """
+
+ super(PE_Decoder, self).__init__()
+ self.de_blocks = Decoder(n_blocks)
+ self.after_conv1 = ResEncoderBlock(32, 32, n_blocks, None) # Refinement block without pooling
+ self.after_conv2 = nn.Conv2d(32, 1, (1, 1)) # Compress feature channels down to 1
+
+ # Temporal smoothing and classification network block sequence
+ self.fc = nn.Sequential(
+ BiGRU(
+ (1, window_length // 2),
+ 1,
+ seq_layers
+ ),
+ nn.Linear(
+ window_length // 2,
+ n_class
+ ),
+ nn.Sigmoid() # Normalize outputs to class probabilities/salience maps
+ )
+ init_layer(self.after_conv2)
+
+ def forward(self, x, concat_tensors):
+ """Predicts vocal pitch probabilities from abstract feature maps.
+
+ Args:
+ x (torch.Tensor): Bottleneck feature tensor.
+ concat_tensors (list): Encoder features for skip connections.
+
+ Returns:
+ torch.Tensor: Decoded pitch bin activations squeezed.
+ """
+
+ # Reconstruct, refine channels, flatten/evaluate temporal GRU sequence, and remove redundant axis
+ return self.fc(self.after_conv2(self.after_conv1(self.de_blocks(x, concat_tensors)))).squeeze(1)
+
+class SVS_Decoder(nn.Module):
+ """
+ Singing Voice Separation (SVS) Decoder module.
+
+ Maps bottleneck tensors back into target audio separation masks or complex spectrogram dimensions
+ corresponding to separated signal tracks.
+ """
+
+ def __init__(self, in_channels, n_blocks):
+ """
+ Initializes the Singing Voice Separation Decoder module.
+
+ Args:
+ in_channels (int): Base audio input channels.
+ n_blocks (int): Number of internal block structures.
+ """
+
+ super(SVS_Decoder, self).__init__()
+ self.de_blocks = Decoder(n_blocks)
+ self.after_conv1 = ResEncoderBlock(32, 32, n_blocks, None)
+ # Final projecting layer to compute multichannel audio separation targets
+ self.after_conv2 = nn.Conv2d(32, in_channels * 4, (1, 1))
+ self.init_weights()
+
+ def init_weights(self):
+ """Initializes the projection block weights."""
+
+ init_layer(self.after_conv2)
+
+ def forward(self, x, concat_tensors):
+ """
+ Extracts source-separated mask fields from underlying representations.
+
+ Args:
+ x (torch.Tensor): Bottleneck feature representation input.
+ concat_tensors (list): Encoder historical cross-connections mapping list.
+
+ Returns:
+ torch.Tensor: Decoded separation mask tensor matching input shapes.
+ """
+
+ # Execute UNet trunk reconstruction, refine, and project channels back to audio targets
+ return self.after_conv2(self.after_conv1(self.de_blocks(x, concat_tensors)))
\ No newline at end of file
diff --git a/main/library/predictors/DJCM/encoder.py b/main/library/predictors/DJCM/encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..35a86ad1cf7cd5ce9cbbcdf2a6dffe382346bf05
--- /dev/null
+++ b/main/library/predictors/DJCM/encoder.py
@@ -0,0 +1,161 @@
+import os
+import sys
+
+import torch.nn as nn
+
+sys.path.append(os.getcwd())
+
+from main.library.predictors.DJCM.utils import ResConvBlock
+
+class ResEncoderBlock(nn.Module):
+ """
+ Residual Encoder Block combining feature extraction and optional spatial pooling.
+
+ This block applies a series of residual convolutional blocks to increase the feature
+ representation capacity, followed by an optional MaxPool2d layer for temporal/frequency downsampling.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ n_blocks,
+ kernel_size
+ ):
+ """
+ Initializes the ResEncoderBlock.
+
+ Args:
+ in_channels (int): Number of input channels.
+ out_channels (int): Number of target output channels.
+ n_blocks (int): Total number of inner residual convolutional blocks.
+ kernel_size (tuple or None): Pooling kernel shape (e.g., (1, 2)), or None to skip downsampling.
+ """
+
+ super(ResEncoderBlock, self).__init__()
+ # Initialize the first residual layer handling channel size adjustments
+ self.conv = nn.ModuleList([
+ ResConvBlock(
+ in_channels,
+ out_channels
+ )
+ ])
+
+ # Append remaining residual layers keeping the output channels constant
+ for _ in range(n_blocks - 1):
+ self.conv.append(
+ ResConvBlock(
+ out_channels,
+ out_channels
+ )
+ )
+
+ # Optional pooling layer for downsampling across frequency/temporal grids
+ self.pool = nn.MaxPool2d(kernel_size) if kernel_size is not None else None
+
+ def forward(self, x):
+ """
+ Executes the forward pass of the block.
+
+ Args:
+ x (torch.Tensor): Input feature map tensor.
+
+ Returns:
+ torch.Tensor or tuple:
+ - If pooling is active, returns a tuple (unpooled_features, pooled_features)
+ where unpooled_features is cached for UNet skip-connections.
+ - If pooling is None, returns the unpooled feature map tensor directly.
+ """
+
+ # Chain consecutive residual blocks sequentially to extract high-level feature maps
+ for each_layer in self.conv:
+ x = each_layer(x)
+
+ # If pooling is defined, return both the pre-pooled representation (for skip-connection) and the downsampled version
+ if self.pool is not None: return x, self.pool(x)
+ return x
+
+class Encoder(nn.Module):
+ """
+ Core UNet-style Encoder backbone of the DJCM model.
+
+ Compiles a multi-stage downsampling ladder that progressively compresses
+ spatial resolution (specifically frequency bins) while scaling up channel capacities.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ n_blocks
+ ):
+ """
+ Initializes the Encoder module stack.
+
+ Args:
+ in_channels (int): Number of channels in the incoming input audio spectrogram.
+ n_blocks (int): Number of internal residual blocks per encoder stage.
+ """
+
+ super(Encoder, self).__init__()
+ # Stacked configuration expanding channels from base input to deep 384 feature dimensions
+ # Stride/Kernel configuration (1, 2) selectively halves the second spatial dimension (frequency)
+ self.en_blocks = nn.ModuleList([
+ ResEncoderBlock(
+ in_channels,
+ 32,
+ n_blocks,
+ (1, 2)
+ ),
+ ResEncoderBlock(
+ 32,
+ 64,
+ n_blocks,
+ (1, 2)
+ ),
+ ResEncoderBlock(
+ 64,
+ 128,
+ n_blocks,
+ (1, 2)
+ ),
+ ResEncoderBlock(
+ 128,
+ 256,
+ n_blocks,
+ (1, 2)
+ ),
+ ResEncoderBlock(
+ 256,
+ 384,
+ n_blocks,
+ (1, 2)
+ ),
+ ResEncoderBlock(
+ 384,
+ 384,
+ n_blocks,
+ (1, 2)
+ )
+ ])
+
+ def forward(self, x):
+ """
+ Processes input feature representations through the multi-stage downsampling architecture.
+
+ Args:
+ x (torch.Tensor): Input spectrogram feature tensor.
+
+ Returns:
+ tuple: (bottleneck_tensor, concat_tensors)
+ - bottleneck_tensor (torch.Tensor): Deep latent representation at the narrowest bottleneck.
+ - concat_tensors (list of torch.Tensor): Collected high-resolution feature tensors cached at each block stage for decoder skip-connection mirroring.
+ """
+
+ concat_tensors = []
+ # Progressively extract features, downsample, and store pre-pooled maps for UNet skip-connections
+ for layer in self.en_blocks:
+ # '_' captures the pre-pooled, high-resolution features; 'x' receives the downsampled result
+ _, x = layer(x)
+ concat_tensors.append(_)
+
+ return x, concat_tensors
\ No newline at end of file
diff --git a/main/library/predictors/DJCM/model.py b/main/library/predictors/DJCM/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4a7485b7f29411f7a6e4318d7f608f952fea999
--- /dev/null
+++ b/main/library/predictors/DJCM/model.py
@@ -0,0 +1,198 @@
+import os
+import sys
+
+import torch.nn as nn
+
+sys.path.append(os.getcwd())
+
+from main.library.predictors.DJCM.utils import init_bn
+from main.library.predictors.DJCM.decoder import PE_Decoder, SVS_Decoder
+from main.library.predictors.DJCM.encoder import ResEncoderBlock, Encoder
+
+class LatentBlocks(nn.Module):
+ """
+ Latent space processing blocks at the UNet bottleneck.
+
+ Applies a series of residual blocks without any spatial downsampling/pooling
+ to deep feature maps to further extract complex abstract representations.
+ """
+
+ def __init__(
+ self,
+ n_blocks,
+ latent_layers
+ ):
+ """
+ Initializes the LatentBlocks layer.
+
+ Args:
+ n_blocks (int): Number of internal residual blocks per layer.
+ latent_layers (int): The total number of consecutive sequence layers.
+ """
+
+ super(LatentBlocks, self).__init__()
+ # Chain multiple ResEncoderBlocks together with pooling explicitly deactivated (None)
+ self.latent_blocks = nn.ModuleList([
+ ResEncoderBlock(
+ 384,
+ 384,
+ n_blocks,
+ None
+ )
+ for _ in range(latent_layers)
+ ])
+
+ def forward(self, x):
+ """
+ Executes the forward pass of the bottleneck latent representation layer.
+
+ Args:
+ x (torch.Tensor): Bottleneck feature tensor.
+
+ Returns:
+ torch.Tensor: Enhanced deep latent space feature map tensor.
+ """
+
+ # Sequentially evaluate each unpooled residual block layer
+ for layer in self.latent_blocks:
+ x = layer(x)
+
+ return x
+
+class DJCMM(nn.Module):
+ """DJCMM Main Module: A Deep Joint Cascade Model for Singing Voice Separation (SVS) and Vocal Pitch Estimation (PE).
+
+ This model can work in two modes:
+ 1. Pitch Estimation Only (svs=False): Directly predicts pitch tracks from input spectrograms.
+ 2. Joint Cascade Mode (svs=True): First separates the singing voice from background audio/music, then routes the cleaned vocal spectrogram into the pitch tracker for high-precision F0 extraction.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ n_blocks,
+ latent_layers,
+ svs=False,
+ window_length=1024,
+ n_class=360
+ ):
+ """
+ Initializes the integrated DJCMM model.
+
+ Args:
+ in_channels (int): Input feature map channel dimensions.
+ n_blocks (int): Number of residual blocks per encoder/decoder stage.
+ latent_layers (int): Number of unpooled blocks situated at the bottlenecks.
+ svs (bool): Enables the integrated Singing Voice Separation cascade. Default is False.
+ window_length (int): FFT window size constraint parameter. Default is 1024.
+ n_class (int): Number of final target pitch bin classification classes. Default is 360.
+ """
+
+ super(DJCMM, self).__init__()
+ # Batch Normalization layer dedicated to input frequency configurations
+ self.bn = nn.BatchNorm2d(
+ window_length // 2 + 1,
+ momentum=0.01
+ )
+
+ # Core Vocal Pitch Estimation (PE) network components initialization
+ self.pe_encoder = Encoder(
+ in_channels,
+ n_blocks
+ )
+ self.pe_latent = LatentBlocks(
+ n_blocks,
+ latent_layers
+ )
+ self.pe_decoder = PE_Decoder(
+ n_blocks,
+ window_length=window_length,
+ n_class=n_class
+ )
+
+ self.svs = svs
+ # Conditionally initialize Singing Voice Separation (SVS) components if cascading is active
+ if svs:
+ self.svs_encoder = Encoder(
+ in_channels,
+ n_blocks
+ )
+ self.svs_latent = LatentBlocks(
+ n_blocks,
+ latent_layers
+ )
+ self.svs_decoder = SVS_Decoder(
+ in_channels,
+ n_blocks
+ )
+
+ init_bn(self.bn)
+
+ def spec(self, x, spec_m):
+ """
+ Applies decoded SVS separation masks to filter and isolate the raw spectrogram.
+
+ Args:
+ x (torch.Tensor): Unprocessed multitrack representation from the SVS decoder.
+ spec_m (torch.Tensor): Original reference mixed audio input spectrogram.
+
+ Returns:
+ torch.Tensor: Refined and masked isolated vocal spectrogram.
+ """
+
+ bs, c, time_steps, freqs_steps = x.shape
+ # Split channels into structured segments representing masking values and linear additives
+ x = x.reshape(bs, c // 4, 4, time_steps, freqs_steps)
+
+ mask_spec = x[:, :, 0, :, :].sigmoid() # Generate soft masking bounds between [0, 1]
+ linear_spec = x[:, :, 3, :, :] # Extract linear residue offsets
+
+ # Apply mask directly onto target mixture and inject linear residue additions
+ out_spec = (
+ spec_m.detach() * mask_spec + linear_spec
+ ).relu()
+
+ return out_spec
+
+ def forward(self, spec):
+ """
+ Executes the complete cascade forward pass.
+
+ Args:
+ spec (torch.Tensor): Input audio mix spectrogram tensor.
+
+ Returns:
+ torch.Tensor: Pitch bin activation probabilities profile.
+ """
+
+ # Normalize input frequency bands across axis boundaries, then clip trailing pad tokens
+ x = self.bn(
+ spec.transpose(1, 3)
+ ).transpose(1, 3)[..., :-1]
+
+ # Stage 1 Cascade Branch: Singing Voice Separation (SVS) processing pipeline
+ if self.svs:
+ # Upsample and extract isolated targets from input representation mappings
+ x, concat_tensors = self.svs_encoder(x)
+
+ x = self.svs_decoder(
+ self.svs_latent(x),
+ concat_tensors
+ )
+
+ # Re-pad and reconstruct mask outputs to extract purified vocal components
+ x = self.spec(
+ nn.functional.pad(x, pad=(0, 1)),
+ spec
+ )[..., :-1]
+
+ # Stage 2 Cascade Branch: Core Vocal Pitch Estimation (PE) processing pipeline
+ # processes either the raw mix spectrogram (svs=False) or the isolated vocal spectrogram (svs=True)
+ x, concat_tensors = self.pe_encoder(x)
+
+ pe_out = self.pe_decoder(
+ self.pe_latent(x),
+ concat_tensors
+ )
+
+ return pe_out
\ No newline at end of file
diff --git a/main/library/predictors/DJCM/spec.py b/main/library/predictors/DJCM/spec.py
new file mode 100644
index 0000000000000000000000000000000000000000..62426a97b77b80f025968a084372bacb981dad1b
--- /dev/null
+++ b/main/library/predictors/DJCM/spec.py
@@ -0,0 +1,104 @@
+import os
+import sys
+import torch
+
+import numpy as np
+import torch.nn as nn
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config
+
+class Spectrogram(nn.Module):
+ """
+ A flexible spectrogram extraction module with multi-backend STFT support.
+
+ This module computes the magnitude spectrogram of multi-channel input audio signals.
+ It automatically routes computation through native PyTorch STFT or specialized alternative
+ backends (e.g., OpenCL, DirectML via 'ocl' or 'privateuseone' device prefixes) depending on
+ the active execution hardware environment.
+ """
+
+ def __init__(
+ self,
+ hop_length,
+ win_length,
+ n_fft=None,
+ clamp=1e-10
+ ):
+ """
+ Initializes the Spectrogram module configurations.
+
+ Args:
+ hop_length (int): Number of audio samples between successive STFT columns.
+ win_length (int): The size of the window signal applied per frame chunk.
+ n_fft (int, optional): The length of the windowed signal after zero-padding. Defaults to match `win_length`.
+ clamp (float): Minimum absolute threshold value used to prevent numerical underflow. Default is 1e-10.
+ """
+
+ super(Spectrogram, self).__init__()
+ self.n_fft = win_length if n_fft is None else n_fft
+ self.hop_length = hop_length
+ self.win_length = win_length
+ self.clamp = clamp
+ self.stft = None
+ # Pre-calculate a standard Hanning analysis window function and register as non-persistent buffer
+ self.register_buffer("window", torch.hann_window(win_length), persistent=False)
+ # Dynamically route backend processing logic depending on runtime hardware acceleration signatures
+ self.stftt = self._stft_other_backends if config.device.startswith(("ocl", "privateuseone")) else self._stft_torch
+
+ def forward(self, audio, center=True):
+ """
+ Extracts the magnitude spectrogram from batched multi-channel raw waveforms.
+
+ Args:
+ audio (torch.Tensor): Audio waveform tensor of shape (batch, channels, samples).
+ center (bool): Pads waveforms symmetrically so frames align precisely with time points. Default is True.
+
+ Returns:
+ torch.Tensor: Magnitude spectrogram block tensor of shape (batch, channels, frequency, time).
+ """
+
+ bs, c, segment_samples = audio.shape
+ # Flatten the batch and channel axis down together to fit standard 2D signal entry profiles
+ audio = audio.reshape(bs * c, segment_samples)
+ # Calculate Short-Time Fourier Transform, transpose time/frequency axis, and floor low signals via clamp
+ mag = self.stftt(audio, center).transpose(1, 2).clamp(self.clamp, np.inf)
+ # Unflatten and reconstruct structured dimensions back out to match incoming shapes
+ return mag.reshape(bs, c, mag.shape[1], mag.shape[2])
+
+ def _stft_other_backends(self, audio, center=True):
+ """
+ Computes Short-Time Fourier Transform utilizing specialized secondary system libraries.
+
+ This branch resolves runtime execution errors encountered on non-native environments
+ such as custom OpenCL integrations by mapping calls out to custom module implementations.
+ """
+
+ if self.stft is None:
+ from main.library.backends.utils import STFT
+
+ # Lazy initial loading of custom STFT instance configurations mapped to target calculation device
+ self.stft = STFT(
+ filter_length=self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_length
+ ).to(audio.device)
+
+ return self.stft.transform(audio, 1e-9, center=center)
+
+ def _stft_torch(self, audio, center=True):
+ """Computes Magnitude Short-Time Fourier Transform using standard native PyTorch kernels."""
+
+ fft = torch.stft(
+ audio,
+ n_fft=self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_length,
+ window=self.window,
+ center=center,
+ pad_mode="reflect",
+ return_complex=True # Mandated parameter choice for torch compatibility guarantees
+ )
+ # Extract and return absolute real magnitudes from complex output spectrum fields
+ return fft.abs()
\ No newline at end of file
diff --git a/main/library/predictors/DJCM/utils.py b/main/library/predictors/DJCM/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..73e494e9f84ab7418e625acfa06a5fc4d7484b59
--- /dev/null
+++ b/main/library/predictors/DJCM/utils.py
@@ -0,0 +1,207 @@
+import torch
+
+from torch import nn
+from einops.layers.torch import Rearrange
+
+def init_layer(layer):
+ """
+ Initializes a linear or convolutional layer's weights using Xavier Uniform.
+
+ If the layer has a learnable bias, it is reset to zero.
+
+ Args:
+ layer (nn.Module): The target layer to initialize.
+ """
+
+ nn.init.xavier_uniform_(layer.weight)
+
+ if hasattr(layer, "bias") and layer.bias is not None:
+ layer.bias.data.fill_(0.0)
+
+def init_bn(bn):
+ """
+ Initializes Batch Normalization parameters to standard baseline starting values.
+
+ Sets biases to 0.0, scaling weights to 1.0, tracking means to 0.0, and variances to 1.0.
+
+ Args:
+ bn (nn.Module): The BatchNorm layer to initialize.
+ """
+
+ bn.bias.data.fill_(0.0)
+ bn.weight.data.fill_(1.0)
+ bn.running_mean.data.fill_(0.0)
+ bn.running_var.data.fill_(1.0)
+
+class BiGRU(nn.Module):
+ """
+ Bidirectional GRU module that extracts sequential temporal relationships from image patches.
+
+ Flattens spatial 2D macro-patches into sequential tokens using `einops.Rearrange`,
+ then processes them via a Bidirectional GRU layer.
+ """
+
+ def __init__(
+ self,
+ patch_size,
+ channels,
+ depth
+ ):
+ """
+ Initializes the BiGRU module.
+
+ Args:
+ patch_size (tuple): Dimensions of each patch (patch_width, patch_height).
+ channels (int): Number of feature channels in the incoming input maps.
+ depth (int): The total number of stacked recurrent layers within the GRU.
+ """
+
+ super(BiGRU, self).__init__()
+ patch_width, patch_height = patch_size
+ # Calculate the flattened feature vector dimension per patch step token
+ patch_dim = channels * patch_height * patch_width
+
+ # Rearrange 4D tensor maps to sequential patch representations
+ self.to_patch_embedding = nn.Sequential(
+ Rearrange(
+ 'b c (w p1) (h p2) -> b (w h) (p1 p2 c)',
+ p1=patch_width,
+ p2=patch_height
+ )
+ )
+
+ # Bidirectional GRU splits the target capacity between forward and backward passes (hence hidden_size = patch_dim // 2)
+ self.gru = nn.GRU(
+ patch_dim,
+ patch_dim // 2,
+ num_layers=depth,
+ batch_first=True,
+ bidirectional=True
+ )
+
+ def forward(self, x):
+ """
+ Executes the forward pass of the patch-based BiGRU.
+
+ Args:
+ x (torch.Tensor): 4D spatial feature tensor.
+
+ Returns:
+ torch.Tensor: Sequential hidden features matrix from the GRU.
+ """
+
+ x = self.to_patch_embedding(x)
+ # Gracefully handle cuDNN hidden sequence state mismatches across different GPU architectures
+ try:
+ return self.gru(x)[0]
+ except:
+ # Fallback routine: temporarily disable cuDNN to calculate on vanilla backend if an exception occurs
+ # Exceptions typically occur when the input is too large.
+ torch.backends.cudnn.enabled = False
+ return self.gru(x)[0]
+
+class ResConvBlock(nn.Module):
+ """
+ Pre-activation Residual Convolutional Block.
+
+ Applies BatchNorm -> PReLU -> Conv twice, along with an integrated automatic
+ identity shortcut connection router.
+ """
+
+ def __init__(
+ self,
+ in_planes,
+ out_planes
+ ):
+ """
+ Initializes the ResConvBlock.
+
+ Args:
+ in_planes (int): Number of incoming input channels.
+ out_planes (int): Number of target output channels.
+ """
+
+ super(ResConvBlock, self).__init__()
+ self.bn1 = nn.BatchNorm2d(
+ in_planes,
+ momentum=0.01
+ )
+ self.bn2 = nn.BatchNorm2d(
+ out_planes,
+ momentum=0.01
+ )
+ self.act1 = nn.PReLU()
+ self.act2 = nn.PReLU()
+ # Standard 3x3 padded convolutions that preserve spatial dimensions
+ self.conv1 = nn.Conv2d(
+ in_planes,
+ out_planes,
+ (3, 3),
+ padding=(1, 1),
+ bias=False
+ )
+ self.conv2 = nn.Conv2d(
+ out_planes,
+ out_planes,
+ (3, 3),
+ padding=(1, 1),
+ bias=False
+ )
+
+ # Determine if a 1x1 projection shortcut is needed to align channel dimensions
+ self.is_shortcut = False
+ if in_planes != out_planes:
+ self.shortcut = nn.Conv2d(
+ in_planes,
+ out_planes,
+ (1, 1)
+ )
+ self.is_shortcut = True
+
+ self.init_weights()
+ # Performance optimization: pre-assign the forward projection method to bypass conditional execution branches
+ self._return = self.return_shortcut if self.is_shortcut else self.return_non_shortcut
+
+ def init_weights(self):
+ """Initializes internal weights across all parameters."""
+
+ init_bn(self.bn1)
+ init_bn(self.bn2)
+
+ init_layer(self.conv1)
+ init_layer(self.conv2)
+
+ if self.is_shortcut: init_layer(self.shortcut)
+
+ def return_shortcut(self, x, out):
+ """Sums projection mapped input modifications with processed output features."""
+
+ return self.shortcut(x) + out
+
+ def return_non_shortcut(self, x, out):
+ """Applies basic residual identity additions directly."""
+
+ return out + x
+
+ def forward(self, x):
+ """
+ Executes the pre-activation forward pass loop.
+
+ Args:
+ x (torch.Tensor): Input activation maps.
+
+ Returns:
+ torch.Tensor: Accumulated residual output tensor map.
+ """
+
+ # First Block: BN -> PReLU -> Conv
+ out = self.conv1(
+ self.act1(self.bn1(x))
+ )
+ # Second Block: BN -> PReLU -> Conv
+ out = self.conv2(
+ self.act2(self.bn2(out))
+ )
+
+ # Return combined result using the pre-mapped execution pathway function pointer
+ return self._return(x, out)
\ No newline at end of file
diff --git a/main/library/predictors/FCPE/FCPE.py b/main/library/predictors/FCPE/FCPE.py
new file mode 100644
index 0000000000000000000000000000000000000000..b346a56dc66791763e8400e0e71490caf0d227ec
--- /dev/null
+++ b/main/library/predictors/FCPE/FCPE.py
@@ -0,0 +1,536 @@
+import os
+import sys
+import torch
+
+import numpy as np
+import torch.nn as nn
+import onnxruntime as ort
+
+from einops import rearrange
+from torch.nn.utils.parametrizations import weight_norm
+
+sys.path.append(os.getcwd())
+
+# Limit Least Recently Used (LRU) cache capacity for specific backend operations
+os.environ["LRU_CACHE_CAPACITY"] = "3"
+
+from main.app.variables import config, configs
+from main.library.predictors.FCPE.stft import Wav2Mel
+from main.library.predictors.FCPE.utils import decrypt_model, DotDict
+from main.library.predictors.FCPE.encoder import EncoderLayer, ConformerNaiveEncoder
+
+@torch.no_grad()
+def cent_to_f0(cent):
+ """
+ Convert pitch values from cents to Frequency (Hz).
+
+ Args:
+ cent (torch.Tensor): Pitch values in cents.
+
+ Returns:
+ torch.Tensor: Pitch frequency values in Hz.
+ """
+
+ return 10 * 2 ** (cent / 1200)
+
+@torch.no_grad()
+def f0_to_cent(f0):
+ """
+ Convert pitch values from Frequency (Hz) to cents.
+
+ Args:
+ f0 (torch.Tensor): Pitch frequency values in Hz.
+
+ Returns:
+ torch.Tensor: Pitch values in cents.
+ """
+
+ return 1200 * (f0 / 10).log2()
+
+@torch.no_grad()
+def latent2cents_local_decoder(cent_table, out_dims, y, threshold = 0.05):
+ """
+ Decodes latent probabilities into cent values using a local neighborhood (GPU-optimized).
+
+ This decodes the model output distribution into a continuous pitch value by calculating
+ a weighted average around the peak index (argmax) within a window of 9 bins.
+
+ Args:
+ cent_table (torch.Tensor): A reference table containing cent mapping values.
+ out_dims (int): The number of output dimensions (bins) in the probability distribution.
+ y (torch.Tensor): Model prediction probabilities, shape (B, N, out_dims).
+ threshold (float, optional): Confidence threshold. Values below this are masked. Defaults to 0.05.
+
+ Returns:
+ torch.Tensor: Continuous cent values masked by confidence, shape (B, N, 1).
+ """
+
+ B, N, _ = y.size()
+ # Expand cent table to match batch and time frames dimensions
+ ci = cent_table[None, None, :].expand(B, N, -1)
+ confident, max_index = y.max(dim=-1, keepdim=True)
+
+ # Define a local window of 9 bins around the argmax position [max_index - 4, max_index + 4]
+ local_argmax_index = torch.arange(0, 9).to(max_index.device) + (max_index - 4)
+ # Clip indices to prevent index out of bounds error
+ local_argmax_index[local_argmax_index < 0] = 0
+ local_argmax_index[local_argmax_index >= out_dims] = out_dims - 1
+
+ # Gather local probabilities and their corresponding cent values
+ y_l = y.gather(-1, local_argmax_index)
+ # Perform a center-of-mass weighted average within the local window
+ rtn = (ci.gather(-1, local_argmax_index) * y_l).sum(dim=-1, keepdim=True) / y_l.sum(dim=-1, keepdim=True)
+
+ # Mask unconfident predictions by assigning negative infinity
+ confident_mask = torch.ones_like(confident)
+ confident_mask[confident <= threshold] = float("-INF")
+ rtn = rtn * confident_mask
+
+ return rtn
+
+def cents_local_decoder(cent_table, y, n_out, threshold = 0.05):
+ """
+ Alternative implementation of local center-of-mass decoding for cent values.
+
+ Args:
+ cent_table (torch.Tensor): Reference cent mapping values.
+ y (torch.Tensor): Model output logits/probabilities.
+ n_out (int): Total number of output frequency bins.
+ threshold (float, optional): Confidence threshold. Defaults to 0.05.
+
+ Returns:
+ torch.Tensor: Calculated continuous cent values.
+ """
+
+ B, N, _ = y.size()
+ confident, max_index = y.max(dim=-1, keepdim=True)
+ # Define and clamp the local window indices to safety constraints
+ local_argmax_index = (torch.arange(0, 9).to(max_index.device) + (max_index - 4)).clamp(0, n_out - 1)
+ y_l = y.gather(-1, local_argmax_index)
+ # Compute center of mass mapping
+ rtn = (cent_table[None, None, :].expand(B, N, -1).gather(-1, local_argmax_index) * y_l).sum(dim=-1, keepdim=True) / y_l.sum(dim=-1, keepdim=True)
+
+ # Filter out low-confidence frame predictions
+ confident_mask = torch.ones_like(confident)
+ confident_mask[confident <= threshold] = float("-INF")
+ rtn = rtn * confident_mask
+
+ return rtn
+
+def latent2cents_local_decoder_cpu(cent_table, out_dims, y, threshold = 0.05):
+ """Wrapper to force latent2cents decoding on the CPU (for specialized hardware backends)."""
+
+ cent_table, y = cent_table.cpu(), y.cpu()
+ return latent2cents_local_decoder(cent_table, out_dims, y, threshold)
+
+def cents_local_decoder_cpu(cent_table, y, n_out, threshold = 0.05):
+ """Wrapper to force cents decoding on the CPU (for specialized hardware backends)."""
+
+ cent_table, y = cent_table.cpu(), y.cpu()
+ return cents_local_decoder(cent_table, y, n_out, threshold)
+
+class PCmer(nn.Module):
+ """Parallel Conformer-based module used for processing frame representations."""
+
+ def __init__(
+ self,
+ num_layers,
+ num_heads,
+ dim_model,
+ dim_keys,
+ dim_values,
+ residual_dropout,
+ attention_dropout
+ ):
+ """
+ Initializes PCmer layer configurations.
+
+ Args:
+ num_layers (int): Number of Conformer Encoder blocks.
+ num_heads (int): Number of attention heads.
+ dim_model (int): Hidden size dimension.
+ dim_keys (int): Feature dimensionality for key projections.
+ dim_values (int): Feature dimensionality for value projections.
+ residual_dropout (float): Dropout probability for residual connections.
+ attention_dropout (float): Dropout probability for attention weights.
+ """
+
+ super().__init__()
+ self.num_layers = num_layers
+ self.num_heads = num_heads
+ self.dim_model = dim_model
+ self.dim_values = dim_values
+ self.dim_keys = dim_keys
+ self.residual_dropout = residual_dropout
+ self.attention_dropout = attention_dropout
+ # Instantiate sequence of sequential encoder layers
+ self._layers = nn.ModuleList([EncoderLayer(self) for _ in range(num_layers)])
+
+ def forward(self, phone, mask=None):
+ """
+ Forward pass through the PCmer block layers.
+
+ Args:
+ phone (torch.Tensor): Feature representations.
+ mask (torch.Tensor, optional): Optional padding masks. Defaults to None.
+ """
+
+ for layer in self._layers:
+ phone = layer(phone, mask)
+
+ return phone
+
+class CFNaiveMelPE(nn.Module):
+ """Conformer-based Naive Mel-spectrogram Pitch Estimator (Modern Architecture)."""
+
+ def __init__(
+ self,
+ input_channels,
+ out_dims,
+ hidden_dims = 512,
+ n_layers = 6,
+ n_heads = 8,
+ use_fa_norm = False,
+ conv_only = False,
+ conv_dropout = 0,
+ atten_dropout = 0
+ ):
+ super().__init__()
+ self.input_channels = input_channels
+ self.out_dims = out_dims
+ self.hidden_dims = hidden_dims
+ self.n_layers = n_layers
+ self.n_heads = n_heads
+ self.use_fa_norm = use_fa_norm
+ # Front-end 1D convolution processing blocks
+ self.input_stack = nn.Sequential(
+ nn.Conv1d(
+ input_channels,
+ hidden_dims,
+ 3,
+ 1,
+ 1
+ ),
+ nn.GroupNorm(
+ 4,
+ hidden_dims
+ ),
+ nn.LeakyReLU(),
+ nn.Conv1d(
+ hidden_dims,
+ hidden_dims,
+ 3,
+ 1,
+ 1
+ )
+ )
+ # Core encoder network based on Conformer architecture
+ self.net = ConformerNaiveEncoder(
+ num_layers=n_layers,
+ num_heads=n_heads,
+ dim_model=hidden_dims,
+ use_norm=use_fa_norm,
+ conv_only=conv_only,
+ conv_dropout=conv_dropout,
+ atten_dropout=atten_dropout
+ )
+ self.norm = nn.LayerNorm(hidden_dims)
+ # Output projection utilizing Weight Normalization for stabilization
+ self.output_proj = weight_norm(
+ nn.Linear(
+ hidden_dims,
+ out_dims
+ )
+ )
+
+ # Pre-calculate constant buffers for mapping index predictions back to Hz
+ self.cent_table_b = torch.linspace(
+ f0_to_cent(torch.Tensor([32.70]))[0],
+ f0_to_cent(torch.Tensor([1975.5]))[0],
+ out_dims
+ ).detach()
+ self.gaussian_blurred_cent_mask_b = (
+ 1200 * torch.Tensor([197.55]).log2()
+ )[0].detach()
+
+ # Register non-trainable state variables
+ self.register_buffer("cent_table", self.cent_table_b)
+ self.register_buffer("gaussian_blurred_cent_mask", self.gaussian_blurred_cent_mask_b)
+ # Adapt decoding strategy dynamically according to hardware limitations
+ self.latent2cents_local_decoder = latent2cents_local_decoder_cpu if config.device.startswith("privateuseone") else latent2cents_local_decoder
+
+ def forward(self, mel, threshold = 0.006):
+ """
+ Infers F0 tracks natively given extracted Mel spectrogram vectors.
+
+ Args:
+ mel (torch.Tensor): Input mel-spectrogram.
+ threshold (float, optional): Confidence decoding threshold. Defaults to 0.006.
+
+ Returns:
+ torch.Tensor: Extracted continuous fundamental frequency values (Hz).
+ """
+
+ with torch.no_grad():
+ # Reorganize batch sequence structures safely
+ mels = rearrange(torch.stack([mel], -1), "B T C K -> (B K) T C")
+
+ # Extract internal intermediate latents via spatial blocks
+ x = self.input_stack(mels.transpose(-1, -2)).transpose(-1, -2)
+ # Keep it as Float; using Half will cause NaN issues and crash RVC/SVC
+ latent = self.output_proj(self.norm(self.net(x))).float().sigmoid()
+
+ # Local probabilistic center-of-mass cent mapping decoding
+ x = cent_to_f0(
+ self.latent2cents_local_decoder(
+ self.cent_table,
+ self.out_dims,
+ latent,
+ threshold=threshold
+ )
+ )
+
+ # Post-process dimensions and clamp extreme boundaries
+ f0 = rearrange(x, "(B K) T 1 -> B T (K 1)", K=1)
+ f0 = f0 * (1 - (f0 < 32.70).type(f0.dtype)) # Zero out lower noise floor
+ f0[f0 > 1975.5] = 1975.5
+
+ return f0
+
+class CFNaiveMelPE_LEGACY(nn.Module):
+ """Legacy Conformer-based Naive Mel-spectrogram Pitch Estimator architecture."""
+
+ def __init__(
+ self,
+ input_channel=128,
+ out_dims=360,
+ n_layers=12,
+ n_chans=512
+ ):
+ super().__init__()
+ self.n_out = out_dims
+
+ # Pre-computed linear space table for cent conversions
+ self.cent_table_b = torch.linspace(
+ f0_to_cent(torch.Tensor([32.70]))[0],
+ f0_to_cent(torch.Tensor([1975.5]))[0],
+ out_dims
+ ).detach()
+
+ self.register_buffer("cent_table", self.cent_table_b)
+ # Processing stack for structural feature downsampling/upsampling
+ self.stack = nn.Sequential(
+ nn.Conv1d(
+ input_channel,
+ n_chans,
+ 3,
+ 1,
+ 1
+ ),
+ nn.GroupNorm(
+ 4,
+ n_chans
+ ),
+ nn.LeakyReLU(),
+ nn.Conv1d(
+ n_chans,
+ n_chans,
+ 3,
+ 1,
+ 1
+ )
+ )
+ self.decoder = PCmer(
+ num_layers=n_layers,
+ num_heads=8,
+ dim_model=n_chans,
+ dim_keys=n_chans,
+ dim_values=n_chans,
+ residual_dropout=0.1,
+ attention_dropout=0.1
+ )
+ self.norm = nn.LayerNorm(n_chans)
+ self.dense_out = weight_norm(
+ nn.Linear(
+ n_chans,
+ self.n_out
+ )
+ )
+ # Dynamic strategy alignment for target device backends
+ self.cents_local_decoder = cents_local_decoder_cpu if config.device.startswith("privateuseone") else cents_local_decoder
+
+ def forward(self, mel, threshold=0.05):
+ """Infers F0 tracks via the legacy model forward routine pipeline."""
+
+ x = self.decoder(self.stack(mel.transpose(1, 2)).transpose(1, 2))
+ x = self.dense_out(self.norm(x)).sigmoid()
+
+ # Unpack indices to frequency
+ x = cent_to_f0(
+ self.cents_local_decoder(
+ self.cent_table,
+ x,
+ self.n_out,
+ threshold=threshold
+ )
+ )
+
+ return x
+
+class FCPE:
+ """
+ FCPE: A Fast Context-based Pitch Estimation Model wrapper class.
+
+ This class serves as the production-ready wrapper for the FCPE model architecture,
+ supporting high-performance inference pipelines including PyTorch Native (FP32/FP16),
+ compiled Torchscript/Dynamo execution, and optimized ONNX Runtime evaluation
+ leveraging zero-copy memory IO-Binding features.
+ """
+
+ def __init__(
+ self,
+ model_path,
+ device=None,
+ threshold=0.05,
+ providers=None,
+ onnx=False,
+ legacy=False,
+ is_half=False,
+ compile_model=False,
+ compile_mode=None
+ ):
+ """
+ Initializes the Fast Context-based Pitch Estimation (FCPE) pipeline backend.
+
+ Args:
+ model_path (str): Path leading to the local model checkpoint file.
+ device (str/torch.device, optional): Device context execution binding (e.g., 'cuda', 'cpu').
+ threshold (float, optional): Local extraction confidence window thresholds. Defaults to 0.05.
+ providers (list, optional): Execution engine providers specified for ONNX Runtime (e.g., ['CUDAExecutionProvider']).
+ onnx (bool, optional): Toggles ONNX Runtime inference usage instead of PyTorch. Defaults to False.
+ legacy (bool, optional): Fallback flag for loading old-format model structures. Defaults to False.
+ is_half (bool, optional): Enables FP16 precision calculations. Defaults to False.
+ compile_model (bool, optional): Calls torch.compile over submodules for enhanced speed. Defaults to False.
+ compile_mode (str, optional): Target compilation strategy modes parameterization (e.g., 'reduce-overhead').
+ """
+
+ self.device = device
+ self.threshold = threshold
+ self.wav2mel = Wav2Mel(device=self.device, dtype=torch.float32)
+
+ if onnx:
+ # Setup configuration optimized execution boundaries for ONNX runtimes
+ sess_options = ort.SessionOptions()
+ sess_options.log_severity_level = 3
+ # Decrypt parameters safely and initialize session state contexts
+ # My FCPE ONNX models previously encountered issues with Hugging Face, so I chose to encode the models to ensure compatibility.
+ # I haven't tried it again since then to see if the problem persists.
+ model = ort.InferenceSession(decrypt_model(configs, model_path), sess_options=sess_options, providers=providers)
+ elif legacy:
+ # Parse classic architecture format checkpoints safely
+ ckpt = torch.load(model_path, map_location="cpu", weights_only=True)
+ self.args = DotDict(ckpt["config"])
+
+ model = CFNaiveMelPE_LEGACY(
+ input_channel=self.args.model.input_channel,
+ out_dims=self.args.model.out_dims,
+ n_layers=self.args.model.n_layers,
+ n_chans=self.args.model.n_chans
+ )
+
+ model.load_state_dict(ckpt["model"])
+ if compile_model: model.decoder, model.stack = torch.compile(model.decoder, mode=compile_mode), torch.compile(model.stack, mode=compile_mode)
+ model = model.to(self.device).eval()
+ if is_half: model = model.half()
+ else:
+ # Load modern architecture configurations
+ ckpt = torch.load(model_path, map_location="cpu", weights_only=True)
+ self.args = DotDict(ckpt["config_dict"])
+
+ model = CFNaiveMelPE(
+ input_channels=self.args.mel.num_mels,
+ out_dims=self.args.model.out_dims,
+ hidden_dims=self.args.model.hidden_dims,
+ n_layers=self.args.model.n_layers,
+ n_heads=self.args.model.n_heads,
+ use_fa_norm=self.args.model.use_fa_norm,
+ conv_only=self.args.model.conv_only
+ )
+
+ model.load_state_dict(ckpt["model"])
+ if compile_model: model.net, model.input_stack = torch.compile(model.net, mode=compile_mode), torch.compile(model.input_stack, mode=compile_mode)
+ model = model.to(self.device).eval()
+ if is_half: model = model.half()
+
+ self.model = model
+ # Identify standard processing target strings for system contexts
+ self._device = "cuda" if providers[0][0].startswith("CUDA") else "cpu"
+ # Preallocate memory buffers depending on execution driver features
+ self._threshold = (torch.zeros((), device=self.device, dtype=torch.float32) if providers[0][0].startswith(("CUDA", "CPU")) else np.empty((), dtype=np.float32)) if onnx else None
+ # Explicitly assign function execution routing pathways to minimize checking costs
+ self.infer = (self._infer_onnx_io if providers[0][0].startswith(("CUDA", "CPU")) else self._infer_onnx_non_io) if onnx else (self._infer_torch_fp16 if is_half else self._infer_torch_fp32)
+
+ def compute_f0(self, wav):
+ """
+ Extract fundamental frequency (F0) tracking directly from raw waveform arrays.
+
+ Args:
+ wav (np.ndarray | torch.Tensor): Input audio waveform tensor or array.
+
+ Returns:
+ torch.Tensor: Frame-level F0 series estimations.
+ """
+
+ if not torch.is_tensor(wav): wav = torch.from_numpy(wav).float().to(self.device)
+
+ with torch.inference_mode():
+ # Extract mel configurations and pass tensors down to active routing backends
+ f0 = self.infer(self.wav2mel(audio=wav[None, :]), self.threshold)
+ # Squeeze output to present simple 1D structures
+ f0 = f0[:] if f0.dim() == 1 else f0[0, :, 0]
+
+ return f0
+
+ def _infer_onnx_non_io(self, mel, threshold):
+ """Inference routing utilizing conventional NumPy array transitions (Slower)."""
+
+ self._threshold.fill(threshold)
+
+ return torch.as_tensor(
+ self.model.run(
+ ["pitchf"], {"mel": mel.detach().cpu().numpy(), "threshold": self._threshold}
+ )[0],
+ dtype=torch.float32,
+ device=self.device
+ )
+
+ def _infer_onnx_io(self, mel, threshold):
+ """High-performance ONNX Inference using device IO-Binding to avoid CPU host copies."""
+
+ mel = mel.contiguous()
+ self._threshold.fill_(threshold)
+ device_idx = mel.device.index or 0
+
+ # Bind hardware native allocations directly to bypass host-device copy roundtrips
+ io_binding = self.model.io_binding()
+ io_binding.bind_input(name="mel", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(mel.shape), buffer_ptr=mel.data_ptr())
+ io_binding.bind_input(name="threshold", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(self._threshold.shape), buffer_ptr=self._threshold.data_ptr())
+
+ io_binding.bind_output(name="pitchf", device_type=self._device, device_id=device_idx)
+ self.model.run_with_iobinding(io_binding)
+
+ return torch.as_tensor(
+ io_binding.get_outputs()[0].numpy(),
+ dtype=torch.float32,
+ device=self.device
+ )
+
+ def _infer_torch_fp32(self, mel, threshold):
+ """Standard PyTorch inference runner using FP32 precision."""
+
+ return self.model(mel, threshold=threshold)
+
+ def _infer_torch_fp16(self, mel, threshold):
+ """Standard PyTorch inference runner using FP16 half-precision."""
+
+ return self.model(mel.half(), threshold=threshold)
\ No newline at end of file
diff --git a/main/library/predictors/FCPE/attentions.py b/main/library/predictors/FCPE/attentions.py
new file mode 100644
index 0000000000000000000000000000000000000000..93007fc644cefe822c5292ab5849e2ed79cbe93e
--- /dev/null
+++ b/main/library/predictors/FCPE/attentions.py
@@ -0,0 +1,530 @@
+import math
+import torch
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torch import einsum
+from functools import partial
+from einops import rearrange, repeat, pack, unpack
+
+def exists(val):
+ """Check if a value is not None."""
+
+ return val is not None
+
+def default(value, d):
+ """Return the value if it exists, otherwise return a default value."""
+
+ return value if exists(value) else d
+
+def empty(tensor):
+ """Check if a given tensor contains zero elements."""
+
+ return tensor.numel() == 0
+
+def pad_to_multiple(tensor, multiple, dim=-1, value=0):
+ """
+ Pad a tensor's specified dimension to be a clean multiple of a target integer.
+
+ Args:
+ tensor (torch.Tensor): Input sequence tensor.
+ multiple (int): Target factor requirement (e.g., window size).
+ dim (int, optional): Dimensional axis to apply padding. Defaults to -1.
+ value (float, optional): Scalar pad value filler. Defaults to 0.0.
+
+ Returns:
+ Tuple[bool, torch.Tensor]: A tuple containing a boolean flag indicating if padding
+ was applied, and the resulting padded tensor.
+ """
+
+ seqlen = tensor.shape[dim]
+ m = seqlen / multiple
+
+ if m.is_integer(): return False, tensor
+ return True, F.pad(tensor, (*((0,) * (-1 - dim) * 2), 0, (math.ceil(m) * multiple - seqlen)), value = value)
+
+def look_around(x, backward = 1, forward = 0, pad_value = -1, dim = 2):
+ """
+ Creates overlapping temporal context blocks by gathering frames across a local window.
+
+ Used to perform sliding window block partitions for localized self-attention layers.
+
+ Args:
+ x (torch.Tensor): Padded chunked windows tensor, shape (B, W, N, D).
+ backward (int, optional): Number of preceding blocks to look into. Defaults to 1.
+ forward (int, optional): Number of succeeding blocks to look into. Defaults to 0.
+ pad_value (float, optional): Out-of-bounds constant mask representation. Defaults to -1.0.
+ dim (int, optional): Focus target concatenating axis. Defaults to 2.
+ """
+
+ t = x.shape[1]
+ dims = (len(x.shape) - dim) * (0, 0)
+ padded_x = F.pad(x, (*dims, backward, forward), value = pad_value)
+
+ # Slice overlapping context sections and concatenate them across the target axis
+ return torch.cat([padded_x[:, ind:(ind + t), ...] for ind in range(forward + backward + 1)], dim = dim)
+
+def rotate_half(x):
+ """
+ Rotates the feature dimensions of the input tensor by splitting and swapping halves.
+
+ Helper function used for applying Rotary Position Embeddings (RoPE).
+ """
+
+ x1, x2 = rearrange(x, 'b ... (r d) -> b ... r d', r = 2).unbind(dim = -2)
+ return torch.cat((-x2, x1), dim = -1)
+
+def apply_rotary_pos_emb(q, k, freqs, scale = 1):
+ """
+ Applies Rotary Position Embeddings (RoPE) to Query and Key tensors.
+
+ Args:
+ q (torch.Tensor): Query states tensor.
+ k (torch.Tensor): Key states tensor.
+ freqs (torch.Tensor): Trigonometric frequency tensor values.
+ scale (float | torch.Tensor, optional): Adaptive feature scaling values. Defaults to 1.0.
+ """
+
+ q_len = q.shape[-2]
+ # Slice the frequencies to match the specific query length context
+ q_freqs = freqs[..., -q_len:, :]
+
+ inv_scale = scale ** -1
+ if scale.ndim == 2: scale = scale[-q_len:, :]
+
+ # Rotate states using the trigonometric Euler formula identity expansion
+ q = (q * q_freqs.cos() * scale) + (rotate_half(q) * q_freqs.sin() * scale)
+ k = (k * freqs.cos() * inv_scale) + (rotate_half(k) * freqs.sin() * inv_scale)
+
+ return q, k
+
+def orthogonal_matrix_chunk(cols, qr_uniform_q=False, device=None):
+ """Generates a square random orthogonal matrix using QR decomposition."""
+
+ unstructured_block = torch.randn((cols, cols), device=device)
+ # Compute QR decomposition on CPU for stability reasons
+ q, r = torch.linalg.qr(unstructured_block.cpu(), mode="reduced")
+ q, r = map(lambda t: t.to(device), (q, r))
+
+ if qr_uniform_q:
+ # Enforce statistical uniformity over the distribution
+ d = r.diag(0)
+ q *= d.sign()
+
+ return q.t()
+
+def gaussian_orthogonal_random_matrix(nb_rows, nb_columns, scaling=0, qr_uniform_q=False, device=None):
+ """
+ Constructs a Gaussian Orthogonal Random Matrix for kernel projection blocks.
+
+ Used by Performer/FastAttention architectures to map states into lower-rank features.
+ """
+
+ nb_full_blocks = int(nb_rows / nb_columns)
+ block_list = []
+
+ for _ in range(nb_full_blocks):
+ block_list.append(orthogonal_matrix_chunk(nb_columns, qr_uniform_q=qr_uniform_q, device=device))
+
+ remaining_rows = nb_rows - nb_full_blocks * nb_columns
+
+ if remaining_rows > 0:
+ block_list.append(
+ orthogonal_matrix_chunk(
+ nb_columns,
+ qr_uniform_q=qr_uniform_q,
+ device=device
+ )[:remaining_rows]
+ )
+
+ # Scale variables according to specific variance choices
+ if scaling == 0:
+ multiplier = torch.randn(
+ (nb_rows, nb_columns),
+ device=device
+ ).norm(dim=1)
+ elif scaling == 1:
+ multiplier = math.sqrt(
+ (float(nb_columns))
+ ) * torch.ones(
+ (nb_rows,),
+ device=device
+ )
+ else: raise ValueError(f"Unsupported scaling factor parameter: {scaling} != 0, 1")
+
+ return multiplier.diag() @ torch.cat(block_list)
+
+def linear_attention(q, k, v):
+ """
+ Computes kernelized linear attention without materializing the full attention matrix.
+
+ After replacing the softmax similarity with a positive kernel feature map,
+ the associative matrix multiplication can be reordered from
+ (QKᵀ)V to Q(KᵀV), reducing the complexity from quadratic to linear
+ with respect to sequence length.
+ """
+
+ return einsum( # Compute kernel feature interactions.
+ "...ed,...nd->...ne",
+ k,
+ q
+ ) if v is None else einsum( # Compute normalized linear attention output.
+ "...de,...nd,...n->...ne",
+ einsum(
+ "...nd,...ne->...de",
+ k,
+ v
+ ),
+ q,
+ 1.0 / (einsum(
+ "...nd,...d->...n",
+ q,
+ k.sum(dim=-2).type_as(q)
+ ) + 1e-8)
+ )
+
+def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4):
+ """
+ Approximates the standard Softmax Attention matrix using random Fourier features.
+
+ Implements the positive random feature (ORF) mapping method described in the
+ Performer architecture paper.
+ """
+
+ b, h, *_ = data.shape
+
+ data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1.0
+ ratio = projection_matrix.shape[0] ** -0.5
+
+ # Project raw data onto the random orthogonal matrix basis space
+ data_dash = torch.einsum("...id,...jd->...ij", (data_normalizer * data), repeat(projection_matrix, "j d -> b h j d", b=b, h=h).type_as(data))
+ # Compute the squared-norm correction term required by the softmax kernel feature map.
+ diag_data = (((data**2).sum(dim=-1) / 2.0) * (data_normalizer**2)).unsqueeze(dim=-1)
+
+ return (ratio * ((data_dash - diag_data - data_dash.max(dim=-1, keepdim=True).values).exp() + eps) if is_query else ratio * ((data_dash - diag_data + eps).exp())).type_as(data)
+
+class SinusoidalEmbeddings(nn.Module):
+ """Generates continuous sinusoidal and frequency scalar values for position encoding."""
+
+ def __init__(
+ self,
+ dim,
+ theta = 10000
+ ):
+ super().__init__()
+ inv_freq = 1. / (theta ** (torch.arange(0, dim, 2).float() / dim))
+ self.register_buffer('inv_freq', inv_freq)
+ scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
+ self.register_buffer('scale', scale, persistent = False)
+
+ def forward(self, x):
+ """Generates raw sinusoidal frequency vectors mapping positions."""
+
+ seq_len, device = x.shape[-2], x.device
+ t = torch.arange(seq_len, device = x.device).type_as(self.inv_freq)
+
+ freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
+ freqs = torch.cat((freqs, freqs), dim = -1)
+
+ return freqs, torch.ones(1, device = device)
+
+class LocalAttention(nn.Module):
+ """
+ Sliding Window Local Attention Block.
+
+ Restricts memory computation overhead costs by containing attention calculation fields
+ within localized adjacent block neighborhoods.
+ """
+
+ def __init__(
+ self,
+ window_size,
+ look_backward = 1,
+ look_forward = None,
+ dropout = 0.,
+ shared_qk = False,
+ rel_pos_emb_config = None,
+ dim = None,
+ scale = None,
+ use_rotary_pos_emb = True,
+ ):
+ super().__init__()
+ look_forward = default(look_forward, 1)
+ self.scale = scale
+ self.window_size = window_size
+ self.look_backward = look_backward
+ self.look_forward = look_forward
+ self.dropout = nn.Dropout(dropout)
+ self.shared_qk = shared_qk
+ self.rel_pos = None
+ # Instantiate rotary structural embeddings if parameters are provided
+ if use_rotary_pos_emb and (exists(rel_pos_emb_config) or exists(dim)):
+ if exists(rel_pos_emb_config): dim = rel_pos_emb_config[0]
+ self.rel_pos = SinusoidalEmbeddings(dim)
+
+ def forward(self, q, k, v, mask = None, input_mask = None, attn_bias = None, window_size = None):
+ """Executes window-partitioned localized self-attention steps."""
+
+ mask = default(mask, input_mask)
+ assert not exists(window_size)
+
+ (
+ _,
+ pad_value,
+ window_size,
+ look_backward,
+ look_forward,
+ shared_qk
+ ) = (
+ q.shape,
+ -1,
+ default(
+ window_size,
+ self.window_size
+ ),
+ self.look_backward,
+ self.look_forward,
+ self.shared_qk
+ )
+
+ # Flatten variable shapes into combined uniform tracking sequences
+ (q, packed_shape), (k, _), (v, _) = map(lambda t: pack([t], '* n d'), (q, k, v))
+
+ orig_seq_len = q.shape[1]
+ # Pad sequence lengths to fit exact window boundary conditions
+ (_, q), (_, k), (_, v) = map(lambda t: pad_to_multiple(t, self.window_size, dim = -2), (q, k, v))
+
+ b, n, dim_head, device, dtype = *q.shape, q.device, q.dtype
+ scale = default(self.scale, dim_head ** -0.5)
+
+ assert (n % window_size) == 0
+ windows = n // window_size
+
+ if shared_qk: k = F.normalize(k, dim = -1).type(k.dtype)
+
+ # Reshape vectors from sequential tracks into chunked window spaces
+ seq = torch.arange(n, device = device)
+ b_t = rearrange(seq, '(w n) -> 1 w n', w = windows, n = window_size)
+ bq, bk, bv = map(lambda t: rearrange(t, 'b (w n) d -> b w n d', w = windows), (q, k, v))
+
+ bq = bq * scale
+ look_around_kwargs = dict(backward=look_backward, forward=look_forward, pad_value=pad_value)
+
+ # Gather context blocks using overlapping window looks
+ bk = look_around(bk, **look_around_kwargs)
+ bv = look_around(bv, **look_around_kwargs)
+
+ if exists(self.rel_pos):
+ pos_emb, xpos_scale = self.rel_pos(bk)
+ bq, bk = apply_rotary_pos_emb(bq, bk, pos_emb, scale = xpos_scale)
+
+ bq_t = b_t
+ bq_k = look_around(b_t, **look_around_kwargs)
+ bq_t = rearrange(bq_t, '... i -> ... i 1')
+ bq_k = rearrange(bq_k, '... j -> ... 1 j')
+
+ pad_mask = bq_k == pad_value
+ sim = einsum('b h i e, b h j e -> b h i j', bq, bk)
+
+ if exists(attn_bias):
+ heads = attn_bias.shape[0]
+ assert (b % heads) == 0
+
+ attn_bias = repeat(attn_bias, 'h i j -> (b h) 1 i j', b = b // heads)
+ sim = sim + attn_bias
+
+ mask_value = -torch.finfo(sim.dtype).max
+ if shared_qk:
+ self_mask = bq_t == bq_k
+ sim = sim.masked_fill(self_mask, -5e4)
+ del self_mask
+
+ sim = sim.masked_fill(pad_mask, mask_value)
+
+ if exists(mask):
+ # Parse custom padding masks over grouped sliding segments
+ batch = mask.shape[0]
+ assert (b % batch) == 0
+
+ h = b // mask.shape[0]
+ _, mask = pad_to_multiple(mask, window_size, dim = -1, value = False)
+
+ mask = repeat(
+ rearrange(
+ look_around(
+ rearrange(
+ mask,
+ '... (w n) -> (...) w n',
+ w = windows,
+ n = window_size
+ ),
+ **{
+ **look_around_kwargs,
+ 'pad_value': False
+ }
+ ),
+ '... j -> ... 1 j'
+ ),
+ 'b ... -> (b h) ...',
+ h = h
+ )
+
+ sim = sim.masked_fill(~mask, mask_value)
+ del mask
+
+ # Map scores using Softmax activation, apply dropout, and combine values
+ out = rearrange(
+ einsum(
+ 'b h i j, b h j e -> b h i e',
+ self.dropout(sim.softmax(dim = -1)),
+ bv
+ ),
+ 'b w n d -> b (w n) d'
+ )
+
+ # Unpad output states to restore original sequence length parameters
+ out = out[:, :orig_seq_len, :]
+ out, *_ = unpack(out, packed_shape, '* n d')
+
+ return out
+
+class FastAttention(nn.Module):
+ """
+ Fast Attention Module (Performer Implementation).
+
+ Approximates standard kernel attention computations via randomized orthogonal matrix projections
+ to bypass quadratic memory consumption locks.
+ """
+
+ def __init__(
+ self,
+ dim_heads,
+ nb_features=None,
+ ortho_scaling=0,
+ generalized_attention=False,
+ kernel_fn=nn.ReLU(),
+ qr_uniform_q=False
+ ):
+ super().__init__()
+ # Calculate random feature projection dimensions using recommended default heuristics
+ nb_features = default(nb_features, int(dim_heads * math.log(dim_heads)))
+ self.dim_heads = dim_heads
+ self.nb_features = nb_features
+ self.ortho_scaling = ortho_scaling
+ self.create_projection = partial(
+ gaussian_orthogonal_random_matrix,
+ nb_rows=self.nb_features,
+ nb_columns=dim_heads,
+ scaling=ortho_scaling,
+ qr_uniform_q=qr_uniform_q
+ )
+ projection_matrix = self.create_projection()
+ self.register_buffer("projection_matrix", projection_matrix)
+ self.generalized_attention = generalized_attention
+ self.kernel_fn = kernel_fn
+
+ @torch.no_grad()
+ def redraw_projection_matrix(self):
+ """Regenerates a fresh random orthogonal basis to preserve kernel approximation variance quality."""
+
+ projections = self.create_projection()
+ self.projection_matrix.copy_(projections)
+ del projections
+
+ def forward(self, q, k, v):
+ """Forward pass executing generalized linear matrix attention."""
+ q_dtype = q.dtype
+ if v is not None: v = v.float()
+
+ # Map continuous multi-head features into linearized feature representations
+ q, k = softmax_kernel(q.float(), is_query=True, projection_matrix=self.projection_matrix), softmax_kernel(k.float(), is_query=False, projection_matrix=self.projection_matrix)
+ return linear_attention(q, k, v).to(q_dtype)
+
+class SelfAttention(nn.Module):
+ """
+ Hybrid Attention Layer combining Fast (Global) and Local (Sliding Window) strategies.
+
+ Splits processing heads into two paths: a localized attention track for fine-grained spatial dependencies
+ and a linear global Performer attention track for long-range context.
+ """
+
+ def __init__(
+ self,
+ dim,
+ heads=8,
+ dim_head=64,
+ local_heads=0,
+ local_window_size=256,
+ nb_features=None,
+ feature_redraw_interval=1000,
+ generalized_attention=False,
+ kernel_fn=nn.ReLU(),
+ qr_uniform_q=False,
+ dropout=0.0
+ ):
+ super().__init__()
+ assert dim % heads == 0
+ dim_head = default(dim_head, dim // heads)
+ inner_dim = dim_head * heads
+ self.fast_attention = FastAttention(
+ dim_head,
+ nb_features,
+ generalized_attention=generalized_attention,
+ kernel_fn=kernel_fn,
+ qr_uniform_q=qr_uniform_q
+ )
+ self.heads = heads
+ self.global_heads = heads - local_heads
+ # Instantiate local attention routing only if local heads are explicitly allocated
+ self.local_attn = (
+ LocalAttention(
+ window_size=local_window_size,
+ dropout=dropout,
+ look_forward=1,
+ rel_pos_emb_config=(dim_head, local_heads)
+ ) if local_heads > 0 else None
+ )
+ self.to_q = nn.Linear(dim, inner_dim)
+ self.to_k = nn.Linear(dim, inner_dim)
+ self.to_v = nn.Linear(dim, inner_dim)
+ self.to_out = nn.Linear(inner_dim, dim)
+ self.dropout = nn.Dropout(dropout)
+
+ @torch.no_grad()
+ def redraw_projection_matrix(self):
+ """Triggers random feature resampling across the underlying fast global estimator module."""
+
+ self.fast_attention.redraw_projection_matrix()
+
+ def forward(self, x, context=None, mask=None, context_mask=None, name=None, inference=False, **kwargs):
+ """Executes hybrid combined context attention passes."""
+
+ _, _, _, h, gh = *x.shape, self.heads, self.global_heads
+ cross_attend = exists(context)
+ context = default(context, x)
+ context_mask = default(context_mask, mask) if not cross_attend else context_mask
+
+ # Linearly project and split values into multi-head tensor representations
+ q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (self.to_q(x), self.to_k(context), self.to_v(context)))
+ # Segment heads between global linear processing streams and local sliding streams
+ (q, lq), (k, lk), (v, lv) = map(lambda t: (t[:, :gh], t[:, gh:]), (q, k, v))
+
+ attn_outs = []
+ # 1. Process Global Attention Track
+ if not empty(q):
+ if exists(context_mask): v.masked_fill_(~context_mask[:, None, :, None], 0.0)
+ if cross_attend: pass
+ else: out = self.fast_attention(q, k, v)
+
+ attn_outs.append(out)
+
+ # 2. Process Local Window Attention Track
+ if not empty(lq):
+ assert not cross_attend
+
+ out = self.local_attn(lq, lk, lv, input_mask=mask)
+ attn_outs.append(out)
+
+ # Recombine outputs from both paths and project back to original model dimension
+ return self.dropout(self.to_out(rearrange(torch.cat(attn_outs, dim=1), "b h n d -> b n (h d)")))
\ No newline at end of file
diff --git a/main/library/predictors/FCPE/encoder.py b/main/library/predictors/FCPE/encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfa1b4dc6707f125fd4fb4618961cc7aa5d179f0
--- /dev/null
+++ b/main/library/predictors/FCPE/encoder.py
@@ -0,0 +1,269 @@
+import os
+import sys
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+sys.path.append(os.getcwd())
+
+from main.library.predictors.FCPE.attentions import SelfAttention
+from main.library.predictors.FCPE.utils import calc_same_padding, Transpose, GLU, Swish
+
+class ConformerConvModule_LEGACY(nn.Module):
+ """
+ Legacy Convolution Module for the Conformer architecture block.
+
+ Processes sequential features using LayerNorm, point-wise 1D convolutions,
+ Gated Linear Units (GLU), and explicit manual depth-wise padding/convolutions.
+ """
+
+ def __init__(
+ self,
+ dim,
+ causal=False,
+ expansion_factor=2,
+ kernel_size=31,
+ dropout=0.0
+ ):
+ """
+ Initializes the legacy Conformer convolutional structure block.
+
+ Args:
+ dim (int): Input channel or feature size dimension.
+ causal (bool, optional): If True, applies causal asymmetric padding. Defaults to False.
+ expansion_factor (int, optional): Bottleneck channel expansion scale. Defaults to 2.
+ kernel_size (int, optional): Width of 1D conv filters. Defaults to 31.
+ dropout (float, optional): Conv block dropout probability. Defaults to 0.0.
+ """
+
+ super().__init__()
+ inner_dim = dim * expansion_factor
+ # Sequentially map, gate, filter, and project feature frames
+ self.net = nn.Sequential(
+ nn.LayerNorm(dim),
+ Transpose((1, 2)),
+ nn.Conv1d(dim, inner_dim * 2, 1), # Point-wise expansion
+ GLU(dim=1), # Gated Linear Unit split along channel dimension
+ DepthWiseConv1d_LEGACY(
+ inner_dim,
+ inner_dim,
+ kernel_size=kernel_size,
+ padding=(
+ calc_same_padding(kernel_size) if not causal else (kernel_size - 1, 0)
+ )
+ ),
+ Swish(),
+ nn.Conv1d(inner_dim, dim, 1), # Point-wise projection back to model dim
+ Transpose((1, 2)),
+ nn.Dropout(dropout)
+ )
+
+ def forward(self, x):
+ """Forward pass through the legacy Conformer convolution module."""
+
+ return self.net(x)
+
+class ConformerConvModule(nn.Module):
+ """
+ Modernized Convolution Module for the Conformer block architecture.
+
+ Utilizes standardized PyTorch components (`nn.GLU`, `nn.SiLU`) and native 1D
+ same-padding depth-wise operations to optimize runtime efficiency.
+ """
+
+ def __init__(
+ self,
+ dim,
+ expansion_factor=2,
+ kernel_size=31,
+ dropout=0
+ ):
+ """Initializes the modernized Conformer convolutional block."""
+
+ super().__init__()
+ inner_dim = dim * expansion_factor
+ self.net = nn.Sequential(
+ nn.LayerNorm(dim),
+ Transpose((1, 2)),
+ nn.Conv1d(dim, inner_dim * 2, 1),
+ nn.GLU(dim=1), # Standardized PyTorch GLU activation
+ DepthWiseConv1d(
+ inner_dim,
+ inner_dim,
+ kernel_size=kernel_size,
+ padding=calc_same_padding(kernel_size)[0],
+ groups=inner_dim # Forces exact depth-wise channel segregation
+ ),
+ nn.SiLU(), # Equivalent to Swish activation block
+ nn.Conv1d(inner_dim, dim, 1),
+ Transpose((1, 2)),
+ nn.Dropout(dropout)
+ )
+
+ def forward(self, x):
+ """Forward pass through the standard Conformer convolution module."""
+
+ return self.net(x)
+
+class DepthWiseConv1d_LEGACY(nn.Module):
+ """Legacy Depth-wise 1D Convolution wrapper with explicit functional padding."""
+
+ def __init__(
+ self,
+ chan_in,
+ chan_out,
+ kernel_size,
+ padding
+ ):
+ super().__init__()
+ self.padding = padding
+ # groups=chan_in applies convolution independently per channel layer
+ self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, groups=chan_in)
+
+ def forward(self, x):
+ """Pads and executes depthwise convolutions across sequential inputs."""
+
+ return self.conv(F.pad(x, self.padding))
+
+class DepthWiseConv1d(nn.Module):
+ """Modernized Depth-wise 1D Convolution leveraging internal native padding parameters."""
+
+ def __init__(
+ self,
+ chan_in,
+ chan_out,
+ kernel_size,
+ padding,
+ groups
+ ):
+ super().__init__()
+ self.conv = nn.Conv1d(chan_in, chan_out, kernel_size=kernel_size, padding=padding, groups=groups)
+
+ def forward(self, x):
+ """Executes native convolution pass."""
+
+ return self.conv(x)
+
+class EncoderLayer(nn.Module):
+ """
+ Legacy Conformer Encoder block layer wrapper.
+
+ Applies alternating multi-head self-attention mechanisms alongside structural
+ legacy convolution blocks bounded by residual addition loops.
+ """
+
+ def __init__(
+ self,
+ parent
+ ):
+ """
+ Initializes encoder layer variables using parent properties.
+
+ Args:
+ parent (Any): Parent context module containing structural hyper-parameters.
+ """
+
+ super().__init__()
+ self.conformer = ConformerConvModule_LEGACY(parent.dim_model)
+ self.norm = nn.LayerNorm(parent.dim_model)
+ self.dropout = nn.Dropout(parent.residual_dropout)
+ self.attn = SelfAttention(dim=parent.dim_model, heads=parent.num_heads)
+
+ def forward(self, phone, mask=None):
+ """Applies sequence blocks sequentially wrapped with residual logic lanes.
+
+ Args:
+ phone (torch.Tensor): Extracted voice/phone target vectors.
+ mask (torch.Tensor, optional): Optional frame sequence masks. Defaults to None.
+ """
+
+ # Multi-Head Attention step with residual summation
+ phone = phone + (self.attn(self.norm(phone), mask=mask))
+ # Convolution step with residual summation
+ return phone + (self.conformer(phone))
+
+class ConformerNaiveEncoder(nn.Module):
+ """
+ A Naive stack of Conformer Encoder Layers (CFNEncoderLayer).
+
+ Acts as the primary text or feature contextual transformer within the system architecture.
+ """
+
+ def __init__(
+ self,
+ num_layers,
+ num_heads,
+ dim_model,
+ use_norm = False,
+ conv_only = False,
+ conv_dropout = 0,
+ atten_dropout = 0
+ ):
+ """Initializes the Conformer Naive Encoder Stack."""
+
+ super().__init__()
+ self.num_layers = num_layers
+ self.num_heads = num_heads
+ self.dim_model = dim_model
+ self.use_norm = use_norm
+ self.residual_dropout = 0.1
+ self.attention_dropout = 0.1
+ # Instantiate uniform structural transformer layers
+ self.encoder_layers = nn.ModuleList([
+ CFNEncoderLayer(dim_model, num_heads, use_norm, conv_only, conv_dropout, atten_dropout)
+ for _ in range(num_layers)
+ ])
+
+ def forward(self, x, mask=None):
+ """Sequentially updates latents across all stack layer blocks."""
+
+ for layer in self.encoder_layers:
+ x = layer(x, mask)
+
+ return x
+
+class CFNEncoderLayer(nn.Module):
+ """
+ Modern Naive Conformer Encoder block layer.
+
+ Supports optional self-attention tracking mechanisms (toggled by `conv_only`)
+ and conditional dropout wrapper assignments on top of modern convolution pipelines.
+ """
+
+ def __init__(
+ self,
+ dim_model,
+ num_heads = 8,
+ use_norm = False,
+ conv_only = False,
+ conv_dropout = 0,
+ atten_dropout = 0
+ ):
+ super().__init__()
+ # Conditionally wrap ConformerConvModule with Dropout if specified
+ self.conformer = (
+ nn.Sequential(
+ ConformerConvModule(dim_model),
+ nn.Dropout(conv_dropout)
+ )
+ ) if conv_dropout > 0 else (
+ ConformerConvModule(dim_model)
+ )
+
+ self.norm = nn.LayerNorm(dim_model)
+ self.dropout = nn.Dropout(0.1)
+ # Explicitly skip Self-Attention instantiation if conv_only configuration is True
+ self.attn = SelfAttention(
+ dim=dim_model,
+ heads=num_heads,
+ use_norm=use_norm,
+ dropout=atten_dropout
+ ) if not conv_only else None
+
+ def forward(self, x, mask=None):
+ """Forward pass applying structural attention logic and spatial convolutions."""
+
+ # Optional self-attention layer pass with residual summation
+ if self.attn is not None: x = x + (self.attn(self.norm(x), mask=mask))
+ # Standard convolution block pass with residual summation
+ return x + (self.conformer(x))
\ No newline at end of file
diff --git a/main/library/predictors/FCPE/stft.py b/main/library/predictors/FCPE/stft.py
new file mode 100644
index 0000000000000000000000000000000000000000..7627274f791326719c9c1462b5a997e9a88a84c6
--- /dev/null
+++ b/main/library/predictors/FCPE/stft.py
@@ -0,0 +1,154 @@
+import os
+import sys
+import torch
+
+import torch.nn.functional as F
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config
+from main.library.audio.features import mel
+
+class STFT:
+ """Short-Time Fourier Transform (STFT) and Mel-Spectrogram extraction processing module.
+
+ Supports native PyTorch STFT backends as well as fallback structures for custom acceleration
+ backends (e.g., OpenCL / privateuseone devices).
+ """
+
+ def __init__(
+ self,
+ sr=22050,
+ n_mels=80,
+ n_fft=1024,
+ win_size=1024,
+ hop_length=256,
+ fmin=20,
+ fmax=11025,
+ clip_val=1e-5
+ ):
+ """
+ Initializes the STFT filterbank and target Mel transformation matrix bases.
+
+ Args:
+ sr (int): Sampling rate of input raw waveform audio. Defaults to 22050.
+ n_mels (int): Target number of frequency bands. Defaults to 80.
+ n_fft (int): Size of the FFT window windowing frames. Defaults to 1024.
+ win_size (int): Size of the analytical window. Defaults to 1024.
+ hop_length (int): Number of audio samples between successive frames. Defaults to 256.
+ fmin (int): Minimum audio frequency bound. Defaults to 20.
+ fmax (int): Maximum audio frequency bound. Defaults to 11025.
+ clip_val (float): Lower bound floor constant value before log mapping. Defaults to 1e-5.
+ """
+
+ self.stft = None
+ self.n_fft = n_fft
+ self.clip_val = clip_val
+ self.win_size = win_size
+ self.hop_length = hop_length
+ # Instantiate window function and register directly on targeted runtime hardware device
+ self.hann_window = torch.hann_window(self.win_size).to(config.device)
+ # Route execution pathways conditionally depending on targeted deployment backend environments
+ self.stftt = self._stft_other_backends if config.device.startswith(("ocl", "privateuseone")) else self._stft_torch
+ # Precompute the fixed weight matrix projecting linear frequencies into log Mel spaces
+ self.mel_basis = mel(sr=sr, n_fft=self.n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax, device=config.device)
+
+ def get_mel(self, y, center=False):
+ """
+ Extracts log Mel-Spectrogram features from standard 1D raw waveform signals.
+
+ Args:
+ y (torch.Tensor): Audio waveform matrix, expected shape (B, T).
+ center (bool, optional): Pads sequence edges symmetry frames internally. Defaults to False.
+
+ Returns:
+ torch.Tensor: Log Mel frequency values, shape (B, n_mels, Frames).
+ """
+
+ # Calculate strict framing reflection pad constants for non-centered boundary alignments
+ pad_left = (self.win_size - self.hop_length) // 2
+ pad_right = max((self.win_size - self.hop_length + 1) // 2, self.win_size - y.size(-1) - pad_left)
+
+ # 1. Transform audio into frequency spectrum space using the designated backend
+ # 2. Multiply by the Mel basis matrix, bound under floor tolerances, and compute the natural log
+ return (self.mel_basis @ self.stftt(F.pad(y.unsqueeze(1), (pad_left, pad_right), mode="reflect" if pad_right < y.size(-1) else "constant").squeeze(1), center)).clamp(min=self.clip_val).log()
+
+ def _stft_other_backends(self, pad, center=True):
+ """Fallback wrapper performing STFT analysis using custom hardware backends."""
+
+ if self.stft is None:
+ from main.library.backends.utils import STFT as _STFT
+
+ self.stft = _STFT(
+ filter_length=self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_size,
+ ).to(pad.device)
+
+ return self.stft.transform(pad, center=center, eps=1e-5)
+
+ def _stft_torch(self, pad, center=True):
+ """Executes native optimized PyTorch STFT calculations."""
+
+ spec = torch.stft(
+ pad,
+ n_fft=self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_size,
+ window=self.hann_window,
+ center=center,
+ pad_mode="reflect",
+ normalized=False,
+ onesided=True,
+ return_complex=True
+ )
+
+ # Extract absolute scalar magnitudes and apply spatial numerical clamps
+ return spec.abs().clamp_min_(1e-9)
+
+class Wav2Mel:
+ """Convenience pipeline processor mapping raw audio vectors directly to transposed Mel features."""
+
+ def __init__(
+ self,
+ device=None,
+ dtype=torch.float32
+ ):
+ """
+ Initializes processing properties and instantiates standard 16kHz STFT targets.
+
+ Args:
+ device (str / torch.device, optional): Inference device destination. Defaults to None.
+ dtype (torch.dtype, optional): Precision format parsing inputs. Defaults to torch.float32.
+ """
+
+ self.sample_rate = 16000
+ self.hop_size = 160
+ self.device = device
+ self.dtype = dtype
+ # Instantiate configured instance mapping exact 16kHz audio sample guidelines
+ self.stft = STFT(self.sample_rate, 128, 1024, 1024, self.hop_size, 0, 8000)
+
+ def __call__(self, audio):
+ """
+ Processes raw waveform chunks into aligned frames.
+
+ Args:
+ audio (torch.Tensor): Audio waveform tensor.
+
+ Returns:
+ torch.Tensor: Padded log Mel frame outputs.
+ """
+
+ # Transfer input data variables onto target computational devices and types
+ audio = audio.to(self.dtype).to(self.device)
+ # Extract features and transpose axis format
+ mel = self.stft.get_mel(audio).transpose(1, 2)
+
+ # Calculate expected standard downsampled frame structures
+ n_frames = int(audio.shape[1] // self.hop_size) + 1
+ # Pad boundary edges manually if sequence elements fall short of frame counts
+ mel = (torch.cat((mel, mel[:, -1:, :]), 1) if n_frames > int(mel.shape[1]) else mel)
+
+ # Slice trailing bounds or return formatted tensor matrices uniformly
+ return mel[:, :n_frames, :] if n_frames < int(mel.shape[1]) else mel
\ No newline at end of file
diff --git a/main/library/predictors/FCPE/utils.py b/main/library/predictors/FCPE/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..55c28451356ed1c00ec461f9795449d7eec3962c
--- /dev/null
+++ b/main/library/predictors/FCPE/utils.py
@@ -0,0 +1,173 @@
+import os
+import torch
+
+from torch import nn
+from io import BytesIO
+from Crypto.Cipher import AES
+from Crypto.Util.Padding import unpad
+
+def decrypt_model(configs, input_path):
+ """
+ Decrypts a model checkpoint file encrypted using AES-CBC 256/128 encryption.
+
+ Extracts an initialization vector (IV) from the first 16 bytes of the file,
+ decrypts the remaining payload, and strips the cryptographic padding.
+
+ Args:
+ configs (Dict[str, Any]): Dictionary containing configuration keys,
+ specifically 'binary_path' pointing to the decryption key directory.
+ input_path (str | os.PathLike): File path to the encrypted model binary file.
+
+ Returns:
+ bytes: Raw decrypted binary buffer ready to be loaded by torch.load.
+ """
+
+ # 1. Read the encrypted binary payload from disk
+ with open(input_path, "rb") as f:
+ data = f.read()
+
+ # 2. Load the raw shared symmetric key matrix configuration
+ with open(
+ os.path.join(configs["binary_path"], "decrypt.bin"),
+ "rb"
+ ) as f:
+ key = f.read()
+
+ # 4. Remove standard block padding and return data inside an in-memory stream wrapper
+ return BytesIO(
+ unpad(
+ AES.new(
+ key,
+ AES.MODE_CBC,
+ data[:16]
+ ).decrypt(data[16:]),
+ AES.block_size
+ )
+ ).read()
+
+def calc_same_padding(kernel_size):
+ """Calculates asymmetric padding size parameters required for 1D convolutions.
+
+ Maintains output sequences of matching lengths across standard 'same' configurations.
+
+ Args:
+ kernel_size (int): Size of the 1D convolution filter window.
+
+ Returns:
+ Tuple[int, int]: Explicit (left_padding, right_padding) integers context.
+ """
+
+ pad = kernel_size // 2
+ # Adjust right pad value if the kernel size is an even number
+ return (pad, pad - (kernel_size + 1) % 2)
+
+def torch_interp(x, xp, fp):
+ """
+ Performs 1D piecewise linear interpolation for PyTorch tensors.
+
+ Mimics the behavior of `numpy.interp` natively on the target device context.
+
+ Args:
+ x (torch.Tensor): Coordinates at which to evaluate the interpolated values.
+ xp (torch.Tensor): 1D sequence of float data points defining the x-coordinates.
+ fp (torch.Tensor): 1D sequence of float data points defining the y-coordinates.
+
+ Returns:
+ torch.Tensor: Interpolated scalar values of the same shape as `x`.
+ """
+
+ # 1. Sort the reference baseline vectors to guarantee monotonically increasing coordinates
+ sort_idx = xp.argsort()
+ xp = xp[sort_idx]
+ fp = fp[sort_idx]
+
+ # 2. Search for index positions using bin boundary conditions
+ right_idxs = torch.searchsorted(xp, x).clamp(max=len(xp) - 1)
+ left_idxs = (right_idxs - 1).clamp(min=0)
+ x_left = xp[left_idxs]
+ y_left = fp[left_idxs]
+
+ # 3. Calculate slopes and apply standard linear extrapolation intervals
+ # Prevent divide-by-zero errors on flat duplicate coordinate values
+ interp_vals = y_left + ((x - x_left) * (fp[right_idxs] - y_left) / (xp[right_idxs] - x_left))
+ # 4. Clamp out-of-bounds inputs to boundary endpoints explicitly (constant boundary padding)
+ interp_vals[x < xp[0]] = fp[0]
+ interp_vals[x > xp[-1]] = fp[-1]
+
+ return interp_vals
+
+class DotDict(dict):
+ """
+ Custom dictionary wrapper allowing attribute-style dot access notation.
+
+ Enables accessing standard dictionary fields using `obj.key` syntax while
+ recursively handling nested dictionary nodes.
+ """
+
+ def __getattr__(*args):
+ """Retrieves dictionary attributes, wrapping nested dicts on the fly."""
+
+ val = dict.get(*args)
+ return DotDict(val) if type(val) is dict else val
+
+ # Directly map native internal attribute overrides to dictionary methods
+ __setattr__ = dict.__setitem__
+ __delattr__ = dict.__delitem__
+
+class Swish(nn.Module):
+ """
+ Swish (or SiLU) activation function wrapper module.
+
+ Computes the standard equation element-wise.
+ """
+
+ def forward(self, x):
+ """Applies the Swish activation function to the input tensor."""
+
+ return x * x.sigmoid()
+
+class Transpose(nn.Module):
+ """Utility wrapper for swapping arbitrary dimension pairs in a tensor."""
+
+ def __init__(self, dims):
+ """
+ Initializes the layer configuration.
+
+ Args:
+ dims (Tuple[int, int]): Pair of dimension indices target to transpose.
+ """
+
+ super().__init__()
+ assert len(dims) == 2
+ self.dims = dims
+
+ def forward(self, x):
+ """Transposes the input tensor along the pre-configured axis targets."""
+
+ return x.transpose(*self.dims)
+
+class GLU(nn.Module):
+ """
+ Custom Gated Linear Unit (GLU) implementation block.
+
+ Splits the input tensor along a target axis and applies a Sigmoid gate activation
+ to regulate the output values.
+ """
+
+ def __init__(self, dim):
+ """
+ Initializes the GLU layer.
+
+ Args:
+ dim (int): Axis dimension index along which to split and evaluate features.
+ """
+
+ super().__init__()
+ self.dim = dim
+
+ def forward(self, x):
+ """Executes splitting gating calculations."""
+
+ # Split the feature map evenly into content structures and filter channels
+ out, gate = x.chunk(2, dim=self.dim)
+ return out * gate.sigmoid()
\ No newline at end of file
diff --git a/main/library/predictors/Generator.py b/main/library/predictors/Generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..5daf132328907c68d43da93e85661e9c8a4ccac8
--- /dev/null
+++ b/main/library/predictors/Generator.py
@@ -0,0 +1,1113 @@
+import os
+import re
+import sys
+import math
+import torch
+import parselmouth
+
+import numba as nb
+import numpy as np
+
+from scipy.signal import medfilt
+from librosa import yin, pyin, piptrack
+
+sys.path.append(os.getcwd())
+
+from main.library.utils import circular_write
+from main.library.predictors.CREPE.filter import mean, median
+from main.library.predictors.WORLD.SWIPE import swipe, stonemask
+from main.app.variables import config, configs, logger, translations
+
+NOTE = [
+ 49.00, # G1
+ 51.91, # G#1 / Ab1
+ 55.00, # A1
+ 58.27, # A#1 / Bb1
+ 61.74, # B1
+ 65.41, # C2
+ 69.30, # C#2 / Db2
+ 73.42, # D2
+ 77.78, # D#2 / Eb2
+ 82.41, # E2
+ 87.31, # F2
+ 92.50, # F#2 / Gb2
+ 98.00, # G2
+ 103.83, # G#2 / Ab2
+ 110.00, # A2
+ 116.54, # A#2 / Bb2
+ 123.47, # B2
+ 130.81, # C3
+ 138.59, # C#3 / Db3
+ 146.83, # D3
+ 155.56, # D#3 / Eb3
+ 164.81, # E3
+ 174.61, # F3
+ 185.00, # F#3 / Gb3
+ 196.00, # G3
+ 207.65, # G#3 / Ab3
+ 220.00, # A3
+ 233.08, # A#3 / Bb3
+ 246.94, # B3
+ 261.63, # C4
+ 277.18, # C#4 / Db4
+ 293.66, # D4
+ 311.13, # D#4 / Eb4
+ 329.63, # E4
+ 349.23, # F4
+ 369.99, # F#4 / Gb4
+ 392.00, # G4
+ 415.30, # G#4 / Ab4
+ 440.00, # A4
+ 466.16, # A#4 / Bb4
+ 493.88, # B4
+ 523.25, # C5
+ 554.37, # C#5 / Db5
+ 587.33, # D5
+ 622.25, # D#5 / Eb5
+ 659.25, # E5
+ 698.46, # F5
+ 739.99, # F#5 / Gb5
+ 783.99, # G5
+ 830.61, # G#5 / Ab5
+ 880.00, # A5
+ 932.33, # A#5 / Bb5
+ 987.77, # B5
+ 1046.50, # C6
+]
+
+def medfilts(x, kernel_size = 3):
+ """
+ Applies a 1D median filter to the input signal.
+ Supports both NumPy arrays and PyTorch tensors (including specialized OpenCL/PrivateUse1 hardware).
+
+ Args:
+ x: The input 1D signal (tensor or array).
+ kernel_size: The size of the window. Must be an odd integer.
+
+ Returns:
+ The filtered signal matching the input type and device.
+
+ Raises:
+ ValueError: If kernel_size is even or x is not 1D.
+ """
+
+ # Validation checks
+ if kernel_size % 2 == 0: raise ValueError("Kernel size must be an odd number.")
+ if x.ndim != 1: raise ValueError("Input signal must be 1-dimensional.")
+
+ if torch.is_tensor(x):
+ # Fallback for specific hardware backends that lack native unfold/median implementations
+ if x.device.type.startswith(("ocl", "privateuseone")):
+ # Convert to CPU numpy, run standard medfilt, convert back to target device
+ return torch.from_numpy(medfilt(x.float().cpu().numpy(), kernel_size)).float().to(x.device)
+
+ # Native PyTorch implementation using tensor sliding window (unfold)
+ # Pad symmetrically on both sides with constant zeros
+ return torch.nn.functional.pad(
+ x.unsqueeze(0).unsqueeze(0),
+ (kernel_size // 2, kernel_size // 2),
+ mode="constant",
+ value=0.0
+ ).squeeze(0).squeeze(0).unfold(0, kernel_size, 1).median(dim=-1).values # Extract sliding windows, find the median along the last dimension, and return the values
+ else:
+ # Standard SciPy median filter for NumPy arrays
+ return medfilt(x, kernel_size)
+
+def autotune_f0(note_dict, f0, f0_autotune_strength):
+ """
+ Snaps the pitch (F0) to the nearest musical notes in the dictionary based on tuning strength.
+
+ Args:
+ note_dict: List or array containing target musical note frequencies.
+ f0: The original fundamental frequency sequence.
+ f0_autotune_strength: Blend factor (0.0 = no change, 1.0 = fully snapped to note).
+
+ Returns:
+ The autotuned pitch array/tensor.
+ """
+
+ if torch.is_tensor(f0):
+ # Convert note dictionary to matching tensor
+ notes = torch.as_tensor(note_dict, dtype=f0.dtype, device=f0.device)
+ # Calculate pairwise distance between each f0 point and all target notes, pick the index of the closest note
+ nearest = notes[torch.cdist(f0[:, None], notes[:, None]).argmin(dim=1)]
+ else:
+ # NumPy counterpart utilizing absolute distance mapping
+ notes = np.asarray(note_dict, dtype=f0.dtype)
+ nearest = notes[np.abs(f0[:, None] - notes).argmin(axis=1)]
+
+ # Linearly interpolate between raw pitch and nearest note frequencies using the strength factor
+ return f0 + (nearest - f0) * f0_autotune_strength
+
+def extract_median_f0(f0):
+ """
+ Calculates the global median frequency of an F0 sequence, excluding unvoiced frames (0 Hz).
+ Unvoiced areas are filled in using linear interpolation before taking the median.
+
+ Args:
+ f0: The fundamental frequency sequence.
+
+ Returns:
+ The median pitch value as a float.
+ """
+
+ # Replace unvoiced 0 frames with NaN to separate them from valid pitch values
+ f0 = np.where(f0 == 0, np.nan, f0)
+
+ return float(
+ np.median(
+ # Linearly interpolate missing NaN boundaries to keep sequence continuous
+ np.interp(
+ np.arange(len(f0)),
+ np.where(~np.isnan(f0))[0], # Target indices containing true numerical values
+ f0[~np.isnan(f0)] # True numerical values
+ )
+ )
+ )
+
+def proposal_f0_up_key(f0, target_f0 = 155.0, limit = 12):
+ """
+ Proposes a semi-tone shift key to align the median of the input F0 to a target pitch.
+
+ Args:
+ f0: Input pitch sequence.
+ target_f0: Desired target baseline frequency (default 155.0 Hz).
+ limit: Max semi-tone ceiling and floor boundaries allowed for shifting.
+
+ Returns:
+ The computed integer value representing the pitch key change.
+ """
+
+ if torch.is_tensor(f0): f0 = f0.cpu().numpy()
+
+ try:
+ # Clamp shift amount to within range [-limit, limit]
+ return max(
+ -limit,
+ min(
+ limit, int(np.round(12 * np.log2(target_f0 / extract_median_f0(f0)))) # Calculate required semi-tone offset: 12 * log2(f2 / f1)
+ )
+ )
+ except ValueError:
+ # Fallback if log calculation encounters math exceptions or all values are unvoiced
+ return 0
+
+@nb.jit(nopython=True)
+def post_process(
+ f0,
+ f0_up_key,
+ manual_x_pad,
+ f0_mel_min,
+ f0_mel_max,
+ manual_f0 = None
+):
+ """
+ Processes F0 arrays by applying key shifts, overrides, and quantizing to standard Mel scales.
+ Optimized via Numba JIT compilation.
+
+ Args:
+ f0: Original continuous pitch sequence array.
+ f0_up_key: Number of semi-tones to shift pitch up/down.
+ manual_x_pad: Padding configuration adjustment timestamp value.
+ f0_mel_min: Mel range boundary floor.
+ f0_mel_max: Mel range boundary ceiling.
+ manual_f0: Optional 2D array of manually specified override pitch frames [[frame_idx, hz], ...].
+
+ Returns:
+ A tuple containing:
+ - np.ndarray: Quantized coarse Mel pitch array mapped between integers [1, 255].
+ - np.ndarray: Adjusted continuous real frequency F0 sequence.
+ """
+
+ # Scale pitch sequence based on semi-tone shift factor
+ f0 *= pow(2, f0_up_key / 12)
+
+ if manual_f0 is not None: # Apply manual manual override boundaries if provided
+ start_frame = int(manual_x_pad * 100)
+ # Interpolate custom values across override window length
+ replace_f0 = np.interp(
+ np.linspace(
+ manual_f0[:, 0][0],
+ manual_f0[:, 0][-1],
+ len(manual_f0[:, 0])
+ ),
+ manual_f0[:, 0],
+ manual_f0[:, 1]
+ )
+
+ end_frame = start_frame + len(replace_f0)
+ # Prevent manual sequence from extending past original array limits
+ if end_frame > len(f0):
+ end_frame = len(f0)
+ replace_f0 = replace_f0[:end_frame - start_frame]
+
+ # Splice manual values over specified target segment window
+ f0[start_frame:end_frame] = replace_f0
+
+ # Convert Hz frequencies into logarithmic Mel scale representation
+ f0_mel = 1127 * np.log(1 + f0 / 700)
+ # Standardize scale distribution mapping range onto token index values between 1 and 255
+ f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
+ f0_mel[f0_mel <= 1] = 1
+ f0_mel[f0_mel > 255] = 255
+
+ return np.rint(f0_mel).astype(np.int32), f0
+
+def realtime_post_process(
+ f0,
+ pitch,
+ pitchf,
+ f0_up_key = 0,
+ f0_min = 50.0,
+ f0_max = 1100.0
+):
+ """
+ Applies pitch shifts and Mel mapping optimized for streaming or real-time pipelines.
+
+ Args:
+ f0: Incoming real-time pitch tensor frame block.
+ pitch: Optional tracking target buffer destination for coarse tokens.
+ pitchf: Optional tracking target buffer destination for continuous floats.
+ f0_up_key: Number of semi-tones to shift pitch.
+ f0_min: Minimum tracking boundary cutoff (Hz).
+ f0_max: Maximum tracking boundary cutoff (Hz).
+
+ Returns:
+ A tuple of tensors containing batched unsqueezed pitch outputs: (pitch, pitchf).
+ """
+
+ # Shift pitch based on semi-tone factor
+ f0 *= 2 ** (f0_up_key / 12)
+
+ # Convert frequency to Mel scale tensor layout
+ f0_mel = 1127.0 * (1.0 + f0 / 700.0).log()
+ # Normalize, range-bound [1, 255] out-of-place or in-place safely using torch clip
+ f0_mel = torch.clip((f0_mel - f0_min) * 254 / (f0_max - f0_min) + 1, 1, 255, out=f0_mel)
+ f0_coarse = torch.round(f0_mel, out=f0_mel).long()
+
+ # If stream pipeline tracking targets are assigned, pipe frame block chunks to circular storage hooks
+ if pitch is not None and pitchf is not None:
+ # Slice padding artifacts away if chunk length is larger than base boundary sizing threshold
+ f0_coarse = f0_coarse[3:-1] if f0_coarse.shape[0] > 4 else f0_coarse
+ f0 = f0[3:-1] if f0.shape[0] > 4 else f0
+
+ circular_write(f0_coarse, pitch)
+ circular_write(f0, pitchf)
+ else:
+ # Fallback allocation targets if no stream buffer contexts were provided
+ pitch = f0_coarse
+ pitchf = f0
+
+ return pitch.unsqueeze(0), pitchf.unsqueeze(0)
+
+class Generator:
+ """Pitch conversion controller managing models, feature estimation routing, and inference hooks."""
+
+ def __init__(
+ self,
+ sample_rate = 16000,
+ hop_length = 160,
+ f0_min = 50,
+ f0_max = 1100,
+ alpha = 0.5,
+ is_half = False,
+ device = "cpu",
+ predictor_onnx = False,
+ return_tensor = False
+ ):
+ """Initializes configuration properties, caching structures, and model prediction backends."""
+
+ self.alpha = alpha
+ self.f0_min = f0_min
+ self.f0_max = f0_max
+ self.hop_length = hop_length
+ self.sample_rate = sample_rate
+ # Pre-calculate constant baseline Mel boundaries
+ self.f0_mel_min = 1127 * math.log(1 + self.f0_min / 700)
+ self.f0_mel_max = 1127 * math.log(1 + self.f0_max / 700)
+
+ self.window = 160
+ self.batch_size = 512
+ self.ref_freqs = NOTE
+ self.frame_period = self.window / self.sample_rate * 1000
+ self.time_step = self.frame_period / 1000
+
+ self.device = device
+ self.is_half = is_half
+ self.providers = config.providers # Expected globally scoped configuration access
+ # Lazily loaded predictors initialized dynamically on-demand during pipeline calls
+ self.pw = None
+ self.fcpe = None
+ self.djcm = None
+ self.penn = None
+ self.pesto = None
+ self.swift = None
+ self.rmvpe = None
+ self.crepe = None
+ self.mangio_penn = None
+ self.mangio_crepe = None
+ # Method cache trackers to optimize parsing lookups
+ self.cache_args = None
+ self.cache_flags = None
+ self.cache_f0_method = None
+
+ self.int8 = config.int8
+ self.return_tensor = return_tensor
+ self.compile_model = config.compile_all
+ self.compile_mode = config.compile_mode
+ self.predictor_onnx = predictor_onnx or config.int8
+ # Dynamic strategy selection based on operational mode configurations
+ self.onnx_mode = '.onnx' if self.predictor_onnx else '.pt'
+ self.resize = configs.get("turn_on_resize_f0_for_all", False)
+ self.int8_mode = "-int8" if config.int8 and self.predictor_onnx else ""
+ self.resize_f0 = self._resize_tensor_f0 if self.return_tensor else self._resize_array_f0
+ self.resize_dtype = torch.float64 if self.device.startswith(("cpu", "cuda")) else torch.float32
+ self.quantile_audio = self._easy_quantile_audio if self.device.startswith(("ocl", "privateuseone")) else self._quantile_audio
+
+ def calculator(
+ self,
+ x_pad,
+ f0_method,
+ x,
+ f0_up_key = 0,
+ p_len = None,
+ filter_radius = 3,
+ f0_autotune = False,
+ f0_autotune_strength = 1,
+ manual_f0 = None,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 255.0
+ ):
+ """
+ Calculates F0 track profiles from standard offline audio vectors.
+
+ Handles model routing, automatic shift recommendations, key modification, and quantization formats.
+ """
+
+ if p_len is None: p_len = x.shape[0] // self.window
+ # Route processing through standard routines or hybrid combinations depending on naming strings
+ compute_fn = self.get_f0_hybrid if "hybrid" in f0_method else self.compute_f0
+
+ # Extract pitch values (enforces odd radius value limits for filters)
+ f0 = compute_fn(
+ f0_method,
+ x,
+ p_len,
+ filter_radius if filter_radius % 2 != 0 else filter_radius + 1
+ )
+
+ # Automatically recommend key signature transformations if requested
+ if proposal_pitch:
+ up_key = proposal_f0_up_key(
+ f0,
+ proposal_pitch_threshold,
+ configs["limit_f0"]
+ )
+
+ logger.debug(translations["proposal_f0"].format(up_key=up_key))
+ f0_up_key += up_key
+
+ # Quantize results to strict discrete musical notes via auto-tuning algorithms
+ if f0_autotune:
+ logger.debug(translations["startautotune"])
+
+ f0 = autotune_f0(
+ self.ref_freqs,
+ f0,
+ f0_autotune_strength
+ )
+
+ return post_process(f0, f0_up_key, x_pad, self.f0_mel_min, self.f0_mel_max, manual_f0)
+
+ def realtime_calculator(
+ self,
+ audio,
+ f0_method,
+ pitch,
+ pitchf,
+ f0_up_key = 0,
+ filter_radius = 3,
+ f0_autotune = False,
+ f0_autotune_strength = 1,
+ proposal_pitch = False,
+ proposal_pitch_threshold = 255.0
+ ):
+ """Calculates and streams F0 frames optimized for low-latency pipelines."""
+
+ p_len = audio.shape[0] // self.window
+
+ f0 = self.compute_f0(
+ f0_method,
+ audio,
+ p_len,
+ filter_radius if filter_radius % 2 != 0 else filter_radius + 1
+ )
+
+ if f0_autotune:
+ f0 = autotune_f0(
+ self.ref_freqs,
+ f0,
+ f0_autotune_strength
+ )
+
+ if proposal_pitch:
+ up_key = proposal_f0_up_key(
+ f0,
+ proposal_pitch_threshold,
+ configs["limit_f0"]
+ )
+
+ f0_up_key += up_key
+
+ return realtime_post_process(f0, pitch, pitchf, f0_up_key, self.f0_min, self.f0_max)
+
+ def _resize_array_f0(self, x, target_len, resize = False):
+ """Resizes standard continuous numpy tracking vectors using linear data interpolation."""
+
+ if torch.is_tensor(x): x = x.cpu().numpy().astype(np.float32)
+ if not resize or len(x) == target_len: return x
+
+ source = np.array(x)
+ source[source < 0.001] = np.nan # Isolate unvoiced parts to avoid interpolation artifacts
+
+ return np.nan_to_num(
+ np.interp(
+ np.arange(0, len(source) * target_len, len(source)) / target_len,
+ np.arange(0, len(source)),
+ source
+ )
+ )
+
+ def _resize_tensor_f0(self, x, target_len, resize = False):
+ """Resizes GPU tracking data directly inside PyTorch memory maps via linear mapping logic."""
+
+ if not torch.is_tensor(x): x = torch.from_numpy(x).to(self.device)
+ if not resize or len(x) == target_len: return x
+
+ source = torch.as_tensor(x, dtype=self.resize_dtype, device=self.device).clone()
+ source[source < 0.001] = torch.nan
+ n = source.shape[0]
+
+ # Calculate exact linear sample positions across targeted frame indices
+ xp = torch.arange(target_len, dtype=self.resize_dtype, device=self.device) * (n / target_len)
+ x0 = xp.floor().long()
+
+ y0 = source[x0]
+ y1 = source[(x0 + 1).clamp(max=n - 1)]
+ w = xp - x0.to(self.resize_dtype) # Distance delta weights
+
+ # Perform manual interpolation calculation step
+ return torch.nan_to_num(
+ torch.where(
+ w == 0,
+ y0,
+ y0 + (y1 - y0) * w
+ )
+ )
+
+ def _quantile_audio(self, audio):
+ """Normalizes audio dynamics by dividing amplitudes by the 99.9th percentile value."""
+
+ x = audio.float()
+ x /= x.abs().quantile(0.999)
+ return x.unsqueeze(dim=0)
+
+ def _easy_quantile_audio(self, audio):
+ """Normalizes audio dynamics by dividing amplitudes by the 99.9th percentile value."""
+
+ x = audio.float()
+ y = x.abs().flatten()
+
+ if y.numel() == 0: raise ValueError("Input tensor must contain at least one element.")
+
+ values = y.sort().values
+ x /= values[min(int(0.999 * (values.numel() - 1)), values.numel() - 1)] + 1e-8
+
+ return x.unsqueeze(dim=0)
+
+ def compute_f0(self, f0_method, x, p_len, filter_radius):
+ """
+ Routes processing flags to designated pitch estimator components.
+
+ Caches string configurations to bypass string processing overhead on subsequent iterations.
+ """
+
+ # Parse and cache string settings upon format transitions
+ if f0_method != self.cache_f0_method:
+ self.cache_f0_method = f0_method
+ self.cache_args = f0_method.split("-")
+ self.cache_flags = set(self.cache_args)
+
+ # Execution Model Routing tree
+ if "pm" in self.cache_flags:
+ f0 = self.get_f0_pm(
+ x,
+ p_len,
+ filter_radius=filter_radius,
+ mode=self.cache_args[1]
+ )
+ elif self.cache_args[0] in {"harvest", "dio"}:
+ f0 = self.get_f0_pyworld(
+ x,
+ p_len,
+ filter_radius,
+ self.cache_args[0],
+ use_stonemask="stonemask" in self.cache_flags
+ )
+ elif "crepe" in self.cache_flags:
+ f0 = (
+ self.get_f0_mangio_crepe(
+ x,
+ p_len,
+ self.cache_args[2]
+ )
+ ) if "mangio" in self.cache_flags else (
+ self.get_f0_crepe(
+ x,
+ p_len,
+ self.cache_args[1],
+ filter_radius=filter_radius
+ )
+ )
+ elif "fcpe" in self.cache_flags:
+ f0 = self.get_f0_fcpe(
+ x,
+ p_len,
+ legacy="legacy" in self.cache_flags and "previous" not in self.cache_flags,
+ previous="previous" in self.cache_flags,
+ filter_radius=filter_radius
+ )
+ elif "rmvpe" in self.cache_flags:
+ f0 = self.get_f0_rmvpe(
+ x,
+ p_len,
+ clipping="clipping" in self.cache_flags,
+ filter_radius=filter_radius,
+ hpa="hpa" in self.cache_flags,
+ previous="previous" in self.cache_flags,
+ mix="mix" in self.cache_flags,
+ v4="v4" in self.cache_flags
+ )
+ elif self.cache_args[0] in {"yin", "pyin", "piptrack"}:
+ f0 = self.get_f0_librosa(
+ x,
+ p_len,
+ mode=self.cache_args[0],
+ filter_radius=filter_radius
+ )
+ elif "swipe" in self.cache_flags:
+ f0 = self.get_f0_swipe(
+ x,
+ p_len,
+ filter_radius=filter_radius,
+ use_stonemask="stonemask" in self.cache_flags
+ )
+ elif "penn" in self.cache_flags:
+ f0 = (
+ self.get_f0_mangio_penn(
+ x,
+ p_len
+ )
+ ) if self.cache_args[0] == "mangio" else (
+ self.get_f0_penn(
+ x,
+ p_len,
+ filter_radius=filter_radius
+ )
+ )
+ elif "djcm" in self.cache_flags:
+ f0 = self.get_f0_djcm(
+ x,
+ p_len,
+ clipping="clipping" in self.cache_flags,
+ svs="svs" in self.cache_flags,
+ filter_radius=filter_radius
+ )
+ elif "pesto" in self.cache_flags:
+ f0 = self.get_f0_pesto(
+ x,
+ p_len
+ )
+ elif "swift" in self.cache_flags:
+ f0 = self.get_f0_swift(
+ x,
+ p_len,
+ filter_radius=filter_radius
+ )
+ else:
+ raise ValueError(translations["option_not_valid"])
+
+ if isinstance(f0, tuple): f0 = f0[0]
+ if "medfilt" in self.cache_flags or "svs" in self.cache_flags: f0 = medfilts(f0, kernel_size=5)
+
+ return f0
+
+ def get_f0_hybrid(self, methods_str, x, p_len, filter_radius):
+ """Combines multiple pitch estimation methods by computing a weighted geometric mean of their results."""
+
+ logger.debug(translations["hybrid_calc"].format(f0_method=methods_str))
+ # Parse inside bracket strings: hybrid[rmvpe+fcpe] -> ['rmvpe', 'fcpe']
+ methods_str = re.search(r"hybrid\[(.+)\]", methods_str)
+ if methods_str: methods = [method.strip() for method in methods_str.group(1).split("+")]
+
+ n = len(methods)
+ f0_stack = []
+
+ for method in methods: # Gather estimation tracks from all designated sub-algorithms
+ f0_stack.append(
+ self.resize_f0(
+ self.compute_f0(
+ method,
+ x,
+ p_len,
+ filter_radius
+ ),
+ p_len,
+ True
+ )
+ )
+
+ f0_mix = np.zeros(p_len)
+
+ if not f0_stack: return f0_mix
+ if len(f0_stack) == 1: return f0_stack[0]
+
+ # Calculate geometric weight allocations influenced by interpolation parameters (alpha)
+ weights = (1 - np.abs(np.arange(n) / (n - 1) - (1 - self.alpha))) ** 2
+ weights /= weights.sum()
+
+ stacked = np.vstack(f0_stack)
+ voiced_mask = np.any(stacked > 0, axis=0) # Process active tracking regions only
+
+ # Compute the weighted geometric mean in log-space to ensure smooth transitions
+ f0_mix[voiced_mask] = np.exp(
+ np.nansum(
+ np.log(stacked + 1e-6) * weights[:, None], axis=0
+ )[voiced_mask]
+ )
+
+ return f0_mix
+
+ def get_f0_pm(self, x, p_len, filter_radius=3, mode="ac"):
+ """Extracts pitch tracks utilizing Praat Parselmouth estimators (AC, CC, or SHS algorithms)."""
+
+ if torch.is_tensor(x): x = x.cpu().numpy()
+
+ pm = parselmouth.Sound(x, self.sample_rate)
+ # Map functional calls to corresponding target backends
+ pm_fn = {
+ "ac": pm.to_pitch_ac,
+ "cc": pm.to_pitch_cc,
+ "shs": pm.to_pitch_shs
+ }.get(mode, pm.to_pitch_ac)
+
+ # Execute estimation with specific target property arguments
+ pitch = (
+ pm_fn(
+ time_step=self.time_step,
+ voicing_threshold=filter_radius / 10 * 2,
+ pitch_floor=self.f0_min,
+ pitch_ceiling=self.f0_max
+ )
+ ) if mode != "shs" else (
+ pm_fn(
+ time_step=self.time_step,
+ minimum_pitch=self.f0_min,
+ maximum_frequency_component=self.f0_max
+ )
+ )
+
+ f0 = pitch.selected_array["frequency"]
+ # Center-pad or truncate arrays to maintain alignment matching the expected sequence length
+ pad_size = (p_len - len(f0) + 1) // 2
+
+ if pad_size > 0 or p_len - len(f0) - pad_size > 0: f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
+ return self.resize_f0(f0, p_len, self.resize)
+
+ def get_f0_mangio_crepe(self, x, p_len, model="full"):
+ """Runs inference via Mangio-CREPE neural network architectures."""
+
+ if self.mangio_crepe is None:
+ from main.library.predictors.CREPE.CREPE import CREPE
+
+ self.mangio_crepe = CREPE(
+ os.path.join(
+ configs["predictors_path"],
+ f"crepe_{model}{self.int8_mode}{self.onnx_mode}" + ("" if self.predictor_onnx else "h")
+ ),
+ model_size=model,
+ hop_length=self.hop_length,
+ batch_size=self.hop_length * 2,
+ f0_min=self.f0_min,
+ f0_max=self.f0_max,
+ device=self.device,
+ sample_rate=self.sample_rate,
+ providers=self.providers,
+ onnx=self.predictor_onnx,
+ is_half=False, # Don't use Is Half; it's terrible.
+ return_periodicity=False,
+ compile_model=self.compile_model,
+ compile_mode=self.compile_mode
+ )
+
+ if not torch.is_tensor(x): x = torch.from_numpy(x).to(device=self.device)
+ audio = self.quantile_audio(x)
+
+ # Average multi-channel audio tracks down to 1D mono signals
+ if audio.ndim == 2 and audio.shape[0] > 1: audio = audio.mean(dim=0, keepdim=True).detach()
+ f0 = self.mangio_crepe.compute_f0(audio.detach(), pad=True).squeeze(0)
+
+ return self.resize_f0(f0, p_len, True)
+
+ def get_f0_crepe(self, x, p_len, model="full", filter_radius=3):
+ """Runs estimation via standard CREPE model distributions incorporating confidence masks."""
+
+ if self.crepe is None:
+ from main.library.predictors.CREPE.CREPE import CREPE
+
+ self.crepe = CREPE(
+ os.path.join(
+ configs["predictors_path"],
+ f"crepe_{model}{self.int8_mode}{self.onnx_mode}" + ("" if self.predictor_onnx else "h")
+ ),
+ model_size=model,
+ hop_length=self.window,
+ batch_size=self.batch_size,
+ f0_min=self.f0_min,
+ f0_max=self.f0_max,
+ device=self.device,
+ sample_rate=self.sample_rate,
+ providers=self.providers,
+ onnx=self.predictor_onnx,
+ is_half=False, # Don't use Is Half; it's terrible.
+ return_periodicity=True,
+ compile_model=self.compile_model,
+ compile_mode=self.compile_mode
+ )
+
+ if not torch.is_tensor(x): x = torch.from_numpy(x).to(device=self.device)
+
+ f0, pd = self.crepe.compute_f0(x.float().unsqueeze(0), pad=True)
+ # Smooth pitch tracking curves using mean and median filter runs
+ f0, pd = mean(f0, filter_radius), median(pd, filter_radius)
+ # Clear noise artifacts by zeroing out tracking frames with low periodicity confidence
+ f0[pd < 0.1] = 0
+
+ return self.resize_f0(f0[0], p_len, self.resize)
+
+ def get_f0_fcpe(self, x, p_len, legacy=False, previous=False, filter_radius=3):
+ """Extracts tracking pitch via Fast Context-aware Pitch Estimation (FCPE) pipelines."""
+
+ if self.fcpe is None:
+ from main.library.predictors.FCPE.FCPE import FCPE
+
+ self.fcpe = FCPE(
+ os.path.join(
+ configs["predictors_path"],
+ ( # Pick target model checkpoint names based on version options
+ "fcpe_legacy"
+ if legacy else
+ ("fcpe" if previous else "ddsp_200k")
+ ) + self.int8_mode + self.onnx_mode
+ ),
+ device=self.device,
+ threshold=(filter_radius / 100) if legacy else (filter_radius / 1000 * 2),
+ providers=self.providers,
+ onnx=self.predictor_onnx,
+ legacy=legacy,
+ is_half=self.is_half,
+ compile_model=self.compile_model,
+ compile_mode=self.compile_mode
+ )
+
+ f0 = self.fcpe.compute_f0(x)
+ return self.resize_f0(f0, p_len, self.resize)
+
+ def get_f0_rmvpe(self, x, p_len, clipping=False, filter_radius=3, hpa=False, previous=False, mix=False, v4=False):
+ """Extracts pitch sequences via Robust Minimum Variance Pitch Estimation (RMVPE) networks."""
+
+ if self.rmvpe is None:
+ from main.library.predictors.RMVPE.RMVPE import RMVPE
+
+ self.rmvpe = RMVPE(
+ os.path.join(
+ configs["predictors_path"],
+ ( # Multi-tier selection logic determining model checkpoint targeting names
+ (
+ "hpa-v4" if v4 else (
+ "hpa-rmvpe-76000"
+ if previous else
+ "hpa-rmvpe-112000"
+ )
+ ) if hpa else (
+ "rmvpe-mix"
+ if mix else
+ "rmvpe"
+ )
+ ) + self.int8_mode + self.onnx_mode
+ ),
+ is_half=self.is_half,
+ device=self.device,
+ onnx=self.predictor_onnx,
+ providers=self.providers,
+ hpa=hpa,
+ compile_model=self.compile_model,
+ compile_mode=self.compile_mode,
+ enable_chunk=not self.return_tensor and (configs.get("enable_rmvpe_chunk", False) or self.device.startswith(("privateuseone", "ocl"))),
+ chunk_size=8000 if hpa else configs.get("rmvpe_chunk_size", 32000),
+ return_tensor=self.return_tensor,
+ f0_min=self.f0_min,
+ f0_max=self.f0_max
+ )
+ # Route method call hooks to custom alternative routines if clipping flags are set
+ if clipping: self.rmvpe.infer_from_audio = self.rmvpe.infer_from_audio_with_pitch
+
+ f0 = self.rmvpe.infer_from_audio(x, thred=filter_radius / 100)
+ return self.resize_f0(f0, p_len, self.resize)
+
+ def get_f0_pyworld(self, x, p_len, filter_radius, model="harvest", use_stonemask=True):
+ """Extracts pitch using PyWorld vocoder analysis frameworks (Harvest or Dio models)."""
+
+ if self.pw is None:
+ from main.library.predictors.WORLD.WORLD import PYWORLD
+
+ self.pw = PYWORLD(
+ os.path.join(configs["predictors_path"], "world"),
+ os.path.join(configs["binary_path"], "world.bin"),
+ harvest=model == "harvest"
+ )
+
+ if torch.is_tensor(x): x = x.cpu().numpy()
+ x = x.astype(np.double)
+
+ f0, t = self.pw.infer(
+ x,
+ fs=self.sample_rate,
+ f0_ceil=self.f0_max,
+ f0_floor=self.f0_min,
+ frame_period=self.frame_period
+ )
+
+ if use_stonemask: # Refine coarse pitch estimations using StoneMask refinements
+ f0 = self.pw.stonemask(
+ x,
+ self.sample_rate,
+ t,
+ f0
+ )
+
+ # Apply noise-spike filters to harvest tracks, or precision rounding rules on dio tracks
+ if filter_radius > 2 and model == "harvest": f0 = medfilts(f0, filter_radius)
+ elif model == "dio":
+ for index, pitch in enumerate(f0):
+ f0[index] = round(pitch, 1)
+
+ return self.resize_f0(f0, p_len, self.resize)
+
+ def get_f0_swipe(self, x, p_len, filter_radius=3, use_stonemask=True):
+ """Extracts F0 estimates utilizing SWIPE algorithms."""
+
+ if torch.is_tensor(x): x = x.cpu().numpy()
+
+ f0, t = swipe(
+ x.astype(np.float32),
+ self.sample_rate,
+ f0_floor=self.f0_min,
+ f0_ceil=self.f0_max,
+ frame_period=self.frame_period,
+ sTHR=filter_radius / 10
+ )
+
+ if use_stonemask:
+ f0 = stonemask(
+ x,
+ self.sample_rate,
+ t,
+ f0
+ )
+
+ return self.resize_f0(f0, p_len, self.resize)
+
+ def get_f0_librosa(self, x, p_len, mode="yin", filter_radius=3):
+ """Extracts pitch using classical DSP analysis routines provided via Librosa (YIN, pYIN, or PipTrack)."""
+
+ if torch.is_tensor(x): x = x.cpu().numpy()
+
+ if mode != "piptrack":
+ if_yin = mode == "yin"
+ yin_fn = yin if if_yin else pyin
+
+ f0 = yin_fn(
+ x.astype(np.float32),
+ sr=self.sample_rate,
+ fmin=self.f0_min,
+ fmax=self.f0_max,
+ hop_length=self.hop_length
+ )
+
+ # Extracted structures from pyin contain lists of alternative items; extract index zero tracking values
+ if not if_yin: f0 = f0[0]
+ else:
+ pitches, magnitudes = piptrack(
+ y=x.astype(np.float32),
+ sr=self.sample_rate,
+ fmin=self.f0_min,
+ fmax=self.f0_max,
+ hop_length=self.hop_length,
+ threshold=filter_radius / 10
+ )
+ # Find candidate bins with the highest energy response across frames
+ f0 = pitches[np.argmax(magnitudes, axis=0), range(magnitudes.shape[1])]
+
+ return self.resize_f0(f0, p_len, True)
+
+ def get_f0_penn(self, x, p_len, filter_radius=3):
+ """Runs pitch inference with standard PENN models."""
+
+ if self.penn is None:
+ from main.library.predictors.PENN.PENN import PENN
+
+ self.penn = PENN(
+ os.path.join(
+ configs["predictors_path"],
+ f"fcn{self.int8_mode}{self.onnx_mode}"
+ ),
+ hop_length=self.window // 2,
+ batch_size=self.batch_size,
+ f0_min=self.f0_min,
+ f0_max=self.f0_max,
+ sample_rate=self.sample_rate,
+ device=self.device,
+ providers=self.providers,
+ onnx=self.predictor_onnx,
+ is_half=self.is_half,
+ compile_model=self.compile_model,
+ compile_mode=self.compile_mode
+ )
+
+ if not torch.is_tensor(x): x = torch.from_numpy(x).to(device=self.device)
+
+ f0, pd = self.penn.compute_f0(x.float().unsqueeze(0))
+ f0, pd = mean(f0, filter_radius), median(pd, filter_radius)
+ f0[pd < 0.1] = 0 # Zero out frames under confidence thresholds
+
+ f0 = medfilts(f0[0], kernel_size=5)
+ return self.resize_f0(f0, p_len, False)
+
+ def get_f0_mangio_penn(self, x, p_len):
+ """Runs pitch inference using Mangio-variant optimized configurations of the PENN architecture."""
+
+ if self.mangio_penn is None:
+ from main.library.predictors.PENN.PENN import PENN
+
+ self.mangio_penn = PENN(
+ os.path.join(
+ configs["predictors_path"],
+ f"fcn{self.int8_mode}{self.onnx_mode}"
+ ),
+ hop_length=self.hop_length,
+ batch_size=self.hop_length * 2,
+ f0_min=self.f0_min,
+ f0_max=self.f0_max,
+ sample_rate=self.sample_rate,
+ device=self.device,
+ providers=self.providers,
+ onnx=self.predictor_onnx,
+ is_half=self.is_half,
+ interp_unvoiced_at=0.1,
+ compile_model=self.compile_model,
+ compile_mode=self.compile_mode
+ )
+
+ if not torch.is_tensor(x): x = torch.from_numpy(x).to(device=self.device)
+ audio = self.quantile_audio(x)
+
+ if audio.ndim == 2 and audio.shape[0] > 1: audio = audio.mean(dim=0, keepdim=True).detach()
+ f0 = self.mangio_penn.compute_f0(audio.detach()).squeeze(0)
+ f0 = medfilts(f0, kernel_size=5)
+
+ return self.resize_f0(f0, p_len, True)
+
+ def get_f0_djcm(self, x, p_len, clipping=False, svs=False, filter_radius=3):
+ """Extracts tracking sequences using Deep Joint Creak and Melody (DJCM) models."""
+
+ if self.djcm is None:
+ from main.library.predictors.DJCM.DJCM import DJCM
+
+ self.djcm = DJCM(
+ os.path.join(
+ configs["predictors_path"],
+ (
+ "djcm-svs"
+ if svs else
+ "djcm"
+ ) + self.int8_mode + self.onnx_mode
+ ),
+ is_half=self.is_half,
+ device=self.device,
+ onnx=self.predictor_onnx,
+ svs=svs,
+ providers=self.providers,
+ compile_model=self.compile_model,
+ compile_mode=self.compile_mode,
+ return_tensor=self.return_tensor,
+ f0_min=self.f0_min,
+ f0_max=self.f0_max
+ )
+ if clipping: self.djcm.infer_from_audio = self.djcm.infer_from_audio_with_pitch
+
+ f0 = self.djcm.infer_from_audio(x, thred=filter_radius / 10)
+ return self.resize_f0(f0, p_len, self.resize)
+
+ def get_f0_swift(self, x, p_len, filter_radius=3):
+ """Runs pitch estimation with high-efficiency SWIFT ONNX runtimes."""
+
+ if self.swift is None:
+ from main.library.predictors.SWIFT.SWIFT import SWIFT
+
+ self.swift = SWIFT(
+ os.path.join(
+ configs["predictors_path"],
+ "swift-int8.onnx" if self.int8_mode else "swift.onnx"
+ ),
+ fmin=self.f0_min,
+ fmax=self.f0_max,
+ confidence_threshold=filter_radius / 4 + 0.137,
+ providers=self.providers
+ )
+
+ if torch.is_tensor(x): x = x.cpu().numpy()
+
+ pitch_hz, _, _ = self.swift.detect_from_array(x)
+ return self.resize_f0(pitch_hz, p_len, True)
+
+ def get_f0_pesto(self, x, p_len):
+ """Infers pitch using PESTO neural estimators."""
+
+ if self.pesto is None:
+ from main.library.predictors.PESTO.PESTO import PESTO
+
+ self.pesto = PESTO(
+ os.path.join(
+ configs["predictors_path"],
+ f"pesto{self.int8_mode}{self.onnx_mode}"
+ ),
+ step_size=self.frame_period,
+ reduction="alwa",
+ sample_rate=self.sample_rate,
+ device=self.device,
+ providers=self.providers,
+ onnx=self.predictor_onnx,
+ is_half=self.is_half,
+ compile_model=self.compile_model,
+ compile_mode=self.compile_mode,
+ chunk_size=(30 * self.sample_rate) if not self.return_tensor else None
+ )
+
+ if not torch.is_tensor(x): x = torch.from_numpy(x).to(device=self.device)
+ audio = self.quantile_audio(x)
+
+ if audio.ndim == 2 and audio.shape[0] > 1: audio = audio.mean(dim=0, keepdim=True).detach()
+ f0, _ = self.pesto.compute_f0(audio.detach())
+
+ return self.resize_f0(f0.squeeze(0), p_len, self.resize)
\ No newline at end of file
diff --git a/main/library/predictors/PENN/PENN.py b/main/library/predictors/PENN/PENN.py
new file mode 100644
index 0000000000000000000000000000000000000000..abd4b64d71b542e60b261e6b60e36b1545136588
--- /dev/null
+++ b/main/library/predictors/PENN/PENN.py
@@ -0,0 +1,267 @@
+import os
+import sys
+import torch
+import torchaudio
+
+import numpy as np
+import torch.nn.functional as F
+
+sys.path.append(os.getcwd())
+
+from main.library.algorithm.viterbi import viterbi
+from main.library.predictors.PENN.core import bins_to_cents, cents_to_frequency
+from main.library.predictors.PENN.core import PITCH_BINS, CENTS_PER_BIN, frequency_to_bins, entropy, interpolate
+
+SAMPLE_RATE, WINDOW_SIZE = 16000, 1024
+
+class Viterbi:
+ """
+ Viterbi Decoder Module for hidden Markov state pitch sequence tracking.
+
+ Smooths sequential pitch contours using a Gaussian transition penalty matrix
+ and estimates localized cent frequencies via windowed centroid interpolation.
+ """
+
+ def __init__(
+ self,
+ pitch_bins=1440,
+ hop_length=80,
+ sample_rate=16000,
+ local_pitch_window_size=19,
+ max_octaves_per_second=32,
+ cents_per_bin=5
+ ):
+ """
+ Initializes Viterbi decoder parameters.
+
+ Args:
+ pitch_bins (int): Discretized frequency tracking bin count. Defaults to 1440.
+ hop_length (int): Distance increment separating audio frames. Defaults to 80.
+ sample_rate (int): Frequency sample tracking configuration. Defaults to 16000.
+ local_pitch_window_size (int): Local window width for fine cent adjustments. Defaults to 19.
+ max_octaves_per_second (float): Tracking velocity constraint bound. Defaults to 32.0.
+ cents_per_bin (int): Cent logarithmic width per discrete index. Defaults to 5.
+ """
+
+ self.transition = None
+ self.pitch_bins = pitch_bins
+ self.hop_length = hop_length
+ self.sample_rate = sample_rate
+ self.cents_per_bin = cents_per_bin
+ self.window_size = local_pitch_window_size
+ self.max_octaves_per_second = max_octaves_per_second
+
+ def __call__(self, logits):
+ """
+ Decodes logit tensors into absolute continuous frequencies in Hertz.
+
+ Args:
+ logits (torch.Tensor): Output model prediction matrix.
+
+ Returns:
+ torch.Tensor: Tracked pitch tensor.
+ """
+
+ # Lazily instantiate the Gaussian probability transition matrix based on runtime constraints
+ if self.transition is None:
+ idx = torch.arange(self.pitch_bins, device=logits.device, dtype=logits.dtype)
+ # Formulate tracking standard deviations based on time steps and bin resolution
+ # Compute Gaussian log probability transitions: exp(-0.5 * (diff / sigma)^2)
+ transition = (-0.5 * ((idx[:, None] - idx[None, :]).abs() / ((self.max_octaves_per_second * 1200 * (self.hop_length / self.sample_rate)) / self.cents_per_bin)) ** 2).exp()
+ self.transition = transition / transition.sum(dim=1, keepdim=True)
+
+ # 1. Transform raw logit fields into normalized probabilities across frame paths
+ distributions = F.softmax(logits, dim=1)
+ # 2. Extract global optimum state tracks via the classic Viterbi dynamic programming block
+ bins = viterbi(distributions, self.transition)
+
+ # 3. Apply a localized center-of-mass expected value function to retrieve high-resolution pitches
+ pitch = self.local_expected_value_from_bins(bins.T, logits)
+ return pitch
+
+ def local_expected_value_from_bins(self, bins, logits):
+ """
+ Calculates fine-grained frequency estimations via center-of-mass cent pooling.
+
+ Restricts calculations to a localized window centered around decoded discrete peak bins.
+ """
+
+ batch, pitch_bins, frames = logits.shape
+ logits_flat = logits.reshape(-1, pitch_bins)
+ bins_flat = bins.reshape(-1)
+
+ # 1. Construct a local evaluation index map bounded around structural centroids
+ half_win = self.window_size // 2
+ steps = torch.arange(-half_win, half_win + 1, device=bins.device)
+ indices = (bins_flat[:, None] + steps[None, :]).clamp(0, pitch_bins - 1)
+
+ # 2. Gather regional logits and normalize them locally into a probability distribution
+ # 3. Weight localized cent representations to get expected values, then map back to Hertz
+ return cents_to_frequency((F.softmax(logits_flat.gather(1, indices), dim=1) * bins_to_cents(indices)).sum(dim=1, keepdim=True)).reshape(batch, frames)
+
+class PENN:
+ """
+ Pitch Estimating Neural Network (PENN) Wrapper class.
+
+ Handles audio pre-processing, chunk framing, inference routing
+ (supporting PyTorch, compiled torch, and zero-copy ONNX formats),
+ and decoding backends.
+ """
+
+ def __init__(
+ self,
+ model_path,
+ hop_length = 80,
+ batch_size = None,
+ f0_min = 31,
+ f0_max = 1984,
+ sample_rate = 8000,
+ interp_unvoiced_at = None,
+ device = None,
+ providers = None,
+ onnx = False,
+ is_half = False,
+ compile_model = False,
+ compile_mode = None
+ ):
+ """
+ Initializes and builds the complete neural pitch tracker execution module.
+
+ Args:
+ model_path (str): File path location pointing to model weight binaries or ONNX files.
+ hop_length (int): Step distance between adjacent frames. Defaults to 80.
+ batch_size (int, optional): Parallel evaluation batch split. Defaults to 384.
+ f0_min (float): Floor limit tracking threshold parameter in Hertz. Defaults to 31.0.
+ f0_max (float): Ceiling limit tracking threshold parameter in Hertz. Defaults to 1984.
+ sample_rate (int): Raw source sample rate property configuration. Defaults to 8000.
+ interp_unvoiced_at (float, optional): Threshold for interpolating over unvoiced frames. Defaults to None.
+ device (str / torch.device, optional): Destination hardware context targets. Defaults to None.
+ providers (List, optional): Backends configuring ONNX execution layers. Defaults to None.
+ onnx (bool): Toggles ONNX runtime acceleration structures. Defaults to False.
+ is_half (bool): Forces FP16 half-precision execution loops. Defaults to False.
+ compile_model (bool): Enables torch.compile graph optimizations. Defaults to False.
+ compile_mode (str, optional): Optimization profiles matching torch compile. Defaults to None.
+ """
+
+ self.device = device
+ self.hop_length = hop_length
+ self.batch_size = batch_size or 384
+ # Compute bound margins in bin space indices
+ self.f0_min_bin = frequency_to_bins(torch.tensor(f0_min))
+ self.f0_max_bin = frequency_to_bins(torch.tensor(f0_max), torch.ceil)
+ self.sample_rate = sample_rate
+ self.interp_unvoiced_at = interp_unvoiced_at
+ # Instantiate tracking decoders
+ self.decoder = Viterbi(
+ PITCH_BINS,
+ hop_length,
+ SAMPLE_RATE,
+ 19,
+ 32,
+ CENTS_PER_BIN
+ )
+
+ # Precompute resampling pipelines to convert non-standard inputs down to 8kHz vectors
+ self.resample = torchaudio.transforms.Resample(orig_freq=self.sample_rate, new_freq=8000, dtype=torch.float32).to(device) if self.sample_rate != 8000 else None
+
+ if onnx:
+ import onnxruntime as ort
+
+ sess_options = ort.SessionOptions()
+ sess_options.log_severity_level = 3
+ model = ort.InferenceSession(model_path, sess_options=sess_options, providers=providers)
+ else:
+ from main.library.predictors.PENN.fcn import FCN
+
+ model = FCN(256, PITCH_BINS, (2, 2))
+ model.load_state_dict(torch.load(model_path, map_location="cpu", weights_only=True)['model'])
+ model.to(device).eval()
+ if is_half: model = model.half()
+ if compile_model: model = torch.compile(model, mode=compile_mode)
+
+ self.model = model
+ # Choose specific device configuration mappings
+ self._device = "cuda" if providers[0][0].startswith("CUDA") else "cpu"
+ # Select target inference function routes based on deployment settings
+ self.infer = (self._infer_onnx_io if providers[0][0].startswith(("CUDA", "CPU")) else self._infer_onnx_non_io) if onnx else (self._infer_torch_fp16 if is_half else self._infer_torch_fp32)
+
+ def preprocess(self, audio):
+ """Applies resampling, context padding, and matrix unfolding to generate overlapping audio frames."""
+
+ if self.resample is not None: audio = self.resample(audio.float()).to(audio.dtype)
+ # Apply padding symmetrically to ensure frame centers align with the original audio boundaries
+ audio = F.pad(audio.to(self.device), (WINDOW_SIZE // 2, WINDOW_SIZE // 2))
+
+ # Fragment continuous vectors into standalone overlapping contextual matrices
+ frames = audio.unfold(-1, WINDOW_SIZE, self.hop_length).squeeze(0)
+ frames = frames.unsqueeze(1).contiguous()
+
+ return frames
+
+ def postprocess(self, logits):
+ """Filters frequency boundaries and decodes logit fields into pitch and periodicity maps."""
+
+ # Enforce analytical masking penalties over frequency bounds
+ logits[:, :self.f0_min_bin] = -float('inf')
+ logits[:, self.f0_max_bin:] = -float('inf')
+
+ # Extract absolute continuous pitch tracks and pitch stability tracking matrices
+ pitch = self.decoder(logits)
+ periodicity = entropy(logits)
+
+ return pitch.T, periodicity.T
+
+ def compute_f0(self, audio):
+ """Main pipeline orchestration function evaluating fundamental frequencies from raw waveforms."""
+
+ with torch.inference_mode():
+ frames = self.preprocess(audio)
+ # Execute inference chunks in batch steps to prevent out-of-memory errors
+ pitch, periodicity = self.postprocess(torch.cat([self.infer(frames[i:i + self.batch_size]).detach() for i in range(0, frames.shape[0], self.batch_size)], dim=0))
+
+ # Interpolate over unvoiced sections if an interpolation threshold is provided
+ if self.interp_unvoiced_at is not None:
+ pitch = interpolate(pitch, periodicity, self.interp_unvoiced_at)
+ return pitch
+
+ return pitch, periodicity
+
+ def _infer_onnx_non_io(self, frames):
+ """Executes traditional ONNX inference copies onto Host CPU memory blocks."""
+
+ return torch.tensor(
+ self.model.run(
+ ["f0"], {"frames": frames.cpu().numpy()}
+ )[0],
+ device=self.device,
+ dtype=torch.float32
+ )
+
+ def _infer_onnx_io(self, frames):
+ """Executes optimized zero-copy inference leveraging ONNX IO-Binding features."""
+
+ frames = frames.float().contiguous()
+ device_idx = frames.device.index or 0
+
+ io_binding = self.model.io_binding()
+ # Bind the input tensor directly to its memory address on the target device
+ io_binding.bind_input(name="frames", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(frames.shape), buffer_ptr=frames.data_ptr())
+ # Allocate memory for output buffers directly on the target device execution context
+ io_binding.bind_output(name="f0", device_type=self._device, device_id=device_idx)
+ self.model.run_with_iobinding(io_binding)
+
+ return torch.tensor(
+ io_binding.get_outputs()[0].numpy(),
+ device=self.device,
+ dtype=torch.float32
+ )
+
+ def _infer_torch_fp32(self, frames):
+ """Executes standard single-precision FP32 PyTorch forward inference passes."""
+
+ return self.model(frames)
+
+ def _infer_torch_fp16(self, frames):
+ """Executes half-precision FP16 PyTorch inference, upcasting outputs back to FP32."""
+
+ return self.model(frames.half()).float()
\ No newline at end of file
diff --git a/main/library/predictors/PENN/core.py b/main/library/predictors/PENN/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..79cbb51ba52ecb6bea750c69ad0985070fba64b4
--- /dev/null
+++ b/main/library/predictors/PENN/core.py
@@ -0,0 +1,152 @@
+import math
+import torch
+
+import torch.nn.functional as F
+
+PITCH_BINS, CENTS_PER_BIN, OCTAVE = 1440, 5, 1200
+
+def frequency_to_bins(frequency, quantize_fn=torch.floor):
+ """
+ Converts continuous frequencies in Hertz to discrete pitch bin indices.
+
+ Args:
+ frequency (torch.Tensor): Tensor containing frequency values in Hz.
+ quantize_fn (Callable, optional): Math rounding strategy wrapper. Defaults to torch.floor.
+
+ Returns:
+ torch.Tensor: Long tensor containing bounded bin indices.
+ """
+
+ return cents_to_bins(frequency_to_cents(frequency), quantize_fn)
+
+def cents_to_bins(cents, quantize_fn=torch.floor):
+ """
+ Converts logarithmic cent values into discrete index bins bounded within valid ranges.
+
+ Args:
+ cents (torch.Tensor): Tensor containing logarithmic values in cents.
+ quantize_fn (Callable, optional): Rounding function (e.g., floor or ceil). Defaults to torch.floor.
+
+ Returns:
+ torch.Tensor: Clamped tensor array of type torch.long.
+ """
+
+ # 1. Map continuous cents space down into discrete bins
+ bins = quantize_fn(cents / CENTS_PER_BIN).long()
+ # 2. Pin indices strictly inside valid filterbank limits to prevent segmentation faults
+ bins[bins < 0] = 0
+ bins[bins >= PITCH_BINS] = PITCH_BINS - 1
+ return bins
+
+def cents_to_frequency(cents):
+ """
+ Converts pitch values from logarithmic cents back to continuous Hertz.
+
+ Calculated using the base-frequency equation.
+ """
+
+ return 31 * 2 ** (cents / OCTAVE)
+
+def bins_to_cents(bins):
+ """Converts discrete bin indices back into continuous logarithmic cents."""
+
+ return CENTS_PER_BIN * bins
+
+def frequency_to_cents(frequency):
+ """
+ Converts continuous frequency values in Hertz to logarithmic cents.
+
+ Calculated relative to a 31 Hz baseline reference pitch.
+ """
+
+ return OCTAVE * (frequency / 31).log2()
+
+def interpolate(pitch, periodicity, value):
+ """
+ Fills unvoiced gaps by performing linear interpolation over log-frequency fields.
+
+ Forces boundary edges to match the nearest voiced frame to prevent unvoiced leaking.
+
+ Args:
+ pitch (torch.Tensor): Extracted continuous pitch tracks in Hertz.
+ periodicity (torch.Tensor): Confidence or periodicity values matching the pitch frames.
+ value (float): Confidence threshold boundary used to identify voiced segments.
+
+ Returns:
+ torch.Tensor: Interpolated continuous pitch curve array in Hertz.
+ """
+
+ # 1. Create a boolean mask identifying reliable voiced frames
+ voiced = periodicity > value
+ if not voiced.any(): return pitch
+
+ # 2. Linear operations require tracking across flat logarithmic spaces
+ pitch = pitch.log2()
+ # 3. Pin boundary frames to the nearest valid voiced endpoints to ensure stable extrapolation
+ pitch[..., 0] = pitch[voiced][..., 0]
+ pitch[..., -1] = pitch[voiced][..., -1]
+ voiced[..., 0] = True
+ voiced[..., -1] = True
+
+ # 4. Interpolate unvoiced indices using coordinates from valid voiced regions
+ pitch[~voiced] = _interpolate(
+ torch.where(~voiced[0])[0][None],
+ torch.where(voiced[0])[0][None],
+ pitch[voiced][None]
+ )
+
+ # 5. Project the values back into standard linear Hertz space
+ return 2 ** pitch
+
+def _interpolate(x, xp, fp):
+ """Helper function that executes optimized 1D linear matrix interpolation across tensors."""
+
+ if xp.shape[-1] == 0: return x
+
+ if xp.shape[-1] == 1:
+ return torch.full(
+ x.shape,
+ fp.squeeze(),
+ device=fp.device,
+ dtype=fp.dtype
+ )
+
+ # 1. Calculate the slope (m) and intercept (b) segments
+ m = (fp[:, 1:] - fp[:, :-1]) / (xp[:, 1:] - xp[:, :-1])
+ b = fp[:, :-1] - (m.mul(xp[:, :-1]))
+
+ # 2. Find the target segment index bounds using comparative operations
+ indicies = x[:, :, None].ge(xp[:, None, :]).sum(-1) - 1
+ indicies = indicies.clamp(0, m.shape[-1] - 1)
+
+ # 3. Build a sequence line index tracker for safe dynamic assignment
+ line_idx = torch.linspace(
+ 0,
+ indicies.shape[0],
+ 1,
+ device=indicies.device
+ ).to(torch.long).expand(indicies.shape)
+
+ # 4. Evaluate the linear equations simultaneously
+ return m[line_idx, indicies].mul(x) + b[line_idx, indicies]
+
+def entropy(logits):
+ """
+ Calculates normalized Shannon entropy across pitch logit distributions.
+ Can be used as a proxy for pitch tracking uncertainty or unvoiced periodicity.
+
+ Args:
+ logits (torch.Tensor): Unnormalized network predictions.
+
+ Returns:
+ torch.Tensor: Normalized periodicity values bound between [0.0, 1.0].
+ """
+
+ # 1. Map logit values into valid probabilities
+ distribution = F.softmax(logits, dim=1)
+ # 2. Compute classic normalized negative entropy values
+ return (
+ 1 + 1 / math.log(PITCH_BINS) * (
+ distribution * (distribution + 1e-7).log()
+ ).sum(dim=1)
+ )
\ No newline at end of file
diff --git a/main/library/predictors/PENN/fcn.py b/main/library/predictors/PENN/fcn.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef92e8989c2cfffa8fe72a2314d3bf1228775ba1
--- /dev/null
+++ b/main/library/predictors/PENN/fcn.py
@@ -0,0 +1,134 @@
+import torch
+
+class FCN(torch.nn.Sequential):
+ """
+ Fully Convolutional Network (FCN) model tailored for PENN pitch estimation.
+
+ Processes overlapping contextual audio frames through a series of specialized
+ 1D convolution layers to output discrete pitch bin probability logits.
+ """
+
+ def __init__(
+ self,
+ channels = 256,
+ pitch_bins = 1440,
+ pooling = (2, 2)
+ ):
+ """
+ Builds and orchestrates the deep sequential network layers.
+
+ Args:
+ channels (int): Base configuration multiplier for channel width tracking. Defaults to 256.
+ pitch_bins (int): Final output logit classes (total discrete pitch bins). Defaults to 1440.
+ pooling (Tuple[int, int]): Pooling factor configuration passed to feature blocks.
+ """
+
+ super().__init__(*(
+ # Stage 1: Dense multi-channel projection with early structural downsampling
+ Block(
+ 1,
+ channels,
+ 481,
+ pooling
+ ),
+ Block(
+ channels,
+ channels // 8,
+ 225,
+ pooling
+ ),
+ Block(
+ channels // 8,
+ channels // 8,
+ 97,
+ pooling
+ ),
+ # Stage 2: Feature expansion without spatial downsampling
+ Block(
+ channels // 8,
+ channels // 2,
+ 66
+ ),
+ Block(
+ channels // 2,
+ channels,
+ 35
+ ),
+ Block(
+ channels,
+ channels * 2,
+ 4
+ ),
+ # Stage 3: Final linear mapping down to target pitch bin structures
+ torch.nn.Conv1d(
+ channels * 2,
+ pitch_bins,
+ 4
+ )
+ ))
+
+ def forward(self, frames):
+ """Executes the forward network tracking logic.
+
+ Args:
+ frames (torch.Tensor): Audio frames tensor.
+
+ Returns:
+ torch.Tensor: Unnormalized output logits.
+ """
+
+ # Crop explicit boundary margins to clip off unaligned context padding elements
+ # Formulates structural inputs mapping exactly from 1024 down to 993 samples
+ return super().forward(
+ frames[:, :, 16:-15]
+ )
+
+class Block(torch.nn.Sequential):
+ """
+ A core building block for the PENN FCN pitch estimation architecture.
+
+ Consists of a 1D Convolution, a ReLU activation, an optional MaxPool1d layer,
+ and a trailing LayerNorm layer tracking precise sequence dimensions.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ length=1,
+ pooling=None,
+ kernel_size=32
+ ):
+ """
+ Initializes structural block layers.
+
+ Args:
+ in_channels (int): Number of input feature maps or audio channels.
+ out_channels (int): Number of output filters or hidden channels.
+ length (int): Expected temporal length of the feature map for LayerNorm tracking.
+ pooling (Tuple[int, int], optional): MaxPool1d configuration (kernel_size, stride).
+ kernel_size (int): Size of the convolving 1D filter. Defaults to 32.
+ """
+
+ # 1. Instantiate basic convolution projection and its non-linear activation
+ layers = (
+ torch.nn.Conv1d(
+ in_channels,
+ out_channels,
+ kernel_size
+ ),
+ torch.nn.ReLU()
+ )
+
+ # 2. Append downsampling pool layers if specified
+ if pooling is not None:
+ layers += (
+ torch.nn.MaxPool1d(*pooling),
+ )
+
+ # 3. Add spatial LayerNorm tracking exactly across (Channels, Length) dimensions
+ layers += (
+ torch.nn.LayerNorm((out_channels, length)),
+ )
+
+ super().__init__(*layers)
\ No newline at end of file
diff --git a/main/library/predictors/PESTO/PESTO.py b/main/library/predictors/PESTO/PESTO.py
new file mode 100644
index 0000000000000000000000000000000000000000..b80d87ddc95c3d2664fdb9f79a8430c239ae688a
--- /dev/null
+++ b/main/library/predictors/PESTO/PESTO.py
@@ -0,0 +1,166 @@
+import os
+import sys
+import torch
+
+import numpy as np
+
+sys.path.append(os.getcwd())
+
+class PESTO:
+ """
+ PESTO (Pitch Estimation with Self-supervised Transposition-equivariant Objective) Pipeline Wrapper.
+
+ Handles continuous fundamental pitch (F0) estimation across streaming audio
+ tensors. Supports dynamic windowed chunk slicing, raw PyTorch execution graphs,
+ compiled PyTorch configurations, and zero-copy ONNX IO-Binding optimizations.
+ """
+
+ def __init__(
+ self,
+ model_path,
+ step_size=10,
+ reduction="alwa",
+ sample_rate=16000,
+ device=None,
+ providers=None,
+ onnx=False,
+ is_half=False,
+ compile_model=False,
+ compile_mode=None,
+ chunk_size = None
+ ):
+ """
+ Initializes processing properties and compiles the Transposition-equivariant model graph.
+
+ Args:
+ model_path (str | os.PathLike): File path pointing to checkpoint weights or ONNX models.
+ step_size (int): Hop size factor step between sequential frame outputs. Defaults to 10.
+ reduction (str): Network temporal resolution grouping policy. Defaults to "alwa".
+ sample_rate (int): Sampling frequency of targeted raw waveform files. Defaults to 16000.
+ device (str / torch.device, optional): Destination computational device. Defaults to None.
+ providers (List, optional): Execution backends managing ONNX nodes. Defaults to None.
+ onnx (bool): If True, activates ONNX runtime execution paths. Defaults to False.
+ is_half (bool): Forces FP16 precision computation pipelines. Defaults to False.
+ compile_model (bool): Compiles PyTorch modules via torch.compile. Defaults to False.
+ compile_mode (str, optional): Compilation profile parameters. Defaults to None.
+ chunk_size (int, optional): Sample count limit for block slicing. Defaults to None.
+ """
+
+ self.device = device
+ self.step_size = step_size
+ self.sample_rate = sample_rate
+ self.chunk_size = chunk_size
+
+ if onnx:
+ import onnxruntime as ort
+
+ # Configure runtime engine sessions to suppress non-critical logging
+ sess_options = ort.SessionOptions()
+ sess_options.log_severity_level = 3
+ model = ort.InferenceSession(model_path, sess_options=sess_options, providers=providers)
+ else:
+ from main.library.predictors.PESTO.model import PPESTO, Resnet1d
+ from main.library.predictors.PESTO.preprocessor import Preprocessor
+
+ # Load model parameters and state weights via standard PyTorch loaders
+ ckpt = torch.load(model_path, map_location="cpu", weights_only=True)
+ # Dynamically instantiate structural submodules using saved hyperparameters
+ model = PPESTO(
+ Resnet1d(
+ **ckpt["hparams"]["encoder"]
+ ),
+ preprocessor=Preprocessor(
+ hop_size=step_size,
+ sampling_rate=sample_rate,
+ **ckpt["hcqt_params"]
+ ),
+ crop_kwargs=ckpt["hparams"]["pitch_shift"],
+ reduction=reduction or ckpt["hparams"]["reduction"]
+ )
+ model.load_state_dict(ckpt["state_dict"], strict=False)
+ model.to(self.device).eval()
+ if is_half: model = model.half()
+ if compile_model: model = torch.compile(model, mode=compile_mode)
+
+ self.model = model
+ # 1. Establish hardware string values to map execution devices correctly
+ self._device = "cuda" if providers[0][0].startswith("CUDA") else "cpu"
+ # 2. Select the orchestration strategy based on chunk constraints
+ self.compute_f0 = self._compute_f0_chunk if self.chunk_size else self._compute_f0
+ # 3. Route structural execution methods based on backend and datatype configurations
+ self.infer = (self._infer_onnx_io if providers[0][0].startswith(("CUDA", "CPU")) else self._infer_onnx_non_io) if onnx else (self._infer_torch_fp16 if is_half else self._infer_torch_fp32)
+
+ def _compute_f0_chunk(self, x):
+ """Slices incoming waveforms into sequential blocks to manage memory consumption."""
+
+ with torch.inference_mode():
+ assert x.ndim <= 2
+
+ preds, confidence = [], []
+ total_samples = x.shape[-1]
+
+ # Forward the raw data directly if it fits within a single chunk length boundary
+ if total_samples <= self.chunk_size: return self.infer(x)
+
+ # Iteratively process the signal step-by-step using slice windows
+ for i in range(0, total_samples, self.chunk_size):
+ # Maintain consistent tensor dimension structures for both mono and multi-channel audio
+ pred_chunk, conf_chunk = self.infer(x[i : i + self.chunk_size] if x.ndim == 1 else x[:, i : i + self.chunk_size])
+
+ preds.append(pred_chunk)
+ confidence.append(conf_chunk)
+
+ return torch.cat(preds, dim=-1), torch.cat(confidence, dim=-1)
+
+ def _compute_f0(self, x):
+ """Passes full waveform arrays directly into the target execution engine."""
+
+ with torch.inference_mode():
+ assert x.ndim <= 2
+ return self.infer(x)
+
+ def _infer_onnx_non_io(self, x):
+ """Runs fallback ONNX inference by copying raw numpy arrays over the host memory."""
+
+ ouput = self.model.run(
+ ["preds", "conf"], {"chunk": x.cpu().numpy()}
+ )
+
+ return torch.tensor(ouput[0], device=self.device), torch.tensor(ouput[1], device=self.device)
+
+ def _infer_onnx_io(self, x):
+ """Runs accelerated ONNX inference using zero-copy IO-Binding structures."""
+
+ x = x.float().contiguous()
+ device_idx = x.device.index or 0
+
+ io_binding = self.model.io_binding()
+ # Bind the input data matrix pointer address directly onto the device context
+ io_binding.bind_input(name="chunk", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(x.shape), buffer_ptr=x.data_ptr())
+
+ # Pre-allocate corresponding output arrays directly on the destination hardware
+ io_binding.bind_output(name="preds", device_type=self._device, device_id=device_idx)
+ io_binding.bind_output(name="conf", device_type=self._device, device_id=device_idx)
+ self.model.run_with_iobinding(io_binding)
+
+ return torch.tensor(io_binding.get_outputs()[0].numpy(), device=self.device), torch.tensor(io_binding.get_outputs()[1].numpy(), device=self.device)
+
+ def _infer_torch_fp32(self, x):
+ """Executes native single-precision FP32 PyTorch inference evaluations."""
+
+ return self.model(
+ x,
+ sr=self.sample_rate,
+ convert_to_freq=True,
+ return_activations=False
+ )
+
+ def _infer_torch_fp16(self, x):
+ """Executes half-precision FP16 PyTorch inference evaluations."""
+
+ return self.model(
+ x.half(),
+ sr=self.sample_rate,
+ convert_to_freq=True,
+ return_activations=False
+ )
\ No newline at end of file
diff --git a/main/library/predictors/PESTO/model.py b/main/library/predictors/PESTO/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..919caa88e1bd37fcf8dd76c6a7ab05c8b442502c
--- /dev/null
+++ b/main/library/predictors/PESTO/model.py
@@ -0,0 +1,301 @@
+import math
+import torch
+
+from functools import partial
+
+class PPESTO(torch.nn.Module):
+ """
+ Core neural execution block for the Transposition-equivariant PESTO system.
+
+ Processes preprocessed Constant-Q Transform (CQT) feature representations
+ to predict high-fidelity monophonic fundamental frequencies along
+ with accompanying voicing confidence markers.
+ """
+
+ def __init__(
+ self,
+ encoder,
+ preprocessor,
+ crop_kwargs = None,
+ reduction = "alwa"
+ ):
+ """
+ Initializes structural tracking submodules and parameters.
+
+ Args:
+ encoder (torch.nn.Module): Underlying spatial feature extractor network block.
+ preprocessor (torch.nn.Module): Spectral transformer converting audio to CQT maps.
+ crop_kwargs (Dict[str, Any], optional): Parameters passed to the CropCQT layer.
+ reduction (str): Discretization resolution collapse policy ('argmax', 'mean', 'alwa').
+ """
+
+ super(PPESTO, self).__init__()
+ self.encoder = encoder
+ self.preprocessor = preprocessor
+ self.confidence = ConfidenceClassifier()
+ if crop_kwargs is None: crop_kwargs = {}
+ self.crop_cqt = CropCQT(**crop_kwargs)
+ self.reduction = reduction
+ # Register a persistent baseline pitch-shift parameter buffer tracking equivariant translations
+ self.register_buffer('shift', torch.zeros((), dtype=torch.float), persistent=True)
+
+ def forward(self, audio_waveforms, sr = 16000, convert_to_freq = True, return_activations = False):
+ """
+ Executes the complete core pitch tracking pipeline logic.
+
+ Args:
+ audio_waveforms (torch.Tensor): Raw temporal acoustic waves.
+ sr (int): Target incoming playback sample rate. Defaults to 16000.
+ convert_to_freq (bool): Automatically maps internal cents back to Hertz scales if True.
+ return_activations (bool): Returns intermediate activations when True.
+
+ Returns:
+ Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, ...]]:
+ - predicted_pitch: Decoded fundamental sequence values.
+ - confidence_scores: Accompanying spatial voicing indicators bounded [0.0, 1.0].
+ """
+
+ # 1. Evaluate batch properties and transform standard time series inputs to spectrogram grids
+ batch_size = audio_waveforms.size(0) if audio_waveforms.ndim == 2 else None
+ x = self.preprocessor(audio_waveforms, sr=sr).flatten(0, 1)
+
+ # 2. Extract spectral energy metrics via dB-to-linear mapping equations
+ energy = x.mul_(math.log(10) / 10.).exp().squeeze_(1)
+ vol = energy.sum(dim=-1)
+ confidence = self.confidence(energy)
+
+ # 3. Apply operational crops and evaluate encoded deep activations
+ x = self.crop_cqt(x)
+ activations = self.encoder(x)
+
+ # 4. Reshape outputs to preserve batch dimensions if working with a 2D tensor
+ if batch_size is None: confidence.squeeze_(0)
+ else:
+ activations = activations.view(batch_size, -1, activations.size(-1))
+ confidence = confidence.view(batch_size, -1)
+ vol = vol.view(batch_size, -1)
+
+ # 5. Compensate for pitch-shift variations by rolling the activation tensors along the semitone bins
+ activations = activations.roll(-(self.shift * self.preprocessor.hcqt_kwargs["bins_per_semitone"]).round().int().item(), -1)
+ # 6. Fallback fallback paths onto host memory space for unmapped custom hardware structures
+ preds = self.reduce_activations(activations.cpu()).to(activations.device) if activations.device.type.startswith(("ocl", "privateuseone")) else self.reduce_activations(activations)
+ # 7. Convert continuous pitch states back into standard linear frequency targets
+ if convert_to_freq: preds = 440 * 2 ** ((preds - 69) / 12)
+
+ if return_activations: return preds, confidence, vol, activations
+ return preds, confidence
+
+ def reduce_activations(self, activations):
+ """Collapses discrete activation probability maps into absolute continuous cent scales."""
+
+ device = activations.device
+ num_bins = activations.size(-1)
+ if torch.is_tensor(num_bins): num_bins = num_bins.item()
+
+ # Ensure feature vectors remain perfectly divisible by baseline musical grids
+ bps, r = divmod(num_bins, 128)
+ assert r == 0
+
+ if self.reduction == "argmax": return activations.argmax(dim=-1).float() / bps
+
+ all_pitches = torch.arange(num_bins, dtype=torch.float, device=device).div_(bps)
+ if self.reduction == "mean": return activations.matmul(all_pitches)
+
+ # ALWA: 'Argmax Local Weighted Average' window calculation strategy
+ if self.reduction == "alwa":
+ # Formulate local coordinate patches centered dynamically around peak positions
+ indices = (
+ activations.argmax(dim=-1, keepdim=True) +
+ (torch.arange(1, 2 * bps, device=device) - bps)
+ ).clip_(min=0, max=num_bins - 1)
+ # Crop localized activations and calculate the weighted average center-of-mass
+ cropped_activations = activations.gather(-1, indices)
+
+ return (
+ cropped_activations * all_pitches.unsqueeze(0).expand_as(activations).gather(-1, indices)
+ ).sum(dim=-1) / cropped_activations.sum(dim=-1)
+
+ raise ValueError(f"Unsupported reduction type: {self.reduction}")
+
+class ConfidenceClassifier(torch.nn.Module):
+ """Evaluates voicing confidence by combining spectral flux features with geometric mean ratios."""
+
+ def __init__(self):
+ super(ConfidenceClassifier, self).__init__()
+ self.conv = torch.nn.Conv1d(1, 1, 39, stride=3)
+ self.linear = torch.nn.Linear(72, 1)
+
+ def forward(self, x):
+ """Processes energy matrices to generate confidence scores bounded by a sigmoid."""
+
+ # Combine descriptors, evaluate linear maps, and bound outputs to standard probabilities
+ return self.linear(
+ torch.cat(
+ (
+ # Compute local spatial features via standard 1D convolutions
+ torch.nn.functional.relu(
+ self.conv(x.unsqueeze(1)).squeeze(1)
+ ),
+ # Calculate a spectral flatness measure using the geometric-to-arithmetic mean ratio
+ x.log().mean(dim=-1, keepdim=True).exp() / x.mean(dim=-1, keepdim=True).clip_(min=1e-8)
+ ),
+ dim=-1
+ )
+ ).sigmoid().squeeze(-1)
+
+class CropCQT(torch.nn.Module):
+ """Crops explicit boundary margins across Constant-Q Transform bins."""
+
+ def __init__(self, min_steps, max_steps):
+ super(CropCQT, self).__init__()
+ self.min_steps = min_steps
+ self.max_steps = max_steps
+ self.lower_bin = self.max_steps
+
+ def forward(self, spectrograms):
+ """Slices away unwanted edge bins from the input spectrogram matrices."""
+
+ return spectrograms[..., self.max_steps: self.min_steps]
+
+class Resnet1d(torch.nn.Module):
+ """Deep 1D ResNet model architecture configured for PESTO's encoder."""
+
+ def __init__(
+ self,
+ n_chan_input=1,
+ n_chan_layers=(20, 20, 10, 1),
+ n_prefilt_layers=1,
+ prefilt_kernel_size=15,
+ residual=False,
+ n_bins_in=216,
+ output_dim=128,
+ activation_fn = "leaky",
+ a_lrelu=0.3,
+ p_dropout=0.2,
+ **unused
+ ):
+ super(Resnet1d, self).__init__()
+ self.hparams = dict(
+ n_chan_input=n_chan_input,
+ n_chan_layers=n_chan_layers,
+ n_prefilt_layers=n_prefilt_layers,
+ prefilt_kernel_size=prefilt_kernel_size,
+ residual=residual,
+ n_bins_in=n_bins_in,
+ output_dim=output_dim,
+ activation_fn=activation_fn,
+ a_lrelu=a_lrelu,
+ p_dropout=p_dropout
+ )
+
+ # 1. Bind target non-linear activation modules
+ if activation_fn == "relu":
+ activation_layer = torch.nn.ReLU
+ elif activation_fn == "silu":
+ activation_layer = torch.nn.SiLU
+ elif activation_fn == "leaky":
+ activation_layer = partial(torch.nn.LeakyReLU, negative_slope=a_lrelu)
+ else:
+ raise ValueError(f"Unknown activation target: {activation_fn}")
+
+ n_in = n_chan_input
+ n_ch = n_chan_layers
+ if len(n_ch) < 5: n_ch.append(1)
+
+ # 2. Build early normalizers and prefilter convolution layer stacks
+ self.layernorm = torch.nn.LayerNorm(normalized_shape=[n_in, n_bins_in])
+ prefilt_padding = prefilt_kernel_size // 2
+
+ self.conv1 = torch.nn.Sequential(
+ torch.nn.Conv1d(
+ in_channels=n_in,
+ out_channels=n_ch[0],
+ kernel_size=prefilt_kernel_size,
+ padding=prefilt_padding,
+ stride=1
+ ),
+ activation_layer(),
+ torch.nn.Dropout(p=p_dropout)
+ )
+
+ self.n_prefilt_layers = n_prefilt_layers
+ self.prefilt_layers = torch.nn.ModuleList([
+ torch.nn.Sequential(
+ torch.nn.Conv1d(
+ in_channels=n_ch[0],
+ out_channels=n_ch[0],
+ kernel_size=prefilt_kernel_size,
+ padding=prefilt_padding,
+ stride=1
+ ),
+ activation_layer(),
+ torch.nn.Dropout(p=p_dropout)
+ )
+ for _ in range(n_prefilt_layers-1)
+ ])
+
+ # 3. Assemble point-wise intermediate layer blocks
+ self.residual = residual
+ conv_layers = []
+
+ for i in range(len(n_chan_layers)-1):
+ conv_layers.extend([
+ torch.nn.Conv1d(
+ in_channels=n_ch[i],
+ out_channels=n_ch[i + 1],
+ kernel_size=1,
+ padding=0,
+ stride=1
+ ),
+ activation_layer(),
+ torch.nn.Dropout(p=p_dropout)
+ ])
+
+ self.conv_layers = torch.nn.Sequential(*conv_layers)
+ self.flatten = torch.nn.Flatten(start_dim=1)
+ # 4. Use specialized ToeplitzLinear layers to preserve translation equivariance
+ self.fc = ToeplitzLinear(n_bins_in * n_ch[-1], output_dim)
+ self.final_norm = torch.nn.Softmax(dim=-1)
+
+ def forward(self, x):
+ """Runs forward passes through the ResNet encoder."""
+
+ x = self.conv1(self.layernorm(x))
+
+ for p in range(0, self.n_prefilt_layers - 1):
+ prefilt_layer = self.prefilt_layers[p]
+ # Apply residual shortcut additions if configured
+ if self.residual:
+ x = prefilt_layer(x) + x
+ else:
+ x = prefilt_layer(x)
+
+ return self.final_norm(self.fc(self.flatten(self.conv_layers(x))))
+
+class ToeplitzLinear(torch.nn.Conv1d):
+ """
+ Toeplitz Matrix-structured Linear layer variant.
+
+ Implements translation-invariant dense mapping behaviors by routing inputs
+ through a highly structured 1D cross-correlation sequence pass.
+ """
+
+ def __init__(
+ self,
+ in_features,
+ out_features
+ ):
+ """Initializes weight dimensions to support Toeplitz structures via 1D convolutions."""
+
+ super(ToeplitzLinear, self).__init__(
+ in_channels=1,
+ out_channels=1,
+ kernel_size=in_features+out_features - 1,
+ padding=out_features - 1,
+ bias=False
+ )
+
+ def forward(self, input):
+ """Applies convolutional sliding maps to mimic a Toeplitz matrix multiplication."""
+
+ return super(ToeplitzLinear, self).forward(input.unsqueeze(-2)).squeeze(-2)
\ No newline at end of file
diff --git a/main/library/predictors/PESTO/preprocessor.py b/main/library/predictors/PESTO/preprocessor.py
new file mode 100644
index 0000000000000000000000000000000000000000..2aabef8e4e272bb14053094728a3eab58a05b221
--- /dev/null
+++ b/main/library/predictors/PESTO/preprocessor.py
@@ -0,0 +1,419 @@
+import torch
+
+import numpy as np
+
+from scipy.signal import get_window
+
+class Preprocessor(torch.nn.Module):
+ """
+ PESTO feature engineering preprocessor.
+
+ Transforms streaming raw audio waveforms into log-magnitude Harmonic Constant-Q
+ Transform (HCQT) representations for pitch decoding.
+ """
+
+ def __init__(
+ self,
+ hop_size,
+ sampling_rate = None,
+ **hcqt_kwargs
+ ):
+ """
+ Initializes internal processing configurations.
+
+ Args:
+ hop_size (float): Frame step increment in milliseconds.
+ sampling_rate (int, optional): Audio sampling rate frequency. Defaults to None.
+ **hcqt_kwargs: Arbitrary parameters passed down to the HarmonicCQT module.
+ """
+
+ super(Preprocessor, self).__init__()
+ self.hcqt_sr = None
+ self.hcqt_kernels = None
+ self.hop_size = hop_size
+ self.hcqt_kwargs = hcqt_kwargs
+ self.to_log = ToLogMagnitude()
+ # Use a non-persistent reference buffer to infer active accelerator devices safely
+ self.register_buffer("_device", torch.zeros(()), persistent=False)
+ if sampling_rate is not None:
+ self.hcqt_sr = sampling_rate
+ self._reset_hcqt_kernels()
+
+ def forward(self, x, sr = None):
+ """
+ Transforms multi-channel temporal audio waves to structured log-magnitude matrices.
+
+ Args:
+ x (torch.Tensor): Waveform audio.
+ sr (int, optional): Explicit runtime override samplerate. Defaults to None.
+
+ Returns:
+ torch.Tensor: Log-magnitude spectral grid maps.
+ """
+
+ # Re-arrange sequence indices to line up features
+ return self.to_log(self.hcqt(x, sr=sr).permute(0, 3, 1, 2, 4))
+
+ def hcqt(self, audio, sr = None) :
+ """Evaluates raw unscaled complex multi-harmonic transform filterbanks."""
+
+ if sr is not None and sr != self.hcqt_sr:
+ self.hcqt_sr = sr
+ self._reset_hcqt_kernels()
+
+ return self.hcqt_kernels(audio)
+
+ def _reset_hcqt_kernels(self):
+ """Re-compiles the internal HarmonicCQT kernel weights when sample rate changes occur."""
+
+ self.hcqt_kernels = HarmonicCQT(
+ sr=self.hcqt_sr,
+ hop_length=int(self.hop_size * self.hcqt_sr / 1000 + 0.5), # Calculate discrete hop length size samples from continuous millisecond trackers
+ **self.hcqt_kwargs
+ ).to(self._device.device)
+
+class ToLogMagnitude(torch.nn.Module):
+ """Maps continuous complex fields into safe base-10 decibel scale spaces."""
+
+ def __init__(self):
+ super(ToLogMagnitude, self).__init__()
+ self.eps = torch.finfo(torch.float32).eps
+
+ def forward(self, x):
+ """Transforms raw energy values into log magnitude footprints."""
+
+ # 1. Resolve complex multi-component dimensions if present
+ x = (x[..., 0] ** 2 + x[..., 1] ** 2).sqrt() if x.shape[-1] == 2 else x.abs()
+ # 2. Rescale inputs to standard decibels safely: dB = 20 * log10(magnitude)
+ x.clamp_(min=self.eps).log10_().mul_(20)
+
+ return x
+
+class HarmonicCQT(torch.nn.Module):
+ """Orchestrates an array of parallel Constant-Q Transforms tracking multiple harmonics."""
+
+ def __init__(
+ self,
+ harmonics,
+ sr = 22050,
+ hop_length = 512,
+ fmin = 32.7,
+ fmax = None,
+ bins_per_semitone = 1,
+ n_bins = 84,
+ center_bins = True,
+ gamma = 0,
+ center = True,
+ streaming = False,
+ mirror = 0,
+ max_batch_size = 1
+ ):
+ super(HarmonicCQT, self).__init__()
+ # Center-align bin configurations by adjusting the base reference frequency if requested
+ if center_bins: fmin = fmin / 2 ** ((bins_per_semitone - 1) / (24 * bins_per_semitone))
+ # Construct separate parallel scaling filter kernels across every specified integer harmonic fraction
+ self.cqt_kernels = torch.nn.ModuleList([
+ CQT(
+ sr=sr,
+ hop_length=hop_length,
+ fmin=h * fmin,
+ fmax=fmax,
+ n_bins=n_bins,
+ bins_per_octave=12 * bins_per_semitone,
+ gamma=gamma,
+ center=center,
+ streaming=streaming,
+ mirror=mirror,
+ max_batch_size=max_batch_size,
+ output_format="Complex"
+ )
+ for h in harmonics
+ ])
+
+ def forward(self, audio_waveforms):
+ """Evaluates and stacks parallel multi-harmonic transform outputs."""
+
+ return torch.stack([cqt(audio_waveforms) for cqt in self.cqt_kernels], dim=1)
+
+class BaseCQT(torch.nn.Module):
+ """Base mathematical foundational layer for execution of Constant-Q Transforms."""
+
+ def __init__(
+ self,
+ sr=22050,
+ hop_length=512,
+ fmin=32.70,
+ fmax=None,
+ n_bins=84,
+ bins_per_octave=12,
+ gamma=0,
+ filter_scale=1,
+ norm=1,
+ window="hann",
+ center = True,
+ trainable=False,
+ output_format="Magnitude"
+ ):
+ super(BaseCQT, self).__init__()
+ self.trainable = trainable
+ self.n_bins = n_bins
+ self.hop_length = hop_length
+ self.center = center
+ self.output_format = output_format
+ # 1. Synthesize static filter coefficients via NumPy execution blocks
+ cqt_kernels, self.kernel_width, lengths, freqs = self.create_cqt_kernels(
+ # Calculate the bounding quality factor Q parameter constants
+ float(filter_scale) / (2 ** (1 / bins_per_octave) - 1),
+ sr,
+ fmin,
+ n_bins,
+ bins_per_octave,
+ norm, window,
+ fmax,
+ gamma=gamma
+ )
+ # 2. Cache spatial scale factor matrices on the local device graph
+ self.sqrt_lengths = lengths.sqrt_().unsqueeze_(-1)
+ self.frequencies = freqs
+ self.cqt_kernels = torch.from_numpy(cqt_kernels).unsqueeze(1)
+
+ def create_cqt_kernels(self, Q, fs, fmin, n_bins=84, bins_per_octave=12, norm=1, window="hann", fmax=None, topbin_check=True, gamma=0):
+ """Synthesizes time-domain complex tracking kernel arrays based on spacing frequencies."""
+
+ if (fmax != None) and (n_bins == None):
+ n_bins = np.ceil(bins_per_octave * np.log2(fmax / fmin))
+ freqs = fmin * 2.0 ** (np.r_[0:n_bins] / np.double(bins_per_octave))
+ elif (fmax == None) and (n_bins != None): freqs = fmin * 2.0 ** (np.r_[0:n_bins] / np.double(bins_per_octave))
+ else:
+ n_bins = np.ceil(bins_per_octave * np.log2(fmax / fmin))
+ freqs = fmin * 2.0 ** (np.r_[0:n_bins] / np.double(bins_per_octave))
+
+ if np.max(freqs) > fs / 2 and topbin_check == True: raise ValueError("Calculated frequencies exceed Nyquist limits.")
+
+ # Compute varying geometric temporal lengths across discrete tracking filters
+ lengths = np.ceil(Q * fs / (freqs + gamma / (2.0 ** (1.0 / bins_per_octave) - 1.0)))
+ fftLen = int(2 ** (np.ceil(np.log2(int(max(lengths))))))
+ tempKernel = np.zeros((int(n_bins), int(fftLen)), dtype=np.complex64)
+
+ for k in range(0, int(n_bins)):
+ l = lengths[k]
+ start = (int(np.ceil(fftLen / 2.0 - l / 2.0)) - 1) if l % 2 == 1 else int(np.ceil(fftLen / 2.0 - l / 2.0))
+ N = int(l)
+
+ if isinstance(window, str):
+ sig = get_window(window, N, fftbins=True)
+ elif isinstance(window, tuple):
+ if window[0] == "gaussian":
+ assert window[1] >= 0
+ sig = get_window(("gaussian", np.floor(-N / 2 / np.sqrt(-2 * np.log(10 ** (-window[1] / 20))))), N, fftbins=True)
+ else: raise TypeError("Unsupported window data structure type.")
+
+ # Modulate base windows onto complex exponential frequency carriers
+ sig = sig * np.exp(np.r_[-l // 2: l // 2] * 1j * 2 * np.pi * freqs[k] / fs) / l
+
+ if norm:
+ tempKernel[k, start: start + int(l)] = sig / np.linalg.norm(sig, norm)
+ else:
+ tempKernel[k, start: start + int(l)] = sig
+
+ return tempKernel, fftLen, torch.tensor(lengths).float(), freqs
+
+ @torch.no_grad()
+ def init_weights(self):
+ """Converts raw complex arrays to parallel multichannel 1D Convolution parameters."""
+
+ self.conv.weight.copy_(torch.cat((self.cqt_kernels.real, -self.cqt_kernels.imag), dim=0))
+ self.conv.weight.requires_grad = self.trainable
+
+ def forward(self, x, output_format=None, normalization_type="librosa"):
+ """Processes input audio streams through the mapped multi-channel kernel weight filters."""
+
+ output_format = output_format or self.output_format
+ x = self.broadcast_dim(x)
+ cqt = self.conv(x).view(x.size(0), 2, self.n_bins, -1)
+
+ if normalization_type == "librosa": cqt *= self.sqrt_lengths.to(cqt.device)
+ elif normalization_type == "convolutional": pass
+ elif normalization_type == "wrap": cqt *= 2
+ else: raise ValueError(f"Unknown scaling normalization type configuration: {normalization_type}")
+
+ if output_format == "Magnitude": return cqt.pow(2).sum(-3).add(1e-8 if self.trainable else 0).sqrt()
+ if output_format == "Complex": return cqt.permute(0, 2, 3, 1)
+
+ cqt_real, cqt_imag = cqt.split(self.n_bins, dim=-2)
+ if output_format == "Phase": return torch.stack((cqt_imag.atan2(cqt_real).cos(), cqt_imag.atan2(cqt_real).sin()), -1)
+
+ raise ValueError(f"Unsupported target output format schema choice: {output_format}")
+
+ def broadcast_dim(self, x):
+ """Normalizes audio dimensions to maintain consistent 3D sequence arrays."""
+
+ if x.dim() == 2: x = x[:, None, :]
+ elif x.dim() == 1: x = x[None, None, :]
+ elif x.dim() == 3: pass
+ else: raise ValueError(f"Invalid structural shape dimension array limits: {x.dim()}")
+
+ return x
+
+class RegularCQT(BaseCQT):
+ """Standard execution pass of the Constant-Q Transform tracking full static offline boundaries."""
+
+ def __init__(self, *args, pad_mode="reflect", **kwargs):
+ super().__init__(*args, **kwargs)
+ padding = self.kernel_width // 2 if self.center else 0
+ self.conv = torch.nn.Conv1d(
+ 1,
+ 2 * self.n_bins,
+ kernel_size=self.kernel_width,
+ stride=self.hop_length,
+ padding=padding,
+ padding_mode=pad_mode,
+ bias=False
+ )
+ self.init_weights()
+
+class StreamingCQT(BaseCQT):
+ """Online streaming implementation of the CQT layer tracking state caches over frame steps."""
+
+ def __init__(
+ self,
+ *args,
+ mirror = 0,
+ max_batch_size = 1,
+ **kwargs
+ ):
+ super(StreamingCQT, self).__init__(*args, **kwargs)
+ if self.center:
+ mirrored_samples = int(mirror * (self.kernel_width - self.hop_length) / 2)
+ padding = self.kernel_width - self.hop_length - mirrored_samples
+ else:
+ mirrored_samples = 0
+ padding = 0
+
+ self.conv = CachedConv1d(
+ 1,
+ 2 * self.n_bins,
+ kernel_size=self.kernel_width,
+ stride=self.hop_length,
+ padding=padding,
+ mirror=mirrored_samples,
+ max_batch_size=max_batch_size,
+ bias=False
+ )
+
+ self.init_weights()
+
+class CQT:
+ """Factory design router class creating standard offline or continuous online streaming variants."""
+
+ regular_only_kwargs = ["pad_mode"]
+ streaming_only_kwargs = ["mirror", "max_batch_size"]
+
+ def __new__(cls, *args, **kwargs):
+ streaming = kwargs.pop("streaming", False)
+
+ if streaming:
+ for kwarg in cls.regular_only_kwargs:
+ kwargs.pop(kwarg, None)
+
+ return StreamingCQT(*args, **kwargs)
+
+ for kwarg in cls.streaming_only_kwargs:
+ kwargs.pop(kwarg, None)
+
+ return RegularCQT(*args, **kwargs)
+
+class CachedConv1d(torch.nn.Conv1d):
+ """Convolution layer containing a persistent sliding window ring-buffer cache for real-time tracking."""
+
+ def __init__(
+ self,
+ *args,
+ **kwargs
+ ):
+ kwargs["padding"] = 0
+ super(CachedConv1d, self).__init__(*args, **kwargs)
+ padding = kwargs.get("padding", 0)
+ max_batch_size = kwargs.pop("max_batch_size", 1)
+ mirror = kwargs.pop("mirror", 0)
+ mirror_fn = kwargs.pop("mirror_fn", "zeros")
+ cumulative_delay = kwargs.pop("cumulative_delay", 0)
+
+ if isinstance(padding, int): r_pad = padding
+ elif isinstance(padding, list) or isinstance(padding, tuple):
+ r_pad = padding[1]
+ padding = padding[0] + padding[1]
+ else: raise TypeError("Invalid type configuration passed inside spatial padding trackers.")
+
+ s = self.stride[0]
+ cd = cumulative_delay
+
+ self.cumulative_delay = (r_pad + ((s - ((r_pad + cd) % s)) % s) + cd) // s
+ # Instantiate continuous history tracking padding caches
+ self.cache = CachedPadding1d(padding, max_batch_size=max_batch_size)
+
+ if mirror == 0:
+ mirroring_fn = torch.nn.Identity
+ elif mirror_fn == "reflection":
+ mirroring_fn = torch.nn.ReflectionPad1d
+ elif mirror_fn == "zeros":
+ mirroring_fn = torch.nn.ZeroPad1d
+ elif mirror_fn == "refill":
+ mirroring_fn = RefillPad1d
+ else:
+ mirroring_fn = torch.nn.Identity
+
+ self.mirror = mirroring_fn((0, mirror))
+
+ def forward(self, x):
+ """Applies spatial transforms across sequential historical feature blocks."""
+
+ return super(CachedConv1d, self).forward(self.mirror(self.cache(x)))
+
+class RefillPad1d(torch.nn.Module):
+ """Pads tracking segments by duplicating the final sequence sample boundaries on the right."""
+
+ def __init__(
+ self,
+ padding
+ ):
+ super(RefillPad1d, self).__init__()
+ self.right_padding = padding[1]
+
+ def forward(self, x):
+ """Applies boundary duplication across multi-channel tracking audio tensors."""
+
+ return torch.cat((x, x[..., -self.right_padding:]), dim=-1)
+
+class CachedPadding1d(torch.nn.Module):
+ """Manages overlapping boundaries across consecutive blocks during real-time streaming."""
+
+ def __init__(
+ self,
+ padding,
+ max_batch_size = 1,
+ crop=False
+ ):
+ super().__init__()
+ self.padding = padding
+ self.max_batch_size = max_batch_size
+ self.crop = crop
+ self.init_cache()
+
+ @torch.jit.unused
+ @torch.no_grad()
+ def init_cache(self):
+ """Initializes un-tracked baseline zeros mapping arrays inside memory configurations."""
+
+ self.register_buffer("pad", torch.zeros(self.max_batch_size, 1, self.padding), persistent=False)
+
+ def forward(self, x):
+ """Appends history cache buffers onto newly received audio packages."""
+
+ bs = x.size(0)
+ if self.padding:
+ # Prepend historical boundary context and refresh the tracking cache
+ x = torch.cat((self.pad[:bs], x), -1)
+ self.pad[:bs].copy_(x[..., -self.padding:])
+
+ return x
\ No newline at end of file
diff --git a/main/library/predictors/RMVPE/RMVPE.py b/main/library/predictors/RMVPE/RMVPE.py
new file mode 100644
index 0000000000000000000000000000000000000000..9522549f4157aebeb900a1ce32a130b67d6e1370
--- /dev/null
+++ b/main/library/predictors/RMVPE/RMVPE.py
@@ -0,0 +1,259 @@
+import os
+import sys
+import torch
+
+import numpy as np
+import torch.nn.functional as F
+
+sys.path.append(os.getcwd())
+
+from main.library.predictors.RMVPE.mel import MelSpectrogram
+
+N_MELS, N_CLASS = 128, 360
+
+class RMVPE:
+ """
+ Robust Model for Vocal Pitch Estimation in Polyphonic Music (RMVPE).
+
+ This class handles feature extraction (MelSpectrogram), model initialization
+ (PyTorch or ONNX Runtime), inference execution, and posterior decoding
+ into frequency cents for robust fundamental frequency (F0) estimation.
+ """
+
+ def __init__(
+ self,
+ model_path,
+ is_half,
+ device=None,
+ providers=None,
+ onnx=False,
+ hpa=False,
+ compile_model=False,
+ compile_mode=None,
+ enable_chunk = False,
+ chunk_size = 8000,
+ return_tensor = False,
+ f0_min = 50,
+ f0_max = 1100
+ ):
+ """
+ Initializes the RMVPE predictor instance.
+
+ Args:
+ model_path (str): File path to the weights or ONNX model checkpoint.
+ is_half (bool): Whether to use FP16 half precision initialization.
+ device (str or torch.device, optional): Device context execution handler.
+ providers (list, optional): ONNX Execution Providers list configuration.
+ onnx (bool, default=False): Flag to trigger ONNX backend instead of PyTorch.
+ hpa (bool, default=False): Flag to trigger the YOLOv13-based Hypergraph and FullPAD backbone variant.
+ compile_model (bool, default=False): Triggers torch.compile optimization.
+ compile_mode (str, optional): Mode passed directly to torch.compile backend.
+ enable_chunk (bool, default=False): Process long audio segments using chunk sliding.
+ chunk_size (int, default=8000): Target frame window size for chunked inference.
+ return_tensor (bool, default=False): Decodes via GPU/CPU Tensors instead of NumPy.
+ f0_min (float, default=50): Minimum allowable voice boundary.
+ f0_max (float, default=1100): Maximum allowable voice boundary.
+ """
+
+ if onnx:
+ import onnxruntime as ort
+
+ # Configure basic logging level constraints to suppress verbose initialization logs
+ sess_options = ort.SessionOptions()
+ sess_options.log_severity_level = 3
+ model = ort.InferenceSession(model_path, sess_options=sess_options, providers=providers)
+ else:
+ from main.library.predictors.RMVPE.e2e import E2E
+
+ # Setup the End-to-End network architecture and load checkpoint weights
+ model = E2E(4, 1, (2, 2), 5, 4, 1, 16, hpa=hpa)
+ model.load_state_dict(torch.load(model_path, map_location="cpu", weights_only=True))
+ model.to(device).eval()
+
+ # Apply runtime optimization transformations (Precision / Compilation)
+ if is_half: model = model.half()
+ if compile_model: model = torch.compile(model, mode=compile_mode)
+
+ # Set foundational class object configurations
+ self.model = model
+ self.f0_min = f0_min
+ self.f0_max = f0_max
+ self.device = device
+ self.chunk_size = chunk_size
+ self.dtype = torch.float16 if is_half else torch.float32
+
+ # Cents mapping vector definition representing localized pitch classes + center padding adjustments
+ self.cents_mapping = np.pad(20 * np.arange(N_CLASS) + 1997.3794084376191, (4, 4))
+ # Initialize native MelSpectrogram layer module on specified hardware target
+ self.mel_extractor = MelSpectrogram(N_MELS, 16000, 1024, 160, 1024, 30, 8000).to(device)
+ # If tensor tracking is requested, push tracking weights data array to target hardware execution context
+ if return_tensor: self.cents_mapping = torch.as_tensor(self.cents_mapping, dtype=self.dtype, device=device)
+
+ # Dynamically map processing handles and dynamic dispatch interfaces based on parameters
+ self._device = "cuda" if providers[0][0].startswith("CUDA") else "cpu"
+ self.mel2hidden = self._mel2hidden_chunk if enable_chunk else self._mel2hidden
+ self.offsets = torch.arange(-4, 5, device=device) if return_tensor else np.arange(-4, 5)
+ self.to_local_average_cents = self._to_local_average_cents_tensor if return_tensor else self._to_local_average_cents_array
+ # Determine the runtime execution function depending on ONNX/PyTorch and Precision flags
+ self.infer = (self._infer_onnx_io if providers[0][0].startswith(("CUDA", "CPU")) else self._infer_onnx_non_io) if onnx else (self._infer_torch_fp16 if is_half else self._infer_torch_fp32)
+
+ def decode(self, hidden, thred=0.03):
+ """
+ Converts model activation salience states into frequencies (Hz).
+
+ Args:
+ hidden (torch.Tensor): Raw matrix output from inference.
+ thred (float): Salience masking threshold. Default is 0.03.
+
+ Returns:
+ torch.Tensor or numpy.ndarray: Estimated F0 pitch matrix.
+ """
+
+ # Convert cents array estimates out to regular frequency values
+ f0 = 10 * (2 ** (self.to_local_average_cents(hidden, thred=thred) / 1200))
+ # Mask out trivial floor configurations representing silence or invalid bounds
+ f0[f0 == 10] = 0
+ return f0
+
+ def infer_from_audio(self, audio, thred=0.03):
+ """
+ Extracts F0 values directly from raw audio waveforms.
+
+ Args:
+ audio (numpy.ndarray or torch.Tensor): Input audio signal buffer.
+ thred (float, default=0.03): Threshold boundary for activation suppression.
+
+ Returns:
+ torch.Tensor or numpy.ndarray: Predicted fundamental frequency array.
+ """
+
+ # Convert audio buffer to Torch tensor if it is provided as a NumPy array
+ audio = torch.from_numpy(audio).float().to(self.device) if not torch.is_tensor(audio) else audio
+ hidden = self.mel2hidden(self.mel_extractor(audio.unsqueeze(0), center=True)).squeeze(0)
+
+ return self.decode(hidden, thred=thred)
+
+ def infer_from_audio_with_pitch(self, audio, thred=0.03):
+ """
+ Extracts F0 values directly from audio waveforms with post-filtered frequency clipping.
+
+ Args:
+ audio (numpy.ndarray or torch.Tensor): Input audio signal buffer.
+ thred (float, default=0.03): Threshold boundary for activation suppression.
+
+ Returns:
+ torch.Tensor or numpy.ndarray: Post-filtered fundamental frequency array.
+ """
+
+ # Call standard inference mapping sequence
+ f0 = self.infer_from_audio(audio, thred)
+ # Zero out values that fall outside the explicitly requested minimum and maximum F0 range
+ f0[(f0 < self.f0_min) | (f0 > self.f0_max)] = 0
+
+ return f0
+
+ def _mel2hidden(self, mel):
+ """Processes complete mel spectrogram through the inference pipeline with reflection padding."""
+
+ with torch.inference_mode():
+ n_frames = mel.shape[-1]
+ # Ensure the frame length is an exact multiple of 32 via reflection padding configuration
+ # Slice and discard extra padding tail values out after model inference execution finishes
+ return self.infer(F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"))[:, :n_frames]
+
+ def _mel2hidden_chunk(self, mel):
+ """Processes long mel spectrogram configurations using chunk-by-chunk slicing strategy."""
+
+ with torch.inference_mode():
+ n_frames = mel.shape[-1]
+ # Ensure the frame length matches block padding alignment multiples
+ mel = F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect")
+ # Slice out input buffers into sequential blocks, run inference, and concatenate outcomes
+ hidden = torch.cat([self.infer(mel[..., start:min(start + self.chunk_size, mel.shape[-1])]) for start in range(0, mel.shape[-1], self.chunk_size)], dim=1)
+
+ # Strip padding frames before returning internal hidden state representations
+ return hidden[:, :n_frames]
+
+ def _infer_torch_fp32(self, mel):
+ """Runs PyTorch inference in standard FP32 floating point mode."""
+
+ return self.model(mel)
+
+ def _infer_torch_fp16(self, mel):
+ """Runs PyTorch inference casting target input structures to FP16 half precision."""
+
+ return self.model(mel.half())
+
+ def _infer_onnx_non_io(self, mel):
+ """Executes ONNX Runtime inference using standard CPU/GPU memory copy operations."""
+
+ # Convert PyTorch Tensor input layout to native NumPy structures required by standard ONNX bindings
+ mel = mel.cpu().numpy().astype(np.float32)
+ # Run model session execution mapping arrays back onto standard Torch Tensor objects
+ return torch.as_tensor(
+ self.model.run(
+ ["f0"], {"mel": mel}
+ )[0],
+ device=self.device,
+ dtype=self.dtype
+ )
+
+ def _infer_onnx_io(self, mel):
+ """Executes optimized ONNX Runtime inference using low-overhead I/O binding allocations."""
+ mel = mel.float().contiguous()
+ device_idx = mel.device.index or 0
+
+ # Create I/O binding instance context to pin memory references directly onto hardware device
+ io_binding = self.model.io_binding()
+ # Bind input layer pointers bypassing intermediate CPU-GPU memory context copies
+ io_binding.bind_input(name="mel", device_type=self._device, device_id=device_idx, element_type=np.float32, shape=tuple(mel.shape), buffer_ptr=mel.data_ptr())
+
+ # Bind output structure memory allocations directly
+ io_binding.bind_output(name="f0", device_type=self._device, device_id=device_idx)
+ # Run optimized graph inference execution
+ self.model.run_with_iobinding(io_binding)
+
+ # Wrap unmanaged internal memory pointer buffer objects into standard PyTorch abstractions
+ return torch.as_tensor(
+ io_binding.get_outputs()[0].numpy(),
+ device=self.device,
+ dtype=self.dtype
+ )
+
+ def _to_local_average_cents_array(self, salience, thred=0.05):
+ """Decodes pitch tracking salience into smooth cents via NumPy arrays."""
+
+ salience = salience.cpu().numpy().astype(np.float32)
+
+ # Extract argmax activation positions
+ center = np.argmax(salience, axis=1)
+ salience = np.pad(salience, ((0, 0), (4, 4)))
+ center += 4 # Offset position map adjustment due to padding shift
+
+ # Slice out a neighborhood window of 9 elements around the argmax peak position
+ idx = center[:, None] + self.offsets[None, :]
+ local_salience = salience[np.arange(salience.shape[0])[:, None], idx]
+
+ # Calculate localized center of mass (weighted average) to achieve sub-bin frequency interpolation
+ devided = np.sum(local_salience * self.cents_mapping[idx], axis=1) / np.sum(local_salience, axis=1)
+ # Suppress noisy frames falling below confidence threshold constraints
+ devided[np.max(salience, axis=1) <= thred] = 0
+
+ return devided
+
+ def _to_local_average_cents_tensor(self, salience, thred=0.05):
+ """Decodes pitch tracking salience into smooth cents via PyTorch Tensors on GPU/CPU."""
+
+ center = torch.argmax(salience, dim=1)
+ salience = F.pad(salience, (4, 4))
+ center += 4
+
+ # Vectorized gathering operation across tensor indices
+ idx = center[:, None] + self.offsets[None, :]
+ local_salience = salience[torch.arange(salience.shape[0], device=salience.device)[:, None], idx]
+
+ # Parallel math matrix computation for weighted frequency centroid decoding
+ devided = (local_salience * self.cents_mapping[idx]).sum(dim=1) / local_salience.sum(dim=1)
+ devided = torch.where(salience.max(dim=1).values <= thred, torch.zeros_like(devided), devided)
+
+ return devided
\ No newline at end of file
diff --git a/main/library/predictors/RMVPE/deepunet.py b/main/library/predictors/RMVPE/deepunet.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f2ba42efd638a92c372f637c4df6ff081157f70
--- /dev/null
+++ b/main/library/predictors/RMVPE/deepunet.py
@@ -0,0 +1,444 @@
+import os
+import sys
+
+import torch
+
+import torch.nn as nn
+
+sys.path.append(os.getcwd())
+
+from main.library.predictors.RMVPE.yolo import YOLO13Encoder, YOLO13FullPADDecoder, HyperACE
+
+class ConvBlockRes(nn.Module):
+ """
+ Standard 2D Convolutional Block with Residual/Shortcut Connection.
+
+ Applies two sequential blocks of Conv2d-BatchNorm2d-ReLU. If the input channels
+ do not match the output channels, an additional 1x1 2D Convolution is initialized
+ as a shortcut projector to enable residual identity addition.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ momentum=0.01
+ ):
+ """
+ Initializes the ConvBlockRes layer module.
+
+ Args:
+ in_channels (int): Number of channels in the input tensor.
+ out_channels (int): Number of channels produced by the convolution.
+ momentum (float, default=0.01): BatchNorm2d momentum configuration.
+ """
+
+ super(ConvBlockRes, self).__init__()
+ self.conv = nn.Sequential(
+ nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=(3, 3),
+ stride=(1, 1),
+ padding=(1, 1),
+ bias=False
+ ),
+ nn.BatchNorm2d(
+ out_channels,
+ momentum=momentum
+ ),
+ nn.ReLU(),
+ nn.Conv2d(
+ in_channels=out_channels,
+ out_channels=out_channels,
+ kernel_size=(3, 3),
+ stride=(1, 1),
+ padding=(1, 1),
+ bias=False
+ ),
+ nn.BatchNorm2d(
+ out_channels,
+ momentum=momentum
+ ),
+ nn.ReLU()
+ )
+
+ # Dynamic shortcut configuration block
+ self.is_shortcut = False
+ if in_channels != out_channels:
+ self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
+ self.is_shortcut = True
+
+ # Optimize execution path branch by assigning the forward method dynamically
+ self.forward = self.forward_shortcut if self.is_shortcut else self.forward_non_shortcut
+
+ def forward_shortcut(self, x):
+ """Executes convolution with a 1x1 projected skip connection."""
+
+ return self.conv(x) + self.shortcut(x)
+
+ def forward_non_shortcut(self, x):
+ """Executes convolution with a direct identity skip connection."""
+
+ return self.conv(x) + x
+
+class ResEncoderBlock(nn.Module):
+ """Residual Encoder Block with an optional Average Pooling downsampler."""
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ n_blocks=1,
+ momentum=0.01
+ ):
+ """
+ Initializes the ResEncoderBlock module.
+
+ Args:
+ in_channels (int): Number of input channels.
+ out_channels (int): Number of output channels.
+ kernel_size (int or tuple or None): Pooling window size. If None, downsampling is skipped.
+ n_blocks (int, default=1): Number of stacked ConvBlockRes layers.
+ momentum (float, default=0.01): BatchNorm2d momentum.
+ """
+
+ super(ResEncoderBlock, self).__init__()
+ self.n_blocks = n_blocks
+ self.conv = nn.ModuleList()
+ # First block processes the input channel shift channel space transition
+ self.conv.append(
+ ConvBlockRes(
+ in_channels,
+ out_channels,
+ momentum
+ )
+ )
+
+ # Subsequent internal blocks retain identical output channel sizes
+ for _ in range(n_blocks - 1):
+ self.conv.append(
+ ConvBlockRes(
+ out_channels,
+ out_channels,
+ momentum
+ )
+ )
+
+ self.kernel_size = kernel_size
+ if self.kernel_size is not None: self.pool = nn.AvgPool2d(kernel_size=kernel_size)
+
+ def forward(self, x):
+ """Processes sequential convolutions and returns skip-connections and pooled representations."""
+
+ for i in range(self.n_blocks):
+ x = self.conv[i](x)
+
+ if self.kernel_size is not None: return x, self.pool(x) # Returns features for skip connections along with downsampled maps
+ else: return x
+
+class Encoder(nn.Module):
+ """The complete multi-stage Downsampling Encoder pipeline for DeepUnet."""
+
+ def __init__(
+ self,
+ in_channels,
+ in_size,
+ n_encoders,
+ kernel_size,
+ n_blocks,
+ out_channels=16,
+ momentum=0.01
+ ):
+ super(Encoder, self).__init__()
+ self.n_encoders = n_encoders
+ self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
+ self.layers = nn.ModuleList()
+
+ for _ in range(self.n_encoders):
+ self.layers.append(
+ ResEncoderBlock(
+ in_channels,
+ out_channels,
+ kernel_size,
+ n_blocks,
+ momentum=momentum
+ )
+ )
+ # Channel configurations double while resolution scales halve at each stage sequence boundary
+ in_channels = out_channels
+ out_channels *= 2
+ in_size //= 2
+
+ self.out_size = in_size
+ self.out_channel = out_channels
+
+ def forward(self, x):
+ """Performs multi-stage encoding and records spatial features for U-Net skip-connections."""
+
+ concat_tensors = []
+ x = self.bn(x)
+
+ for layer in self.layers:
+ t, x = layer(x)
+ concat_tensors.append(t) # Stashes encoder feature maps for Decoder concatenation bounds
+
+ return x, concat_tensors
+
+class Intermediate(nn.Module):
+ """The bottleneck layer structure connecting Encoder and Decoder chains."""
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ n_inters,
+ n_blocks,
+ momentum=0.01
+ ):
+ super(Intermediate, self).__init__()
+ self.layers = nn.ModuleList()
+ self.layers.append(
+ ResEncoderBlock(
+ in_channels,
+ out_channels,
+ None,
+ n_blocks,
+ momentum
+ )
+ )
+
+ for _ in range(n_inters - 1):
+ self.layers.append(
+ ResEncoderBlock(
+ out_channels,
+ out_channels,
+ None,
+ n_blocks,
+ momentum
+ )
+ )
+
+ def forward(self, x):
+ """Passes features through the sequential non-downsampling bottleneck blocks."""
+
+ for layer in self.layers:
+ x = layer(x)
+
+ return x
+
+class ResDecoderBlock(nn.Module):
+ """Residual Decoder Block executing Transposed Convolutions and feature map concatenation."""
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ stride,
+ n_blocks=1,
+ momentum=0.01
+ ):
+ super(ResDecoderBlock, self).__init__()
+ out_padding = (0, 1) if stride == (1, 2) else (1, 1)
+ # Up-sampling layer path via Transposed Convolutional operations
+ self.conv1 = nn.Sequential(
+ nn.ConvTranspose2d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=(3, 3),
+ stride=stride,
+ padding=(1, 1),
+ output_padding=out_padding,
+ bias=False
+ ),
+ nn.BatchNorm2d(
+ out_channels,
+ momentum=momentum
+ ),
+ nn.ReLU()
+ )
+
+ self.conv2 = nn.ModuleList()
+ # Takes concatenated channels (doubled channel space size) from up-sampled and encoder features
+ self.conv2.append(
+ ConvBlockRes(
+ out_channels * 2,
+ out_channels,
+ momentum
+ )
+ )
+
+ for _ in range(n_blocks - 1):
+ self.conv2.append(
+ ConvBlockRes(
+ out_channels,
+ out_channels,
+ momentum
+ )
+ )
+
+ def forward(self, x, concat_tensor):
+ """Upsamples and concatenates corresponding encoder feature maps before final convolutions."""
+
+ x = torch.cat((self.conv1(x), concat_tensor), dim=1)
+ for conv2 in self.conv2:
+ x = conv2(x)
+
+ return x
+
+class Decoder(nn.Module):
+ """The multi-stage Upsampling Decoder pipeline matching corresponding Encoder blocks."""
+
+ def __init__(
+ self,
+ in_channels,
+ n_decoders,
+ stride,
+ n_blocks,
+ momentum=0.01
+ ):
+ super(Decoder, self).__init__()
+ self.layers = nn.ModuleList()
+
+ for _ in range(n_decoders):
+ out_channels = in_channels // 2
+ self.layers.append(
+ ResDecoderBlock(
+ in_channels,
+ out_channels,
+ stride,
+ n_blocks,
+ momentum
+ )
+ )
+ in_channels = out_channels
+
+ def forward(self, x, concat_tensors):
+ """Iterates through decoding loops processing stored encoder tensors in reversed order."""
+
+ for i, layer in enumerate(self.layers):
+ x = layer(x, concat_tensors[-1 - i])
+
+ return x
+
+class DeepUnet(nn.Module):
+ """Standard Baseline Deep U-Net encoder-decoder network used in standard RMVPE."""
+
+ def __init__(
+ self,
+ kernel_size,
+ n_blocks,
+ en_de_layers=5,
+ inter_layers=4,
+ in_channels=1,
+ en_out_channels=16
+ ):
+ super(DeepUnet, self).__init__()
+ self.encoder = Encoder(
+ in_channels,
+ 128,
+ en_de_layers,
+ kernel_size,
+ n_blocks,
+ en_out_channels
+ )
+ self.intermediate = Intermediate(
+ self.encoder.out_channel // 2,
+ self.encoder.out_channel,
+ inter_layers,
+ n_blocks
+ )
+ self.decoder = Decoder(
+ self.encoder.out_channel,
+ en_de_layers,
+ kernel_size,
+ n_blocks
+ )
+
+ def forward(self, x):
+ """Main core DeepUnet forward flow execution pipeline."""
+
+ x, concat_tensors = self.encoder(x)
+
+ return self.decoder(
+ self.intermediate(x),
+ concat_tensors
+ )
+
+class HPADeepUnet(nn.Module):
+ """
+ YOLOv13-enhanced U-Net architecture integrating Hypergraph and FullPAD paradigms.
+
+ This advanced variant replaces standard convolution layers with a YOLOv13 Backbone
+ Encoder, processes high-order correlations via a Hypergraph-based Adaptive Correlation
+ Enhancement (HyperACE) module, and fuses feature hierarchies using a Full-Pipeline
+ Aggregation-and-Distribution (FullPAD) Decoder scheme.
+ """
+
+ def __init__(
+ self,
+ in_channels=1,
+ en_out_channels=16,
+ base_channels=64,
+ hyperace_k=2,
+ hyperace_l=1,
+ num_hyperedges=16,
+ num_heads=8
+ ):
+ """
+ Initializes the YOLOv13-based HPADeepUnet block.
+
+ Args:
+ in_channels (int, default=1): Dimensionality layout of raw spectrogram tracks.
+ en_out_channels (int, default=16): Final target channel layout leaving the decoder module.
+ base_channels (int, default=64): Multiplier factor channel layer settings for YOLO13 stages.
+ hyperace_k (int, default=2): Parameter scale governing HyperACE cross-location clustering boundaries.
+ hyperace_l (int, default=1): Level bounds configuration parameters for hypergraph convolution steps.
+ num_hyperedges (int, default=16): Total continuous differentiable hyperedges allocation count.
+ num_heads (int, default=8): Head splits for the multi-head hypergraph query sub-spaces.
+ """
+
+ super().__init__()
+ # Step 1: Initialize YOLOv13 Multi-Stage Feature Extraction Backbone Encoder
+ self.encoder = YOLO13Encoder(
+ in_channels,
+ base_channels
+ )
+
+ enc_ch = self.encoder.out_channels
+ # Step 2: Initialize Hypergraph-based Adaptive Correlation Enhancement (HyperACE) Module
+ self.hyperace = HyperACE(
+ in_channels=enc_ch,
+ out_channels=enc_ch[-1],
+ num_hyperedges=num_hyperedges,
+ num_heads=num_heads,
+ k=hyperace_k,
+ l=hyperace_l
+ )
+
+ # Step 3: Initialize Full-Pipeline Aggregation-and-Distribution (FullPAD) Decoder
+ self.decoder = YOLO13FullPADDecoder(
+ encoder_channels=enc_ch,
+ hyperace_out_c=enc_ch[-1],
+ out_channels_final=en_out_channels
+ )
+
+ def forward(self, x):
+ """Executes the complete YOLOv13 Hypergraph + FullPAD feature mapping route."""
+
+ # Extract hierarchical features across multiple multi-scale visual layers
+ features = self.encoder(x)
+
+ # Dynamically scale spatial dimensional sizes back to perfectly match raw audio shape domains
+ return nn.functional.interpolate(
+ # Pass multi-level features into HyperACE to construct global high-order correlations,
+ # then map and project everything down inside the specialized FullPAD Decoder pipeline.
+ self.decoder(
+ features,
+ self.hyperace(features)
+ ),
+ size=x.shape[2:],
+ mode='bilinear',
+ align_corners=False
+ )
\ No newline at end of file
diff --git a/main/library/predictors/RMVPE/e2e.py b/main/library/predictors/RMVPE/e2e.py
new file mode 100644
index 0000000000000000000000000000000000000000..22875fed917f8d5f977b2209d849f31a259605d7
--- /dev/null
+++ b/main/library/predictors/RMVPE/e2e.py
@@ -0,0 +1,158 @@
+import os
+import sys
+import torch
+
+import torch.nn as nn
+
+sys.path.append(os.getcwd())
+
+from main.library.predictors.RMVPE.deepunet import DeepUnet, HPADeepUnet
+
+N_MELS, N_CLASS = 128, 360
+
+class BiGRU(nn.Module):
+ """
+ Bidirectional Gated Recurrent Unit (BiGRU) wrapper network block.
+
+ This module encloses a standard PyTorch GRU configured for bidirectional execution.
+ It includes an embedded error handling fallback mechanism to temporarily disable
+ cuDNN backend execution if it encounters memory layout or driver compatibility errors.
+ """
+
+ def __init__(
+ self,
+ input_features,
+ hidden_features,
+ num_layers
+ ):
+ """
+ Initializes the BiGRU block.
+
+ Args:
+ input_features (int): Dimensionality size of the incoming feature sequence vectors.
+ hidden_features (int): Size of internal hidden state memory dimensions per direction.
+ num_layers (int): Total number of stacked recurrent network layers.
+ """
+
+ super(BiGRU, self).__init__()
+ self.gru = nn.GRU(
+ input_features,
+ hidden_features,
+ num_layers=num_layers,
+ batch_first=True,
+ bidirectional=True
+ )
+
+ def forward(self, x):
+ """
+ Executes sequential data forward pass processing with a cuDNN safety fallback shield.
+
+ Args:
+ x (torch.Tensor): Sequenced input tensor.
+
+ Returns:
+ torch.Tensor: Combined hidden sequence representations from both directions.
+ """
+
+ try:
+ return self.gru(x)[0]
+ except:
+ # Fallback routine: temporarily disable cuDNN to calculate on vanilla backend if an exception occurs
+ # Exceptions typically occur when the input is too large.
+ torch.backends.cudnn.enabled = False
+ return self.gru(x)[0]
+
+class E2E(nn.Module):
+ """
+ End-to-End (E2E) Neural Network Architecture for the RMVPE Pitch Tracker.
+
+ This architecture processes input Mel-spectrogram vectors using a choice of standard
+ DeepUnet or a High-Performance Hypergraph-enhanced (HPA) DeepUnet encoder backbone.
+ The intermediate outputs are refined using 2D CNN mapping and decoded via temporal
+ recurrent blocks (BiGRU) or feedforward linear blocks into multi-class pitch salience vectors.
+ """
+
+ def __init__(
+ self,
+ n_blocks,
+ n_gru,
+ kernel_size,
+ en_de_layers=5,
+ inter_layers=4,
+ in_channels=1,
+ en_out_channels=16,
+ hpa=False
+ ):
+ """Initializes the End-to-End (E2E) feature processing architecture.
+
+ Args:
+ n_blocks (int): Number of internal layer blocks inside standard DeepUnet modules.
+ n_gru (int): Total recurrent layer stack count inside the BiGRU network. If 0, replaces GRUs with Feedforward layers.
+ kernel_size (int or tuple): Transposed convolution kernel window specifications.
+ en_de_layers (int, default=5): Total layer depths of encoder-decoder chains.
+ inter_layers (int, default=4): Total layer counts inside bottleneck layers.
+ in_channels (int, default=1): Number of structural input channels for 2D convolutions.
+ en_out_channels (int, default=16): Number of target feature channels leaving the network encoder block.
+ hpa (bool, default=False): Flag to trigger the YOLOv13-based Hypergraph and FullPAD backbone variant.
+ """
+
+ super(E2E, self).__init__()
+ # Branch dynamic instantiation of target network structural backbone
+ self.unet = (
+ HPADeepUnet(
+ in_channels=in_channels,
+ en_out_channels=en_out_channels,
+ base_channels=64,
+ hyperace_k=2,
+ hyperace_l=1,
+ num_hyperedges=16,
+ num_heads=4
+ )
+ ) if hpa else (
+ DeepUnet(
+ kernel_size,
+ n_blocks,
+ en_de_layers,
+ inter_layers,
+ in_channels,
+ en_out_channels
+ )
+ )
+ # 2D CNN projection to collapse spatial representations into standard mapping boundaries
+ self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
+ # Configure the final classification head based on the requested GRU recurrent setup
+ self.fc = (
+ nn.Sequential(
+ BiGRU(3 * 128, 256, n_gru), # Input features mapped across 3 channels * 128 Mel frequencies
+ nn.Linear(512, N_CLASS), # Map combined bidirectional outputs (256 * 2 = 512) to N_CLASS (360)
+ nn.Dropout(0.25),
+ nn.Sigmoid() # Output normalized independent frame-level probabilities
+ )
+ ) if n_gru else (
+ nn.Sequential(
+ nn.Linear(3 * N_MELS, N_CLASS),
+ nn.Dropout(0.25),
+ nn.Sigmoid()
+ )
+ )
+
+ def forward(self, mel):
+ """Processes input Mel spectrogram features to predict F0 pitch salience profiles.
+
+ Args:
+ mel (torch.Tensor): Input Mel-spectrogram tensor maps with shape (batch, channels, time_frames).
+
+ Returns:
+ torch.Tensor: Multi-class fundamental frequency classification matrix of shape (batch, time_frames, N_CLASS).
+ """
+
+ # Step 3: Classify sequence arrays across target pitch classes using the selected FC header network block
+ return self.fc(
+ # Step 2: Reduce channel spaces down using local convolutional wrappers
+ self.cnn(
+ # Step 1: Feed into structural Unet backbone networks
+ self.unet(
+ mel.transpose(-1, -2).unsqueeze(1)
+ )
+ ).transpose(1, 2).flatten(-2)
+ )
\ No newline at end of file
diff --git a/main/library/predictors/RMVPE/mel.py b/main/library/predictors/RMVPE/mel.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9e5376ad447cb5fc7658f138dcf0aaefe3f4dc3
--- /dev/null
+++ b/main/library/predictors/RMVPE/mel.py
@@ -0,0 +1,110 @@
+import os
+import sys
+import torch
+
+import torch.nn as nn
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config
+from main.library.audio.features import mel
+
+class MelSpectrogram(nn.Module):
+ """
+ A PyTorch module for extracting Mel-Spectrogram features from raw audio waveforms.
+
+ This module computes the Short-Time Fourier Transform (STFT) of an audio signal
+ and projects the linear frequency magnitude spectrum onto the Mel scale using
+ a predefined filter bank matrix. It supports dynamic backend switching for alternative
+ hardware architectures (e.g., OpenCL, DirectML/PrivateUse1).
+ """
+
+ def __init__(
+ self,
+ n_mel_channels,
+ sample_rate,
+ win_length,
+ hop_length,
+ n_fft=None,
+ mel_fmin=0,
+ mel_fmax=None,
+ clamp=1e-5
+ ):
+ """
+ Initializes the MelSpectrogram feature extractor.
+
+ Args:
+ n_mel_channels (int): Total number of Mel bands to construct.
+ sample_rate (int): Target sampling rate of input audio signals.
+ win_length (int): Window duration size for frame localized windowing.
+ hop_length (int): Step/stride size between consecutive overlapping windows.
+ n_fft (int, optional): Length of the FFT window. Defaults to win_length if not provided.
+ mel_fmin (float, default=0): Minimum frequency bound for the Mel scale filter bank.
+ mel_fmax (float, optional): Maximum frequency bound for the Mel scale filter bank.
+ clamp (float, default=1e-5): Floor restriction value utilized before log mapping operations.
+ """
+
+ super().__init__()
+ self.clamp = clamp
+ self.n_fft = n_fft
+ self.hop_length = hop_length
+ self.win_length = win_length
+ self.sample_rate = sample_rate
+ self.n_mel_channels = n_mel_channels
+ # Pre-compute and cache the Hann window function to minimize runtime overhead
+ self.hann_window = torch.hann_window(win_length).to(config.device)
+
+ self.stft = None
+ # Dynamically dispatch the appropriate STFT calculation routine depending on device backend
+ self.stftt = self._stft_other_backends if config.device.startswith(("ocl", "privateuseone")) else self._stft_torch
+ # Compute the HTK-scaled Mel filter bank basis matrix and register it as a non-trainable module buffer
+ self.register_buffer("mel_basis", mel(sr=sample_rate, n_fft=n_fft, n_mels=n_mel_channels, fmin=mel_fmin, fmax=mel_fmax, htk=True, device=config.device))
+
+ def forward(self, audio, center=True):
+ """Processes raw audio waveforms into log-compressed Mel-Spectrogram features.
+
+ Args:
+ audio (torch.Tensor): Input audio waveform tensor.
+ center (bool, default=True): If True, pads input waveform on both sides so that the t-th frame is centered around time t * hop_length.
+
+ Returns:
+ torch.Tensor: Log-compressed Mel-spectrogram feature maps.
+ """
+
+ # Step 1: Perform STFT magnitude extraction followed by matrix multiplication projection onto the Mel scale matrix
+ mel_output = self.mel_basis @ self.stftt(audio, center)
+ # Step 2: Apply floor clamping to guarantee mathematical stability, then convert to the logarithmic domain
+ return mel_output.clamp(min=self.clamp).log()
+
+ def _stft_other_backends(self, audio, center=True):
+ """Computes STFT magnitude for specialized hardware backends (e.g., OpenCL/Intel/AMD Extensions)."""
+
+ if self.stft is None:
+ # Lazy import alternative STFT module wrapper to optimize startup performance overhead
+
+ from main.library.backends.utils import STFT
+
+ self.stft = STFT(
+ filter_length=self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_length
+ ).to(audio.device)
+
+ # Run custom backend transform logic applying a tiny floor constant for division guards
+ return self.stft.transform(audio, 1e-9, center=center)
+
+ def _stft_torch(self, audio, center=True):
+ """Computes STFT magnitude using the standard native PyTorch implementation wrapper."""
+
+ # Step 1: Calculate the complex frequency representation via native short-time fourier transform
+ fft = torch.stft(
+ audio,
+ n_fft=self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_length,
+ window=self.hann_window,
+ center=center,
+ return_complex=True
+ )
+ # Step 2: Extract the absolute value (magnitude spectrum), discarding phase information
+ return fft.abs()
\ No newline at end of file
diff --git a/main/library/predictors/RMVPE/yolo.py b/main/library/predictors/RMVPE/yolo.py
new file mode 100644
index 0000000000000000000000000000000000000000..c728dd5c7faa0ef627e5f282c681321144787663
--- /dev/null
+++ b/main/library/predictors/RMVPE/yolo.py
@@ -0,0 +1,729 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+def autopad(k, p=None):
+ """
+ Computes the padding value dynamically to retain identical spatial dimensions (same padding).
+
+ Args:
+ k (int or list): Kernel size used in the convolution layer.
+ p (int or list, optional): Explicit padding value. Defaults to None.
+
+ Returns:
+ int or list: Dynamically calculated padding value.
+ """
+
+ if p is None: p = k // 2 if isinstance(k, int) else [x // 2 for x in k]
+ return p
+
+class Conv(nn.Module):
+ """
+ Standard Convolutional Block utilizing the SiLU activation function.
+
+ Consists of a structured sequence: 2D Convolution -> 2D Batch Normalization -> SiLU/Identity.
+ """
+
+ def __init__(
+ self,
+ c1,
+ c2,
+ k=1,
+ s=1,
+ p=None,
+ g=1,
+ act=True
+ ):
+ """
+ Initializes the standard Conv block.
+
+ Args:
+ c1 (int): Input channel count.
+ c2 (int): Output channel count.
+ k (int): Convolution kernel dimension. Default is 1.
+ s (int): Convolution stride dimension. Default is 1.
+ p (int, optional): Spatial padding. Evaluated automatically if None.
+ g (int): Grouped convolution configurations. Default is 1.
+ act (bool): Flag toggling SiLU activation vs Identity. Default is True.
+ """
+
+ super().__init__()
+ self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
+ self.bn = nn.BatchNorm2d(c2)
+ self.act = nn.SiLU() if act else nn.Identity()
+
+ def forward(self, x):
+ """Executes sequential Conv2d -> BatchNorm -> Activation operations."""
+
+ return self.act(
+ self.bn(
+ self.conv(x)
+ )
+ )
+
+class DSConv(nn.Module):
+ """
+ Depthwise Separable Convolution (DSConv) Block.
+
+ Reduces computational complexity by splitting filtering and combination steps into a Depthwise Conv (spatial)
+ followed by a Pointwise Conv (channel cross-mapping).
+ """
+
+ def __init__(
+ self,
+ c1,
+ c2,
+ k=3,
+ s=1,
+ p=None,
+ act=True
+ ):
+ """
+ Initializes the Depthwise Separable Convolution block.
+
+ Args:
+ c1 (int): Input channel dimension.
+ c2 (int): Output channel dimension.
+ k (int): Depthwise kernel dimension size. Default is 3.
+ s (int): Depthwise stride tracking. Default is 1.
+ p (int, optional): Spatial padding parameters.
+ act (bool): Toggles SiLU activation constraints. Default is True.
+ """
+
+ super().__init__()
+ # Channel dimension-wise isolated spatial filters (groups=c1)
+ self.dwconv = nn.Conv2d(c1, c1, k, s, autopad(k, p), groups=c1, bias=False)
+ # Pointwise 1x1 cross-channel combination layer
+ self.pwconv = nn.Conv2d(c1, c2, 1, 1, 0, bias=False)
+ self.bn = nn.BatchNorm2d(c2)
+ self.act = nn.SiLU() if act else nn.Identity()
+
+ def forward(self, x):
+ """Applies depthwise processing followed by pointwise cross-channel expansion."""
+
+ return self.act(
+ self.bn(
+ self.pwconv(
+ self.dwconv(x)
+ )
+ )
+ )
+
+class DS_Bottleneck(nn.Module):
+ """
+ Standard Bottleneck Block built using Depthwise Separable Convolutions.
+
+ Utilizes residual skip-connections if input and output dimensions match identically.
+ """
+
+ def __init__(
+ self,
+ c1,
+ c2,
+ k=3,
+ shortcut=True
+ ):
+ """
+ Initializes the DS_Bottleneck block.
+
+ Args:
+ c1 (int): Incoming spatial channel dimensions.
+ c2 (int): Outgoing spatial channel dimensions.
+ k (int): Target kernel window constraint for the second DSConv layer. Default is 3.
+ shortcut (bool): Triggers residual identity pathing logic. Default is True.
+ """
+
+ super().__init__()
+ self.dsconv1 = DSConv(c1, c1, k=3, s=1)
+ self.dsconv2 = DSConv(c1, c2, k=k, s=1)
+ self.shortcut = shortcut and c1 == c2
+
+ def forward(self, x):
+ """Passes inputs through dual DSConvs with an optional residual identity shortcut path."""
+
+ return x + self.dsconv2(self.dsconv1(x)) if self.shortcut else self.dsconv2(self.dsconv1(x))
+
+class DS_C3k(nn.Module):
+ """YOLOv13 Cross-Stage Partial (CSP) block built via Depthwise Separable Bottlenecks."""
+
+ def __init__(
+ self,
+ c1,
+ c2,
+ n=1,
+ k=3,
+ e=0.5
+ ):
+ """
+ Initializes the DS_C3k module.
+
+ Args:
+ c1 (int): Input channel dimension.
+ c2 (int): Output channel dimension.
+ n (int): Multi-stack count for internal DS_Bottleneck layers. Default is 1.
+ k (int): Kernel settings inside bottleneck blocks. Default is 3.
+ e (float): Scaling factor adjusting bottleneck channel width. Default is 0.5.
+ """
+
+ super().__init__()
+ self.cv1 = Conv(c1, int(c2 * e), 1, 1)
+ self.cv2 = Conv(c1, int(c2 * e), 1, 1)
+ self.cv3 = Conv(2 * int(c2 * e), c2, 1, 1)
+ self.m = nn.Sequential(
+ *[
+ DS_Bottleneck(
+ int(c2 * e),
+ int(c2 * e),
+ k=k,
+ shortcut=True
+ )
+ for _ in range(n)
+ ]
+ )
+
+ def forward(self, x):
+ """Fuses deep bottleneck features and shallow skip features before spatial projection."""
+
+ return self.cv3(
+ torch.cat(
+ (self.m(self.cv1(x)), self.cv2(x)),
+ dim=1
+ )
+ )
+
+class DS_C3k2(nn.Module):
+ """Advanced YOLOv13 CSP Variant wrapping a nested DS_C3k module block."""
+
+ def __init__(
+ self,
+ c1,
+ c2,
+ n=1,
+ k=3,
+ e=0.5
+ ):
+ """
+ Initializes the DS_C3k2 module wrapper.
+
+ Args:
+ c1 (int): Input channel count.
+ c2 (int): Output channel count.
+ n (int): Depth loop multiplier count passed to the inner DS_C3k block. Default is 1.
+ k (int): Kernel size for bottlenecks. Default is 3.
+ e (float): Ratio constraint adjusting the hidden channel depth profiles. Default is 0.5.
+ """
+
+ super().__init__()
+ self.cv1 = Conv(c1, int(c2 * e), 1, 1)
+ self.m = DS_C3k(int(c2 * e), int(c2 * e), n=n, k=k, e=1.0)
+ self.cv2 = Conv(int(c2 * e), c2, 1, 1)
+
+ def forward(self, x):
+ """Routes execution through cascading Conv -> DS_C3k -> Conv pipelines."""
+
+ return self.cv2(
+ self.m(
+ self.cv1(x)
+ )
+ )
+
+class AdaptiveHyperedgeGeneration(nn.Module):
+ """
+ Generates an adaptive incidence matrix to model relationships across spatial points.
+
+ Combines dynamic context maps via global average/max pooling alongside global hyperedge
+ prototypes, mapping high-order correlations through a multi-head projection space.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ num_hyperedges,
+ num_heads
+ ):
+ """
+ Initializes the Hyperedge Generator.
+
+ Args:
+ in_channels (int): Input feature depth dimensions.
+ num_hyperedges (int): Total number of hyperedges (M) to generate dynamically.
+ num_heads (int): Head splits for the multi-head correlation space.
+ """
+
+ super().__init__()
+ self.num_hyperedges = num_hyperedges
+ self.num_heads = num_heads
+ self.head_dim = max(1, in_channels // num_heads)
+ # Learnable global dictionary tracking reference structural shapes
+ self.global_proto = nn.Parameter(torch.randn(num_hyperedges, in_channels))
+ # Maps pooled statistical descriptors back into localized scaling weights
+ self.context_mapper = nn.Linear(2 * in_channels, num_hyperedges * in_channels, bias=False)
+ self.query_proj = nn.Linear(in_channels, in_channels, bias=False)
+ self.scale = self.head_dim ** -0.5
+
+ def forward(self, x):
+ """Generates the soft hypergraph incidence matrix"""
+ B, N, C = x.shape
+ P = (
+ # Step 2: Combine local context projections with the global learnable prototypes
+ self.global_proto.unsqueeze(0) +
+ self.context_mapper(
+ # Step 1: Accumulate spatial context statistics using parallel Pooling operations
+ torch.cat(
+ (
+ F.adaptive_avg_pool1d(x.permute(0, 2, 1), 1).squeeze(-1),
+ F.adaptive_max_pool1d(x.permute(0, 2, 1), 1).squeeze(-1)
+ ),
+ dim=1
+ )
+ ).view(B, self.num_hyperedges, C))
+
+ # Step 3: Scale affinities, average across multiple heads, and apply Softmax normalization
+ return F.softmax((
+ (
+ # Step 3: Project inputs into query states and calculate dot-product affinity maps across heads
+ self.query_proj(x).view(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3) @ P.view(B, self.num_hyperedges, self.num_heads, self.head_dim).permute(0, 2, 3, 1)
+ ) * self.scale
+ ).mean(dim=1).permute(0, 2, 1), dim=-1)
+
+class HypergraphConvolution(nn.Module):
+ """
+ Executes high-order spatial message-passing updates over a soft hypergraph network.
+
+ Applies the mathematical projection sequence: Vertex -> Hyperedge -> Vertex.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels
+ ):
+ """
+ Initializes the Hypergraph Convolution block.
+
+ Args:
+ in_channels (int): Incoming token feature dimensions.
+ out_channels (int): Outgoing token feature dimensions.
+ """
+
+ super().__init__()
+ self.W_e = nn.Linear(in_channels, in_channels, bias=False)
+ self.W_v = nn.Linear(in_channels, out_channels, bias=False)
+ self.act = nn.SiLU()
+
+ def forward(self, x, A):
+ """Performs graph-based vertex feature updates using the incidence matrix."""
+
+ # Vertex-to-Hyperedge aggregation followed by Hyperedge-to-Vertex distribution
+ return x + self.act(self.W_v(A.transpose(1, 2).bmm(self.act(self.W_e(A.bmm(x))))))
+
+class AdaptiveHypergraphComputation(nn.Module):
+ """Orchestrates hypergraph generation and structural convolution over 2D feature maps."""
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ num_hyperedges,
+ num_heads
+ ):
+ super().__init__()
+ self.adaptive_hyperedge_gen = AdaptiveHyperedgeGeneration(in_channels, num_hyperedges, num_heads)
+ self.hypergraph_conv = HypergraphConvolution(in_channels, out_channels)
+
+ def forward(self, x):
+ """Flattens 2D feature grids into sequence tokens for hypergraph operations."""
+
+ B, _, H, W = x.shape
+ # Flatten spatial dimensions
+ x_flat = x.flatten(2).permute(0, 2, 1)
+
+ # Generate hypergraph structures, run graph convolutions, and reconstruct the 2D grid
+ return self.hypergraph_conv(x_flat, self.adaptive_hyperedge_gen(x_flat)).permute(0, 2, 1).view(B, -1, H, W)
+
+class C3AH(nn.Module):
+ """Cross-Stage Partial (CSP) module integrated with Adaptive Hypergraph Computation (AHC)."""
+
+ def __init__(
+ self,
+ c1,
+ c2,
+ num_hyperedges,
+ num_heads,
+ e=0.5
+ ):
+ super().__init__()
+ self.cv1 = Conv(c1, int(c1 * e), 1, 1)
+ self.cv2 = Conv(c1, int(c1 * e), 1, 1)
+ self.ahc = AdaptiveHypergraphComputation(int(c1 * e), int(c1 * e), num_hyperedges, num_heads)
+ self.cv3 = Conv(2 * int(c1 * e), c2, 1, 1)
+
+ def forward(self, x):
+ """Splits the feature routing path into an active AHC stream and a standard skip stream."""
+
+ return self.cv3(
+ torch.cat(
+ (self.ahc(self.cv2(x)), self.cv1(x)),
+ dim=1
+ )
+ )
+
+class HyperACE(nn.Module):
+ """
+ Hypergraph-based Adaptive Correlation Enhancement (HyperACE) block from YOLOv13.
+
+ Aggregates multi-scale visual features from all four encoder stages onto a single reference grid.
+ It then splits processing into a high-order hypergraph relationship branch and a low-order
+ local spatial convolution branch to capture comprehensive cross-frequency associations.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ num_hyperedges=16,
+ num_heads=8,
+ k=2,
+ l=1,
+ c_h=0.5,
+ c_l=0.25
+ ):
+ """
+ Initializes the multi-branch HyperACE module.
+
+ Args:
+ in_channels (list): A list containing channel counts across the four encoder stages [P2, P3, P4, P5].
+ out_channels (int): Output channel configuration leaving the fusion layer.
+ num_hyperedges (int): Hyperedge count allocations. Default is 16.
+ num_heads (int): Head splits inside AHC submodules. Default is 8.
+ k (int): Number of stacked high-order hypergraph processing layers. Default is 2.
+ l (int): Number of stacked low-order local DS_C3k convolution layers. Default is 1.
+ c_h (float): Channel width ratio assigned to the high-order branch. Default is 0.5.
+ c_l (float): Channel width ratio assigned to the low-order branch. Default is 0.25.
+ """
+
+ super().__init__()
+ c2, c3, c4, c5 = in_channels
+ c_mid = c4 # Target intermediate destination reference channel footprint width
+ self.fuse_conv = Conv(c2 + c3 + c4 + c5, c_mid, 1, 1)
+ # Calculate split slice channel allocations
+ self.c_h = int(c_mid * c_h)
+ self.c_l = int(c_mid * c_l)
+ self.c_s = c_mid - self.c_h - self.c_l # Residual skip channel remainder size
+ # Branch A: High-Order Structural Relationships (Cascaded C3AH Hypergraph layers)
+ self.high_order_branch = nn.ModuleList([
+ C3AH(
+ self.c_h,
+ self.c_h,
+ num_hyperedges=num_hyperedges,
+ num_heads=num_heads, e=1.0
+ )
+ for _ in range(k)
+ ])
+ self.high_order_fuse = Conv(self.c_h * k, self.c_h, 1, 1)
+ # Branch B: Low-Order Structural Relationships (Stacked Local DS Convolutions)
+ self.low_order_branch = nn.Sequential(
+ *[
+ DS_C3k(
+ self.c_l,
+ self.c_l,
+ n=1,
+ k=3,
+ e=1.0
+ )
+ for _ in range(l)
+ ]
+ )
+ self.final_fuse = Conv(self.c_h + self.c_l + self.c_s, out_channels, 1, 1)
+
+ def forward(self, x):
+ """Rescales and groups multi-scale features to compute adaptive high/low-order correlations."""
+
+ B2, B3, B4, B5 = x
+ _, _, H4, W4 = B4.shape
+
+ # Interpolate and concatenate all feature maps onto the reference grid resolution of stage B4
+ x_h, x_l, x_s = self.fuse_conv(
+ torch.cat(
+ (
+ F.interpolate(
+ B2,
+ size=(H4, W4),
+ mode='bilinear',
+ align_corners=False
+ ),
+ F.interpolate(
+ B3,
+ size=(H4, W4),
+ mode='bilinear',
+ align_corners=False
+ ),
+ B4,
+ F.interpolate(
+ B5,
+ size=(H4, W4),
+ mode='bilinear',
+ align_corners=False
+ )
+ ),
+ dim=1
+ )
+ ).split([self.c_h, self.c_l, self.c_s], dim=1) # Split into separate processing streams: high-order, low-order, and direct identity skip
+
+ # Execute concurrent branch operations and combine the outputs through the final fusion layer
+ return self.final_fuse(
+ torch.cat(
+ (
+ self.high_order_fuse(torch.cat([m(x_h) for m in self.high_order_branch], dim=1)),
+ self.low_order_branch(x_l),
+ x_s
+ ),
+ dim=1
+ )
+ )
+
+class GatedFusion(nn.Module):
+ """Applies a learnable residual gate parameter ($\gamma$) to dynamically fuse two feature fields."""
+
+ def __init__(
+ self,
+ in_channels
+ ):
+ super().__init__()
+ self.gamma = nn.Parameter(torch.zeros(1, in_channels, 1, 1))
+
+ def forward(self, f_in, h):
+ # Initialized to zero so that the network initially relies on the base feature stream
+ return f_in + self.gamma * h
+
+class YOLO13Encoder(nn.Module):
+ """
+ Multi-stage Feature Downsampling Encoder Backbone styled after YOLOv13.
+
+ Sequentially processes raw spectrogram maps down across 4 spatial dimensions [P2, P3, P4, P5],
+ utilizing Depthwise Separable Convolutions to optimize efficiency.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ base_channels=32
+ ):
+ super().__init__()
+ # Initial downsampling block
+ self.stem = DSConv(
+ in_channels,
+ base_channels,
+ k=3,
+ s=1
+ )
+ # Stage P2: Downsample and process features
+ self.p2 = nn.Sequential(
+ DSConv(
+ base_channels,
+ base_channels*2, k=3, s=(2, 2)),
+ DS_C3k2(
+ base_channels*2,
+ base_channels*2,
+ n=1
+ )
+ )
+ # Stage P3
+ self.p3 = nn.Sequential(
+ DSConv(
+ base_channels*2,
+ base_channels*4,
+ k=3,
+ s=(2, 2)
+ ),
+ DS_C3k2(
+ base_channels*4,
+ base_channels*4,
+ n=2
+ )
+ )
+ # Stage P4
+ self.p4 = nn.Sequential(
+ DSConv(
+ base_channels*4,
+ base_channels*8,
+ k=3,
+ s=(2, 2)
+ ),
+ DS_C3k2(
+ base_channels*8,
+ base_channels*8,
+ n=2
+ )
+ )
+ # Stage P5
+ self.p5 = nn.Sequential(
+ DSConv(
+ base_channels*8,
+ base_channels*16,
+ k=3,
+ s=(2, 2)
+ ),
+ DS_C3k2(
+ base_channels*16,
+ base_channels*16,
+ n=1
+ )
+ )
+
+ self.out_channels = [base_channels*2, base_channels*4, base_channels*8, base_channels*16]
+
+ def forward(self, x):
+ """Passes inputs forward through the cascading encoder stages, returning all intermediate multi-scale feature maps."""
+
+ p2 = self.p2(self.stem(x))
+ p3 = self.p3(p2)
+ p4 = self.p4(p3)
+ p5 = self.p5(p4)
+
+ return [p2, p3, p4, p5]
+
+class YOLO13FullPADDecoder(nn.Module):
+ """
+ Full-Pipeline Aggregation-and-Distribution (FullPAD) Decoder module.
+
+ Implements top-down feature distribution pathways by dynamically incorporating
+ global relational cues from the HyperACE module into each decoder stage
+ using learnable GatedFusion gates.
+ """
+
+ def __init__(self, encoder_channels, hyperace_out_c, out_channels_final):
+ super().__init__()
+ c_p2, c_p3, c_p4, c_p5 = encoder_channels
+ c_d5, c_d4, c_d3, c_d2 = c_p5, c_p4, c_p3, c_p2
+
+ # Projection convolutions mapping HyperACE features to respective decoder stage depths
+ self.h_to_d5 = Conv(
+ hyperace_out_c,
+ c_d5,
+ 1,
+ 1
+ )
+ self.h_to_d4 = Conv(
+ hyperace_out_c,
+ c_d4,
+ 1,
+ 1
+ )
+ self.h_to_d3 = Conv(
+ hyperace_out_c,
+ c_d3,
+ 1,
+ 1
+ )
+ self.h_to_d2 = Conv(
+ hyperace_out_c,
+ c_d2,
+ 1,
+ 1
+ )
+
+ # Dynamic gated fusion blocks
+ self.fusion_d5 = GatedFusion(c_d5)
+ self.fusion_d4 = GatedFusion(c_d4)
+ self.fusion_d3 = GatedFusion(c_d3)
+ self.fusion_d2 = GatedFusion(c_d2)
+
+ # Lateral skip-connection layers mapping encoder features to decoder spaces
+ self.skip_p5 = Conv(
+ c_p5,
+ c_d5,
+ 1,
+ 1
+ )
+ self.skip_p4 = Conv(
+ c_p4,
+ c_d4,
+ 1,
+ 1
+ )
+ self.skip_p3 = Conv(
+ c_p3,
+ c_d3,
+ 1,
+ 1
+ )
+ self.skip_p2 = Conv(
+ c_p2,
+ c_d2,
+ 1,
+ 1
+ )
+
+ # Top-down upsampling CSP convolvers
+ self.up_d5 = DS_C3k2(
+ c_d5,
+ c_d4,
+ n=1
+ )
+ self.up_d4 = DS_C3k2(
+ c_d4,
+ c_d3,
+ n=1
+ )
+ self.up_d3 = DS_C3k2(
+ c_d3,
+ c_d2,
+ n=1
+ )
+
+ self.final_d2 = DS_C3k2(
+ c_d2,
+ c_d2,
+ n=1
+ )
+ self.final_conv = Conv(
+ c_d2,
+ out_channels_final,
+ 1,
+ 1
+ )
+
+ def forward(self, enc_feats, h_ace):
+ """Executes FullPAD multi-level top-down aggregation loops with hypergraph feature injection."""
+
+ p2, p3, p4, p5 = enc_feats
+ d5 = self.skip_p5(p5)
+
+ d4 = self.up_d5(
+ F.interpolate(
+ self.fusion_d5(d5, self.h_to_d5(F.interpolate(h_ace, size=d5.shape[2:], mode='bilinear', align_corners=False))),
+ size=p4.shape[2:],
+ mode='bilinear',
+ align_corners=False
+ )
+ ) + self.skip_p4(p4)
+
+ d3 = self.up_d4(
+ F.interpolate(
+ self.fusion_d4(d4, self.h_to_d4(F.interpolate(h_ace, size=d4.shape[2:], mode='bilinear', align_corners=False))),
+ size=p3.shape[2:],
+ mode='bilinear',
+ align_corners=False
+ )
+ ) + self.skip_p3(p3)
+
+ d2 = self.up_d3(
+ F.interpolate(
+ self.fusion_d3(d3, self.h_to_d3(F.interpolate(h_ace, size=d3.shape[2:], mode='bilinear', align_corners=False))),
+ size=p2.shape[2:],
+ mode='bilinear',
+ align_corners=False
+ )
+ ) + self.skip_p2(p2)
+
+ return self.final_conv(
+ self.final_d2(
+ self.fusion_d2(
+ d2,
+ self.h_to_d2(
+ F.interpolate(h_ace, size=d2.shape[2:], mode='bilinear', align_corners=False)
+ )
+ )
+ )
+ )
\ No newline at end of file
diff --git a/main/library/predictors/SWIFT/SWIFT.py b/main/library/predictors/SWIFT/SWIFT.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd8643b9e26503ea7d38b6db16f7b165faebe350
--- /dev/null
+++ b/main/library/predictors/SWIFT/SWIFT.py
@@ -0,0 +1,114 @@
+import onnxruntime
+
+import numpy as np
+
+SAMPLE_RATE, HOP_LENGTH, FRAME_LENGTH = 16000, 256, 1024
+
+class SWIFT:
+ """
+ SWIFT: Fast and Accurate Monophonic Pitch Detection ONNX Inference Wrapper class.
+
+ Provides accelerated, lightweight fundamental frequency (F0) estimation pipeline
+ execution. Supports standard sequential Numpy matrix extraction alongside advanced
+ zero-copy IO-Binding features optimized for hardware deployment loops.
+ """
+
+ def __init__(
+ self,
+ model_path,
+ fmin = 50,
+ fmax = 1100,
+ confidence_threshold = 0.9,
+ providers = ["CPUExecutionProvider"]
+ ):
+ """
+ Initializes runtime environments and execution graph nodes for the SwiftF0 wrapper.
+
+ Args:
+ model_path (str): File path pointing to the serialized standalone .onnx model asset.
+ fmin (float, optional): Floor frequency boundary value filter threshold. Defaults to 50.0.
+ fmax (float, optional): Ceiling frequency boundary value filter threshold. Defaults to 1100.0.
+ confidence_threshold (float, optional): Scalar criteria for continuous voicing filtering. Defaults to 0.9.
+ providers (List[str], optional): List of target execution engines. Defaults to ["CPUExecutionProvider"].
+ """
+
+ self.fmin = fmin
+ self.fmax = fmax
+ self.confidence_threshold = confidence_threshold
+
+ # 1. Parameterize runtime execution threads to enforce deterministic serialization benchmarks
+ session_options = onnxruntime.SessionOptions()
+ session_options.inter_op_num_threads = 1
+ session_options.intra_op_num_threads = 1
+ session_options.log_severity_level = 3 # Suppress non-critical warning alerts
+
+ # 2. Track device context variables depending on target hardware strings
+ self._device = "cuda" if providers[0][0].startswith("CUDA") else "cpu"
+ # 3. Compile backend runtime graph engine structures
+ self.model = onnxruntime.InferenceSession(model_path, session_options, providers=providers)
+ # 4. Bind dynamic pointer configurations for execution loops based on device types
+ self._extract_pitch_and_confidence = self._infer_onnx_io if providers[0][0].startswith(("CUDA", "CPU")) else self._infer_onnx_non_io
+
+ def _infer_onnx_non_io(self, audio_16k):
+ """Runs inference via standard runtime paths using copy-heavy array inputs."""
+
+ # Add singleton dimension tracking to fulfill structural (Batch, Audio_samples) mandates
+ outputs = self.model.run(
+ ["pitch_hz", "confidence"], {"input_audio": audio_16k[None, :].astype(np.float32)}
+ )
+
+ return outputs[0][0], outputs[1][0]
+
+ def _infer_onnx_io(self, audio_16k):
+ """Runs accelerated model inference leveraging Zero-Copy IO-Binding setups."""
+
+ io_binding = self.model.io_binding()
+ # Bind input tensor memory addresses directly onto the processing pipeline
+ io_binding.bind_cpu_input("input_audio", audio_16k[None, :].astype(np.float32))
+ # Pre-allocate output buffers explicitly on the target execution device contexts
+ io_binding.bind_output(name="pitch_hz", device_type=self._device)
+ io_binding.bind_output(name="confidence", device_type=self._device)
+ # Execute model path optimization passes
+ self.model.run_with_iobinding(io_binding)
+
+ # Unpack structural tensor data pointers back into basic NumPy format arrays
+ return io_binding.get_outputs()[0].numpy()[0], io_binding.get_outputs()[1].numpy()[0]
+
+ def _compute_voicing(self, pitch_hz, confidence):
+ """Evaluates boolean masks pinpointing unvoiced and voiced frames across threshold ranges."""
+
+ return (confidence > self.confidence_threshold) & (pitch_hz >= self.fmin) & (pitch_hz <= self.fmax)
+
+ def _calculate_timestamps(self, n_frames):
+ """Calculates precise temporal center locations corresponding to generated spectral frames.
+
+ Returns:
+ np.ndarray: Vector array mapping sample positions out in seconds.
+ """
+
+ # Formulate framing offsets based on window sizes and framing shifts
+ frame_centers = np.arange(n_frames) * HOP_LENGTH + ((FRAME_LENGTH - 1) / 2 - ((FRAME_LENGTH - HOP_LENGTH) // 2))
+ return frame_centers / SAMPLE_RATE
+
+ def detect_from_array(self, audio_array):
+ """
+ Extracts fundamental pitches, tracking matrices, and time sequences from an audio array.
+
+ Args:
+ audio_array (np.ndarray): Target raw float voice data matrix array.
+
+ Returns:
+ Tuple[np.ndarray, np.ndarray, np.ndarray]: A tuple containing:
+ - pitch_hz: Continuous pitch predictions in Hertz (F0).
+ - voicing_mask: Boolean indicator flags tracking valid voiced audio segments.
+ - timestamps: Sequence positions mapping estimated points out in seconds.
+ """
+
+ # 1. Downmix multi-channel stereo arrays down to uniform mono format if required
+ if audio_array.ndim > 1: audio_array = np.mean(audio_array, axis=-1)
+ # 2. Enforce minimum sample boundaries to prevent engine runtime crashes
+ if len(audio_array) < 256: audio_array = np.pad(audio_array, (0, max(0, 256 - len(audio_array))), mode="constant")
+ # 3. Extract core prediction matrices using the configured device pipeline
+ pitch_hz, confidence = self._extract_pitch_and_confidence(audio_array)
+
+ return pitch_hz, self._compute_voicing(pitch_hz, confidence), self._calculate_timestamps(len(pitch_hz))
\ No newline at end of file
diff --git a/main/library/predictors/WORLD/SWIPE.py b/main/library/predictors/WORLD/SWIPE.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0ac0654d620ac769767cfd7334bfa1d1d2ff94e
--- /dev/null
+++ b/main/library/predictors/WORLD/SWIPE.py
@@ -0,0 +1,340 @@
+import math
+
+import numba as nb
+import numpy as np
+
+from matplotlib import mlab
+from scipy import interpolate
+from decimal import Decimal, ROUND_HALF_UP
+
+def swipe(
+ x,
+ fs,
+ f0_floor=50,
+ f0_ceil=1100,
+ frame_period=10,
+ sTHR=0.3
+):
+ """
+ SWIPE (Sawtooth Waveform Inspired Pitch Estimator) algorithm.
+ Estimates the fundamental frequency (f0) of an audio signal by matching
+ the spectrum with a sawtooth-like harmonic mask on the ERB scale.
+
+ Parameters:
+ -----------
+ x : numpy.ndarray
+ 1D input audio signal array.
+ fs : int or float
+ Sampling frequency in Hz.
+ f0_floor : float, optional
+ Minimum f0 search frequency in Hz (default: 50).
+ f0_ceil : float, optional
+ Maximum f0 search frequency in Hz (default: 1100).
+ frame_period : float, optional
+ Time step between frames in milliseconds (default: 10).
+ sTHR : float, optional
+ Pitch strength threshold. Frames below this are considered unvoiced (default: 0.3).
+
+ Returns:
+ --------
+ p : numpy.ndarray
+ Estimated fundamental frequency (f0) trajectory in Hz.
+ t : numpy.ndarray
+ Time positions for each frame in seconds.
+ """
+
+ # Define pitch limit search range
+ plim = np.array([f0_floor, f0_ceil])
+ # Generate time grid for frames based on frame_period (converted to seconds)
+ t = np.arange(0, int(1000 * len(x) / fs / (frame_period) + 1)) * (frame_period / 1000)
+
+ # Pitch candidates spaced at 1/96th of an octave (high-resolution pitch grid)
+ log2pc = np.arange(np.log2(plim[0]) * 96, np.log2(plim[-1]) * 96)
+ log2pc *= (1 / 96)
+
+ pc = 2 ** log2pc
+ # Initialize Pitch Strength matrix
+ S = np.zeros((len(pc), len(t)))
+ # Determine window sizes (ws) based on optimal length for different f0 ranges
+ logWs = [round_matlab(elm) for elm in np.log2(4 * 2 * fs / plim)]
+ ws = 2 ** np.arange(logWs[0], logWs[1] - 1, -1)
+ p0 = 4 * 2 * fs / ws
+
+ # 'd' tracks which octave band/window size covers which pitch candidate
+ d = 1 + log2pc - np.log2(4 * 2 * fs / ws[0])
+ # Define ERB frequency points from a quarter of the lowest pitch to Nyquist
+ fERBs = erbs2hz(np.arange(hz2erbs(pc[0] / 4), hz2erbs(fs / 2), 0.1))
+
+ # Multi-resolution STFT analysis (Loop through different window sizes)
+ for i in range(len(ws)):
+ dn = round_matlab(4 * fs / p0[i])
+
+ # Compute Complex Spectrogram
+ X, f, ti = mlab.specgram(
+ x=np.r_[np.zeros(int(ws[i] / 2)), np.r_[x, np.zeros(int(dn + ws[i] / 2))]], # Pad signal symmetrically to prevent edge artifacts in spectrogram
+ NFFT=ws[i],
+ Fs=fs,
+ window=np.hanning(ws[i] + 2)[1:-1], noverlap=max(0, np.round(ws[i] - dn)),
+ mode='complex'
+ )
+
+ ti = np.r_[0, ti[:-1]] # Align time vectors
+ # Interpolate the magnitude spectrum from linear Hz scale to ERB scale
+ M = np.maximum(0, interpolate.interp1d(f, np.abs(X.T), kind='cubic')(fERBs)).T
+
+ # Define pitch candidate boundaries applicable to the current window size
+ if i == len(ws) - 1:
+ j = np.where(d - (i + 1) > -1)[0]
+ k = np.where(d[j] - (i + 1) < 0)[0]
+ elif i == 0:
+ j = np.where(d - (i + 1) < 1)[0]
+ k = np.where(d[j] - (i + 1) > 0)[0]
+ else:
+ j = np.where(np.abs(d - (i + 1)) < 1)[0]
+ k = np.arange(len(j))
+
+ # Calculate pitch strength for the active candidates
+ Si = pitchStrengthAllCandidates(fERBs, np.sqrt(M), pc[j])
+ # Interpolate the sub-sampled pitch strength matrix to match target time grid 't'
+ Si = interpolate.interp1d(
+ ti,
+ Si,
+ bounds_error=False,
+ fill_value='nan'
+ )(t) if Si.shape[1] > 1 else np.full(
+ (len(Si), len(t)),
+ np.nan
+ )
+
+ # Apply a smooth window function (mu) to blend overlapping resolution layers
+ mu = np.ones(j.shape)
+ mu[k] = 1 - np.abs(d[j[k]] - i - 1)
+ S[j, :] = S[j, :] + np.tile(mu.reshape(-1, 1), (1, Si.shape[1])) * Si
+
+ # Post-processing: Extract pitch trajectories and find optimal peaks
+ p = np.full((S.shape[1], 1), np.nan)
+ s = np.full((S.shape[1], 1), np.nan)
+
+ for j in range(S.shape[1]):
+ s[j] = np.max(S[:, j]) # Max pitch strength for current frame
+ i = np.argmax(S[:, j]) # Index of the max candidate
+
+ # If peak strength is lower than threshold, mark it as unvoiced/silent
+ if s[j] < sTHR: continue
+
+ # Handle boundaries
+ if i == 0: p[j] = pc[0]
+ elif i == len(pc) - 1: p[j] = pc[0]
+ else:
+ # Perform Parabolic/Polynomial interpolation around the peak for high resolution
+ I = np.arange(i-1, i+2)
+ tc = 1 / pc[I]
+
+ ntc = (tc / tc[1] - 1) * 2 * np.pi
+ idx = np.isfinite(S[I, j])
+
+ c = np.zeros(len(ntc))
+ c += np.nan
+
+ I_ = I[idx]
+
+ if len(I_) < 2: c[idx] = (S[I, j])[0] / ntc[0]
+ else: c[idx] = np.polyfit(ntc[idx], (S[I_, j]), 2)
+
+ # Re-evaluate fitting function on a fine pitch subgrid
+ pval = np.polyval(
+ c,
+ ((1 / (2 ** np.arange(np.log2(pc[I[0]]), np.log2(pc[I[2]]) + 1 / 12 / 64, 1 / 12 / 64))) / tc[1] - 1) * 2 * np.pi
+ )
+
+ # Update with highly refined pitch and score
+ s[j] = np.max(pval)
+ p[j] = 2 ** (np.log2(pc[I[0]]) + (np.argmax(pval)) / 12 / 64)
+
+ p = p.flatten()
+ p[np.isnan(p)] = 0 # Replace NaNs with 0 (unvoiced indicator)
+
+ return np.array(p, dtype=np.float32), np.array(t, dtype=np.float32)
+
+def round_matlab(n):
+ """
+ Rounds a number to the nearest integer, using the half-up convention
+ consistent with MATLAB's round function.
+ """
+
+ return int(Decimal(n).quantize(0, ROUND_HALF_UP))
+
+def pitchStrengthAllCandidates(f, L, pc):
+ """
+ Computes the pitch strength for all f0 candidates across all frames.
+
+ Parameters:
+ -----------
+ f : numpy.ndarray
+ Frequency vector (on ERB scale).
+ L : numpy.ndarray
+ Normalized magnitude spectrum (matrix form).
+ pc : numpy.ndarray
+ Vector of pitch candidates.
+ """
+
+ # Normalize the spectrum across rows to ensure consistent scaling
+ den = np.sqrt(np.sum(L * L, axis=0))
+ den = np.where(den == 0, 2.220446049250313e-16, den) # Prevent division by zero (eps)
+
+ L = L / den
+ S = np.zeros((len(pc), L.shape[1]))
+ # Evaluate each pitch candidate
+ for j in range(len(pc)):
+ S[j,:] = pitchStrengthOneCandidate(f, L, pc[j])
+
+ return S
+
+def pitchStrengthOneCandidate(f, L, pc):
+ """
+ Computes pitch strength score for a single pitch candidate (pc)
+ by cross-correlating with a harmonic sieve/kernel.
+ """
+
+ k = np.zeros(len(f))
+ q = f / pc # Normalized frequencies relative to candidate f0
+
+ # Apply a harmonic mask using 1 and all prime numbers up to maximum possible harmonic
+ for i in ([1] + sieve(int(np.fix(f[-1] / pc - 0.75)))):
+ a = np.abs(q - i)
+ # Perfect peak matches (valleys and peaks of the sawtooth-like model)
+ p = a < 0.25
+ k[p] = np.cos(2 * np.pi * q[p])
+
+ # Sub-harmonic/secondary peak energy weighting
+ v = np.logical_and((0.25 < a), (a < 0.75))
+ k[v] = k[v] + np.cos(2 * np.pi * q[v]) / 2
+
+ # Global normalization and scaling inversely proportional to root frequency
+ k *= np.sqrt(1 / f)
+ k /= np.linalg.norm(k[k>0])
+
+ return k @ L # Matrix multiplication yields similarity score
+
+def hz2erbs(hz):
+ """Converts frequency from Hertz (Hz) to Equivalent Rectangular Bandwidth (ERB) rate."""
+
+ return 21.4 * np.log10(1 + hz / 229)
+
+def erbs2hz(erbs):
+ """Converts frequency from Equivalent Rectangular Bandwidth (ERB) rate back to Hertz (Hz)."""
+
+ return (10 ** (erbs / 21.4) - 1) * 229
+
+def sieve(n):
+ """
+ Generates a list of prime numbers up to n using the Sieve of Eratosthenes.
+ Used to select prime harmonics for the pitch scoring sieve.
+ """
+
+ primes = list(range(2, n + 1))
+ num = 2
+
+ while num < math.sqrt(n):
+ i = num
+
+ while i <= n:
+ i += num
+
+ if i in primes: primes.remove(i)
+
+ for j in primes:
+ if j > num:
+ num = j
+ break
+
+ return primes
+
+def stonemask(x, fs, temporal_positions, f0):
+ """
+ StoneMask: Refines the f0 trajectory calculated by SWIPE.
+ It checks instantaneous frequency around local components and suppresses unvocal fluctuations.
+
+ Parameters:
+ -----------
+ x : numpy.ndarray
+ Input audio waveform data.
+ fs : float
+ Sampling rate.
+ temporal_positions : numpy.ndarray
+ Time positions (seconds) corresponding to each frame.
+ f0 : numpy.ndarray
+ Coarse baseline f0 estimate from SWIPE.
+ """
+
+ refined_f0 = np.copy(f0)
+
+ for i in range(len(temporal_positions)):
+ if f0[i] != 0:
+ # Refine f0 using instantaneous frequency analysis
+ refined_f0[i] = get_refined_f0(x, fs, temporal_positions[i], f0[i])
+ # If the deviation exceeds 20%, discard refinement and revert to original
+ if abs(refined_f0[i] - f0[i]) / f0[i] > 0.2: refined_f0[i] = f0[i]
+
+ return np.array(refined_f0, dtype=np.float32)
+
+def get_refined_f0(x, fs, current_time, current_f0):
+ """
+ Calculates the exact instantaneous frequency around a local frame
+ using the phase/amplitude spectrum derivative method.
+ """
+
+ f0_initial = current_f0
+ half_window_length = np.ceil(3 * fs / f0_initial / 2)
+ window_length_in_time = (2 * half_window_length + 1) / fs
+ # Build local time vectors for extracting window
+ base_time = np.arange(-half_window_length, half_window_length + 1) / fs
+ fft_size = 2 ** math.ceil(math.log((half_window_length * 2 + 1), 2) + 1)
+
+ base_time = np.array([float("{0:.4f}".format(elm)) for elm in base_time])
+ index_raw = round_matlab_2((current_time + base_time) * fs)
+ # Generate Blackman-Harris / Hanning style window function
+ window_time = ((index_raw - 1) / fs) - current_time
+ main_window = 0.42 + 0.5 * np.cos(2 * math.pi * window_time / window_length_in_time) + 0.08 * np.cos(4 * math.pi * window_time / window_length_in_time)
+ # Avoid out-of-bound sample indices
+ index = np.array(np.maximum(1, np.minimum(len(x), index_raw)), dtype=int)
+ # STFT spectrum of windowed signal segment
+ spectrum = np.fft.fft(x[index - 1] * main_window, fft_size)
+
+ # Calculate derivative spectrum for instantaneous frequency assessment
+ diff_spectrum = np.fft.fft(x[index - 1] * (-(np.diff(np.r_[0, main_window]) + np.diff(np.r_[main_window, 0])) / 2), fft_size)
+ power_spectrum = np.abs(spectrum) ** 2
+
+ from sys import float_info
+
+ power_spectrum[power_spectrum == 0] = float_info.epsilon # Avoid log/div zero errors
+ instantaneous_frequency = (np.arange(fft_size) / fft_size * fs) + (np.real(spectrum) * np.imag(diff_spectrum) - np.imag(spectrum) * np.real(diff_spectrum)) / power_spectrum * fs / 2 / math.pi
+ # First stage optimization: Evaluate the 1st and 2nd harmonic weights
+ trim_index = np.array([1, 2])
+ index_list_trim = np.array(round_matlab_2(f0_initial * fft_size / fs * trim_index) + 1, int)
+
+ amp_list = np.sqrt(power_spectrum[index_list_trim - 1])
+ f0_initial = np.sum(amp_list * instantaneous_frequency[index_list_trim - 1]) / np.sum(amp_list * trim_index)
+
+ if f0_initial < 0: return 0
+ # Second stage optimization: Fine-tune using the first 6 harmonics
+ trim_index = np.array([1, 2, 3, 4, 5, 6])
+ index_list_trim = np.array(round_matlab_2(f0_initial * fft_size / fs * trim_index) + 1, int)
+ amp_list = np.sqrt(power_spectrum[index_list_trim - 1])
+
+ # Return the amplitude-weighted mean instantaneous frequency
+ return np.sum(amp_list * instantaneous_frequency[index_list_trim - 1]) / np.sum(amp_list * trim_index)
+
+@nb.jit((nb.float64[:],), nopython=True, cache=True)
+def round_matlab_2(x):
+ """
+ A fast Numba-compiled element-wise rounding function
+ mimicking MATLAB's round operation for arrays.
+ """
+
+ y = x.copy()
+
+ y[x > 0] += 0.5
+ y[x <= 0] -= 0.5
+
+ return y
\ No newline at end of file
diff --git a/main/library/predictors/WORLD/WORLD.py b/main/library/predictors/WORLD/WORLD.py
new file mode 100644
index 0000000000000000000000000000000000000000..da36995c73777f78c2954c5082818e093f3fd298
--- /dev/null
+++ b/main/library/predictors/WORLD/WORLD.py
@@ -0,0 +1,316 @@
+import os
+import struct
+import ctypes
+import platform
+
+import numpy as np
+
+class DioOption(ctypes.Structure):
+ """
+ C-struct binding for DIO (Distributed Isolation Optimizer) configuration options.
+ Matches the original struct definition in the WORLD vocoder C++ library.
+ """
+
+ _fields_ = [
+ ("F0Floor", ctypes.c_double), # Minimum F0 search boundary (Hz)
+ ("F0Ceil", ctypes.c_double), # Maximum F0 search boundary (Hz)
+ ("ChannelsInOctave", ctypes.c_double), # Number of channels per octave band
+ ("FramePeriod", ctypes.c_double), # Window shift period (ms)
+ ("Speed", ctypes.c_int), # Downsampling/Speed factor (1 to 12)
+ ("AllowedRange", ctypes.c_double) # Allowed deviation parameter for post-processing
+ ]
+
+class HarvestOption(ctypes.Structure):
+ """
+ C-struct binding for Harvest F0 estimation configuration options.
+ Matches the original struct definition in the WORLD vocoder C++ library.
+ """
+
+ _fields_ = [
+ ("F0Floor", ctypes.c_double), # Minimum F0 search boundary (Hz)
+ ("F0Ceil", ctypes.c_double), # Maximum F0 search boundary (Hz)
+ ("FramePeriod", ctypes.c_double) # Window shift period (ms)
+ ]
+
+class PYWORLD:
+ """
+ Python wrapper for the WORLD speech synthesis vocoder C library using ctypes.
+ Handles cross-platform dynamic library extraction, loading, and memory orchestration
+ for Harvest, DIO, and StoneMask F0 processing routines.
+ """
+
+ def __init__(self, world_path, model_path, harvest = True):
+ """
+ Initializes the PYWORLD wrapper, extracts the system-specific binary dependency
+ from a pickled package, and binds default pitch estimation interfaces.
+
+ Parameters:
+ -----------
+ world_path : str
+ Directory path where the extracted dynamic library (.dll/.so/.dylib) will be written.
+ model_path : str
+ File path to the serialized pickle file containing binary objects of the WORLD library.
+ harvest : bool, optional
+ If True, assigns the `harvest` function as the default baseline estimator.
+ If False, falls back to `dio` (default: True).
+ """
+
+ self.world_path = world_path
+ os.makedirs(self.world_path, exist_ok=True)
+
+ # Detect operating system and architecture to identify target binary type
+ # 'world_android64', 'world_android86', 'world_arm_eabi', 'world_arm64', 'world_linux', 'world_mac', 'world_64', 'world_86'
+ if platform.system() == "Windows":
+ model_type, suffix = (
+ ("world_64" if platform.architecture()[0] == "64bit" else "world_86"), ".dll"
+ )
+ elif platform.system() == "Linux":
+ model_type, suffix = (
+ "world_linux", ".so"
+ )
+ elif platform.system() == "Darwin":
+ model_type, suffix = (
+ "world_mac", ".dylib"
+ )
+ else:
+ raise ValueError(f"Unsupported operating system architecture: {platform.system()}")
+
+ self.world_file_path = os.path.join(self.world_path, f"{model_type}{suffix}")
+ # Extract compiled binary file from pickle payload if it does not exist locally
+ if not os.path.exists(self.world_file_path):
+ target_content = None
+
+ with open(model_path, "rb") as f:
+ while 1:
+ name_len_bytes = f.read(4)
+ if not name_len_bytes: break
+
+ current = f.read(struct.unpack("I", name_len_bytes)[0]).decode('utf-8')
+ data_len = struct.unpack("I", f.read(4))[0]
+
+ if current == model_type:
+ target_content = f.read(data_len)
+ break
+ else: f.seek(data_len, 1)
+
+ if target_content is None:
+ raise KeyError(f"World type '{model_type}' not found inside packaged resources.")
+
+ with open(self.world_file_path, "wb") as w:
+ w.write(target_content)
+
+ self.idx_dio = 0
+ self.idx_harvest = 0
+ self.idx_stonemask = 0
+ self.option_dio = None
+ self.option_harvest = None
+ # Load the dynamic library via ctypes
+ self.world_dll = ctypes.CDLL(self.world_file_path)
+ # Bind the primary inference entry point alias
+ self.infer = self.harvest if harvest else self.dio
+
+ def harvest(
+ self,
+ x,
+ fs,
+ f0_floor=50,
+ f0_ceil=1100,
+ frame_period=10
+ ):
+ """
+ Estimates the fundamental frequency (F0) contour using the Harvest algorithm.
+ Harvest offers high-accuracy pitch contour tracking via fundamental component candidates.
+
+ Parameters:
+ -----------
+ x : numpy.ndarray or list
+ Input 1D waveform data array.
+ fs : int
+ Sampling frequency of the audio (Hz).
+ f0_floor : float, optional
+ Lower bound constraint for pitch detection (default: 50).
+ f0_ceil : float, optional
+ Upper bound constraint for pitch detection (default: 1100).
+ frame_period : float, optional
+ Time interval window step between frames in milliseconds (default: 10).
+
+ Returns:
+ --------
+ f0 : numpy.ndarray
+ Estimated fundamental frequency track (Hz) per frame.
+ tpos : numpy.ndarray
+ Time markers (seconds) corresponding to each frame center.
+ """
+
+ if self.idx_harvest == 0:
+ # Configure ctypes signature profiles for underlying C functions
+ self.world_dll.Harvest.argtypes = [
+ ctypes.POINTER(ctypes.c_double), # input waveform array
+ ctypes.c_int, # x length
+ ctypes.c_int, # sampling frequency
+ ctypes.POINTER(HarvestOption), # algorithm configuration parameters
+ ctypes.POINTER(ctypes.c_double), # output time positions array
+ ctypes.POINTER(ctypes.c_double) # output f0 frequency array
+ ]
+
+ self.world_dll.Harvest.restype = None
+ self.world_dll.InitializeHarvestOption.argtypes = [ctypes.POINTER(HarvestOption)]
+ self.world_dll.InitializeHarvestOption.restype = None
+ self.world_dll.GetSamplesForHarvest.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.c_double]
+ self.world_dll.GetSamplesForHarvest.restype = ctypes.c_int
+ # Instantiate option structure and apply defaults from the C source template
+ self.option_harvest = HarvestOption()
+ self.world_dll.InitializeHarvestOption(ctypes.byref(self.option_harvest))
+
+ # Override defaults with runtime configurations
+ self.option_harvest.F0Floor = f0_floor
+ self.option_harvest.F0Ceil = f0_ceil
+ self.option_harvest.FramePeriod = frame_period
+ self.idx_harvest += 1
+
+ # Determine target buffer size allocations needed for output arrays
+ f0_length = self.world_dll.GetSamplesForHarvest(fs, len(x), self.option_harvest.FramePeriod)
+ f0 = (ctypes.c_double * f0_length)()
+ tpos = (ctypes.c_double * f0_length)()
+
+ # Execute C backend processing (casting input to flat continuous double precision array)
+ self.world_dll.Harvest((ctypes.c_double * len(x))(*x), len(x), fs, ctypes.byref(self.option_harvest), tpos, f0)
+ return np.array(f0, dtype=np.float32), np.array(tpos, dtype=np.float32)
+
+ def dio(
+ self,
+ x,
+ fs,
+ f0_floor=50,
+ f0_ceil=1100,
+ channels_in_octave=2,
+ frame_period=10,
+ speed=1,
+ allowed_range=0.1
+ ):
+ """
+ Estimates the fundamental frequency (F0) contour using the DIO algorithm.
+ DIO is computationally faster and utilizes multiple band-pass filters.
+
+ Parameters:
+ -----------
+ x : numpy.ndarray or list
+ Input 1D waveform data array.
+ fs : int
+ Sampling frequency of the audio (Hz).
+ f0_floor : float, optional
+ Lower bound constraint for pitch detection (default: 50).
+ f0_ceil : float, optional
+ Upper bound constraint for pitch detection (default: 1100).
+ channels_in_octave : float, optional
+ Resolution of filter banks distributed per octave (default: 2).
+ frame_period : float, optional
+ Time interval window step between frames in milliseconds (default: 10).
+ speed : int, optional
+ Downsampling execution factor acceleration parameter [1, 12] (default: 1).
+ allowed_range : float, optional
+ Threshold parameter controlling voice tracking deviations (default: 0.1).
+
+ Returns:
+ --------
+ f0 : numpy.ndarray
+ Estimated baseline fundamental frequency track (Hz).
+ tpos : numpy.ndarray
+ Time markers (seconds) corresponding to each frame center.
+ """
+
+ if self.idx_dio == 0:
+ # Configure ctypes signature profiles for underlying DIO functions
+ self.world_dll.Dio.argtypes = [
+ ctypes.POINTER(ctypes.c_double),
+ ctypes.c_int,
+ ctypes.c_int,
+ ctypes.POINTER(DioOption),
+ ctypes.POINTER(ctypes.c_double),
+ ctypes.POINTER(ctypes.c_double)
+ ]
+
+ self.world_dll.Dio.restype = None
+ self.world_dll.InitializeDioOption.argtypes = [ctypes.POINTER(DioOption)]
+ self.world_dll.InitializeDioOption.restype = None
+ self.world_dll.GetSamplesForDIO.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.c_double]
+ self.world_dll.GetSamplesForDIO.restype = ctypes.c_int
+
+ # Instantiate structure and pull internal C library initialization variables
+ self.option_dio = DioOption()
+ self.world_dll.InitializeDioOption(ctypes.byref(self.option_dio))
+ # Assign user parameters to struct memory layout
+ self.option_dio.F0Floor = f0_floor
+ self.option_dio.F0Ceil = f0_ceil
+ self.option_dio.ChannelsInOctave = channels_in_octave
+ self.option_dio.FramePeriod = frame_period
+ self.option_dio.Speed = speed
+ self.option_dio.AllowedRange = allowed_range
+ self.idx_dio += 1
+
+ # Allocate double array storage matching required output structures
+ f0_length = self.world_dll.GetSamplesForDIO(fs, len(x), self.option_dio.FramePeriod)
+ f0 = (ctypes.c_double * f0_length)()
+ tpos = (ctypes.c_double * f0_length)()
+
+ # Execute baseline execution sequence
+ self.world_dll.Dio((ctypes.c_double * len(x))(*x), len(x), fs, ctypes.byref(self.option_dio), tpos, f0)
+ return np.array(f0, dtype=np.float32), np.array(tpos, dtype=np.float32)
+
+ def stonemask(
+ self,
+ x,
+ fs,
+ tpos,
+ f0
+ ):
+ """
+ Refines a coarse F0 trajectory estimate using the StoneMask refinement protocol.
+ Typically executed as a corrective refinement pass over raw DIO trajectories.
+
+ Parameters:
+ -----------
+ x : numpy.ndarray or list
+ Original input audio timeline sequence.
+ fs : int
+ Sampling frequency of the audio (Hz).
+ tpos : numpy.ndarray or list
+ Target analysis frame timestamp index markers (retrieved from DIO/Harvest).
+ f0 : numpy.ndarray or list
+ Coarse initial F0 frequency tracking array values to polish.
+
+ Returns:
+ --------
+ out_f0 : numpy.ndarray
+ Refined high-precision F0 estimation trajectory map (Hz).
+ """
+
+ if self.idx_stonemask == 0:
+ # Define function parameters mapping onto the exact C source definition
+ self.world_dll.StoneMask.argtypes = [
+ ctypes.POINTER(ctypes.c_double), # input waveform reference
+ ctypes.c_int, # x total length
+ ctypes.c_int, # sampling frequency
+ ctypes.POINTER(ctypes.c_double), # timestamps tracking grid list
+ ctypes.POINTER(ctypes.c_double), # target initial coarse f0 values array
+ ctypes.c_int, # length of f0 vector elements
+ ctypes.POINTER(ctypes.c_double) # output array pointer for corrected f0 data
+ ]
+
+ self.world_dll.StoneMask.restype = None
+ self.idx_stonemask += 1
+
+ # Allocate memory space for receiving refined data contents
+ out_f0 = (ctypes.c_double * len(f0))()
+ # Cast elements and invoke the dynamic modification pipeline layer
+ self.world_dll.StoneMask(
+ (ctypes.c_double * len(x))(*x),
+ len(x),
+ fs,
+ (ctypes.c_double * len(tpos))(*tpos),
+ (ctypes.c_double * len(f0))(*f0),
+ len(f0),
+ out_f0
+ )
+
+ return np.array(out_f0, dtype=np.float32)
\ No newline at end of file
diff --git a/main/library/speaker_diarization/ECAPA_TDNN.py b/main/library/speaker_diarization/ECAPA_TDNN.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c12d077c65fa21a3687dc45ca7605949111f96b
--- /dev/null
+++ b/main/library/speaker_diarization/ECAPA_TDNN.py
@@ -0,0 +1,742 @@
+import math
+import torch
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+def length_to_mask(length, max_len=None, dtype=None, device=None):
+ """
+ Converts a 1D tensor of sequence lengths into a 2D binary mask matrix.
+
+ Args:
+ length (torch.Tensor): 1D tensor containing lengths of individual sequences.
+ max_len (int, optional): The maximum sequence length for the mask. Defaults to the maximum value in `length`.
+ dtype (torch.dtype, optional): Desired data type of returned tensor. Defaults to length's dtype.
+ device (torch.device, optional): Desired device of returned tensor. Defaults to length's device.
+
+ Returns:
+ torch.Tensor: A 2D boolean/numeric mask tensor of shape (batch_size, max_len).
+ """
+
+ assert len(length.shape) == 1
+ # Determine maximum length if not explicitly provided
+ if max_len is None: max_len = length.max().long().item()
+
+ # Generate a grid of indices [0, 1, ..., max_len - 1] and broadcast to compare with lengths
+ mask = torch.arange(max_len, device=length.device, dtype=length.dtype).expand(len(length), max_len) < length.unsqueeze(1)
+
+ # Resolve default data type and device constraints
+ if dtype is None: dtype = length.dtype
+ if device is None: device = length.device
+
+ return torch.as_tensor(mask, dtype=dtype, device=device)
+
+def get_padding_elem(L_in, stride, kernel_size, dilation):
+ """
+ Calculates asymmetric or symmetric padding values required for maintaining temporal alignment.
+
+ Args:
+ L_in (int): The dimension size of the input channel sequence.
+ stride (int): Stride step size of the convolution.
+ kernel_size (int): Size of the convolving kernel.
+ dilation (int): Spacing between kernel elements.
+
+ Returns:
+ List[int]: A list of integers specifying padding values [left, right].
+ """
+
+ # For strided convolutions, apply standard half-kernel padding bounds
+ if stride > 1: padding = [math.floor(kernel_size / 2), math.floor(kernel_size / 2)]
+ else:
+ # Calculate strict output sequence lengths to maintain exact internal spatial boundaries
+ L_out = (math.floor((L_in - dilation * (kernel_size - 1) - 1) / stride) + 1)
+ padding = [math.floor((L_in - L_out) / 2), math.floor((L_in - L_out) / 2)]
+
+ return padding
+
+class _BatchNorm1d(nn.Module):
+ """
+ An internal custom wrapper for PyTorch's BatchNorm1d to easily handle
+
+ transpositions and flexible multi-dimensional sequential tensors.
+ """
+
+ def __init__(
+ self,
+ input_shape=None,
+ input_size=None,
+ eps=1e-05,
+ momentum=0.1,
+ affine=True,
+ track_running_stats=True,
+ combine_batch_time=False,
+ skip_transpose=False
+ ):
+ """Initializes the _BatchNorm1d tracking block."""
+
+ super().__init__()
+ self.combine_batch_time = combine_batch_time
+ self.skip_transpose = skip_transpose
+
+ # Deduce input features automatically if input_shape is provided instead of input_size
+ if input_size is None and skip_transpose: input_size = input_shape[1]
+ elif input_size is None: input_size = input_shape[-1]
+
+ self.norm = nn.BatchNorm1d(
+ input_size,
+ eps=eps,
+ momentum=momentum,
+ affine=affine,
+ track_running_stats=track_running_stats
+ )
+
+ def forward(self, x):
+ """Applies normalization over the input sequence."""
+
+ shape_or = x.shape
+
+ if self.combine_batch_time:
+ # Flatten Batch and Time dimensions together to normalize features independently
+
+ x = (
+ x.reshape(shape_or[0] * shape_or[1], shape_or[2])
+ ) if x.ndim == 3 else (
+ x.reshape(shape_or[0] * shape_or[1], shape_or[3], shape_or[2])
+ )
+ elif not self.skip_transpose: x = x.transpose(-1, 1)
+
+ x_n = self.norm(x)
+ # Restore original tensor layouts post-normalization
+ if self.combine_batch_time: x_n = x_n.reshape(shape_or)
+ elif not self.skip_transpose: x_n = x_n.transpose(1, -1)
+
+ return x_n
+
+class _Conv1d(nn.Module):
+ """
+ An internal core 1D Convolution class supporting dynamic padding strategies and flexible initialization methods.
+ """
+
+ def __init__(
+ self,
+ out_channels,
+ kernel_size,
+ input_shape=None,
+ in_channels=None,
+ stride=1,
+ dilation=1,
+ padding="same",
+ groups=1,
+ bias=True,
+ padding_mode="reflect",
+ skip_transpose=False,
+ weight_norm=False,
+ conv_init=None,
+ default_padding=0
+ ):
+ """Initializes custom configurations for 1D convolution layer layers."""
+
+ super().__init__()
+ self.kernel_size = kernel_size
+ self.stride = stride
+ self.dilation = dilation
+ self.padding = padding
+ self.padding_mode = padding_mode
+ self.unsqueeze = False
+ self.skip_transpose = skip_transpose
+
+ if input_shape is None and in_channels is None: raise ValueError("Either input_shape or in_channels must be defined.")
+ if in_channels is None: in_channels = self._check_input_shape(input_shape)
+
+ self.in_channels = in_channels
+ self.conv = nn.Conv1d(
+ in_channels,
+ out_channels,
+ self.kernel_size,
+ stride=self.stride,
+ dilation=self.dilation,
+ padding=default_padding,
+ groups=groups,
+ bias=bias
+ )
+
+ # Initialize weight structures based on standard initialization algorithms
+ if conv_init == "kaiming": nn.init.kaiming_normal_(self.conv.weight)
+ elif conv_init == "zero": nn.init.zeros_(self.conv.weight)
+ elif conv_init == "normal": nn.init.normal_(self.conv.weight, std=1e-6)
+
+ # Apply standard weight normalization transformations
+ if weight_norm: self.conv = nn.utils.weight_norm(self.conv)
+
+ def forward(self, x):
+ """Applies spatial padding configurations and forward convolution pass."""
+
+ if not self.skip_transpose: x = x.transpose(1, -1)
+ if self.unsqueeze: x = x.unsqueeze(1)
+
+ # Evaluate contextual padding configurations
+ if self.padding == "same":
+ x = self._manage_padding(
+ x,
+ self.kernel_size,
+ self.dilation,
+ self.stride
+ )
+ elif self.padding == "causal":
+ # Apply causal padding to prevent information leakage from future frames
+ x = F.pad(
+ x,
+ ((self.kernel_size - 1) * self.dilation, 0)
+ )
+ elif self.padding == "valid": pass
+ else: raise ValueError(f"Unsupported padding strategy: {self.padding}")
+
+ wx = self.conv(x)
+
+ if self.unsqueeze: wx = wx.squeeze(1)
+ if not self.skip_transpose: wx = wx.transpose(1, -1)
+
+ return wx
+
+ def _manage_padding(self, x, kernel_size, dilation, stride):
+ """Wraps functional padding operations with dynamically computed bounds."""
+
+ return F.pad(
+ x,
+ get_padding_elem(
+ self.in_channels,
+ stride,
+ kernel_size,
+ dilation
+ ),
+ mode=self.padding_mode
+ )
+
+ def _check_input_shape(self, shape):
+ """Validates shape semantics and determines operational input channel counts."""
+
+ if len(shape) == 2:
+ self.unsqueeze = True
+ in_channels = 1
+ elif self.skip_transpose: in_channels = shape[1]
+ elif len(shape) == 3: in_channels = shape[2]
+ else: raise ValueError(f"Unsupported input shape dimensions: {len(shape)}")
+
+ if not self.padding == "valid" and self.kernel_size % 2 == 0: raise ValueError("Even kernel sizes are only compatible with 'valid' padding.")
+ return in_channels
+
+ def remove_weight_norm(self):
+ """Removes weight normalization from the internal convolution layer."""
+
+ self.conv = nn.utils.remove_weight_norm(self.conv)
+
+class Linear(torch.nn.Module):
+ """
+ Custom Linear projection block handling higher-order dimension flattening
+
+ and weight normalization (max-norm clipping) techniques.
+ """
+
+ def __init__(
+ self,
+ n_neurons,
+ input_shape=None,
+ input_size=None,
+ bias=True,
+ max_norm=None,
+ combine_dims=False
+ ):
+ """Initializes projection weights and flattening options."""
+
+ super().__init__()
+ self.max_norm = max_norm
+ self.combine_dims = combine_dims
+
+ if input_shape is None and input_size is None: raise ValueError
+ if input_size is None:
+ input_size = input_shape[-1]
+ # Compress spatial dimensions if specific 4D processing conditions are requested
+ if len(input_shape) == 4 and self.combine_dims: input_size = input_shape[2] * input_shape[3]
+
+ self.w = nn.Linear(input_size, n_neurons, bias=bias)
+
+ def forward(self, x):
+ """Executes forward projection passing after checking structural norm criteria."""
+
+ if x.ndim == 4 and self.combine_dims: x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3])
+ # Enforce max-norm constraints by renormalizing row weights inline
+ if self.max_norm is not None: self.w.weight.data = torch.renorm(self.w.weight.data, p=2, dim=0, maxnorm=self.max_norm)
+
+ return self.w(x)
+
+class Conv1d(_Conv1d):
+ def __init__(
+ self,
+ *args,
+ **kwargs
+ ):
+ """An explicit 1D Convolution wrapper subclass defaulting to channel-first layout formatting."""
+
+ super().__init__(
+ skip_transpose=True,
+ *args,
+ **kwargs
+ )
+
+class BatchNorm1d(_BatchNorm1d):
+ """An explicit 1D Batch Normalization wrapper subclass defaulting to channel-first layout formatting."""
+
+ def __init__(
+ self,
+ *args,
+ **kwargs
+ ):
+ super().__init__(
+ skip_transpose=True,
+ *args,
+ **kwargs
+ )
+
+class TDNNBlock(nn.Module):
+ """
+ Time Delay Neural Network (TDNN) base block grouping 1D Convolution,
+
+ activation function, normalization layers, and dropout steps.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ dilation,
+ activation=nn.ReLU,
+ groups=1,
+ dropout=0.0
+ ):
+ """Builds standard structural TDNN sequence operations."""
+
+ super().__init__()
+ self.conv = Conv1d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ dilation=dilation,
+ groups=groups
+ )
+
+ self.activation = activation()
+ self.norm = BatchNorm1d(input_size=out_channels)
+ self.dropout = nn.Dropout1d(p=dropout)
+
+ def forward(self, x):
+ """Passes sequential representations through standard composite layer blocks."""
+
+ return self.dropout(self.norm(self.activation(self.conv(x))))
+
+class Res2NetBlock(torch.nn.Module):
+ """
+ Res2Net component module partitioning channel profiles into multi-stage residual scales
+
+ to establish hierarchical multi-scale receptive processing paths.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ scale=8,
+ kernel_size=3,
+ dilation=1,
+ dropout=0.0
+ ):
+ """Constructs sub-block pipelines according to target scale values."""
+
+ super().__init__()
+ assert in_channels % scale == 0
+ assert out_channels % scale == 0
+ in_channel = in_channels // scale
+ hidden_channel = out_channels // scale
+ # Instantiate recursive processing blocks across segmented groups
+ self.blocks = nn.ModuleList([
+ TDNNBlock(
+ in_channel,
+ hidden_channel,
+ kernel_size=kernel_size,
+ dilation=dilation,
+ dropout=dropout
+ )
+ for _ in range(scale - 1)
+ ])
+ self.scale = scale
+
+ def forward(self, x):
+ """Applies sequential split operations and sums forward channels hierarchically."""
+
+ y = []
+ # Split along channels dimension into equal chunk groups
+ for i, x_i in enumerate(x.chunk(self.scale, dim=1)):
+ if i == 0: y_i = x_i # The first subset passes through unmodified without operation
+ elif i == 1: y_i = self.blocks[i - 1](x_i)
+ else: y_i = self.blocks[i - 1](x_i + y_i) # Accumulate current chunks with the previous sub-block output to form local context links
+
+ y.append(y_i)
+
+ return torch.cat(y, dim=1)
+
+class SEBlock(nn.Module):
+ """
+ Squeeze-and-Excitation (SE) block customized to track context limits
+
+ dynamically using optional length masks.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ se_channels,
+ out_channels
+ ):
+ """Creates compression bottlenecks for feature re-weighting pipelines."""
+
+ super().__init__()
+ self.conv1 = Conv1d(
+ in_channels=in_channels,
+ out_channels=se_channels,
+ kernel_size=1
+ )
+ self.relu = torch.nn.ReLU(inplace=True)
+
+ self.conv2 = Conv1d(
+ in_channels=se_channels,
+ out_channels=out_channels,
+ kernel_size=1
+ )
+ self.sigmoid = torch.nn.Sigmoid()
+
+ def forward(self, x, lengths=None):
+ """Performs masked statistics collection and rescales channel profiles."""
+
+ L = x.shape[-1]
+ # Calculate localized temporal means based on input lengths if available
+ if lengths is not None:
+ mask = length_to_mask(
+ lengths * L,
+ max_len=L,
+ device=x.device
+ ).unsqueeze(1)
+
+ s = (x * mask).sum(dim=2, keepdim=True) / mask.sum(dim=2, keepdim=True)
+ else: s = x.mean(dim=2, keepdim=True)
+
+ # Generate excitation scalars and scale the input activations
+ return self.sigmoid(self.conv2(self.relu(self.conv1(s)))) * x
+
+class AttentiveStatisticsPooling(nn.Module):
+ """
+ Attentive Statistics Pooling (ASP) layer designed to compute weighted
+
+ means and variances across variable-length frame sequences using self-attention mechanisms.
+ """
+
+ def __init__(
+ self,
+ channels,
+ attention_channels=128,
+ global_context=True
+ ):
+ """Initializes internal attention models and multi-scale feature tracking parameters."""
+
+ super().__init__()
+ self.eps = 1e-12
+ self.global_context = global_context
+ # Determine projection paths using global sequence contexts if requested
+ self.tdnn = (
+ TDNNBlock(
+ channels * 3,
+ attention_channels,
+ 1,
+ 1
+ )
+ ) if global_context else (
+ TDNNBlock(
+ channels,
+ attention_channels,
+ 1,
+ 1
+ )
+ )
+ self.tanh = nn.Tanh()
+ self.conv = Conv1d(
+ in_channels=attention_channels,
+ out_channels=channels,
+ kernel_size=1
+ )
+
+ def forward(self, x, lengths=None):
+ """Aggregates temporal frame sequences into fixed-dimensional statistical vectors."""
+
+ L = x.shape[-1]
+
+ def _compute_statistics(x, m, dim=2, eps=self.eps):
+ # Internal helper function to extract attention-weighted mean and standard deviation
+ mean = (m * x).sum(dim)
+ return mean, ((m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(eps)).sqrt()
+
+ if lengths is None: lengths = torch.ones(x.shape[0], device=x.device)
+ mask = length_to_mask(lengths * L, max_len=L, device=x.device).unsqueeze(1)
+
+ if self.global_context:
+ # Squeeze time dimension and merge global mean/std vectors alongside raw sequence frames
+ mean, std = _compute_statistics(
+ x,
+ mask / mask.sum(dim=2, keepdim=True).float()
+ )
+
+ attn = torch.cat([
+ x,
+ mean.unsqueeze(2).repeat(1, 1, L),
+ std.unsqueeze(2).repeat(1, 1, L)
+ ], dim=1)
+ else: attn = x
+
+ # Compute the final attention-weighted mean and standard deviation vectors
+ mean, std = _compute_statistics(
+ x,
+ # Compute raw energy scores, mask invalid sequence padding elements, and normalize with Softmax
+ F.softmax(
+ self.conv(
+ self.tanh(self.tdnn(attn))
+ ).masked_fill(mask == 0, float("-inf")),
+ dim=2
+ )
+ )
+
+ # Concatenate mean and variance descriptors into a unified spatial pooling vector
+ return torch.cat((mean, std), dim=1).unsqueeze(2)
+
+class SERes2NetBlock(nn.Module):
+ """
+ A unified block combining Multi-scale Res2Net features with Squeeze-and-Excitation
+
+ gating logic and identity residual shortcuts.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ res2net_scale=8,
+ se_channels=128,
+ kernel_size=1,
+ dilation=1,
+ activation=torch.nn.ReLU,
+ groups=1,
+ dropout=0.0
+ ):
+ """Initializes internal structural component blocks."""
+
+ super().__init__()
+ self.out_channels = out_channels
+ self.tdnn1 = TDNNBlock(
+ in_channels,
+ out_channels,
+ kernel_size=1,
+ dilation=1,
+ activation=activation,
+ groups=groups, dropout=dropout
+ )
+ self.res2net_block = Res2NetBlock(
+ out_channels,
+ out_channels,
+ res2net_scale,
+ kernel_size,
+ dilation
+ )
+ self.tdnn2 = TDNNBlock(
+ out_channels,
+ out_channels,
+ kernel_size=1,
+ dilation=1,
+ activation=activation,
+ groups=groups,
+ dropout=dropout
+ )
+ self.se_block = SEBlock(
+ out_channels,
+ se_channels,
+ out_channels
+ )
+ # Set up a projection linear shortcut if channel dimension sizes mismatch
+ self.shortcut = None
+ if in_channels != out_channels:
+ self.shortcut = Conv1d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1
+ )
+
+ def forward(self, x, lengths=None):
+ """Executes full block transformations and returns combined shortcut residuals."""
+
+ residual = x
+ if self.shortcut: residual = self.shortcut(x)
+ # Pipeline flow: TDNN -> Res2Net -> TDNN -> SE -> Residual Add
+ return self.se_block(
+ self.tdnn2(self.res2net_block(self.tdnn1(x))),
+ lengths
+ ) + residual
+
+class ECAPA_TDNN(torch.nn.Module):
+ """
+ The complete ECAPA-TDNN architecture tailored for robust speaker embedding extraction
+
+ and audio classification tasks.
+ """
+
+ def __init__(
+ self,
+ input_size,
+ device="cpu",
+ lin_neurons=192,
+ activation=torch.nn.ReLU,
+ channels=[512, 512, 512, 512, 1536],
+ kernel_sizes=[5, 3, 3, 3, 1],
+ dilations=[1, 2, 3, 4, 1],
+ attention_channels=128,
+ res2net_scale=8,
+ se_channels=128,
+ global_context=True,
+ groups=[1, 1, 1, 1, 1],
+ dropout=0.0
+ ):
+ """Constructs consecutive layers and multi-layer feature aggregation (MFA) blocks."""
+
+ super().__init__()
+ assert len(channels) == len(kernel_sizes)
+ assert len(channels) == len(dilations)
+
+ self.channels = channels
+ self.blocks = nn.ModuleList()
+ # Layer 1: Initial feature processing TDNN block
+ self.blocks.append(
+ TDNNBlock(
+ input_size,
+ channels[0],
+ kernel_sizes[0],
+ dilations[0],
+ activation,
+ groups[0],
+ dropout
+ )
+ )
+
+ # Layers 2 to 4: Intermediate multi-scale SERes2Net blocks
+ for i in range(1, len(channels) - 1):
+ self.blocks.append(
+ SERes2NetBlock(
+ channels[i - 1],
+ channels[i],
+ res2net_scale=res2net_scale,
+ se_channels=se_channels,
+ kernel_size=kernel_sizes[i],
+ dilation=dilations[i],
+ activation=activation,
+ groups=groups[i],
+ dropout=dropout
+ )
+ )
+
+ # Layer 5: Multi-Stage Feature Aggregation block (MFA)
+ self.mfa = TDNNBlock(
+ channels[-2] * (len(channels) - 2),
+ channels[-1],
+ kernel_sizes[-1],
+ dilations[-1],
+ activation,
+ groups=groups[-1],
+ dropout=dropout
+ )
+ # Layer 6: Attentive Statistics Pooling (ASP) segment
+ self.asp = AttentiveStatisticsPooling(
+ channels[-1],
+ attention_channels=attention_channels,
+ global_context=global_context
+ )
+
+ # Layer 7: Final linear bottleneck projections and normalizations
+ self.asp_bn = BatchNorm1d(
+ input_size=channels[-1] * 2
+ )
+ self.fc = Conv1d(
+ in_channels=channels[-1] * 2,
+ out_channels=lin_neurons,
+ kernel_size=1
+ )
+
+ def forward(self, x, lengths=None):
+ """Processes raw acoustic inputs and yields optimized latent identity representations."""
+
+ x = x.transpose(1, 2)
+ xl = []
+ # Forward step through foundational layer hierarchies
+ for layer in self.blocks:
+ try:
+ x = layer(x, lengths=lengths)
+ except TypeError:
+ x = layer(x) # Fallback if block does not support sequence length parameters
+
+ xl.append(x)
+
+ return self.fc(
+ self.asp_bn(self.asp(self.mfa(torch.cat(xl[1:], dim=1)), lengths=lengths))
+ ).transpose(1, 2)
+
+class Classifier(torch.nn.Module):
+ """
+ Cosine-similarity classifier layer equipped with customizable linear
+
+ projection blocks and Xavier weight initializations.
+ """
+
+ def __init__(
+ self,
+ input_size,
+ device="cpu",
+ lin_blocks=0,
+ lin_neurons=192,
+ out_neurons=1211
+ ):
+ """Constructs projection paths and initializes prediction weight weights."""
+ super().__init__()
+ self.blocks = nn.ModuleList()
+ # Interleave additional linear scaling projections if requested
+ for _ in range(lin_blocks):
+ self.blocks.extend([
+ _BatchNorm1d(input_size=input_size),
+ Linear(input_size=input_size, n_neurons=lin_neurons)
+ ])
+ input_size = lin_neurons
+
+ # Linear classification matrix initialized via Xavier distribution mapping
+ self.weight = nn.Parameter(
+ torch.FloatTensor(
+ out_neurons,
+ input_size,
+ device=device
+ )
+ )
+
+ nn.init.xavier_uniform_(self.weight)
+
+ def forward(self, x):
+ """Computes normalized cosine logit scores over feature embeddings."""
+
+ for layer in self.blocks:
+ x = layer(x)
+
+ # Apply Cosine similarity metric normalization across activations and targets
+ return F.linear(
+ F.normalize(x.squeeze(1)),
+ F.normalize(self.weight)
+ ).unsqueeze(1)
\ No newline at end of file
diff --git a/main/library/speaker_diarization/audio.py b/main/library/speaker_diarization/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..24478294ef79cf1e60ec5b507caf5c11b36142ec
--- /dev/null
+++ b/main/library/speaker_diarization/audio.py
@@ -0,0 +1,306 @@
+import os
+import math
+import torch
+import random
+import librosa
+import torchaudio
+
+from io import IOBase
+
+class Audio:
+ """
+ A comprehensive utility class for audio processing, loading, resampling,
+
+ downmixing, and precision cropping using PyTorch, torchaudio, and librosa.
+ """
+
+ @staticmethod
+ def power_normalize(waveform):
+ """
+ Normalizes the waveform to have a Root Mean Square (RMS) power of 1.
+
+ Args:
+ waveform (torch.Tensor): Audio waveform tensor of shape (channels, samples).
+
+ Returns:
+ torch.Tensor: Power-normalized waveform tensor.
+ """
+
+ # Calculate RMS along the last dimension and avoid division by zero with a small epsilon (1e-8)
+ return waveform / (waveform.square().mean(dim=-1, keepdim=True).sqrt() + 1e-8)
+
+ @staticmethod
+ def validate_file(file):
+ """
+ Validates and standardizes various audio file input types into a structured dictionary.
+
+ Args:
+ file (Union[str, os.PathLike, IOBase, Dict[str, Any]]): File path, file-like stream, or dictionary containing audio specifications.
+
+ Returns:
+ Dict[str, Any]: Standardized file metadata dictionary.
+
+ Raises:
+ ValueError: If the input format or internal shape metadata is invalid.
+ """
+
+ if isinstance(file, (str, os.PathLike)):
+ # Case 1: Input is a file path string or PathLike object
+ file = {
+ "audio": str(file),
+ "uri": os.path.splitext(os.path.basename(file))[0] # Use file name without extension as URI
+ }
+ elif isinstance(file, IOBase):
+ # Case 2: Input is an open file-like stream (IOBase)
+ return {
+ "audio": file,
+ "uri": "stream"
+ }
+ else:
+ raise ValueError("Unsupported file type provided.")
+
+ # Validation logic if a raw waveform tensor is already provided in the dictionary
+ if "waveform" in file:
+ waveform = file["waveform"]
+ # Ensure waveform is 2D (channels, samples) and channels < samples
+ if len(waveform.shape) != 2 or waveform.shape[0] > waveform.shape[1]: raise ValueError("Waveform must be 2D with shape (channels, samples).")
+
+ # Sample rate is mandatory if an explicit waveform is provided
+ sample_rate = file.get("sample_rate", None)
+ if sample_rate is None: raise ValueError("sample_rate must be provided alongside waveform.")
+
+ file.setdefault("uri", "waveform")
+ elif "audio" in file:
+ # Validation logic if an audio path/stream pointer is provided in the dictionary
+ if isinstance(file["audio"], IOBase): return file
+ # Convert relative paths to absolute paths
+ path = os.path.abspath(file["audio"])
+ file.setdefault("uri", os.path.splitext(os.path.basename(path))[0])
+ else:
+ raise ValueError("The dictionary must contain either 'waveform' or 'audio' keys.")
+
+ return file
+
+ def __init__(
+ self,
+ sample_rate = None,
+ mono=None,
+ backend = None
+ ):
+ """
+ Initializes the Audio processor with specific target configurations.
+
+ Args:
+ sample_rate (int, optional): Target sample rate for resampling. Defaults to None (no resampling).
+ mono (str, optional): Downmix strategy for multi-channel audio. Options: "random", "downmix". Defaults to None.
+ backend (str, optional): I/O backend for torchaudio. Defaults to "soundfile".
+ """
+
+ super().__init__()
+ self.sample_rate = sample_rate
+ self.mono = mono
+
+ # Default to 'soundfile' backend if none is specified
+ if not backend: backend = "soundfile"
+ self.backend = backend
+
+ def downmix_and_resample(self, waveform, sample_rate):
+ """
+ Performs channel downmixing and sample rate conversion based on instance configurations.
+
+ Args:
+ waveform (torch.Tensor): Audio waveform tensor of shape (channels, samples).
+ sample_rate (int): Current sample rate of the input waveform.
+
+ Returns:
+ Tuple[torch.Tensor, int]: Processed waveform tensor and its new sample rate.
+ """
+
+ num_channels = waveform.shape[0]
+ # Handle multi-channel downmixing if mono configuration is active
+ if num_channels > 1:
+ if self.mono == "random":
+ # Select one random channel from the available channels
+ channel = random.randint(0, num_channels - 1)
+ waveform = waveform[channel : channel + 1]
+ elif self.mono == "downmix":
+ # Average all channels to create a single mono channel
+ waveform = waveform.mean(dim=0, keepdim=True)
+
+ # Resample the waveform if a target sample rate is set and differs from current sample rate
+ if (self.sample_rate is not None) and (self.sample_rate != sample_rate):
+ waveform = torchaudio.functional.resample(
+ waveform,
+ sample_rate,
+ self.sample_rate
+ )
+
+ sample_rate = self.sample_rate
+
+ return waveform, sample_rate
+
+ def get_num_samples(self, duration, sample_rate = None):
+ """
+ Calculates the total number of samples for a given duration.
+
+ Args:
+ duration (float): Duration in seconds.
+ sample_rate (int, optional): Sample rate to use. Defaults to instance sample_rate.
+
+ Returns:
+ int: Calculated total number of samples (floored).
+
+ Raises:
+ ValueError: If no valid sample rate is available.
+ """
+
+ sample_rate = sample_rate or self.sample_rate
+ if sample_rate is None: raise ValueError("Sample rate must be provided or configured globally.")
+
+ return math.floor(duration * sample_rate)
+
+ def __call__(self, file):
+ """
+ Loads and processes the complete audio file or dictionary configuration.
+
+ Args:
+ file (Union[str, os.PathLike, IOBase, Dict[str, Any]]): The input audio source descriptor.
+
+ Returns:
+ Tuple[torch.Tensor, int]: Fully processed waveform tensor and its sample rate.
+ """
+
+ file = self.validate_file(file)
+ # Extract waveform directly if already loaded in dictionary
+ if "waveform" in file:
+ waveform = file["waveform"]
+ sample_rate = file["sample_rate"]
+ elif "audio" in file: # Otherwise, read and load the audio data from disk or stream
+ try:
+ # Primary attempt: Use torchaudio with the specified backend
+ waveform, sample_rate = torchaudio.load(file["audio"], backend=self.backend)
+ except:
+ # Fallback attempt: Use librosa if torchaudio fails (e.g., format or backend issue)
+ y, sample_rate = librosa.load(file["audio"], sr=None, mono=False)
+ waveform = torch.from_numpy(y)
+
+ # Reset stream pointer position to the beginning if input is an IOBase object
+ if isinstance(file["audio"], IOBase): file["audio"].seek(0)
+
+ # Slice specific channel if explicitly requested in the file dictionary
+ channel = file.get("channel", None)
+ if channel is not None: waveform = waveform[channel : channel + 1]
+
+ # Apply downmixing and resampling configurations
+ return self.downmix_and_resample(waveform, sample_rate)
+
+ def crop(self, file, segment, duration = None, mode="raise"):
+ """
+ Extracts a precise temporal slice (crop) from an audio file or waveform.
+
+ Args:
+ file (Union[str, os.PathLike, IOBase, Dict[str, Any]]): The input audio source descriptor.
+ segment (Any): An object representing the segment boundaries (must contain .start and .end in seconds).
+ duration (float, optional): Exact duration to extract in seconds. Overrides segment.end if specified.
+ mode (str, optional): Out-of-bounds handling strategy. Options: "raise", "pad". Defaults to "raise".
+
+ Returns:
+ Tuple[torch.Tensor, int]: Cropped waveform slice and its sample rate.
+
+ Raises:
+ ValueError: If constraints are violated under "raise" mode.
+ RuntimeError: On unrecoverable streaming / I/O reading errors.
+ """
+
+ file = self.validate_file(file)
+ # Determine total frames and sample rate based on metadata availability
+ if "waveform" in file:
+ waveform = file["waveform"]
+ frames = waveform.shape[1]
+ sample_rate = file["sample_rate"]
+ elif "torchaudio.info" in file:
+ info = file["torchaudio.info"]
+ frames = info.num_frames
+ sample_rate = info.sample_rate
+ else:
+ # Heavy fallback: Load metadata attributes using librosa
+ info, sr = librosa.load(file["audio"], sr=None)
+ frames = info.shape[0]
+ sample_rate = sr
+
+ channel = file.get("channel", None)
+ start_frame = math.floor(segment.start * sample_rate)
+ # Compute targeted frame ranges based on segment limits or requested duration
+ if duration:
+ num_frames = math.floor(duration * sample_rate)
+ end_frame = start_frame + num_frames
+ else:
+ end_frame = math.floor(segment.end * sample_rate)
+ num_frames = end_frame - start_frame
+
+ # Boundary Management - Strategy 1: Strict Validation ("raise" mode)
+ if mode == "raise":
+ if num_frames > frames: raise ValueError("Requested duration is longer than the file length.")
+ # Allow a tiny tolerance window (1ms) before discarding due to precision issues
+ if end_frame > frames + math.ceil(0.001 * sample_rate): raise ValueError("Requested crop segment extends past the end of the file.")
+ else:
+ end_frame = min(end_frame, frames)
+ start_frame = end_frame - num_frames
+
+ if start_frame < 0: raise ValueError
+ elif mode == "pad":
+ # Boundary Management - Strategy 2: Padding Preparation ("pad" mode)
+ pad_start = -min(0, start_frame)
+ pad_end = max(end_frame, frames) - frames
+
+ start_frame = max(0, start_frame)
+ end_frame = min(end_frame, frames)
+
+ num_frames = end_frame - start_frame
+
+ # Slice the audio data based on the source location
+ if "waveform" in file:
+ # In-memory slice if the tensor is already fully allocated inside the dict
+ data = file["waveform"][:, start_frame:end_frame]
+ else:
+ try:
+ try:
+ # Optimized partial loading using torchaudio with frame offsets
+ data, _ = torchaudio.load(
+ file["audio"],
+ frame_offset=start_frame,
+ num_frames=num_frames,
+ backend=self.backend
+ )
+ except:
+ # Fallback partial loading using librosa
+ y, _ = librosa.load(
+ file["audio"],
+ sr=sample_rate,
+ offset=start_frame / sample_rate,
+ duration=num_frames / sample_rate,
+ mono=False
+ )
+ data = torch.from_numpy(y)
+ # Keep tensor 2D even if loaded as single channel (mono)
+ data = data.unsqueeze(0) if len(data.shape) == 1 else data
+
+ if isinstance(file["audio"], IOBase): file["audio"].seek(0)
+ except RuntimeError:
+ # Streams cannot be re-read efficiently via fallback, raise immediately
+ if isinstance(file["audio"], IOBase): raise RuntimeError("Failed to seek or slice the given streaming IO handle.")
+
+ # Fallback: Load entire file into cache structure, then execute localized index slice
+ waveform, sample_rate = self.__call__(file)
+ data = waveform[:, start_frame:end_frame]
+
+ file["waveform"] = waveform
+ file["sample_rate"] = sample_rate
+
+ # Filter channel tracks post-crop if a channel constraint exists
+ if channel is not None: data = data[channel : channel + 1, :]
+ # Apply Zero-Padding if mode was set to 'pad'
+ if mode == "pad": data = torch.nn.functional.pad(data, (pad_start, pad_end))
+
+ # Pass cropped array through structural normalization/resampling filters
+ return self.downmix_and_resample(data, sample_rate)
\ No newline at end of file
diff --git a/main/library/speaker_diarization/embedding.py b/main/library/speaker_diarization/embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6f0e665708a032041bf3dc82a245d75ea21493f
--- /dev/null
+++ b/main/library/speaker_diarization/embedding.py
@@ -0,0 +1,153 @@
+import os
+import sys
+import torch
+
+import numpy as np
+import torch.nn.functional as F
+
+from functools import cached_property
+from torch.nn.utils.rnn import pad_sequence
+
+sys.path.append(os.getcwd())
+
+from main.library.speaker_diarization.speechbrain import EncoderClassifier
+
+class SpeechBrainPretrainedSpeakerEmbedding:
+ """
+ A wrapper class for SpeechBrain's pretrained EncoderClassifier to extract
+
+ speaker embeddings from audio waveforms, with support for temporal masking.
+ """
+
+ def __init__(
+ self,
+ embedding,
+ device = None
+ ):
+ """
+ Initializes the SpeechBrain pretrained model wrapper.
+
+ Args:
+ embedding (str): Path or HuggingFace source ID of the pretrained embedding model.
+ device (Optional[Union[str, torch.device]]): Target execution device (e.g., 'cuda' or 'cpu'). Defaults to CPU if not specified.
+ """
+
+ super().__init__()
+
+ self.embedding = embedding
+ self.device = device or torch.device("cpu")
+ # Load the pretrained SpeechBrain model using hyperparameter files
+ self.classifier_ = EncoderClassifier.from_hparams(
+ source=self.embedding,
+ run_opts={"device": self.device}
+ )
+
+ @cached_property
+ def dimension(self):
+ """
+ Determines the embedding output dimension dynamically using a dummy forward pass.
+
+ Returns:
+ int: The size/dimension of the generated speaker embedding vectors.
+ """
+
+ # Pass a 1-second dummy tensor (assuming 16kHz) through the encoder to catch the shape
+ *_, dimension = self.classifier_.encode_batch(
+ torch.rand(1, 16000).to(self.device)
+ ).shape
+
+ return dimension
+
+ @cached_property
+ def min_num_samples(self):
+ """
+ Finds the absolute minimum number of audio samples required by the model.
+
+ Uses a binary search strategy to find the lower bound threshold where the
+ forward pass no longer throws a processing RuntimeError.
+
+ Returns:
+ int: Minimum sample count threshold.
+ """
+
+ with torch.inference_mode():
+ # Set search boundaries: between 2 samples and 0.5 seconds of audio
+ lower, upper = 2, round(0.5 * self.classifier_.audio_normalizer.sample_rate)
+ middle = (lower + upper) // 2
+
+ while lower + 1 < upper:
+ try:
+ # Attempt encoder forward pass with a dummy tensor of length 'middle'
+ _ = self.classifier_.encode_batch(torch.randn(1, middle).to(self.device))
+ upper = middle # Successfully processed, try a smaller length
+ except RuntimeError:
+ lower = middle # Errored out, sample size is too short
+
+ middle = (lower + upper) // 2
+
+ return upper
+
+ def __call__(self, waveforms, masks = None):
+ """
+ Extracts speaker embeddings from a batch of waveforms with optional voice activity masks.
+
+ Args:
+ waveforms (torch.Tensor): Audio tensor.
+ masks (Optional[torch.Tensor], optional): Binary temporal mask tensor. Defaults to None.
+
+ Returns:
+ np.ndarray: A 2D NumPy array of shape (batch_size, embedding_dimension)
+ containing the embeddings. Sequences that are too short are populated with NaNs.
+ """
+
+ batch_size, num_channels, num_samples = waveforms.shape
+ assert num_channels == 1
+
+ # Remove the channel dimension -> shape becomes
+ waveforms = waveforms.squeeze(dim=1)
+
+ if masks is None:
+ signals = waveforms.squeeze(dim=1)
+ # All signals within the batch have the same maximum sequence frame length
+ wav_lens = signals.shape[1] * torch.ones(batch_size)
+ else:
+ batch_size_masks, _ = masks.shape
+ assert batch_size == batch_size_masks
+
+ # Interpolate low-res masks to match the exact audio sample length via nearest-neighbor
+ imasks = F.interpolate(
+ masks.unsqueeze(dim=1),
+ size=num_samples,
+ mode="nearest"
+ ).squeeze(dim=1) > 0.5 # Convert to a boolean selection matrix
+
+ # Filter out non-masked regions and pad sequences back to form a uniform tensor block
+ signals = pad_sequence([
+ waveform[imask].contiguous()
+ for waveform, imask in zip(waveforms, imasks)
+ ], batch_first=True)
+
+ # Compute total active sample frames per batch item
+ wav_lens = imasks.sum(dim=1)
+
+ # Edge Case: If the longest sequence in the batch is still under minimum bounds, abort early
+ max_len = wav_lens.max()
+ if max_len < self.min_num_samples: return np.nan * np.zeros((batch_size, self.dimension))
+
+ # Identify which individual elements fail the minimal length requirement
+ too_short = wav_lens < self.min_num_samples
+ # SpeechBrain expects relative lengths scaled between 0.0 and 1.0
+ wav_lens = wav_lens / max_len
+ # Prevent runtime crashes by assigning a fallback dummy length ratio to short tracks
+ wav_lens[too_short] = 1.0
+
+ # Execute SpeechBrain batch forward pass
+ embeddings = (
+ self.classifier_.encode_batch(
+ signals,
+ wav_lens=wav_lens
+ ).squeeze(dim=1).cpu().numpy()
+ )
+ # Overwrite invalid/short sequence entries with NaN flags
+ embeddings[too_short.cpu().numpy()] = np.nan
+ return embeddings
\ No newline at end of file
diff --git a/main/library/speaker_diarization/encoder.py b/main/library/speaker_diarization/encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..742003a69971975d02e2e305049ed823dd5bcfa2
--- /dev/null
+++ b/main/library/speaker_diarization/encoder.py
@@ -0,0 +1,402 @@
+import os
+import sys
+import ast
+import torch
+import itertools
+import collections
+
+sys.path.append(os.getcwd())
+
+from main.library.speaker_diarization.speechbrain import if_main_process, ddp_barrier
+from main.library.speaker_diarization.features import register_checkpoint_hooks, mark_as_saver, mark_as_loader
+
+@register_checkpoint_hooks
+class CategoricalEncoder:
+ """
+ A bidirectional encoder designed to map categorical text labels to continuous
+ numerical indices and vice versa. It supports unknown label fallback, sequence
+ encoding/decoding, and distributed data parallel (DDP) synchronized loading.
+ """
+
+ VALUE_SEPARATOR = " => "
+ EXTRAS_SEPARATOR = "================\n"
+
+ def __init__(
+ self,
+ starting_index=0,
+ **special_labels
+ ):
+ """
+ Initializes mapping dictionaries and registers optional configurations.
+
+ Args:
+ starting_index (int, optional): The lowest boundary integer to begin index mapping. Defaults to 0.
+ **special_labels (Any): Key-value pairs defining specialized token targets (e.g., unk_label="").
+ """
+
+ self.lab2ind = {}
+ self.ind2lab = {}
+ self.starting_index = starting_index
+ self.handle_special_labels(special_labels)
+
+ def handle_special_labels(self, special_labels):
+ """Parses arguments to register a designated out-of-vocabulary (OOV) token."""
+
+ if "unk_label" in special_labels: self.add_unk(special_labels["unk_label"])
+
+ def __len__(self):
+ """Returns total distinct mapped categories."""
+
+ return len(self.lab2ind)
+
+ @classmethod
+ def from_saved(cls, path):
+ """
+ Instantiates the class and instantly populates entries from a flat text record file.
+
+ Args:
+ path (str): File path location containing saved key-to-index dictionaries.
+
+ Returns:
+ CategoricalEncoder: An initialized instance populated with the loaded dataset vocabulary.
+ """
+
+ obj = cls()
+ obj.load(path)
+ return obj
+
+ def update_from_iterable(self, iterable, sequence_input=False):
+ """
+ Iterates over entries or flat sequences to dynamically expand vocabulary fields.
+
+ Args:
+ iterable (Iterable[Any]): Target items or sequence arrays to process.
+ sequence_input (bool, optional): Treat the iterator as a collection of sub-sequences to be flattened. Defaults to False.
+ """
+
+ label_iterator = itertools.chain.from_iterable(iterable) if sequence_input else iter(iterable)
+ for label in label_iterator:
+ self.ensure_label(label)
+
+ def update_from_didataset(self, didataset, output_key, sequence_input=False):
+ """
+ Extracts and parses structural dictionary labels from custom pipeline datasets.
+
+ Args:
+ didataset (Any): Object implementing contextual key restrictions.
+ output_key (str): Targeted target data key holding item labels.
+ sequence_input (bool, optional): Specifies if values are sequences requiring flattening. Defaults to False.
+ """
+
+ with didataset.output_keys_as([output_key]):
+ self.update_from_iterable(
+ (data_point[output_key] for data_point in didataset),
+ sequence_input=sequence_input
+ )
+
+ def limited_labelset_from_iterable(
+ self,
+ iterable,
+ sequence_input=False,
+ n_most_common=None,
+ min_count=1
+ ):
+ """
+ Limits label selection to the top tokens filtering by threshold and occurrence frequency.
+
+ Args:
+ iterable (Iterable[Any]): Elements or series containing data targets.
+ sequence_input (bool, optional): Flattens iterable streams if explicitly set. Defaults to False.
+ n_most_common (Optional[int], optional): Maximum cap limit for selecting elements. Defaults to None.
+ min_count (int, optional): Minimum frequency bound cutoff. Defaults to 1.
+
+ Returns:
+ collections.Counter: Frequency records for the input elements.
+ """
+
+ label_iterator = itertools.chain.from_iterable(iterable) if sequence_input else iter(iterable)
+ counts = collections.Counter(label_iterator)
+
+ for label, count in counts.most_common(n_most_common):
+ if count < min_count: break
+ self.add_label(label)
+
+ return counts
+
+ def load_or_create(
+ self,
+ path,
+ from_iterables=[],
+ from_didatasets=[],
+ sequence_input=False,
+ output_key=None,
+ special_labels={}
+ ):
+ """
+ Attempts to load a dictionary configuration checkpoint. If it doesn't exist,
+ builds it from source iterables across synchronized DDP processes.
+
+ Args:
+ path (str): Intended target load or backup serialization file path.
+ from_iterables (List[Iterable[Any]], optional): Source tracking objects. Defaults to [].
+ from_didatasets (List[Any], optional): Structural database objects. Defaults to [].
+ sequence_input (bool, optional): Sequence context handling flag. Defaults to False.
+ output_key (Optional[str], optional): Targeted evaluation keys inside data dictionaries. Defaults to None.
+ special_labels (Dict[str, Any], optional): Custom dynamic labels dictionary. Defaults to {}.
+ """
+
+ try:
+ if if_main_process():
+ if not self.load_if_possible(path):
+ for iterable in from_iterables:
+ self.update_from_iterable(iterable, sequence_input)
+
+ for didataset in from_didatasets:
+ if output_key is None: raise ValueError
+ self.update_from_didataset(didataset, output_key, sequence_input)
+
+ self.handle_special_labels(special_labels)
+ self.save(path)
+ finally:
+ # Block sub-processes until the main thread completes vocabulary build operations
+ ddp_barrier()
+ self.load(path)
+
+ def add_label(self, label):
+ """
+ Registers a unique label, matching it against the next available integer index.
+
+ Args:
+ label (Any): Unique category name or label token.
+
+ Returns:
+ int: The newly assigned integer index.
+ """
+
+ if label in self.lab2ind: raise KeyError(f"Label '{label}' is already registered in the encoder mapping definitions.")
+ index = self._next_index()
+
+ self.lab2ind[label] = index
+ self.ind2lab[index] = label
+
+ return index
+
+ def ensure_label(self, label):
+ """Saves a category or passes back the index value if it already exists."""
+
+ if label in self.lab2ind: return self.lab2ind[label]
+ else: return self.add_label(label)
+
+ def insert_label(self, label, index):
+ """Inserts a specific category label at a fixed target index location."""
+
+ if label in self.lab2ind: raise KeyError(f"Label '{label}' already exists.")
+ else: self.enforce_label(label, index)
+
+ def enforce_label(self, label, index):
+ """Binds a specific label to an index, shifting competing records if collisions occur."""
+
+ index = int(index)
+ # Clear old indices matching this exact label string reference
+ if label in self.lab2ind:
+ if index == self.lab2ind[label]: return
+ else: del self.ind2lab[self.lab2ind[label]]
+
+ # Detect collisions on the target index
+ if index in self.ind2lab:
+ saved_label = self.ind2lab[index]
+ moving_other = True
+ else: moving_other = False
+
+ self.lab2ind[label] = index
+ self.ind2lab[index] = label
+
+ # Resolve index collisions by migrating displaced labels to the next open index
+ if moving_other:
+ new_index = self._next_index()
+ self.lab2ind[saved_label] = new_index
+ self.ind2lab[new_index] = saved_label
+
+ def add_unk(self, unk_label=""):
+ """Registers an out-of-vocabulary fallback default token identifier."""
+
+ self.unk_label = unk_label
+ return self.add_label(unk_label)
+
+ def _next_index(self):
+ """Scans indexes sequentially to find the first unassigned slot."""
+
+ index = self.starting_index
+ while index in self.ind2lab:
+ index += 1
+
+ return index
+
+ def is_continuous(self):
+ """Checks if the index definitions contain holes or gaps."""
+
+ indices = sorted(self.ind2lab.keys())
+ return self.starting_index in indices and all(j - i == 1 for i, j in zip(indices[:-1], indices[1:]))
+
+ def encode_label(self, label, allow_unk=True):
+ """Transforms an item string/token into its corresponding index target integer."""
+
+ self._assert_len()
+
+ try:
+ return self.lab2ind[label]
+ except KeyError:
+ if hasattr(self, "unk_label") and allow_unk: return self.lab2ind[self.unk_label]
+ else: raise KeyError(f"Label '{label}' not found, and no unknown fallback behavior is enabled.")
+
+ def encode_label_torch(self, label, allow_unk=True):
+ """Wraps the encoded label integer inside a 1D PyTorch LongTensor."""
+
+ return torch.LongTensor([self.encode_label(label, allow_unk)])
+
+ def encode_sequence(self, sequence, allow_unk=True):
+ """Encodes an array list of string labels into an array list of index integers."""
+
+ self._assert_len()
+ return [self.encode_label(label, allow_unk) for label in sequence]
+
+ def encode_sequence_torch(self, sequence, allow_unk=True):
+ """Converts an array list of sequence labels directly into a PyTorch LongTensor."""
+
+ return torch.LongTensor([self.encode_label(label, allow_unk) for label in sequence])
+
+ def decode_torch(self, x):
+ """Recursively parses indices out of PyTorch multi-dimensional arrays back into labels."""
+
+ self._assert_len()
+ decoded = []
+
+ if x.ndim == 1:
+ for element in x:
+ decoded.append(self.ind2lab[int(element)])
+ else:
+ for subtensor in x:
+ decoded.append(self.decode_torch(subtensor))
+
+ return decoded
+
+ def decode_ndim(self, x):
+ """Recursively walks nested iterables or lists to reconstruct raw data labels."""
+
+ self._assert_len()
+ try:
+ decoded = []
+ for subtensor in x:
+ decoded.append(self.decode_ndim(subtensor))
+
+ return decoded
+ except TypeError:
+ # Triggers when item cannot be iterated, treating it as a basic integer key
+ return self.ind2lab[int(x)]
+
+ @mark_as_saver
+ def save(self, path):
+ """Serializes current category dictionaries to disk."""
+
+ self._save_literal(path, self.lab2ind, self._get_extras())
+
+ def load(self, path):
+ """Loads entries into storage, overwriting all current configuration bounds."""
+
+ lab2ind, ind2lab, extras = self._load_literal(path)
+ self.lab2ind = lab2ind
+ self.ind2lab = ind2lab
+ self._set_extras(extras)
+
+ @mark_as_loader
+ def load_if_possible(self, path, end_of_epoch=False):
+ """Gracefully loads checkpoints if they exist without breaking runtime workflows."""
+
+ del end_of_epoch
+
+ try:
+ self.load(path)
+ except FileNotFoundError:
+ return False
+ except (ValueError, SyntaxError):
+ return False
+
+ return True
+
+ def expect_len(self, expected_len):
+ """Sets an expected vocabulary size constraint for runtime validation."""
+
+ self.expected_len = expected_len
+
+ def ignore_len(self):
+ """Disables vocabulary size verification constraints."""
+
+ self.expected_len = None
+
+ def _assert_len(self):
+ """Validates that current state sizing matches requested expectations."""
+
+ if hasattr(self, "expected_len"):
+ if self.expected_len is None: return
+ if len(self) != self.expected_len: raise RuntimeError(f"Size mismatch: Expected {self.expected_len} items, got {len(self)}.")
+ else:
+ self.ignore_len()
+ return
+
+ def _get_extras(self):
+ """Bundles supplementary structural class attributes for serialization processing."""
+
+ extras = {"starting_index": self.starting_index}
+ if hasattr(self, "unk_label"): extras["unk_label"] = self.unk_label
+
+ return extras
+
+ def _set_extras(self, extras):
+ """Restores structural operational states from unmarshalled attributes dictionaries."""
+
+ if "unk_label" in extras: self.unk_label = extras["unk_label"]
+ self.starting_index = extras["starting_index"]
+
+ @staticmethod
+ def _save_literal(path, lab2ind, extras):
+ """Writes plain text data structures with human-readable separators."""
+
+ with open(path, "w", encoding="utf-8") as f:
+ for label, ind in lab2ind.items():
+ f.write(repr(label) + CategoricalEncoder.VALUE_SEPARATOR + str(ind) + "\n")
+
+ f.write(CategoricalEncoder.EXTRAS_SEPARATOR)
+
+ for key, value in extras.items():
+ f.write(repr(key) + CategoricalEncoder.VALUE_SEPARATOR + repr(value) + "\n")
+
+ f.flush()
+
+ @staticmethod
+ def _load_literal(path):
+ """Parses custom flat map texts containing standard serialized Python literals."""
+
+ lab2ind, ind2lab, extras = {}, {}, {}
+
+ with open(path, encoding="utf-8") as f:
+ for line in f:
+ if line == CategoricalEncoder.EXTRAS_SEPARATOR: break
+
+ literal, ind = line.strip().split(
+ CategoricalEncoder.VALUE_SEPARATOR,
+ maxsplit=1
+ )
+
+ label = ast.literal_eval(literal)
+ lab2ind[label] = int(ind)
+ ind2lab[ind] = label # Cast index as int to fix lookups in downstream decoders
+
+ for line in f:
+ literal_key, literal_value = line.strip().split(
+ CategoricalEncoder.VALUE_SEPARATOR,
+ maxsplit=1
+ )
+
+ extras[ast.literal_eval(literal_key)] = ast.literal_eval(literal_value)
+
+ return lab2ind, ind2lab, extras
\ No newline at end of file
diff --git a/main/library/speaker_diarization/features.py b/main/library/speaker_diarization/features.py
new file mode 100644
index 0000000000000000000000000000000000000000..154b001e13228e29c752d497de32cae704cf12f9
--- /dev/null
+++ b/main/library/speaker_diarization/features.py
@@ -0,0 +1,919 @@
+import os
+import sys
+import math
+import torch
+import inspect
+import functools
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config
+from main.library.speaker_diarization.speechbrain import MAIN_PROC_ONLY, is_distributed_initialized, main_process_only
+
+WEAKREF_MARKER = "WEAKREF"
+
+KEYS_MAPPING = {
+ ".mutihead_attn": ".multihead_attn",
+ ".convs_intermedite": ".convs_intermediate"
+}
+
+def map_old_state_dict_weights(state_dict, mapping):
+ """
+ Updates outdated or typo-ridden state_dict keys with modern equivalents.
+
+ Args:
+ state_dict (Dict[str, torch.Tensor]): Model checkpoint parameters state dictionary.
+ mapping (Dict[str, str]): Key substitution configuration patterns.
+
+ Returns:
+ Dict[str, torch.Tensor]: Remapped configuration weights matrix.
+ """
+
+ for replacement_old, replacement_new in mapping.items():
+ for old_key in list(state_dict.keys()):
+ if replacement_old in old_key:
+ state_dict[
+ old_key.replace(replacement_old, replacement_new)
+ ] = state_dict.pop(old_key)
+
+ return state_dict
+
+def hook_on_loading_state_dict_checkpoint(state_dict):
+ """Callback wrapper ensuring state_dict complies with updated key mappings before structural binding."""
+
+ return map_old_state_dict_weights(
+ state_dict,
+ KEYS_MAPPING
+ )
+
+def torch_patched_state_dict_load(path, device="cpu"):
+ """
+ Loads a PyTorch state dict securely using safe-weights filtering and applies key patches.
+
+ Args:
+ path (str): File path location of the checkpoint tensor file.
+ device (Union[str, torch.device]): Device targeting map locations. Defaults to "cpu".
+
+ Returns:
+ Dict[str, torch.Tensor]: Patched state dictionary.
+ """
+
+ return hook_on_loading_state_dict_checkpoint(
+ torch.load(
+ path,
+ map_location=device,
+ weights_only=True
+ )
+ )
+
+@main_process_only
+def torch_save(obj, path):
+ """Saves object state parameters to a disk archive from the main process thread only."""
+
+ state_dict = obj.state_dict()
+ torch.save(state_dict, path)
+
+def torch_recovery(obj, path, end_of_epoch):
+ """
+ Restores saved dictionary parameters into target working modules safely.
+
+ Args:
+ obj (Any): Target object or module instance (e.g., nn.Module, Optimizer).
+ path (str): Checkpoint file path source.
+ end_of_epoch (bool): Indicates if the trigger occurred exactly on an epoch boundary loop.
+ """
+
+ del end_of_epoch
+
+ state_dict = torch_patched_state_dict_load(path, "cpu")
+
+ try:
+ obj.load_state_dict(state_dict, strict=True)
+ except TypeError:
+ # Fallback mechanism if the module's load_state_dict doesn't explicitly accept the strict parameter
+ obj.load_state_dict(state_dict)
+
+def torch_parameter_transfer(obj, path):
+ """Performs a non-strict warm-start parameters transfer between models with partially matching layouts."""
+
+ incompatible_keys = obj.load_state_dict(
+ torch_patched_state_dict_load(path, "cpu"),
+ strict=False
+ )
+ # Hooks to handle shape deviations can be appended safely below
+ for missing_key in incompatible_keys.missing_keys:
+ pass
+ for unexpected_key in incompatible_keys.unexpected_keys:
+ pass
+
+def _cycliclrsaver(obj, path):
+ """Serializes CyclicLR scheduler state while severing weak references to prevent garbage collection errors."""
+
+ state_dict = obj.state_dict()
+ if state_dict.get("_scale_fn_ref") is not None: state_dict["_scale_fn_ref"] = WEAKREF_MARKER
+
+ torch.save(state_dict, path)
+
+def _cycliclrloader(obj, path, end_of_epoch):
+ """Restores CyclicLR schedulers parameters safely."""
+
+ del end_of_epoch
+
+ try:
+ obj.load_state_dict(
+ torch.load(path, map_location="cpu", weights_only=True),
+ strict=True
+ )
+ except TypeError:
+ obj.load_state_dict(
+ torch.load(path, map_location="cpu", weights_only=True)
+ )
+
+# Mapping of standard handlers for PyTorch system objects
+DEFAULT_LOAD_HOOKS = {
+ torch.nn.Module: torch_recovery,
+ torch.optim.Optimizer: torch_recovery,
+ torch.optim.lr_scheduler.ReduceLROnPlateau: torch_recovery,
+ torch.cuda.amp.grad_scaler.GradScaler: torch_recovery
+}
+
+DEFAULT_SAVE_HOOKS = {
+ torch.nn.Module: torch_save,
+ torch.optim.Optimizer: torch_save,
+ torch.optim.lr_scheduler.ReduceLROnPlateau: torch_save,
+ torch.cuda.amp.grad_scaler.GradScaler: torch_save
+}
+
+# Attach standard hooks for general LRSchedulers
+DEFAULT_LOAD_HOOKS[
+ torch.optim.lr_scheduler.LRScheduler
+] = torch_recovery
+
+DEFAULT_SAVE_HOOKS[
+ torch.optim.lr_scheduler.LRScheduler
+] = torch_save
+
+DEFAULT_TRANSFER_HOOKS = {
+ torch.nn.Module: torch_parameter_transfer
+}
+
+# Attach custom handlers for CyclicLR schedulers
+DEFAULT_SAVE_HOOKS[
+ torch.optim.lr_scheduler.CyclicLR
+] = _cycliclrsaver
+
+DEFAULT_LOAD_HOOKS[
+ torch.optim.lr_scheduler.CyclicLR
+] = _cycliclrloader
+
+def register_checkpoint_hooks(cls, save_on_main_only=True):
+ """
+ Class decorator to auto-discover and register custom SpeechBrain saver/loader hooks.
+
+ Args:
+ cls (Any): Target class being modified.
+ save_on_main_only (bool, optional): Wraps saving logic to limit disk writes to the main process thread.
+ Defaults to True.
+
+ Returns:
+ Any: Modified class containing global serialization hook definitions.
+ """
+
+ global DEFAULT_LOAD_HOOKS, DEFAULT_SAVE_HOOKS, DEFAULT_TRANSFER_HOOKS
+
+ for name, method in cls.__dict__.items():
+ if hasattr(method, "_speechbrain_saver"): DEFAULT_SAVE_HOOKS[cls] = main_process_only(method) if save_on_main_only else method
+ if hasattr(method, "_speechbrain_loader"): DEFAULT_LOAD_HOOKS[cls] = method
+ if hasattr(method, "_speechbrain_transfer"): DEFAULT_TRANSFER_HOOKS[cls] = method
+
+ return cls
+
+def mark_as_saver(method):
+ """Marks a class method as the primary checkpoint serialization routine."""
+
+ sig = inspect.signature(method)
+
+ try:
+ sig.bind(object(), "testpath")
+ except TypeError:
+ raise TypeError("Saver function must match signature pattern: method(self, path)")
+
+ method._speechbrain_saver = True
+ return method
+
+def mark_as_transfer(method):
+ """Marks a class method as a valid target for partial parameter weight migration transfers."""
+
+ sig = inspect.signature(method)
+
+ try:
+ sig.bind(object(), "testpath")
+ except TypeError:
+ raise TypeError("Transfer function must match signature pattern: method(self, path)")
+
+ method._speechbrain_transfer = True
+ return method
+
+def mark_as_loader(method):
+ """Marks a class method as the target deserializer decoder routine."""
+
+ sig = inspect.signature(method)
+
+ try:
+ sig.bind(object(), "testpath", True)
+ except TypeError:
+ raise TypeError("Loader function must match signature pattern: method(self, path, end_of_epoch)")
+
+ method._speechbrain_loader = True
+ return method
+
+def ddp_all_reduce(communication_object, reduce_op):
+ """
+ Gathers and performs collective reduction calculations across elements distributed on worker clusters.
+
+ Args:
+ communication_object (torch.Tensor): Metric tensor values requiring multi-GPU sync.
+ reduce_op (Any): PyTorch distributed reduction operation behavior (e.g., SUM, AVG).
+
+ Returns:
+ torch.Tensor: Evaluated tensor value synchronized across distributed processes.
+ """
+
+ if MAIN_PROC_ONLY >= 1 or not is_distributed_initialized(): return communication_object
+ torch.distributed.all_reduce(communication_object, op=reduce_op)
+
+ return communication_object
+
+def fwd_default_precision(fwd = None, cast_inputs = torch.float32):
+ """
+ Decorator ensuring forward operations execute under uniform, predictable floating-point precision levels.
+
+ Args:
+ fwd (Optional[Callable]): Target execution routine block. Defaults to None.
+ cast_inputs (torch.dtype): Target floating precision requirement. Defaults to torch.float32.
+ """
+
+ if fwd is None: return functools.partial(fwd_default_precision, cast_inputs=cast_inputs)
+ # Resolve execution target environment to bind appropriate accelerator backend
+ wrapped_fwd = torch.amp.custom_fwd(fwd, cast_inputs=cast_inputs, device_type=config.device if config.device.startswith(("cuda", "xpu")) else "cpu")
+
+ @functools.wraps(fwd)
+ def wrapper(*args, force_allow_autocast = False, **kwargs):
+ return fwd(*args, **kwargs) if force_allow_autocast else wrapped_fwd(*args, **kwargs)
+
+ return wrapper
+
+def spectral_magnitude(stft, power = 1, log = False, eps = 1e-14):
+ """
+ Computes the magnitude or power spectrogram from STFT complex tensors.
+
+ Args:
+ stft (torch.Tensor): Multi-channel complex STFT tensor array outputs.
+ power (float): Scaling factor exponent for the magnitude matrix. Defaults to 1.
+ log (bool): Converts the signal representation into the logarithmic spectrum domain when True. Defaults to False.
+ eps (float): Small epsilon value to avoid log(0) numerical instabilities. Defaults to 1e-14.
+ """
+
+ spectr = stft.pow(2).sum(-1)
+
+ if power < 1: spectr = spectr + eps
+ spectr = spectr.pow(power)
+
+ if log: return (spectr + eps).log()
+ return spectr
+
+class Filterbank(torch.nn.Module):
+ """Generates custom parametric Mel-scale filterbanks supporting trainable and dynamic initialization."""
+
+ def __init__(
+ self,
+ n_mels=40,
+ log_mel=True,
+ filter_shape="triangular",
+ f_min=0,
+ f_max=8000,
+ n_fft=400,
+ sample_rate=16000,
+ power_spectrogram=2,
+ amin=1e-10,
+ ref_value=1.0,
+ top_db=80.0,
+ param_change_factor=1.0,
+ param_rand_factor=0.0,
+ freeze=True
+ ):
+ """Initializes filterbank geometry and mapping spaces."""
+
+ super().__init__()
+ self.n_mels = n_mels
+ self.log_mel = log_mel
+ self.filter_shape = filter_shape
+ self.f_min = f_min
+ self.f_max = f_max
+ self.n_fft = n_fft
+ self.sample_rate = sample_rate
+ self.power_spectrogram = power_spectrogram
+ self.amin = amin
+ self.ref_value = ref_value
+ self.top_db = top_db
+ self.freeze = freeze
+ self.n_stft = self.n_fft // 2 + 1
+ self.db_multiplier = math.log10(max(self.amin, self.ref_value))
+ self.device_inp = torch.device("cpu")
+ self.param_change_factor = param_change_factor
+ self.param_rand_factor = param_rand_factor
+ self.multiplier = 10 if self.power_spectrogram == 2 else 20
+ # Uniformly segment standard Mel space grid matrices
+ hz = self._to_hz(torch.linspace(self._to_mel(self.f_min), self._to_mel(self.f_max), self.n_mels + 2))
+
+ band = hz[1:] - hz[:-1]
+ self.band = band[:-1]
+ self.f_central = hz[1:-1]
+
+ if not self.freeze:
+ # Map values into fractional learnable parameters
+ self.f_central = torch.nn.Parameter(self.f_central / (self.sample_rate * self.param_change_factor))
+ self.band = torch.nn.Parameter(self.band / (self.sample_rate * self.param_change_factor))
+
+ self.all_freqs_mat = torch.linspace(0, self.sample_rate // 2, self.n_stft).repeat(self.f_central.shape[0], 1)
+
+ def forward(self, spectrogram):
+ """
+ Applies filterbank analysis matrices to input spectrograms.
+
+ Args:
+ spectrogram (torch.Tensor): Tensor matrix shape (batch, time, frequencies) or 4D channel variants.
+ """
+
+ f_central_mat = self.f_central.repeat(self.all_freqs_mat.shape[1], 1).transpose(0, 1)
+ band_mat = self.band.repeat(self.all_freqs_mat.shape[1], 1).transpose(0, 1)
+
+ if not self.freeze:
+ f_central_mat = f_central_mat * (self.sample_rate * self.param_change_factor * self.param_change_factor)
+ band_mat = band_mat * (self.sample_rate * self.param_change_factor * self.param_change_factor)
+ elif self.param_rand_factor != 0 and self.training:
+ rand_change = (1.0 + torch.rand(2) * 2 * self.param_rand_factor - self.param_rand_factor)
+ f_central_mat = f_central_mat * rand_change[0]
+ band_mat = band_mat * rand_change[1]
+
+ fbank_matrix = self._create_fbank_matrix(f_central_mat, band_mat).to(spectrogram.device)
+ sp_shape = spectrogram.shape
+ if len(sp_shape) == 4: spectrogram = spectrogram.permute(0, 3, 1, 2).reshape(sp_shape[0] * sp_shape[3], sp_shape[1], sp_shape[2])
+
+ fbanks = spectrogram @ fbank_matrix
+ if self.log_mel: fbanks = self._amplitude_to_DB(fbanks)
+
+ if len(sp_shape) == 4:
+ fb_shape = fbanks.shape
+ fbanks = fbanks.reshape(sp_shape[0], sp_shape[3], fb_shape[1], fb_shape[2]).permute(0, 2, 3, 1)
+
+ return fbanks
+
+ @staticmethod
+ def _to_mel(hz):
+ """Converts Hertz frequency values into Mel frequency values."""
+
+ return 2595 * math.log10(1 + hz / 700)
+
+ @staticmethod
+ def _to_hz(mel):
+ """Converts Mel scale arrays directly back to standard Hertz metrics."""
+
+ return 700 * (10 ** (mel / 2595) - 1)
+
+ def _triangular_filters(self, all_freqs, f_central, band):
+ """Constructs standard triangular overlap filtering layouts."""
+
+ slope = (all_freqs - f_central) / band
+ return torch.zeros(1, device=self.device_inp).max((slope + 1.0).min(-slope + 1.0)).transpose(0, 1)
+
+ def _rectangular_filters(self, all_freqs, f_central, band):
+ """Constructs standard sharp boxcar rectangular mask arrays."""
+
+ left_side = right_size = all_freqs.ge(f_central - band)
+ right_size = all_freqs.le(f_central + band)
+
+ return (left_side * right_size).float().transpose(0, 1)
+
+ def _gaussian_filters(self, all_freqs, f_central, band, smooth_factor=torch.tensor(2)):
+ """Constructs radial Gaussian curve filtering layouts."""
+
+ return (-0.5 * ((all_freqs - f_central) / (band / smooth_factor)) ** 2).exp().transpose(0, 1)
+
+ def _create_fbank_matrix(self, f_central_mat, band_mat):
+ """Factory dispatcher resolving intended structural shapes into numeric matrix masks."""
+
+ if self.filter_shape == "triangular": fbank_matrix = self._triangular_filters(self.all_freqs_mat, f_central_mat, band_mat)
+ elif self.filter_shape == "rectangular": fbank_matrix = self._rectangular_filters(self.all_freqs_mat, f_central_mat, band_mat)
+ else: fbank_matrix = self._gaussian_filters(self.all_freqs_mat, f_central_mat, band_mat)
+
+ return fbank_matrix
+
+ def _amplitude_to_DB(self, x):
+ """Converts standard linear filter energy values directly into logarithmic Decibel power metrics."""
+
+ x_db = self.multiplier * x.clamp(min=self.amin).log10()
+ x_db -= self.multiplier * self.db_multiplier
+
+ return x_db.max((x_db.amax(dim=(-2, -1)) - self.top_db).view(x_db.shape[0], 1, 1))
+
+class ContextWindow(torch.nn.Module):
+ """Applies temporal context splicing stacking windows over acoustic frames via localized 1D Convolutions."""
+
+ def __init__(
+ self,
+ left_frames=0,
+ right_frames=0
+ ):
+ """Initializes convolutional kernel configurations to stack temporal frames."""
+
+ super().__init__()
+ self.left_frames = left_frames
+ self.right_frames = right_frames
+ self.context_len = self.left_frames + self.right_frames + 1
+ self.kernel_len = 2 * max(self.left_frames, self.right_frames) + 1
+ self.kernel = torch.eye(self.context_len, self.kernel_len)
+
+ if self.right_frames > self.left_frames: self.kernel = torch.roll(self.kernel, self.right_frames - self.left_frames, 1)
+ self.first_call = True
+
+ def forward(self, x):
+ """Stacks context frames over feature channels sequentially."""
+
+ x = x.transpose(1, 2)
+ if self.first_call:
+ self.first_call = False
+ self.kernel = (self.kernel.repeat(x.shape[1], 1, 1).view(x.shape[1] * self.context_len, self.kernel_len).unsqueeze(1))
+
+ or_shape = x.shape
+ if len(or_shape) == 4: x = x.reshape(or_shape[0] * or_shape[2], or_shape[1], or_shape[3])
+
+ cw_x = torch.nn.functional.conv1d(
+ x,
+ self.kernel.to(x.device),
+ groups=x.shape[1],
+ padding=max(self.left_frames, self.right_frames)
+ )
+
+ if len(or_shape) == 4: cw_x = cw_x.reshape(or_shape[0], cw_x.shape[1], or_shape[2], cw_x.shape[-1])
+ return cw_x.transpose(1, 2)
+
+class FilterProperties:
+ """Calculates operational properties (effective receptive fields, padding offsets) for stacked filters."""
+
+ def __init__(self, window_size = 0, stride = 1, dilation = 1, causal = False):
+ """Initializes physical structural properties."""
+
+ self.window_size = window_size
+ self.stride = stride
+ self.dilation = dilation
+ self.causal = causal
+
+ def __post_init__(self):
+ """Validates filter configurations."""
+
+ assert self.window_size > 0
+ assert self.stride > 0
+ assert (self.dilation > 0)
+
+ @staticmethod
+ def pointwise_filter():
+ """Generates properties for a basic 1x1 pointwise filtering window layer."""
+
+ return FilterProperties(window_size=1, stride=1)
+
+ def get_effective_size(self):
+ """Returns the calculated virtual receptive field footprint considering active dilation properties."""
+
+ return 1 + ((self.window_size - 1) * self.dilation)
+
+ def get_convolution_padding(self):
+ """Computes symmetric padding requirements."""
+
+ if self.window_size % 2 == 0: raise ValueError("Asymmetric padding evaluation for even window boundaries is unsupported.")
+ if self.causal: return self.get_effective_size() - 1
+
+ return (self.get_effective_size() - 1) // 2
+
+ def get_noncausal_equivalent(self):
+ """Transforms properties into standard symmetric bidirectional equivalents."""
+
+ if not self.causal: return self
+
+ return FilterProperties(
+ window_size=(self.window_size - 1) * 2 + 1,
+ stride=self.stride,
+ dilation=self.dilation,
+ causal=False
+ )
+
+ def with_on_top(self, other, allow_approximate=True):
+ """Calculates structural composite property behaviors when stacking layers together."""
+
+ self_size = self.window_size
+
+ if other.window_size % 2 == 0:
+ if allow_approximate: other_size = other.window_size + 1
+ else: raise ValueError("Even structural layer shapes require approximation permissions enabled.")
+ else: other_size = other.window_size
+
+ if (self.causal or other.causal) and not (self.causal and other.causal):
+ if allow_approximate: return self.get_noncausal_equivalent().with_on_top(other.get_noncausal_equivalent())
+ else: raise ValueError("Mixed causal settings require approximation permissions enabled.")
+
+ return FilterProperties(self_size + (self.stride * (other_size - 1)), self.stride * other.stride, self.dilation * other.dilation, self.causal)
+
+class STFT(torch.nn.Module):
+ """Computes Short-Time Fourier Transform (STFT) frames from raw 1D digital audio streams."""
+
+ def __init__(
+ self,
+ sample_rate,
+ win_length=25,
+ hop_length=10,
+ n_fft=400,
+ window_fn=torch.hamming_window,
+ normalized_stft=False,
+ center=True,
+ pad_mode="constant",
+ onesided=True
+ ):
+ """Initializes STFT parameter windows converting millisecond metrics directly to discrete audio sample frames."""
+
+ super().__init__()
+ self.sample_rate = sample_rate
+ self.win_length = win_length
+ self.hop_length = hop_length
+ self.n_fft = n_fft
+ self.normalized_stft = normalized_stft
+ self.center = center
+ self.pad_mode = pad_mode
+ self.onesided = onesided
+ # Transform duration metrics from milliseconds to physical sample frames counts
+ self.win_length = int(round((self.sample_rate / 1000.0) * self.win_length))
+ self.hop_length = int(round((self.sample_rate / 1000.0) * self.hop_length))
+ self.window = window_fn(self.win_length)
+
+ def forward(self, x):
+ """Transforms digital waveforms directly into multi-channel frequency spectrum maps."""
+
+ or_shape = x.shape
+ if len(or_shape) == 3: x = x.transpose(1, 2).reshape(or_shape[0] * or_shape[2], or_shape[1])
+
+ device = x.device
+ if str(device) not in ["cuda", "xpu", "cpu"]: x = x.cpu()
+
+ stft = torch.view_as_real(
+ torch.stft(
+ x,
+ n_fft=self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_length,
+ window=self.window.to(x.device),
+ center=self.center,
+ pad_mode=self.pad_mode,
+ normalized=self.normalized_stft,
+ onesided=self.onesided,
+ return_complex=True
+ )
+ )
+
+ stft = (
+ stft.reshape(
+ or_shape[0],
+ or_shape[2],
+ stft.shape[1],
+ stft.shape[2],
+ stft.shape[3]
+ ).permute(0, 3, 2, 4, 1)
+ ) if len(or_shape) == 3 else (
+ stft.transpose(2, 1)
+ )
+
+ return stft.to(device)
+
+ def get_filter_properties(self):
+ """Exposes operational framing properties."""
+
+ if not self.center: raise ValueError("Framing metadata requires center configuration setting parameters set to True.")
+
+ return FilterProperties(
+ window_size=self.win_length,
+ stride=self.hop_length
+ )
+
+class Deltas(torch.nn.Module):
+ """Computes dynamic delta trajectories (derivatives) across localized temporal spaces."""
+
+ def __init__(
+ self,
+ input_size,
+ window_length=5
+ ):
+ """Initializes delta calculation kernel buffers."""
+
+ super().__init__()
+ self.n = (window_length - 1) // 2
+ self.denom = self.n * (self.n + 1) * (2 * self.n + 1) / 3
+ self.register_buffer("kernel", torch.arange(-self.n, self.n + 1, dtype=torch.float32).repeat(input_size, 1, 1))
+
+ def forward(self, x):
+ """Applies derivative tracking filters over target features arrays streams."""
+
+ x = x.transpose(1, 2).transpose(2, -1)
+ or_shape = x.shape
+
+ if len(or_shape) == 4:
+ x = x.reshape(
+ or_shape[0] * or_shape[2],
+ or_shape[1],
+ or_shape[3]
+ )
+
+ x = torch.nn.functional.pad(
+ x,
+ (self.n, self.n),
+ mode="replicate"
+ )
+
+ delta_coeff = (
+ torch.nn.functional.conv1d(
+ x,
+ self.kernel.to(x.device),
+ groups=x.shape[1]
+ ) / self.denom
+ )
+
+ if len(or_shape) == 4:
+ delta_coeff = delta_coeff.reshape(
+ or_shape[0],
+ or_shape[1],
+ or_shape[2],
+ or_shape[3]
+ )
+
+ return delta_coeff.transpose(1, -1).transpose(2, -1)
+
+class Fbank(torch.nn.Module):
+ """Composite module tracking pipeline extraction of Mel-Filterbank energy configurations from waveforms."""
+
+ def __init__(
+ self,
+ deltas=False,
+ context=False,
+ requires_grad=False,
+ sample_rate=16000,
+ f_min=0,
+ f_max=None,
+ n_fft=400,
+ n_mels=40,
+ filter_shape="triangular",
+ param_change_factor=1.0,
+ param_rand_factor=0.0,
+ left_frames=5,
+ right_frames=5,
+ win_length=25,
+ hop_length=10
+ ):
+ """Initializes underlying submodules (STFT, Filterbank, Deltas, ContextWindow)."""
+
+ super().__init__()
+ self.deltas = deltas
+ self.context = context
+ self.requires_grad = requires_grad
+ if f_max is None: f_max = sample_rate / 2
+
+ self.compute_STFT = STFT(
+ sample_rate=sample_rate,
+ n_fft=n_fft,
+ win_length=win_length,
+ hop_length=hop_length
+ )
+ self.compute_fbanks = Filterbank(
+ sample_rate=sample_rate,
+ n_fft=n_fft,
+ n_mels=n_mels,
+ f_min=f_min,
+ f_max=f_max,
+ freeze=not requires_grad,
+ filter_shape=filter_shape,
+ param_change_factor=param_change_factor,
+ param_rand_factor=param_rand_factor
+ )
+ self.compute_deltas = Deltas(
+ input_size=n_mels
+ )
+ self.context_window = ContextWindow(
+ left_frames=left_frames,
+ right_frames=right_frames
+ )
+
+ @fwd_default_precision(cast_inputs=torch.float32)
+ def forward(self, wav):
+ """Executes full pipeline processing converting raw waveforms into context-aware mel features fields."""
+
+ fbanks = self.compute_fbanks(
+ spectral_magnitude(
+ self.compute_STFT(wav)
+ )
+ )
+
+ if self.deltas:
+ delta1 = self.compute_deltas(fbanks)
+ fbanks = torch.cat([
+ fbanks, delta1,
+ self.compute_deltas(delta1)
+ ], dim=2)
+
+ if self.context: fbanks = self.context_window(fbanks)
+ return fbanks
+
+ def get_filter_properties(self):
+ """Returns filter property descriptions."""
+
+ return self.compute_STFT.get_filter_properties()
+
+@register_checkpoint_hooks
+class InputNormalization(torch.nn.Module):
+ """Normalizes input acoustic feature maps across Sentence, Batch, Speaker, or Global tracks distributions."""
+
+ def __init__(
+ self,
+ mean_norm=True,
+ std_norm=True,
+ norm_type="global",
+ avg_factor=None,
+ requires_grad=False,
+ update_until_epoch=3
+ ):
+ """Initializes storage maps to hold mean and standard deviation properties."""
+
+ super().__init__()
+ self.mean_norm = mean_norm
+ self.std_norm = std_norm
+ self.norm_type = norm_type
+ self.avg_factor = avg_factor
+ self.requires_grad = requires_grad
+ self.glob_mean = torch.tensor([0])
+ self.glob_std = torch.tensor([0])
+ self.spk_dict_mean = {}
+ self.spk_dict_std = {}
+ self.spk_dict_count = {}
+ self.weight = 1.0
+ self.count = 0
+ self.eps = 1e-10
+ self.update_until_epoch = update_until_epoch
+
+ def forward(self, x, lengths, spk_ids = torch.tensor([]), epoch=0):
+ """Applies normalization scaling adjustments following specific distribution mode criteria rules."""
+
+ N_batches = x.shape[0]
+ current_means, current_stds = [], []
+
+ if (
+ self.norm_type == "sentence" or self.norm_type == "speaker"
+ ):
+ out = torch.empty_like(x)
+
+ for snt_id in range(N_batches):
+ actual_size = torch.round(lengths[snt_id] * x.shape[1]).int()
+ current_mean, current_std = self._compute_current_stats(x[snt_id, 0:actual_size, ...])
+
+ current_means.append(current_mean)
+ current_stds.append(current_std)
+
+ if self.norm_type == "sentence": out[snt_id] = (x[snt_id] - current_mean.data) / current_std.data
+
+ if self.norm_type == "speaker":
+ spk_id = int(spk_ids[snt_id][0])
+
+ if self.training:
+ if spk_id not in self.spk_dict_mean:
+ self.spk_dict_mean[spk_id] = current_mean
+ self.spk_dict_std[spk_id] = current_std
+ self.spk_dict_count[spk_id] = 1
+ else:
+ self.spk_dict_count[spk_id] = (self.spk_dict_count[spk_id] + 1)
+ self.weight = (1 / self.spk_dict_count[spk_id]) if self.avg_factor is None else self.avg_factor
+
+ self.spk_dict_mean[spk_id] = (1 - self.weight) * self.spk_dict_mean[spk_id].to(current_mean) + self.weight * current_mean
+ self.spk_dict_std[spk_id] = (1 - self.weight) * self.spk_dict_std[spk_id].to(current_std) + self.weight * current_std
+
+ self.spk_dict_mean[spk_id].detach()
+ self.spk_dict_std[spk_id].detach()
+
+ speaker_mean = self.spk_dict_mean[spk_id].data
+ speaker_std = self.spk_dict_std[spk_id].data
+ else:
+ if spk_id in self.spk_dict_mean:
+ speaker_mean = self.spk_dict_mean[spk_id].data
+ speaker_std = self.spk_dict_std[spk_id].data
+ else:
+ speaker_mean = current_mean.data
+ speaker_std = current_std.data
+
+ out[snt_id] = (x[snt_id] - speaker_mean) / speaker_std
+
+ if self.norm_type == "batch" or self.norm_type == "global":
+ current_mean = ddp_all_reduce(
+ torch.stack(current_means).mean(dim=0),
+ torch.distributed.ReduceOp.AVG
+ )
+
+ current_std = ddp_all_reduce(
+ torch.stack(current_stds).mean(dim=0),
+ torch.distributed.ReduceOp.AVG
+ )
+
+ if self.norm_type == "batch": out = (x - current_mean.data) / (current_std.data)
+
+ if self.norm_type == "global":
+ if self.training:
+ if self.count == 0:
+ self.glob_mean = current_mean
+ self.glob_std = current_std
+ elif epoch is None or epoch < self.update_until_epoch:
+ self.weight = (1 / (self.count + 1)) if self.avg_factor is None else self.avg_factor
+ self.glob_mean = (1 - self.weight) * self.glob_mean.to(current_mean) + self.weight * current_mean
+ self.glob_std = (1 - self.weight) * self.glob_std.to(current_std) + self.weight * current_std
+
+ self.glob_mean.detach()
+ self.glob_std.detach()
+ self.count = self.count + 1
+
+ out = (x - self.glob_mean.data.to(x)) / (self.glob_std.data.to(x))
+
+ return out
+
+ def _compute_current_stats(self, x):
+ """Calculates detached localized variance mean metrics elements targets structures."""
+
+ current_std = x.std(dim=0).detach().data if self.std_norm else torch.tensor([1.0], device=x.device)
+ return (
+ x.mean(dim=0).detach().data if self.mean_norm else torch.tensor([0.0], device=x.device),
+ current_std.max(self.eps * torch.ones_like(current_std))
+ )
+
+ def _statistics_dict(self):
+ """Bundles statistical mapping arrays for custom metadata exports."""
+
+ state = {}
+ state["count"] = self.count
+ state["glob_mean"] = self.glob_mean
+ state["glob_std"] = self.glob_std
+ state["spk_dict_mean"] = self.spk_dict_mean
+ state["spk_dict_std"] = self.spk_dict_std
+ state["spk_dict_count"] = self.spk_dict_count
+
+ return state
+
+ def _load_statistics_dict(self, state):
+ """Reconstructs state trackers tracking dictionary configurations logs objects."""
+
+ self.count = state["count"]
+
+ if isinstance(state["glob_mean"], int):
+ self.glob_mean = state["glob_mean"]
+ self.glob_std = state["glob_std"]
+ else:
+ self.glob_mean = state["glob_mean"]
+ self.glob_std = state["glob_std"]
+
+ self.spk_dict_mean = {}
+ for spk in state["spk_dict_mean"]:
+ self.spk_dict_mean[spk] = state["spk_dict_mean"][spk]
+
+ self.spk_dict_std = {}
+ for spk in state["spk_dict_std"]:
+ self.spk_dict_std[spk] = state["spk_dict_std"][spk]
+
+ self.spk_dict_count = state["spk_dict_count"]
+ return state
+
+ def to(self, device):
+ """Migrates state tracks and mapping buffers directly onto target backend execution hardware."""
+
+ self = super(InputNormalization, self).to(device)
+ self.glob_mean = self.glob_mean.to(device)
+ self.glob_std = self.glob_std.to(device)
+
+ for spk in self.spk_dict_mean:
+ self.spk_dict_mean[spk] = self.spk_dict_mean[spk].to(device)
+ self.spk_dict_std[spk] = self.spk_dict_std[spk].to(device)
+
+ return self
+
+ @mark_as_saver
+ def _save(self, path):
+ """Checkpoint hook callback saving operational parameter maps arrays."""
+
+ torch.save(self._statistics_dict(), path)
+
+ @mark_as_transfer
+ @mark_as_loader
+ def _load(self, path, end_of_epoch=False):
+ """Checkpoint hook callback loading tracked mean parameter archives maps entries."""
+
+ del end_of_epoch
+ stats = torch.load(path, map_location="cpu", weights_only=True)
+ self._load_statistics_dict(stats)
\ No newline at end of file
diff --git a/main/library/speaker_diarization/parameter_transfer.py b/main/library/speaker_diarization/parameter_transfer.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ce18bd979d4ae5ebf4031d260de851d1e4b670e
--- /dev/null
+++ b/main/library/speaker_diarization/parameter_transfer.py
@@ -0,0 +1,205 @@
+import os
+import sys
+import inspect
+
+sys.path.append(os.getcwd())
+
+from main.library.speaker_diarization.speechbrain import fetch, run_on_main
+from main.library.speaker_diarization.features import DEFAULT_TRANSFER_HOOKS, DEFAULT_LOAD_HOOKS
+
+def get_default_hook(obj, default_hooks):
+ """
+ Resolves the appropriate handling hook function based on the object's Method Resolution Order (MRO).
+
+ Args:
+ obj (Any): Target object instance (e.g., nn.Module, Optimizer) to look up.
+ default_hooks (Dict[Any, Callable]): Dictionary mapping class types to hook functions.
+
+ Returns:
+ Optional[Callable]: The resolved callback hook function if matched, otherwise None.
+ """
+
+ for cls in inspect.getmro(type(obj)):
+ if cls in default_hooks: return default_hooks[cls]
+
+ return None
+
+class Pretrainer:
+ """
+ Manages downloading, local caching, and flexible state restoration for pre-trained models.
+
+ Supports custom registration mapping paths, runtime evaluation filters, and hierarchical hook bindings.
+ """
+
+ def __init__(
+ self,
+ loadables=None,
+ paths=None,
+ custom_hooks=None,
+ conditions=None
+ ):
+ """Initializes the Pretrainer system registries."""
+
+ self.loadables = {}
+
+ if loadables is not None: self.add_loadables(loadables)
+ self.paths = {}
+
+ if paths is not None: self.add_paths(paths)
+ self.custom_hooks = {}
+
+ if custom_hooks is not None: self.add_custom_hooks(custom_hooks)
+ self.conditions = {}
+
+ if conditions is not None: self.add_conditions(conditions)
+ self.is_local = []
+
+ def add_loadables(self, loadables):
+ """Registers objects targeting weight restorations (e.g., models, optimizers)."""
+
+ self.loadables.update(loadables)
+
+ def add_paths(self, paths):
+ """Maps loadable keys directly to their respective remote or local asset file locations."""
+
+ self.paths.update(paths)
+
+ def add_custom_hooks(self, custom_hooks):
+ """Binds customized standalone logic callback handlers targeting unique recovery objects."""
+
+ self.custom_hooks.update(custom_hooks)
+
+ def add_conditions(self, conditions):
+ """Sets conditional switches or dynamic callbacks deciding whether a key should be loaded."""
+
+ self.conditions.update(conditions)
+
+ @staticmethod
+ def split_path(path):
+ """
+ Splits a full URI or file system path into a separate source root and file name target.
+
+ Args:
+ path (str): The complete resource identifier path string.
+
+ Returns:
+ Tuple[str, str]: A pair containing (source_directory_or_url, filename).
+ """
+
+ def split(src):
+ if "/" in src: return src.rsplit("/", maxsplit=1)
+ else: return "./", src
+
+ return split(path)
+
+ def collect_files(self, default_source=None):
+ """
+ Downloads or resolves the local cache file path for all active registered loadables.
+
+ Ensures network fetching triggers on the main process thread first in distributed configurations.
+
+ Args:
+ default_source (Optional[str], optional): Fallback remote URL or directory root path. Defaults to None.
+
+ Returns:
+ Dict[str, str]: Resolved local file system path mappings.
+ """
+
+ loadable_paths = {}
+ for name in self.loadables:
+ if not self.is_loadable(name): continue
+ save_filename = name + ".ckpt"
+
+ if name in self.paths:
+ source, filename = self.split_path(
+ self.paths[name]
+ )
+ elif default_source is not None:
+ filename = save_filename
+ source = default_source
+ else: raise ValueError(f"Failed to resolve fetch location for '{name}'. Provide an explicit path or define a default source route fallback.")
+
+ fetch_kwargs = {
+ "filename": filename,
+ "source": source
+ }
+ path = None
+
+ def run_fetch(**kwargs):
+ nonlocal path
+
+ path = fetch(**kwargs)
+
+ # Block multi-GPU worker initialization race conditions by querying solely via the master thread root
+ run_on_main(
+ run_fetch,
+ kwargs=fetch_kwargs,
+ post_func=run_fetch,
+ post_kwargs=fetch_kwargs
+ )
+
+ loadable_paths[name] = path
+ self.paths[name] = str(path)
+ self.is_local.append(name)
+
+ return loadable_paths
+
+ def is_loadable(self, name):
+ """
+ Evaluates whether a loadable component satisfies its defined structural conditions.
+
+ Args:
+ name (str): Key matching targeted parameter objects.
+ """
+
+ if name not in self.conditions: return True
+ condition = self.conditions[name]
+
+ if callable(condition): return condition()
+ else: return bool(condition)
+
+ def load_collected(self):
+ """Triggers local file mapping verification before feeding components into structural loading hooks."""
+
+ paramfiles = {}
+ for name in self.loadables:
+ if not self.is_loadable(name): continue
+
+ if name in self.is_local: paramfiles[name] = self.paths[name]
+ else: raise ValueError(f"Component file target '{name}' must be collected locally before running state loads.")
+
+ self._call_load_hooks(paramfiles)
+
+ def _call_load_hooks(self, paramfiles):
+ """
+ Dispatches parameters restoration targets to their best matching functional hooks pipeline.
+
+ Prioritizes custom user-defined hooks, falls back to loose parameter transfers, then
+ standard system checkpoint recovery mechanisms, and errors out if no matching routine is discovered.
+ """
+
+ for name, obj in self.loadables.items():
+ if not self.is_loadable(name): continue
+ loadpath = paramfiles[name]
+
+ # Priority 1: User defined standalone customized callback injections
+ if name in self.custom_hooks:
+ self.custom_hooks[name](obj, loadpath)
+ continue
+
+ # Priority 2: Safe partial state transfer bindings (e.g., fine-tuning partial layers matches)
+ default_hook = get_default_hook(obj, DEFAULT_TRANSFER_HOOKS)
+
+ if default_hook is not None:
+ default_hook(obj, loadpath)
+ continue
+
+ # Priority 3: Standard matching layout checkpoint recovery loaders routines
+ default_hook = get_default_hook(obj, DEFAULT_LOAD_HOOKS)
+
+ if default_hook is not None:
+ end_of_epoch = False
+ default_hook(obj, loadpath, end_of_epoch)
+ continue
+
+ raise RuntimeError(f"No compatible parameter loading hook mechanism found for object '{name}' of class type: {type(obj)}.")
\ No newline at end of file
diff --git a/main/library/speaker_diarization/segment.py b/main/library/speaker_diarization/segment.py
new file mode 100644
index 0000000000000000000000000000000000000000..67980e0ee69ebf042f5f3299b349c666f6b330a1
--- /dev/null
+++ b/main/library/speaker_diarization/segment.py
@@ -0,0 +1,927 @@
+import os
+import sys
+import numpy as np
+
+sys.path.append(os.getcwd())
+
+from main.tools.sortedcontainers import SortedList
+
+AUTO_ROUND_TIME = False
+SEGMENT_PRECISION = 1e-6
+
+class Timeline:
+ """
+ Represents a collection of unique, ordered time segments.
+
+ Provides high-level geometric operations on time intervals such as union,
+ intersection (cropping), gaps detection, and overlapping lookups.
+ """
+
+ @classmethod
+ def from_df(cls, df, uri = None):
+ """
+ Creates a Timeline instance from a pandas DataFrame.
+
+ Args:
+ df: A pandas DataFrame containing a 'segment' column.
+ uri: Optional unique resource identifier for the timeline.
+
+ Returns:
+ A new Timeline instance containing the extracted segments.
+ """
+
+ return cls(segments=list(df['segment']), uri=uri)
+
+ def __init__(
+ self,
+ segments = None,
+ uri = None
+ ):
+ """
+ Initializes a Timeline with a collection of segments.
+
+ Args:
+ segments: An iterable collection of Segment objects.
+ uri: Optional unique resource identifier.
+ """
+
+ if segments is None: segments = ()
+ # Filter out empty/falsy segments and ensure uniqueness using a set
+ segments_set = set([segment for segment in segments if segment])
+
+ self.segments_set_ = segments_set
+ # Keep segments in a sorted data structure for efficient lookups
+ self.segments_list_ = SortedList(segments_set)
+ # Store all boundary points (starts and ends) sorted linearly
+ self.segments_boundaries_ = SortedList((
+ boundary
+ for segment in segments_set
+ for boundary in segment
+ ))
+ self.uri = uri
+
+ def __len__(self):
+ """Returns the number of segments in the timeline."""
+
+ return len(self.segments_set_)
+
+ def __nonzero__(self):
+ """Python 2 compatibility wrapper for boolean evaluation."""
+
+ return self.__bool__()
+
+ def __bool__(self):
+ """Returns True if the timeline contains at least one segment."""
+
+ return len(self.segments_set_) > 0
+
+ def __iter__(self):
+ """Iterates over the sorted segments."""
+
+ return iter(self.segments_list_)
+
+ def __getitem__(self, k):
+ """Retrieves a segment by its index from the sorted list."""
+
+ return self.segments_list_[k]
+
+ def __eq__(self, other):
+ """Checks if two timelines contain the exact same set of segments."""
+
+ return self.segments_set_ == other.segments_set_
+
+ def __ne__(self, other):
+ """Checks if two timelines are different."""
+
+ return self.segments_set_ != other.segments_set_
+
+ def index(self, segment):
+ """Returns the sorted index position of a given segment."""
+
+ return self.segments_list_.index(segment)
+
+ def add(self, segment):
+ """
+ Adds a new valid segment to the timeline if not already present.
+
+ Args:
+ segment: The Segment instance to be added.
+ """
+
+ segments_set_ = self.segments_set_
+ if segment in segments_set_ or not segment: return self
+
+ # Update set and sorted list structures
+ segments_set_.add(segment)
+ self.segments_list_.add(segment)
+
+ # Update boundaries
+ segments_boundaries_ = self.segments_boundaries_
+ segments_boundaries_.add(segment.start)
+ segments_boundaries_.add(segment.end)
+
+ return self
+
+ def remove(self, segment):
+ """
+ Removes a segment from the timeline. Raises no error if missing.
+
+ Args:
+ segment: The Segment instance to remove.
+ """
+
+ segments_set_ = self.segments_set_
+ if segment not in segments_set_: return self
+
+ # Remove from data structures
+ segments_set_.remove(segment)
+ self.segments_list_.remove(segment)
+
+ # Remove its corresponding boundaries
+ segments_boundaries_ = self.segments_boundaries_
+ segments_boundaries_.remove(segment.start)
+ segments_boundaries_.remove(segment.end)
+
+ return self
+
+ def discard(self, segment):
+ """Alias for remove() to safely discard a segment."""
+
+ return self.remove(segment)
+
+ def __ior__(self, timeline):
+ """In-place union operator (|=). Updates the timeline directly."""
+
+ return self.update(timeline)
+
+ def update(self, timeline):
+ """
+ Merges another timeline into this instance (In-place Union).
+
+ Args:
+ timeline: The timeline to merge from.
+ """
+
+ segments_set = self.segments_set_
+ segments_set |= timeline.segments_set_
+ # Re-build sorted lists based on the updated set
+ self.segments_list_ = SortedList(segments_set)
+ self.segments_boundaries_ = SortedList((
+ boundary
+ for segment in segments_set
+ for boundary in segment
+ ))
+
+ return self
+
+ def __or__(self, timeline):
+ """Union operator (|). Returns a new Timeline combination."""
+
+ return self.union(timeline)
+
+ def union(self, timeline):
+ """
+ Returns a new Timeline containing segments from both timelines.
+
+ Args:
+ timeline: The other timeline to unite with.
+ """
+
+ return Timeline(
+ segments=self.segments_set_ | timeline.segments_set_,
+ uri=self.uri
+ )
+
+ def co_iter(self, other):
+ """
+ Yields pairs of intersecting segments between this timeline and another.
+
+ Args:
+ other: The other timeline to evaluate intersections against.
+ """
+
+ for segment in self.segments_list_:
+ # Create a zero-duration dummy segment at the end boundary to optimize range queries
+ temp = Segment(start=segment.end, end=segment.end)
+
+ # Look up candidate segments from the other timeline within range
+ for other_segment in other.segments_list_.irange(maximum=temp):
+ if segment.intersects(other_segment): yield segment, other_segment
+
+ def crop_iter(self, support, mode = 'intersection', returns_mapping = False):
+ """
+ Iterates over parts of segments that overlap with a support frame.
+
+ Args:
+ support: A Segment or Timeline acting as the cropping window.
+ mode: 'intersection' (slices overlaps), 'loose' (keeps any intersecting), or 'strict' (keeps only fully contained segments).
+ returns_mapping: If True, yields (original_segment, cropped_segment).
+
+ Raises:
+ ValueError: If an unsupported mode is provided.
+ TypeError: If support is not a Segment or Timeline instance.
+ """
+
+ if mode not in {'loose', 'strict', 'intersection'}: raise ValueError("Mode must be 'loose', 'strict', or 'intersection'")
+ if not isinstance(support, (Segment, Timeline)): raise TypeError("Support must be a Segment or Timeline instance")
+
+ # Handle fallback if support is a single Segment
+ if isinstance(support, Segment):
+ support = Timeline(
+ segments=([support] if support else []),
+ uri=self.uri
+ )
+
+ for yielded in self.crop_iter(
+ support,
+ mode=mode,
+ returns_mapping=returns_mapping
+ ):
+ yield yielded
+
+ return
+
+ # Collapse overlapping regions in support to avoid redundant processing
+ support = support.support()
+
+ if mode == 'loose':
+ for segment, _ in self.co_iter(support):
+ yield segment
+
+ return
+
+ if mode == 'strict':
+ for segment, other_segment in self.co_iter(support):
+ if segment in other_segment: yield segment
+
+ return
+
+ for segment, other_segment in self.co_iter(support):
+ mapped_to = segment & other_segment # Logical AND calculates overlap
+ if not mapped_to: continue
+
+ if returns_mapping: yield segment, mapped_to
+ else: yield mapped_to
+
+ def crop(self, support, mode = 'intersection', returns_mapping = False):
+ """
+ Crops the timeline according to a support boundary.
+
+ Args:
+ support: Segment or Timeline indicating the target crop zone.
+ mode: Slicing configuration ('intersection', 'loose', 'strict').
+ returns_mapping: If True, returns a mapping dict along with the Timeline.
+ """
+
+ if mode == 'intersection' and returns_mapping:
+ segments, mapping = [], {}
+
+ for segment, mapped_to in self.crop_iter(
+ support,
+ mode='intersection',
+ returns_mapping=True
+ ):
+ segments.append(mapped_to)
+ # Map cropped segment back to its original segment(s)
+ mapping[mapped_to] = mapping.get(mapped_to, list()) + [segment]
+
+ return Timeline(segments=segments, uri=self.uri), mapping
+
+ return Timeline(segments=self.crop_iter(support, mode=mode), uri=self.uri)
+
+ def overlapping(self, t):
+ """Returns a list of all segments covering timestamp `t`."""
+
+ return list(self.overlapping_iter(t))
+
+ def overlapping_iter(self, t):
+ """Yields segments that cover the target timestamp `t`."""
+
+ for segment in self.segments_list_.irange(maximum=Segment(start=t, end=t)):
+ if segment.overlaps(t): yield segment
+
+ def get_overlap(self):
+ """
+ Finds self-intersecting regions within this timeline.
+
+ Returns:
+ A new Timeline representing the consolidated internal overlapping areas.
+ """
+
+ overlaps_tl = Timeline(uri=self.uri)
+ # Self cross-iteration to look for internal collisions
+ for s1, s2 in self.co_iter(self):
+ if s1 == s2: continue
+
+ overlaps_tl.add(s1 & s2)
+
+ return overlaps_tl.support()
+
+ def extrude(self, removed, mode = 'intersection'):
+ """
+ Removes specific segments or timelines from the current timeline (Inversion/Subtraction).
+
+ Args:
+ removed: The segments/timeline to excise.
+ mode: Cropping strategy mode logic.
+ """
+
+ if isinstance(removed, Segment): removed = Timeline([removed])
+ # Invert the crop modes to switch extraction perspectives
+ if mode == "loose": mode = "strict"
+ elif mode == "strict": mode = "loose"
+
+ # Crop self against the gaps (non-removed regions) of the removed input
+ return self.crop(
+ removed.gaps(
+ support=Timeline(
+ [self.extent()],
+ uri=self.uri
+ )
+ ),
+ mode=mode
+ )
+
+ def __str__(self):
+ """Returns a clean multi-line string representation of the timeline segments."""
+
+ n = len(self.segments_list_)
+ string = "["
+
+ for i, segment in enumerate(self.segments_list_):
+ string += str(segment)
+ string += "\n " if i + 1 < n else ""
+
+ string += "]"
+ return string
+
+ def __repr__(self):
+ """Returns the formal developer representation of the Timeline."""
+
+ return "" % (self.uri, list(self.segments_list_))
+
+ def __contains__(self, included):
+ """Checks containment of a Segment or an entire Timeline."""
+
+ if isinstance(included, Segment): return included in self.segments_set_
+ elif isinstance(included, Timeline): return self.segments_set_.issuperset(included.segments_set_)
+ else: raise TypeError("Can only check containment for Segment or Timeline objects")
+
+ def empty(self):
+ """Returns an empty copy of this timeline holding the same URI."""
+
+ return Timeline(uri=self.uri)
+
+ def covers(self, other):
+ """Verifies if this timeline entirely wraps around another timeline's coverage."""
+
+ gaps = self.gaps(support=other.extent())
+ # If any of our gaps overlap with the other timeline, we don't fully cover it
+ for _ in gaps.co_iter(other):
+ return False
+
+ return True
+
+ def copy(self, segment_func = None):
+ """Returns a copy of the timeline, optionally processing segments through a function.
+
+ Args:
+ segment_func: Optional callable to modify segments during copying.
+ """
+
+ if segment_func is None: return Timeline(segments=self.segments_list_, uri=self.uri)
+ return Timeline(segments=[segment_func(s) for s in self.segments_list_], uri=self.uri)
+
+ def extent(self):
+ """Returns the absolute span of the timeline (from very beginning to the absolute end)."""
+
+ if self.segments_set_:
+ segments_boundaries_ = self.segments_boundaries_
+ return Segment(start=segments_boundaries_[0], end=segments_boundaries_[-1])
+
+ return Segment(start=0.0, end=0.0)
+
+ def support_iter(self, collar = 0.0):
+ """
+ Fuses adjacent/overlapping segments together into continuous blocks.
+
+ Args:
+ collar: Minimum time separation distance required to keep segments separate.
+ """
+
+ if not self: return
+
+ new_segment = self.segments_list_[0]
+
+ for segment in self:
+ possible_gap = segment ^ new_segment # Symmetric difference tracking
+
+ # If they touch, overlap, or the gap is smaller than the collar, merge them
+ if not possible_gap or possible_gap.duration < collar: new_segment |= segment
+ else:
+ yield new_segment
+ new_segment = segment
+
+ yield new_segment
+
+ def support(self, collar = 0.):
+ """
+ Consolidates the timeline into non-overlapping continuous segments.
+
+ Args:
+ collar: Gap padding allowance threshold.
+ """
+
+ return Timeline(segments=self.support_iter(collar), uri=self.uri)
+
+ def duration(self):
+ """Calculates total unique active time duration across all merged segments."""
+
+ return sum(s.duration for s in self.support_iter())
+
+ def gaps_iter(self, support = None):
+ """
+ Discovers unallocated slots (empty time gaps) within a specified window.
+
+ Args:
+ support: Boundary scope for gap mapping. Defaults to total timeline extent.
+ """
+
+ if support is None: support = self.extent()
+ if not isinstance(support, (Segment, Timeline)): raise TypeError("Support must be a Segment or Timeline instance")
+
+ if isinstance(support, Segment):
+ end = support.start
+ # Find holes by tracking steps between continuous blocks
+ for segment in self.crop(support, mode='intersection').support():
+ gap = Segment(start=end, end=segment.start)
+ if gap: yield gap
+
+ end = segment.end
+
+ # Check trailing space gap
+ gap = Segment(start=end, end=support.end)
+ if gap: yield gap
+ elif isinstance(support, Timeline):
+ for segment in support.support():
+ for gap in self.gaps_iter(support=segment):
+ yield gap
+
+ def gaps(self, support = None):
+ """Returns a Timeline composed entirely of the discovered empty spaces."""
+
+ return Timeline(segments=self.gaps_iter(support=support), uri=self.uri)
+
+ def segmentation(self):
+ """Subdivides the timeline into atomic chunks based on all present boundary combinations."""
+
+ support = self.support()
+ timestamps = set([])
+
+ # Collect every boundary point
+ for (start, end) in self:
+ timestamps.add(start)
+ timestamps.add(end)
+
+ timestamps = sorted(timestamps)
+ if len(timestamps) == 0: return Timeline(uri=self.uri)
+
+ segments = []
+ start = timestamps[0]
+ # Form atomic chunks between sequential boundaries
+ for end in timestamps[1:]:
+ segment = Segment(start=start, end=end)
+ # Keep chunk if it matches an active timeline section
+ if segment and support.overlapping(segment.middle): segments.append(segment)
+ start = end
+
+ return Timeline(segments=segments, uri=self.uri)
+
+ def _iter_uem(self):
+ """Internal generator yielding UEM formatted mapping lines."""
+
+ uri = self.uri if self.uri else ""
+
+ for segment in self:
+ yield f"{uri} 1 {segment.start:.3f} {segment.end:.3f}\n"
+
+ def to_uem(self):
+ """Converts timeline data into standard string block matching UEM file format specifications."""
+
+ return "".join([line for line in self._iter_uem()])
+
+ def write_uem(self, file):
+ """
+ Writes the UEM formatted timeline attributes into a file buffer descriptor.
+
+ Args:
+ file: File-like object supporting text write operations.
+ """
+
+ for line in self._iter_uem():
+ file.write(line)
+
+ def _repr_png_(self):
+ """Rich notebook rendering placeholder layout function."""
+
+ return None
+
+class Segment:
+ """Represents a continuous interval of time bounded by a start and end point."""
+
+ def __init__(self, start, end):
+ """Initializes a Segment object."""
+
+ self.start = start
+ self.end = end
+
+ @staticmethod
+ def set_precision(ndigits = None):
+ """
+ Sets the global precision limit rules applied to Segment parameters.
+
+ Args:
+ ndigits: Digits resolution parameter count. Passing None disables rounding constraints.
+ """
+
+ global AUTO_ROUND_TIME, SEGMENT_PRECISION
+
+ if ndigits is None:
+ AUTO_ROUND_TIME = False
+ SEGMENT_PRECISION = 1e-6
+ else:
+ AUTO_ROUND_TIME = True
+ SEGMENT_PRECISION = 10 ** (-ndigits)
+
+ def __bool__(self):
+ """Evaluates segment validity. Returns False if duration falls below resolution precision."""
+
+ return bool((self.end - self.start) > SEGMENT_PRECISION)
+
+ def __post_init__(self):
+ """Applies dynamic runtime rounding filters if AUTO_ROUND_TIME configuration flag is set."""
+
+ if AUTO_ROUND_TIME:
+ object.__setattr__(self, 'start', int(self.start / SEGMENT_PRECISION + 0.5) * SEGMENT_PRECISION)
+ object.__setattr__(self, 'end', int(self.end / SEGMENT_PRECISION + 0.5) * SEGMENT_PRECISION)
+
+ @property
+ def duration(self):
+ """Returns the total duration of the segment. Returns 0 if invalid."""
+
+ return self.end - self.start if self else 0.
+
+ @property
+ def middle(self):
+ """Finds the mathematical midpoint coordinate of the segment instance."""
+
+ return .5 * (self.start + self.end)
+
+ def __iter__(self):
+ """Allows unpacking or looping via a (start, end) format structure."""
+
+ yield self.start
+ yield self.end
+
+ def copy(self):
+ """Returns an identical duplicate clone of the original segment."""
+
+ return Segment(start=self.start, end=self.end)
+
+ def __contains__(self, other):
+ """Checks whether this segment completely encapsulates the other object parameter segment."""
+
+ return (self.start <= other.start) and (self.end >= other.end)
+
+ def __and__(self, other):
+ """Logical intersection mapping operator (&) calculating overlapping range areas."""
+
+ return Segment(start=max(self.start, other.start), end=min(self.end, other.end))
+
+ def intersects(self, other):
+ """Checks if there is any shared overlap area matching standard bounds constraints."""
+
+ return (
+ self.start < other.start and other.start < self.end - SEGMENT_PRECISION
+ ) or (
+ self.start > other.start and self.start < other.end - SEGMENT_PRECISION
+ ) or (
+ self.start == other.start
+ )
+
+ def overlaps(self, t):
+ """Checks if a particular timestamp location point falls anywhere within current frame limits."""
+
+ return self.start <= t and self.end >= t
+
+ def __or__(self, other):
+ """Logical union hull merge operator (|) computing bounding range wrapper bounds."""
+
+ if not self: return other
+ if not other: return self
+
+ return Segment(
+ start=min(self.start, other.start),
+ end=max(self.end, other.end)
+ )
+
+ def __xor__(self, other):
+ """Symmetric difference operator (^) discovering internal gap separator boundaries."""
+
+ if (not self) or (not other): raise ValueError("Both segments must be valid to evaluate XOR")
+
+ return Segment(
+ start=min(self.end, other.end),
+ end=max(self.start, other.start)
+ )
+
+ def _str_helper(self, seconds):
+ """Internal timedelta display encoder string conversion assistant."""
+
+ from datetime import timedelta
+
+ negative = seconds < 0
+ td = timedelta(seconds=abs(seconds))
+
+ hours, remainder = divmod(td.seconds + 86400 * td.days, 3600)
+ minutes, seconds = divmod(remainder, 60)
+
+ return '%s%02d:%02d:%02d.%03d' % ('-' if negative else ' ', hours, minutes, seconds, td.microseconds / 1000)
+
+ def __str__(self):
+ """Returns user legible standard timestamp boundary summary block layout string."""
+
+ if self: return '[%s --> %s]' % (self._str_helper(self.start), self._str_helper(self.end))
+ return '[]'
+
+ def __repr__(self):
+ """Developer inspection representation string layout handler structure."""
+
+ return '' % (self.start, self.end)
+
+ def _repr_png_(self):
+ """Rich notebook visualization support hook entry vector."""
+
+ return None
+
+class SlidingWindow:
+ """Generates sequential window segments sliding across a temporal continuum."""
+
+ def __init__(
+ self,
+ duration=0.030,
+ step=0.010,
+ start=0.000,
+ end=None
+ ):
+ """
+ Initializes a SlidingWindow configuration.
+
+ Args:
+ duration: Width span size matching frame metrics allocations.
+ step: Advancement offset resolution step.
+ start: Tracking window offset coordinates floor.
+ end: Upper bounds execution range roof ceiling target marker.
+ """
+
+ if duration <= 0: raise ValueError("Duration must be strictly positive")
+ self.__duration = duration
+ if step <= 0: raise ValueError("Step step increments must be positive integers")
+
+ self.__step = step
+ self.__start = start
+
+ if end is None: self.__end = np.inf
+ else:
+ if end <= start: raise ValueError("End bounds must surpass tracking start index")
+ self.__end = end
+
+ self.__i = -1 # Internal iteration registry tracking counter variable
+
+ @property
+ def start(self):
+ return self.__start
+
+ @property
+ def end(self):
+ return self.__end
+
+ @property
+ def step(self):
+ return self.__step
+
+ @property
+ def duration(self):
+ return self.__duration
+
+ def closest_frame(self, t):
+ """Finds the nearest discrete step frame index mapping adjacent to target `t` location."""
+
+ return int(np.rint((t - self.__start - .5 * self.__duration) / self.__step))
+
+ def samples(self, from_duration, mode = 'strict'):
+ """
+ Calculates expected total chunk allocations capacity fitting into given input length profile rules.
+
+ Args:
+ from_duration: Total available evaluation duration length tracker.
+ mode: Fitting evaluation structure ('strict', 'loose', 'center').
+ """
+
+ if mode == 'strict': return int(np.floor((from_duration - self.duration) / self.step)) + 1
+ elif mode == 'loose': return int(np.floor((from_duration + self.duration) / self.step))
+ elif mode == 'center': return int(np.rint((from_duration / self.step)))
+
+ def crop(self, focus, mode = 'loose', fixed = None, return_ranges = False):
+ """
+ Crops sliding steps indices window frame matrices bounding into structural regions parameters map trackers.
+
+ Args:
+ focus: Target tracking framework region criteria layer details.
+ mode: Intersection mapping configuration selector.
+ fixed: Fixed constraint window limit variables specifications rules.
+ return_ranges: Toggles returning coordinate arrays raw nested limits.
+ """
+
+ if not isinstance(focus, (Segment, Timeline)): raise TypeError("Focus must be a Segment or Timeline instance")
+
+ if isinstance(focus, Timeline):
+ if fixed is not None: raise ValueError("Fixed setting is unavailable for Timeline inputs")
+
+ if return_ranges:
+ ranges = []
+
+ for i, s in enumerate(focus.support()):
+ rng = self.crop(s, mode=mode, fixed=fixed, return_ranges=True)
+
+ if i == 0 or rng[0][0] > ranges[-1][1]: ranges += rng
+ else: ranges[-1][1] = rng[0][1] # Merge contiguous block bounds
+
+ return ranges
+
+ # Consolidate linear index coordinates arrays directly together
+ return np.unique(
+ np.hstack([
+ self.crop(
+ s,
+ mode=mode,
+ fixed=fixed,
+ return_ranges=False
+ )
+ for s in focus.support()
+ ])
+ )
+
+ # Implementation mapping parameters calculations across modes
+ if mode == 'loose':
+ i = int(np.ceil((focus.start - self.duration - self.start) / self.step))
+
+ if fixed is None:
+ j = int(np.floor((focus.end - self.start) / self.step))
+ rng = (i, j + 1)
+ else:
+ n = self.samples(fixed, mode='loose')
+ rng = (i, i + n)
+ elif mode == 'strict':
+ i = int(np.ceil((focus.start - self.start) / self.step))
+
+ if fixed is None:
+ j = int(np.floor((focus.end - self.duration - self.start) / self.step))
+ rng = (i, j + 1)
+ else:
+ n = self.samples(fixed, mode='strict')
+ rng = (i, i + n)
+ elif mode == 'center':
+ i = self.closest_frame(focus.start)
+
+ if fixed is None:
+ j = self.closest_frame(focus.end)
+ rng = (i, j + 1)
+ else:
+ n = self.samples(fixed, mode='center')
+ rng = (i, i + n)
+ else: raise ValueError("Invalid mode: choose 'loose', 'strict', or 'center'")
+
+ if return_ranges: return [list(rng)]
+ return np.array(range(*rng), dtype=np.int64)
+
+ def segmentToRange(self, segment):
+ """Legacy camelCase alias wrapper for segment_to_range."""
+
+ return self.segment_to_range(segment)
+
+ def segment_to_range(self, segment):
+ """Maps a physical segment onto internal array space coordinates tuple pairs (start_index, count)."""
+
+ return self.closest_frame(segment.start), int(segment.duration / self.step) + 1
+
+ def rangeToSegment(self, i0, n):
+ """Legacy camelCase alias wrapper for range_to_segment."""
+
+ return self.range_to_segment(i0, n)
+
+ def range_to_segment(self, i0, n):
+ """Reconstructs continuous spatial metrics mapping dynamic context targets back to structural block classes."""
+
+ start = self.__start + (i0 - .5) * self.__step + .5 * self.__duration
+
+ if i0 == 0: start = self.start
+ return Segment(start, start + (n * self.__step))
+
+ def samplesToDuration(self, nSamples):
+ """Legacy camelCase alias wrapper for samples_to_duration."""
+
+ return self.samples_to_duration(nSamples)
+
+ def samples_to_duration(self, n_samples):
+ """Calculates numerical span runtime dimensions mapping sample step limits."""
+
+ return self.range_to_segment(0, n_samples).duration
+
+ def durationToSamples(self, duration):
+ """Legacy camelCase alias wrapper for duration_to_samples."""
+
+ return self.duration_to_samples(duration)
+
+ def duration_to_samples(self, duration):
+ """Converts raw window duration metrics into scalar step increments parameters array counts."""
+
+ return self.segment_to_range(Segment(0, duration))[1]
+
+ def __getitem__(self, i):
+ """Generates the exact Segment boundary window matching step sequence position integer location `i`."""
+
+ start = self.__start + i * self.__step
+ if start >= self.__end: return None
+
+ return Segment(start=start, end=start + self.__duration)
+
+ def next(self):
+ return self.__next__()
+
+ def __next__(self):
+ """Advances the internal counter index to return the next consecutive Segment frame sequence slice block."""
+
+ self.__i += 1
+ window = self[self.__i]
+
+ if window: return window
+ else: raise StopIteration()
+
+ def __iter__(self):
+ """Resets tracking sequence states pointers to restart sequential iterable navigation loops."""
+
+ self.__i = -1
+ return self
+
+ def __len__(self):
+ """Determines total available processing windows allocation segments limits matching configuration frames."""
+
+ if np.isinf(self.__end): raise ValueError("Infinite timelines parameters cannot evaluate finite len allocations counters.")
+ i = self.closest_frame(self.__end)
+
+ # Proactively look ahead to scan edge window offsets boundaries accurately
+ while (self[i]):
+ i += 1
+
+ length = i
+ return length
+
+ def copy(self):
+ """Generates a separate cloned copy reflecting current settings structure."""
+
+ return self.__class__(
+ duration=self.duration,
+ step=self.step,
+ start=self.start,
+ end=self.end
+ )
+
+ def __call__(self, support, align_last = False):
+ """
+ Slices a specific support timeline target map using configured sliding step sequences limits.
+
+ Args:
+ support: Target area bounds context.
+ align_last: Forces generation of an extra adjusted window snapped directly to boundary limits.
+ """
+
+ if isinstance(support, Timeline): segments = support
+ elif isinstance(support, Segment): segments = Timeline(segments=[support])
+ else: raise TypeError("Support must be a Segment or Timeline instance")
+
+ for segment in segments:
+ if segment.duration < self.duration: continue
+ # Evaluate slices iteratively
+ for s in SlidingWindow(
+ duration=self.duration,
+ step=self.step,
+ start=segment.start,
+ end=segment.end
+ ):
+ if s in segment:
+ yield s
+ last = s
+
+ # Handle fractional alignment padding if toggled active
+ if align_last and last.end < segment.end: yield Segment(start=segment.end - self.duration, end=segment.end)
\ No newline at end of file
diff --git a/main/library/speaker_diarization/speechbrain.py b/main/library/speaker_diarization/speechbrain.py
new file mode 100644
index 0000000000000000000000000000000000000000..194c513b7b093e4edc93c617c867ad1e6ad79ba7
--- /dev/null
+++ b/main/library/speaker_diarization/speechbrain.py
@@ -0,0 +1,538 @@
+import os
+import torch
+import torchaudio
+
+from functools import wraps
+from types import SimpleNamespace
+from torch.nn import SyncBatchNorm
+from hyperpyyaml import load_hyperpyyaml
+
+from torch.nn import DataParallel as DP
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+MAIN_PROC_ONLY = 0
+
+def fetch(filename, source):
+ """
+ Resolves an absolute file path given a base directory source and a filename.
+
+ Args:
+ filename: Name of the target file.
+ source: Base source directory path.
+
+ Returns:
+ The absolute path to the designated file.
+ """
+
+ return os.path.abspath(os.path.join(source, filename))
+
+def run_on_main(
+ func,
+ args=None,
+ kwargs=None,
+ post_func=None,
+ post_args=None,
+ post_kwargs=None,
+ run_post_on_main=False
+):
+ """
+ Executes a function strictly on the main process in DDP setups, synchronizes ranks,
+ and handles subsequent post-execution callbacks.
+
+ Args:
+ func: Primary function to run on rank 0.
+ args: Positional arguments for the primary function.
+ kwargs: Keyword arguments for the primary function.
+ post_func: Optional callback function to execute after the primary function.
+ post_args: Positional arguments for the callback function.
+ post_kwargs: Keyword arguments for the callback function.
+ run_post_on_main: If True, executes the callback on the main process;
+ otherwise executes it on child processes.
+ """
+
+ if args is None: args = []
+ if kwargs is None: kwargs = {}
+ if post_args is None: post_args = []
+ if post_kwargs is None: post_kwargs = {}
+
+ # Execute main operation on Rank 0 and hold other processes at the barrier
+ main_process_only(func)(*args, **kwargs)
+ ddp_barrier()
+
+ # Handle post-execution operations
+ if post_func is not None:
+ if run_post_on_main: post_func(*post_args, **post_kwargs)
+ else:
+ # If not explicitly run on main, run on all side-worker processes
+ if not if_main_process(): post_func(*post_args, **post_kwargs)
+ ddp_barrier()
+
+def is_distributed_initialized():
+ """
+ Checks if the PyTorch distributed process group is initialized and active.
+
+ Returns:
+ True if distributed environment is operational, otherwise False.
+ """
+
+ return (
+ torch.distributed.is_available() and
+ torch.distributed.is_initialized()
+ )
+
+def if_main_process():
+ """
+ Determines whether the current process is the primary rank (Rank 0).
+
+ Returns:
+ True if running on Rank 0 or non-distributed mode, False otherwise.
+ """
+
+ if is_distributed_initialized(): return torch.distributed.get_rank() == 0
+ else: return True
+
+class MainProcessContext:
+ """Context manager that increments the main process execution depth state flag."""
+
+ def __enter__(self):
+ global MAIN_PROC_ONLY
+
+ MAIN_PROC_ONLY += 1
+ return self
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ global MAIN_PROC_ONLY
+
+ MAIN_PROC_ONLY -= 1
+
+def main_process_only(function):
+ """
+ Decorator ensuring that a function is only executed by the main process (rank 0).
+
+ Args:
+ function: The function to be restricted to the main process.
+
+ Returns:
+ The wrapped process-aware function.
+ """
+
+ @wraps(function)
+ def main_proc_wrapped_func(*args, **kwargs):
+ with MainProcessContext():
+ return function(*args, **kwargs) if if_main_process() else None
+
+ return main_proc_wrapped_func
+
+def ddp_barrier():
+ """
+ Synchronizes all processes across the distributed framework.
+
+ Supports NCCL (CUDA) and XCCL (Intel XPU) backends dynamically.
+ """
+
+ # Bypass barrier tracking if within a main-process-only wrapper or not in distributed mode
+ if MAIN_PROC_ONLY >= 1 or not is_distributed_initialized(): return
+
+ # Check for hardware backend profile types
+ if torch.distributed.get_backend() == torch.distributed.Backend.NCCL:
+ torch.distributed.barrier(
+ device_ids=[torch.cuda.current_device()]
+ )
+ elif hasattr(torch, "xpu") and hasattr(torch.distributed.Backend, "XCCL") and torch.distributed.get_backend() == torch.distributed.Backend.XCCL:
+ torch.distributed.barrier(
+ device_ids=[torch.xpu.current_device()]
+ )
+ else: torch.distributed.barrier()
+
+class Resample(torch.nn.Module):
+ """
+ An audio resampling module wrapping torchaudio.transforms.Resample
+
+ with support for multiple waveform tensor layouts.
+ """
+
+ def __init__(
+ self,
+ orig_freq=16000,
+ new_freq=16000,
+ *args,
+ **kwargs
+ ):
+ """
+ Initializes the Resample layer.
+
+ Args:
+ orig_freq: Initial audio sampling rate frequency.
+ new_freq: Target audio sampling rate frequency.
+ """
+
+ super().__init__()
+
+ self.orig_freq = orig_freq
+ self.new_freq = new_freq
+ self.resampler = torchaudio.transforms.Resample(
+ orig_freq=orig_freq,
+ new_freq=new_freq,
+ *args,
+ **kwargs
+ )
+
+ def forward(self, waveforms):
+ """
+ Resamples the input waveform tensor to the target sample rate.
+
+ Args:
+ waveforms: Input tensor of shape (batch, time) or (batch, time,
+ channels).
+
+ Returns:
+ Resampled waveform tensor matching the expected input layout format.
+ """
+
+ if self.orig_freq == self.new_freq: return waveforms
+
+ unsqueezed = False
+ # Layout tracking and shape normalization
+ if len(waveforms.shape) == 2:
+ waveforms = waveforms.unsqueeze(1)
+ unsqueezed = True
+ elif len(waveforms.shape) == 3: waveforms = waveforms.transpose(1, 2)
+ else: raise ValueError("Unsupported tensor shape. Expected 2D or 3D tensor input layout.")
+
+ # Move the resampler to the current execution target device
+ self.resampler.to(waveforms.device)
+ resampled_waveform = self.resampler(waveforms)
+
+ # Revert tensor to original shape configuration layout
+ return resampled_waveform.squeeze(1) if unsqueezed else resampled_waveform.transpose(1, 2)
+
+class AudioNormalizer:
+ """Normalizes, resamples, and conditions audio channels into consistent format standards."""
+
+ def __init__(
+ self,
+ sample_rate=16000,
+ mix="avg-to-mono"
+ ):
+ """
+ Initializes the AudioNormalizer.
+
+ Args:
+ sample_rate: Target uniform sampling rate frequency.
+ mix: Mixing format strategy ('avg-to-mono' or 'keep').
+ """
+
+ self.sample_rate = sample_rate
+
+ if mix not in ["avg-to-mono", "keep"]: raise ValueError("Mix mode strategy must be 'avg-to-mono' or 'keep'")
+
+ self.mix = mix
+ self._cached_resamplers = {}
+
+ def __call__(self, audio, sample_rate):
+ """
+ Applies dynamic resampling and track configuration down-mixing on an audio tensor.
+
+ Args:
+ audio: Input audio tensor.
+ sample_rate: The native sampling rate of the input audio.
+
+ Returns:
+ Processed and normalized audio tensor.
+ """
+
+ # Instantiate and cache new resamplers dynamically as needed
+ if sample_rate not in self._cached_resamplers:
+ self._cached_resamplers[
+ sample_rate
+ ] = Resample(
+ sample_rate,
+ self.sample_rate
+ )
+
+ return self._mix(
+ # Resample requires a batch dimension; add it, process, then remove it
+ self._cached_resamplers[sample_rate](audio.unsqueeze(0)).squeeze(0)
+ )
+
+ def _mix(self, audio):
+ """Internal down-mixing function based on the selected mix strategy."""
+
+ flat_input = audio.dim() == 1
+
+ if self.mix == "avg-to-mono":
+ if flat_input: return audio
+ # Average multi-channel stream data across channel axis dimension index 1
+ return audio.mean(1)
+
+ if self.mix == "keep": return audio
+
+class Pretrained(torch.nn.Module):
+ """
+ Base class for managing pretrained speech/audio neural network models.
+ Handles hyperparameter parsing, compilation (JIT/Inductor), and parallel wrappers.
+ """
+
+ HPARAMS_NEEDED, MODULES_NEEDED = [], []
+
+ def __init__(
+ self,
+ modules=None,
+ hparams=None,
+ run_opts=None,
+ freeze_params=True
+ ):
+ """
+ Initializes structural base variables and model wrapper attributes.
+
+ Args:
+ modules: Dictionary of torch.nn.Module components.
+ hparams: Pre-loaded dictionary of configurations and hyperparameters.
+ run_opts: Runtime adjustments mapping device and compile overrides.
+ freeze_params: If True, locks network parameters into evaluation modes.
+ """
+
+ super().__init__()
+ # Resolve system parameters mapping defaults configurations profile parameters
+ for arg, default in {
+ "device": "cpu",
+ "data_parallel_count": -1,
+ "data_parallel_backend": False,
+ "distributed_launch": False,
+ "distributed_backend": "nccl",
+ "jit": False,
+ "jit_module_keys": None,
+ "compile": False,
+ "compile_module_keys": None,
+ "compile_mode": "reduce-overhead",
+ "compile_using_fullgraph": False,
+ "compile_using_dynamic_shape_tracing": False
+ }.items():
+ if run_opts is not None and arg in run_opts: setattr(self, arg, run_opts[arg])
+ elif hparams is not None and arg in hparams: setattr(self, arg, hparams[arg])
+ else: setattr(self, arg, default)
+
+ # Store submodule network branches within an accessible ModuleDict container
+ self.mods = torch.nn.ModuleDict(modules)
+ # Relocate modules onto the designated processing device hardware targets
+ for module in self.mods.values():
+ if module is not None: module.to(self.device)
+
+ # Enforce validation checks on critical configuration structures
+ if self.HPARAMS_NEEDED and hparams is None: raise ValueError("Required hyperparameters mapping structure is missing.")
+
+ if hparams is not None:
+ for hp in self.HPARAMS_NEEDED:
+ if hp not in hparams: raise ValueError(f"Missing required parameter attribute key: {hp}")
+
+ self.hparams = SimpleNamespace(**hparams)
+
+ # Prepare code layers and initialize normalizing transformations
+ self._prepare_modules(freeze_params)
+ self.audio_normalizer = hparams.get("audio_normalizer", AudioNormalizer())
+
+ def _prepare_modules(self, freeze_params):
+ """Applies optimization compilation routines and registers distribution wrappers."""
+
+ self._compile()
+ self._wrap_distributed()
+
+ # Freeze weights and switch modules to evaluation mode if configured
+ if freeze_params:
+ self.mods.eval()
+ for p in self.mods.parameters():
+ p.requires_grad = False
+
+ def _compile(self):
+ """Compiles modules using torch.compile (PyTorch 2.x Inductor) or torch.jit.script."""
+
+ compile_available = hasattr(torch, "compile")
+ if not compile_available and self.compile_module_keys is not None: raise ValueError("torch.compile is not supported on this framework build.")
+
+ # Determine target module targets for compilation
+ compile_module_keys = set()
+ if self.compile: compile_module_keys = set(self.mods) if self.compile_module_keys is None else set(self.compile_module_keys)
+
+ jit_module_keys = set()
+ if self.jit: jit_module_keys = set(self.mods) if self.jit_module_keys is None else set(self.jit_module_keys)
+
+ # Verify that all target keys exist in the module structure
+ for name in compile_module_keys | jit_module_keys:
+ if name not in self.mods: raise ValueError(f"Module target key '{name}' not found.")
+
+ # Apply torch.compile to the specified modules
+ for name in compile_module_keys:
+ try:
+ module = torch.compile(
+ self.mods[name],
+ mode=self.compile_mode,
+ fullgraph=self.compile_using_fullgraph,
+ dynamic=self.compile_using_dynamic_shape_tracing
+ )
+ except Exception:
+ # Fall back if an unexpected error occurs during structural trace conversion
+ continue
+
+ self.mods[name] = module.to(self.device)
+ jit_module_keys.discard(name) # Avoid redundant JIT compilation
+
+ # Apply torch.jit.script fallback optimization to remaining keys
+ for name in jit_module_keys:
+ module = torch.jit.script(self.mods[name])
+ self.mods[name] = module.to(self.device)
+
+ def _compile_jit(self):
+ """Explicitly invokes the comprehensive module graph compilation workflow."""
+
+ self._compile()
+
+ def _wrap_distributed(self):
+ """
+ Wraps submodules with DistributedDataParallel (DDP) or DataParallel (DP) wrappers
+
+ based on active distributed cluster launch profiles.
+ """
+
+ if not self.distributed_launch and not self.data_parallel_backend: return
+ elif self.distributed_launch:
+ # Wrap with DistributedDataParallel (DDP) for multi-node/multi-GPU training
+ for name, module in self.mods.items():
+ if any(p.requires_grad for p in module.parameters()):
+ self.mods[name] = DDP(
+ SyncBatchNorm.convert_sync_batchnorm(module),
+ device_ids=[self.device]
+ )
+ else:
+ # Wrap with standard single-node standard multi-GPU DataParallel (DP)
+ for name, module in self.mods.items():
+ if any(p.requires_grad for p in module.parameters()):
+ self.mods[name] = (
+ DP(module)
+ ) if self.data_parallel_count == -1 else (
+ DP(
+ module,
+ [i for i in range(self.data_parallel_count)]
+ )
+ )
+
+ @classmethod
+ def from_hparams(
+ cls,
+ source,
+ hparams_file="hyperparams.yaml",
+ overrides={},
+ download_only=False,
+ overrides_must_match=True,
+ **kwargs
+ ):
+ """
+ Factory method to instantiate the pretrained network class components directly
+ from an external HyperPyYAML configuration layout structure.
+
+ Args:
+ source: Root target path containing configuration settings files.
+ hparams_file: File name of the target configuration file.
+ overrides: Dynamic run runtime override adjustments dictionary.
+ download_only: Toggles checking asset collection setups exclusively.
+ overrides_must_match: If True, enforces override key presence validation.
+ """
+
+ # Load yaml definitions parsing custom class instances descriptors
+ with open(fetch(filename=hparams_file, source=source)) as fin:
+ hparams = load_hyperpyyaml(
+ fin,
+ overrides,
+ overrides_must_match=overrides_must_match
+ )
+
+ pretrainer = hparams.get("pretrainer", None)
+ # Manage external assets if pretrainer class extensions exist
+ if pretrainer is not None:
+ run_on_main(pretrainer.collect_files, kwargs={"default_source": source})
+ if not download_only:
+ pretrainer.load_collected()
+ return cls(hparams["modules"], hparams, **kwargs)
+ else: return cls(hparams["modules"], hparams, **kwargs)
+
+class EncoderClassifier(Pretrained):
+ """
+ An end-to-end network pipeline designed to map raw vocal input wave vectors
+
+ into lower-dimension speaker embedding clusters and classify labels.
+ """
+
+ MODULES_NEEDED = [
+ "compute_features",
+ "mean_var_norm",
+ "embedding_model",
+ "classifier"
+ ]
+
+ def encode_batch(self, wavs, wav_lens=None, normalize=False):
+ """
+ Extracts latent speaker embeddings from a batch of raw waveforms.
+
+ Args:
+ wavs: Input audio tensor waveform data of shape (batch, time) or (time).
+ wav_lens: Normalized relative duration values for each audio sample.
+ normalize: Toggles post-extraction normalization via hyperparameter targets.
+
+ Returns:
+ Extracted latent embedding tensor vectors.
+ """
+
+ # Ensure input tensor matches the required 2D batch dimension layout format
+ if len(wavs.shape) == 1: wavs = wavs.unsqueeze(0)
+ if wav_lens is None: wav_lens = torch.ones(wavs.shape[0], device=self.device)
+
+ # Relocate tensors to device and cast to standard precision float format
+ wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
+ wavs = wavs.float()
+
+ embeddings = self.mods.embedding_model(
+ self.mods.mean_var_norm(
+ self.mods.compute_features(wavs),
+ wav_lens
+ ),
+ wav_lens
+ )
+
+ if normalize:
+ embeddings = self.hparams.mean_var_norm_emb(
+ embeddings,
+ torch.ones(embeddings.shape[0], device=self.device)
+ )
+
+ return embeddings
+
+ def classify_batch(self, wavs, wav_lens=None):
+ """
+ Processes audio inputs to extract class probabilities, confidence scores, and labels.
+
+ Args:
+ wavs: Raw waveform input data tensors.
+ wav_lens: Relative time dimension trackers.
+
+ Returns:
+ A tuple containing:
+ - out_prob: Prediction probability matrices.
+ - score: Highest matched confidence accuracy metrics.
+ - index: Raw argmax network predicted index coordinates.
+ - decoded: String representations of the target output labels.
+ """
+
+ out_prob = self.mods.classifier(self.encode_batch(wavs, wav_lens)).squeeze(1)
+ # Extract argmax confidence scores along the final classification target axis
+ score, index = out_prob.max(dim=-1)
+
+ # Map index coordinates back to string labels via the label encoder
+ return out_prob, score, index, self.hparams.label_encoder.decode_torch(index)
+
+ def forward(self, wavs, wav_lens=None):
+ """
+ Standard torch forward pass method redirection forwarding arguments directly
+
+ to the batch classification pipeline.
+ """
+
+ return self.classify_batch(wavs, wav_lens)
\ No newline at end of file
diff --git a/main/library/speaker_diarization/whisper.py b/main/library/speaker_diarization/whisper.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7324e645c699db1bd1a319b94d039f5c3d0da36
--- /dev/null
+++ b/main/library/speaker_diarization/whisper.py
@@ -0,0 +1,2522 @@
+import os
+import sys
+import gzip
+import zlib
+import tqdm
+import torch
+import base64
+import string
+import tiktoken
+import itertools
+
+import numba as nb
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+
+from dataclasses import replace
+from torch.distributions import Categorical
+from functools import cached_property, lru_cache
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config, configs, logger
+from main.library.algorithm.normalization import Fp32LayerNorm
+
+LANGUAGES = {
+ "en": "english",
+ "zh": "chinese",
+ "de": "german",
+ "es": "spanish",
+ "ru": "russian",
+ "ko": "korean",
+ "fr": "french",
+ "ja": "japanese",
+ "pt": "portuguese",
+ "tr": "turkish",
+ "pl": "polish",
+ "ca": "catalan",
+ "nl": "dutch",
+ "ar": "arabic",
+ "sv": "swedish",
+ "it": "italian",
+ "id": "indonesian",
+ "hi": "hindi",
+ "fi": "finnish",
+ "vi": "vietnamese",
+ "he": "hebrew",
+ "uk": "ukrainian",
+ "el": "greek",
+ "ms": "malay",
+ "cs": "czech",
+ "ro": "romanian",
+ "da": "danish",
+ "hu": "hungarian",
+ "ta": "tamil",
+ "no": "norwegian",
+ "th": "thai",
+ "ur": "urdu",
+ "hr": "croatian",
+ "bg": "bulgarian",
+ "lt": "lithuanian",
+ "la": "latin",
+ "mi": "maori",
+ "ml": "malayalam",
+ "cy": "welsh",
+ "sk": "slovak",
+ "te": "telugu",
+ "fa": "persian",
+ "lv": "latvian",
+ "bn": "bengali",
+ "sr": "serbian",
+ "az": "azerbaijani",
+ "sl": "slovenian",
+ "kn": "kannada",
+ "et": "estonian",
+ "mk": "macedonian",
+ "br": "breton",
+ "eu": "basque",
+ "is": "icelandic",
+ "hy": "armenian",
+ "ne": "nepali",
+ "mn": "mongolian",
+ "bs": "bosnian",
+ "kk": "kazakh",
+ "sq": "albanian",
+ "sw": "swahili",
+ "gl": "galician",
+ "mr": "marathi",
+ "pa": "punjabi",
+ "si": "sinhala",
+ "km": "khmer",
+ "sn": "shona",
+ "yo": "yoruba",
+ "so": "somali",
+ "af": "afrikaans",
+ "oc": "occitan",
+ "ka": "georgian",
+ "be": "belarusian",
+ "tg": "tajik",
+ "sd": "sindhi",
+ "gu": "gujarati",
+ "am": "amharic",
+ "yi": "yiddish",
+ "lo": "lao",
+ "uz": "uzbek",
+ "fo": "faroese",
+ "ht": "haitian creole",
+ "ps": "pashto",
+ "tk": "turkmen",
+ "nn": "nynorsk",
+ "mt": "maltese",
+ "sa": "sanskrit",
+ "lb": "luxembourgish",
+ "my": "myanmar",
+ "bo": "tibetan",
+ "tl": "tagalog",
+ "mg": "malagasy",
+ "as": "assamese",
+ "tt": "tatar",
+ "haw": "hawaiian",
+ "ln": "lingala",
+ "ha": "hausa",
+ "ba": "bashkir",
+ "jw": "javanese",
+ "su": "sundanese",
+ "yue": "cantonese"
+}
+
+TO_LANGUAGE_CODE = {
+ **{
+ language: code for code, language in LANGUAGES.items()
+ },
+ "burmese": "my",
+ "valencian": "ca",
+ "flemish": "nl",
+ "haitian": "ht",
+ "letzeburgesch": "lb",
+ "pushto": "ps",
+ "panjabi": "pa",
+ "moldavian": "ro",
+ "moldovan": "ro",
+ "sinhalese": "si",
+ "castilian": "es",
+ "mandarin": "zh"
+}
+
+_ALIGNMENT_HEADS = {
+ "tiny.en": b"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00",
+ "tiny": b"ABzY8bu8Lr0{>%RKn9Fp%m@SkK7Kt=7ytkO",
+ "base.en": b"ABzY8;40c<0{>%RzzG;p*o+Vo09|#PsxSZm00",
+ "base": b"ABzY8KQ!870{>%RzyTQH3`Q^yNP!>##QT-?_)10{>%RpeA61k&I|OI3I$65C{;;pbCHh0B{qLQ;+}v00",
+ "small": b"ABzY8DmU6=0{>%Rpa?J`kvJ6qF(V^F86#Xh7JUGMK}P%R7%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9",
+ "large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj",
+ "large-v3": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
+ "large": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
+ "large-v3-turbo": b"ABzY8j^C+e0{>%RARaKHP%t(lGR*)0g!tONPyhe`"
+}
+
+SAMPLE_RATE, N_FFT, HOP_LENGTH, CHUNK_LENGTH = 16000, 400, 160, 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE
+N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2
+stft = None
+
+def exact_div(x, y):
+ """
+ Perform exact integer division and ensure there is no remainder.
+
+ Args:
+ x (int): The dividend.
+ y (int): The divisor.
+
+ Returns:
+ int: The quotient of x // y.
+ """
+
+ assert x % y == 0
+ return x // y
+
+# Time-related and token-related scaling constants calculated via exact division
+N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH)
+FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH)
+TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN)
+
+def load_model(name = "base", device = "cpu"):
+ """
+ Load a Whisper model checkpoint from disk and configure its alignment heads.
+
+ Args:
+ name (str): Name of the model architecture configuration (e.g., "base").
+ device (str): Target hardware device to load the model onto (e.g., "cpu", "cuda").
+
+ Returns:
+ torch.nn.Module: The initialized and loaded Whisper model moved to the target device.
+ """
+
+ # Build the absolute path to the weights file
+ checkpoint_file = os.path.join(configs["speaker_diarization_path"], "models", name + ".pt")
+ alignment_heads = _ALIGNMENT_HEADS[name]
+
+ # Safely load the model weights using CPU mapping first to avoid GPU OOM
+ with open(checkpoint_file, "rb") as fp:
+ checkpoint = torch.load(fp, map_location="cpu", weights_only=True)
+
+ del checkpoint_file # Free up string reference
+
+ # Initialize model dimensions and state dict
+ model = Whisper(ModelDimensions(**checkpoint["dims"]))
+ model.load_state_dict(checkpoint["model_state_dict"])
+ model.set_alignment_heads(alignment_heads)
+
+ return model.to(device)
+
+def merge_punctuations(alignment, prepended, appended):
+ """
+ Merge hanging punctuations into their adjacent words based on prefix/suffix rules.
+
+ Args:
+ alignment (list[WordTiming]): List of WordTiming objects representing word boundaries.
+ prepended (str): String containing punctuation marks that should stick to the following word.
+ appended (str): String containing punctuation marks that should stick to the previous word.
+ """
+
+ # Phase 1: Backward pass to merge prepended punctuations
+ i = len(alignment) - 2
+ j = len(alignment) - 1
+
+ while i >= 0:
+ previous = alignment[i]
+ following = alignment[j]
+
+ # If previous item is a standalone punctuation matching prepended rules
+ if previous.word.startswith(" ") and previous.word.strip() in prepended:
+ following.word = previous.word + following.word
+ following.tokens = previous.tokens + following.tokens
+
+ # Clear out the merged element
+ previous.word = ""
+ previous.tokens = []
+ else: j = i # Move anchor pointer
+
+ i -= 1
+
+ # Phase 2: Forward pass to merge appended punctuations (e.g., "hello ." -> "hello.")
+ i = 0
+ j = 1
+
+ while j < len(alignment):
+ previous = alignment[i]
+ following = alignment[j]
+
+ # If following item is an isolated punctuation matching appended rules
+ if not previous.word.endswith(" ") and following.word in appended:
+ previous.word = previous.word + following.word
+ previous.tokens = previous.tokens + following.tokens
+
+ # Clear out the merged element
+ following.word = ""
+ following.tokens = []
+ else: i = j # Move anchor pointer
+
+ j += 1
+
+class WordTiming:
+ """Data structure holding timestamp and probability metadata for an individual word."""
+
+ def __init__(
+ self,
+ word,
+ tokens,
+ start,
+ end,
+ probability
+ ):
+ """Initializes WordTiming with temporal boundary metrics and token metadata."""
+
+ self.word = word
+ self.tokens = tokens
+ self.start = start
+ self.end = end
+ self.probability = probability
+
+def median_filter(x, filter_width):
+ """
+ Apply a 1D median filter along the last dimension of a PyTorch tensor.
+
+ Args:
+ x (torch.Tensor): Input tensor to be filtered.
+ filter_width (int): Odd integer representing the size of the sliding window.
+
+ Returns:
+ torch.Tensor: Median filtered tensor maintaining structural dimensions.
+ """
+
+ pad_width = filter_width // 2
+ # Return early if data is shorter than padding bounds
+ if x.shape[-1] <= pad_width: return x
+ # Temporarily unsqueeze to match expected 3D format for processing if needed
+ if (ndim := x.ndim) <= 2: x = x[None, None, :]
+
+ assert (filter_width > 0 and filter_width % 2 == 1)
+ result = None
+ # Pad tensor symmetrically using reflection on boundaries
+ x = F.pad(
+ x,
+ (filter_width // 2, filter_width // 2, 0, 0),
+ mode="reflect"
+ )
+
+ # Unfold along the last dimension to create windows, sort them, and select the median value
+ if result is None: result = x.unfold(-1, filter_width, 1).sort()[0][..., filter_width // 2]
+ # Revert dimension expansion if applied earlier
+ if ndim <= 2: result = result[0, 0]
+
+ return result
+
+@nb.jit(nopython=True)
+def backtrace(trace):
+ """
+ Trace back the optimal alignment path from a DTW direction matrix.
+
+ Args:
+ trace (np.ndarray): 2D matrix containing transition step history tags (0: diag, 1: up, 2: left).
+
+ Returns:
+ np.ndarray: A 2xN array containing the sequence indices mapping path alignment.
+ """
+
+ i = trace.shape[0] - 1
+ j = trace.shape[1] - 1
+
+ # Force boundary defaults to prevent running out of matrix edge bounds
+ trace[0, :] = 2
+ trace[:, 0] = 1
+
+ result = []
+ while i > 0 or j > 0:
+ result.append((i - 1, j - 1))
+ # Backtrack steps based on encoded direction numbers
+ if trace[i, j] == 0:
+ i -= 1
+ j -= 1
+ elif trace[i, j] == 1: i -= 1
+ elif trace[i, j] == 2: j -= 1
+ else: raise ValueError("Invalid trace matrix element encountered.")
+
+ # Reverse the list path to make it read chronologically from beginning to end
+ return np.array(result)[::-1, :].T
+
+
+@nb.jit(nopython=True, parallel=True)
+def dtw_cpu(x):
+ """
+ Compute Dynamic Time Warping cross-alignment path matrix on CPU using Numba acceleration.
+
+ Args:
+ x (np.ndarray): Cost matrix grid representing computed cross-entropy distance weights.
+
+ Returns:
+ np.ndarray: Reconstructed index map tracking the minimum cost alignment trajectory path.
+ """
+
+ N, M = x.shape
+ # Allocate DP matrices initialized to infinity
+ cost = np.ones((N + 1, M + 1), dtype=np.float32) * np.inf
+ trace = -np.ones((N + 1, M + 1), dtype=np.float32)
+ cost[0, 0] = 0
+
+ # Iteratively evaluate alignment costs cell by cell
+ for j in range(1, M + 1):
+ for i in range(1, N + 1):
+ c0 = cost[i - 1, j - 1] # Diagonal (Match/Substitution)
+ c1 = cost[i - 1, j] # Vertical (Insertion)
+ c2 = cost[i, j - 1] # Horizontal (Deletion)
+
+ # Determine minimum local transition routing paths
+ if c0 < c1 and c0 < c2: c, t = c0, 0
+ elif c1 < c0 and c1 < c2: c, t = c1, 1
+ else: c, t = c2, 2
+
+ # Accumulate cumulative cost steps
+ cost[i, j] = x[i - 1, j - 1] + c
+ trace[i, j] = t
+
+ return backtrace(trace)
+
+def dtw(x):
+ """
+ Bridge interface converting standard PyTorch tensors to NumPy arrays for DTW tracking execution.
+
+ Args:
+ x (torch.Tensor): PyTorch tensor distance grid matrix.
+
+ Returns:
+ np.ndarray: Reconstructed alignment track path mapping.
+ """
+
+ return dtw_cpu(x.double().cpu().numpy())
+
+def find_alignment(model, tokenizer, text_tokens, mel, num_frames, *, medfilt_width = 7, qk_scale = 1.0):
+ """
+ Map explicit token items directly down to corresponding audio frames utilizing cross-attention weights.
+
+ Args:
+ model: Whisper network configuration reference.
+ tokenizer: Active processing token encoder instance.
+ text_tokens (list[int]): Text sequence items to align.
+ mel (torch.Tensor): Audio Mel spectrogram matrix.
+ num_frames (int): Raw quantity number of audio context frames available.
+ medfilt_width (int): Sliding frame size used in filtering operations.
+ qk_scale (float): Multiplier factor for scaling Query-Key cross attention weights.
+
+ Returns:
+ list[WordTiming]: Array list of evaluated timestamps mapping per isolated word item.
+ """
+
+ if len(text_tokens) == 0: return []
+ # Reconstruct whole contextual block format with mandatory structural prompt framing tags
+ tokens = torch.tensor([
+ *tokenizer.sot_sequence,
+ tokenizer.no_timestamps,
+ *text_tokens,
+ tokenizer.eot
+ ]).to(model.device)
+
+ # Setup forward hook collectors to intercept cross-attention layer values seamlessly
+ QKs = [None] * model.dims.n_text_layer
+ hooks = [
+ block.cross_attn.register_forward_hook(
+ lambda _, ins, outs, index=i: QKs.__setitem__(index, outs[-1][0])
+ )
+ for i, block in enumerate(model.decoder.blocks)
+ ]
+
+ with torch.no_grad():
+ # Execute forward pass to collect generation probabilities
+ token_probs = model(
+ mel.unsqueeze(0),
+ tokens.unsqueeze(0)
+ )[0][len(tokenizer.sot_sequence) :, : tokenizer.eot].softmax(dim=-1)
+
+ # Retrieve confidence probabilities matching our active target text tokens
+ text_token_probs = token_probs[np.arange(len(text_tokens)), text_tokens].tolist()
+
+ # Clear hook references immediately to prevent memory leakage
+ for hook in hooks:
+ hook.remove()
+
+ # Extract indices tracking designated alignment heads across the model layers
+ if not config.device.startswith(("privateuseone", "ocl")):
+ alignment_indices = model.alignment_heads.indices().T
+ else:
+ # Fallback loop configuration handling for specialized virtual backends
+ alignment_indices = [
+ (l, h)
+ for l in range(model.alignment_heads.size(0))
+ for h in range(model.alignment_heads.size(1))
+ if model.alignment_heads[l, h]
+ ]
+
+ # Stack cross-attention weights from chosen layers and slice off excess frames
+ weights = (
+ torch.stack([
+ QKs[_l][_h]
+ for _l, _h in alignment_indices
+ ])[:, :, : num_frames // 2] * qk_scale
+ ).softmax(dim=-1)
+
+ # Normalize alignment activations across token sequence dimensions
+ std, mean = torch.std_mean(weights, dim=-2, keepdim=True, unbiased=False)
+
+ if config.device.startswith("privateuseone"):
+ weights = median_filter(
+ ((weights - mean) / std).cpu(),
+ medfilt_width
+ ).to(weights.device)
+ else:
+ weights = median_filter(
+ (weights - mean) / std,
+ medfilt_width
+ )
+
+ # Apply DTW tracking logic over averaged attention maps
+ text_indices, time_indices = dtw(-weights.mean(axis=0)[len(tokenizer.sot_sequence) : -1])
+ words, word_tokens = tokenizer.split_to_word_tokens(text_tokens + [tokenizer.eot])
+ if len(word_tokens) <= 1: return []
+
+ # Reconstruct token boundary offsets mapping to explicit text words
+ word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0))
+ jump_times = time_indices[np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool)] / TOKENS_PER_SECOND
+
+ # Format output items array into systematic structural WordTiming records
+ return [
+ WordTiming(
+ word,
+ tokens,
+ start,
+ end,
+ probability
+ )
+ for word, tokens, start, end, probability in zip(
+ words,
+ word_tokens,
+ jump_times[word_boundaries[:-1]],
+ jump_times[word_boundaries[1:]],
+ [
+ np.mean(text_token_probs[i:j])
+ for i, j in zip(
+ word_boundaries[:-1],
+ word_boundaries[1:]
+ )
+ ]
+ )
+ ]
+
+def add_word_timestamps(
+ *,
+ segments,
+ model,
+ tokenizer,
+ mel,
+ num_frames,
+ prepend_punctuations = "\"'“¿([{-",
+ append_punctuations = "\"'.。,,!!??::”)]}、",
+ last_speech_timestamp,
+ **kwargs
+):
+ """
+ Refine text segment ranges down to fine-grained, word-by-word timestamps using DTW alignment maps.
+
+ Args:
+ segments (list[dict]): Extracted transcription chunk items tracking active window clips.
+ model: Active Whisper neural network instance.
+ tokenizer: Model token sequence manager reference.
+ mel (torch.Tensor): Normalized log-Mel input spectrum tracking frame contents.
+ num_frames (int): Current count of valid processing frames.
+ prepend_punctuations (str): Prefix punctuation filter layout.
+ append_punctuations (str): Suffix punctuation filter layout.
+ last_speech_timestamp (float): The timestamp when the last speech segment ended.
+ **kwargs: Forward tracking argument configs passed directly to find_alignment.
+ """
+
+ if len(segments) == 0: return
+ # Strip out any End-Of-Text or control sequence tokens from tracking maps
+ text_tokens_per_segment = [
+ [token for token in segment["tokens"] if token < tokenizer.eot]
+ for segment in segments
+ ]
+
+ # Combine tokens to compute massive matrix alignment tracks in one pass
+ text_tokens = list(itertools.chain.from_iterable(text_tokens_per_segment))
+ alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **kwargs)
+
+ # Collect statistical metadata about word lengths to filter out outlier anomalies
+ word_durations = np.array([t.end - t.start for t in alignment])
+ word_durations = word_durations[word_durations.nonzero()]
+
+ median_duration = min(0.7, float(np.median(word_durations) if len(word_durations) > 0 else 0.0))
+ max_duration = median_duration * 2
+
+ # Smooth out unrealistic jump spikes across punctuation boundaries
+ if len(word_durations) > 0:
+ sentence_end_marks = ".。!!??"
+ for i in range(1, len(alignment)):
+ if alignment[i].end - alignment[i].start > max_duration:
+ if alignment[i].word in sentence_end_marks: alignment[i].end = alignment[i].start + max_duration
+ elif alignment[i - 1].word in sentence_end_marks: alignment[i].start = alignment[i].end - max_duration
+
+ # Merge isolated punctuation tokens back into their target words
+ merge_punctuations(alignment, prepend_punctuations, append_punctuations)
+ time_offset = segments[0]["seek"] * HOP_LENGTH / SAMPLE_RATE
+ word_index = 0
+
+ # Distribute global alignment tracking information back onto individual segments
+ for segment, text_tokens in zip(segments, text_tokens_per_segment):
+ saved_tokens = 0
+ words = []
+
+ while word_index < len(alignment) and saved_tokens < len(text_tokens):
+ timing = alignment[word_index]
+
+ if timing.word:
+ words.append(
+ dict(
+ word=timing.word,
+ start=round(time_offset + timing.start, 2),
+ end=round(time_offset + timing.end, 2),
+ probability=timing.probability
+ )
+ )
+
+ saved_tokens += len(timing.tokens)
+ word_index += 1
+
+ # Heuristic checks to identify and correct alignment edge-case anomalies
+ if len(words) > 0:
+ if (
+ words[0]["end"] - last_speech_timestamp > median_duration * 4 and (
+ words[0]["end"] - words[0]["start"] > max_duration or
+ (len(words) > 1 and words[1]["end"] - words[0]["start"] > max_duration * 2)
+ )
+ ):
+ if (
+ len(words) > 1 and
+ words[1]["end"] - words[1]["start"] > max_duration
+ ):
+ words[0]["end"] = words[1]["start"] = max(words[1]["end"] / 2, words[1]["end"] - max_duration)
+
+ words[0]["start"] = max(0, words[0]["end"] - max_duration)
+
+ # Re-align segment bounds if they conflict with word boundaries
+ if (
+ segment["start"] < words[0]["end"] and
+ segment["start"] - 0.5 > words[0]["start"]
+ ):
+ words[0]["start"] = max(0, min(words[0]["end"] - median_duration, segment["start"]))
+ else:
+ segment["start"] = words[0]["start"]
+
+ if (
+ segment["end"] > words[-1]["start"] and
+ segment["end"] + 0.5 < words[-1]["end"]
+ ):
+ words[-1]["end"] = max(words[-1]["start"] + median_duration, segment["end"])
+ else:
+ segment["end"] = words[-1]["end"]
+
+ last_speech_timestamp = segment["end"]
+
+ segment["words"] = words
+
+@lru_cache(maxsize=None)
+def mel_filters(device, n_mels):
+ """
+ Load standard Mel-scale frequency filterbanks and cache them in memory.
+
+ Args:
+ device (torch.device): Device destination where weights should reside.
+ n_mels (int): Target frequency band count size (Must be 80 or 128).
+
+ Returns:
+ torch.Tensor: Cached constant filter matrix mapping.
+ """
+
+ assert n_mels in {80, 128}
+
+ with np.load(
+ os.path.join(configs["speaker_diarization_path"], "assets", "mel_filters.npz"),
+ allow_pickle=False
+ ) as f:
+ return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
+
+def log_mel_spectrogram(audio, n_mels = 80, padding = 0, device = None):
+ """
+ Compute the log-Mel spectrogram of a given audio signal.
+
+ Args:
+ audio (str | np.ndarray | torch.Tensor): Input audio source file path or raw data array.
+ n_mels (int): Frequency scaling band resolution size.
+ padding (int): Length of zero padding to apply to the end of the audio.
+ device: Target execution hardware environment.
+
+ Returns:
+ torch.Tensor: Log-frequency spectral power map normalized matrix.
+ """
+
+ global stft
+
+ # If input is a file path string, load it using external utility wrappers
+ if not torch.is_tensor(audio):
+ if isinstance(audio, str):
+ from main.library.audio.audio import load_audio
+ audio = load_audio(audio, sample_rate=SAMPLE_RATE)
+
+ audio = torch.from_numpy(audio).float()
+
+ if device is not None: audio = audio.to(device)
+ if padding > 0: audio = F.pad(audio, (0, padding))
+
+ # Calculate Short-Time Fourier Transform (STFT) based on backend device capability
+ if str(audio.device).startswith(("ocl", "privateuseone")):
+ if stft is None:
+ from main.library.backends.utils import STFT
+
+ stft = STFT(
+ N_FFT,
+ HOP_LENGTH,
+ N_FFT
+ ).to(audio.device)
+
+ fft = stft.transform(
+ audio.unsqueeze(0),
+ eps=1e-9
+ ).squeeze(0)
+ else:
+ fft = torch.stft(
+ audio,
+ n_fft=N_FFT,
+ hop_length=HOP_LENGTH,
+ window=torch.hann_window(N_FFT).to(audio.device),
+ return_complex=True
+ )
+
+ # Map powers across Mel scales and bound lower logs to prevent numeric underflows
+ log_spec = (
+ mel_filters(audio.device, n_mels) @ fft[..., :-1].abs() ** 2
+ ).clamp(min=1e-10).log10()
+
+ # Apply scaling to conform to standard standardized amplitude ranges
+ return (log_spec.maximum(log_spec.max() - 8.0) + 4.0) / 4.0
+
+def pad_or_trim(array, length = N_SAMPLES, *, axis = -1):
+ """
+ Pad or trim a tensor/array along a specified axis to match a target length.
+
+ Args:
+ array (torch.Tensor | np.ndarray): Input sequence buffer layout.
+ length (int): Final desired dimension size constraint.
+ axis (int): Targeting dimension axis index.
+
+ Returns:
+ The uniformly resized data tensor or array structure.
+ """
+
+ if torch.is_tensor(array):
+ # Slice off trailing ends if array exceeds targeted bounds
+ if array.shape[axis] > length:
+ array = array.index_select(
+ dim=axis,
+ index=torch.arange(length, device=array.device)
+ )
+
+ # Pad with zeros if array falls short of targeted bounds
+ if array.shape[axis] < length:
+ pad_widths = [(0, 0)] * array.ndim
+ pad_widths[axis] = (0, length - array.shape[axis])
+ array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
+ else:
+ # NumPy fallback operations path
+ if array.shape[axis] > length: array = array.take(indices=range(length), axis=axis)
+
+ if array.shape[axis] < length:
+ pad_widths = [(0, 0)] * array.ndim
+ pad_widths[axis] = (0, length - array.shape[axis])
+ array = np.pad(array, pad_widths)
+
+ return array
+
+def get_end(segments):
+ """
+ Retrieve the end timestamp of the last valid word found within active segment logs.
+
+ Args:
+ segments (list[dict]): Processed text segment history entries.
+
+ Returns:
+ float: Absolute timestamp indicating where the last vocal speech word item concluded.
+ """
+
+ return next((
+ w["end"]
+ for s in reversed(segments)
+ for w in reversed(s["words"])
+ ), segments[-1]["end"] if segments else None)
+
+def transcribe_function(
+ model,
+ audio,
+ *,
+ verbose = None,
+ temperature = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
+ compression_ratio_threshold = 2.4,
+ logprob_threshold = -1.0,
+ no_speech_threshold = 0.6,
+ condition_on_previous_text = True,
+ initial_prompt = None,
+ carry_initial_prompt = False,
+ word_timestamps = False,
+ prepend_punctuations = "\"'“¿([{-",
+ append_punctuations = "\"'.。,,!!??::”)]}、",
+ clip_timestamps = "0",
+ hallucination_silence_threshold = None,
+ fp16 = False,
+ **decode_options
+):
+ """
+ Transcribe an audio source using a Whisper model with fallback mechanisms and timestamp adjustments.
+
+ Args:
+ model: Loaded instance configuration of the Whisper neural network.
+ audio: Audio source to transcribe (file path or pre-loaded data array).
+ verbose (bool): Set to true to print out runtime logging details.
+ temperature (tuple): Sequence of decoding temperatures to attempt when fallbacks trigger.
+ compression_ratio_threshold (float): Upper compression limit before rejecting bad text loops.
+ logprob_threshold (float): Minimum log probability threshold to accept a generation.
+ no_speech_threshold (float): Silence probability limit above which segments are skipped.
+ condition_on_previous_text (bool): If True, feeds past generation back into model prompts.
+ initial_prompt (str): Text phrase passed into initial context fields.
+ carry_initial_prompt (bool): If True, retains initial prompts across all processing chunks.
+ word_timestamps (bool): Extends metrics output generation down to word level granularities.
+ prepend_punctuations (str): Prefix punctuation mapping string formatting definitions.
+ append_punctuations (str): Suffix punctuation mapping string formatting definitions.
+ clip_timestamps (str): Comma-separated time markers limiting execution processing windows.
+ hallucination_silence_threshold (float): Maximum allowed gap width before assuming hallucinations.
+ fp16 (bool): Switch encoding tensors execution path directly into float16 precision.
+
+ Returns:
+ dict: Complete structured dictionary containing text, segments, and detected language.
+ """
+
+ dtype = torch.float16 if fp16 else torch.float32
+ decode_options["fp16"] = fp16
+
+ # Convert audio into continuous normalized Log-Mel-Spectrogram spectrum frames
+ mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES)
+ content_frames = mel.shape[-1] - N_FRAMES
+ content_duration = float(content_frames * HOP_LENGTH / SAMPLE_RATE)
+
+ # Detect language if not specified explicitly in arguments
+ if decode_options.get("language", None) is None:
+ if not model.is_multilingual: decode_options["language"] = "vi"
+ else:
+ mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype)
+ _, probs = model.detect_language(mel_segment)
+ decode_options["language"] = max(probs, key=probs.get)
+
+ if verbose is not None: logger.info(f"{LANGUAGES[decode_options['language']].title()}")
+
+ language = decode_options["language"]
+ task = decode_options.get("task", "transcribe")
+
+ tokenizer = get_tokenizer(
+ model.is_multilingual,
+ num_languages=model.num_languages,
+ language=language,
+ task=task
+ )
+
+ # Parse timestamp clipping ranges to constrain decoding regions
+ if isinstance(clip_timestamps, str):
+ clip_timestamps = [
+ float(ts)
+ for ts in (
+ clip_timestamps.split(",") if clip_timestamps else []
+ )
+ ]
+
+ seek_points = [round(ts * FRAMES_PER_SECOND) for ts in clip_timestamps]
+
+ if len(seek_points) == 0: seek_points.append(0)
+ if len(seek_points) % 2 == 1: seek_points.append(content_frames)
+
+ seek_clips = list(zip(seek_points[::2], seek_points[1::2]))
+ punctuation = "\"'“¿([{-\"'.。,,!!??::”)]}、"
+
+ def decode_with_fallback(segment):
+ """Helper to retry decoding with higher temperatures if quality thresholds fail."""
+
+ decode_result = None
+ temperatures = (
+ [temperature] if isinstance(temperature, (int, float)) else temperature
+ )
+
+ for t in temperatures:
+ kwargs = {**decode_options}
+
+ # Adjust options based on whether we are doing greedy decoding or sampling
+ if t > 0:
+ kwargs.pop("beam_size", None)
+ kwargs.pop("patience", None)
+ else: kwargs.pop("best_of", None)
+
+ # Perform inference
+ decode_result = model.decode(
+ segment,
+ DecodingOptions(
+ **kwargs,
+ temperature=t
+ )
+ )
+
+ needs_fallback = False
+
+ # Check if output is repetitive (high compression ratio)
+ if (
+ compression_ratio_threshold is not None and
+ decode_result.compression_ratio > compression_ratio_threshold
+ ):
+ needs_fallback = True
+
+ # Check if overall confidence is too low
+ if (
+ logprob_threshold is not None and
+ decode_result.avg_logprob < logprob_threshold
+ ):
+ needs_fallback = True
+
+ # Override fallback if it's likely just silence/no speech
+ if (
+ no_speech_threshold is not None and
+ decode_result.no_speech_prob > no_speech_threshold and
+ logprob_threshold is not None and
+ decode_result.avg_logprob < logprob_threshold
+ ):
+ needs_fallback = False
+
+ if not needs_fallback: break
+
+ return decode_result
+
+ # Initialize tracking variables for the main loop
+ clip_idx = 0
+ seek = seek_clips[clip_idx][0]
+
+ input_stride = exact_div(N_FRAMES, model.dims.n_audio_ctx)
+ time_precision = (input_stride * HOP_LENGTH / SAMPLE_RATE)
+
+ all_tokens, all_segments = [], []
+ prompt_reset_since = 0
+
+ remaining_prompt_length = model.dims.n_text_ctx // 2 - 1
+ # Encode the initial prompt text if provided
+ if initial_prompt is not None:
+ initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
+ all_tokens.extend(initial_prompt_tokens)
+ remaining_prompt_length -= len(initial_prompt_tokens)
+ else: initial_prompt_tokens = []
+
+ def new_segment(*, start, end, tokens, result):
+ """Construct a structured dictionary for a transcribed segment."""
+
+ tokens = tokens.tolist()
+
+ return {
+ "seek": seek,
+ "start": start,
+ "end": end,
+ "text": tokenizer.decode([token for token in tokens if token < tokenizer.eot]),
+ "tokens": tokens,
+ "temperature": result.temperature,
+ "avg_logprob": result.avg_logprob,
+ "compression_ratio": result.compression_ratio,
+ "no_speech_prob": result.no_speech_prob
+ }
+
+ # Main chunk-by-chunk processing loop with a progress bar
+ with tqdm.tqdm(total=content_frames, unit="frames", disable=verbose is not False) as pbar:
+ last_speech_timestamp = 0.0
+ while clip_idx < len(seek_clips):
+ seek_clip_start, seek_clip_end = seek_clips[clip_idx]
+ if seek < seek_clip_start: seek = seek_clip_start
+
+ # Advance to the next time clip if the current one is finished
+ if seek >= seek_clip_end:
+ clip_idx += 1
+ if clip_idx < len(seek_clips): seek = seek_clips[clip_idx][0]
+ continue
+
+ # Calculate current window time offsets
+ time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
+ window_end_time = float((seek + N_FRAMES) * HOP_LENGTH / SAMPLE_RATE)
+
+ segment_size = min(N_FRAMES, content_frames - seek, seek_clip_end - seek)
+ mel_segment = mel[:, seek : seek + segment_size]
+
+ segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
+ mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)
+
+ # Prepare the prompt history for context conditioning
+ if carry_initial_prompt:
+ decode_options["prompt"] = initial_prompt_tokens + all_tokens[max(len(initial_prompt_tokens), prompt_reset_since):][-remaining_prompt_length:]
+ else:
+ decode_options["prompt"] = all_tokens[prompt_reset_since:]
+
+ # Run transcription on the current chunk
+ result = decode_with_fallback(mel_segment)
+ tokens = torch.tensor(result.tokens)
+
+ # Skip processing this segment if it is detected as silence
+ if no_speech_threshold is not None:
+ should_skip = result.no_speech_prob > no_speech_threshold
+ if (logprob_threshold is not None and result.avg_logprob > logprob_threshold):
+ should_skip = False
+
+ if should_skip:
+ seek += segment_size
+ continue
+
+ previous_seek = seek
+ current_segments = []
+
+ def word_anomaly_score(word):
+ """Calculate an anomaly score based on unusual word lengths or low probability."""
+
+ probability = word.get("probability", 0.0)
+ duration = word["end"] - word["start"]
+ score = 0.0
+
+ if probability < 0.15: score += 1.0
+ if duration < 0.133: score += (0.133 - duration) * 15
+ if duration > 2.0: score += duration - 2.0
+
+ return score
+
+ def is_segment_anomaly(segment):
+ """Check if a segment's initial words look like a model hallucination loop."""
+
+ if segment is None or not segment["words"]: return False
+
+ words = [w for w in segment["words"] if w["word"] not in punctuation]
+ words = words[:8]
+
+ score = sum(word_anomaly_score(w) for w in words)
+
+ return score >= 3 or score + 0.01 >= len(words)
+
+ def next_words_segment(segments):
+ """Find the first segment in the list that contains word metadata."""
+
+ return next((s for s in segments if s["words"]), None)
+
+ # Parse timestamp tokens to slice the chunk into sub-segments
+ timestamp_tokens = tokens.ge(tokenizer.timestamp_begin)
+ single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True]
+
+ consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0]
+ consecutive.add_(1)
+
+ if len(consecutive) > 0:
+ slices = consecutive.tolist()
+ if single_timestamp_ending:
+ slices.append(len(tokens))
+
+ last_slice = 0
+ for current_slice in slices:
+ sliced_tokens = tokens[last_slice:current_slice]
+ current_segments.append(
+ new_segment(
+ start=time_offset + (sliced_tokens[0].item() - tokenizer.timestamp_begin) * time_precision,
+ end=time_offset + (sliced_tokens[-1].item() - tokenizer.timestamp_begin) * time_precision,
+ tokens=sliced_tokens,
+ result=result
+ )
+ )
+ last_slice = current_slice
+
+ if single_timestamp_ending: seek += segment_size
+ else: seek += (tokens[last_slice - 1].item() - tokenizer.timestamp_begin) * input_stride
+ else:
+ # Fallback path if no internal timestamp markers were generated
+ duration = segment_duration
+
+ timestamps = tokens[timestamp_tokens.nonzero().flatten()]
+ if (len(timestamps) > 0 and timestamps[-1].item() != tokenizer.timestamp_begin):
+ duration = (timestamps[-1].item() - tokenizer.timestamp_begin) * time_precision
+
+ current_segments.append(
+ new_segment(
+ start=time_offset,
+ end=time_offset + duration,
+ tokens=tokens,
+ result=result
+ )
+ )
+
+ seek += segment_size
+
+ # Compute fine-grained word timestamps if requested
+ if word_timestamps:
+ add_word_timestamps(
+ segments=current_segments,
+ model=model,
+ tokenizer=tokenizer,
+ mel=mel_segment,
+ num_frames=segment_size,
+ prepend_punctuations=prepend_punctuations,
+ append_punctuations=append_punctuations,
+ last_speech_timestamp=last_speech_timestamp
+ )
+
+ if not single_timestamp_ending:
+ last_word_end = get_end(current_segments)
+ if last_word_end is not None and last_word_end > time_offset:
+ seek = round(last_word_end * FRAMES_PER_SECOND)
+
+ # Implement an anti-hallucination guardrail for prolonged periods of silence
+ if hallucination_silence_threshold is not None:
+ threshold = hallucination_silence_threshold
+
+ if not single_timestamp_ending:
+ last_word_end = get_end(current_segments)
+ if last_word_end is not None and last_word_end > time_offset:
+ seek = round(last_word_end * FRAMES_PER_SECOND) if (window_end_time - last_word_end) > threshold else (previous_seek + segment_size)
+
+ first_segment = next_words_segment(current_segments)
+ if first_segment is not None and is_segment_anomaly(first_segment):
+ gap = first_segment["start"] - time_offset
+
+ if gap > threshold:
+ seek = previous_seek + round(gap * FRAMES_PER_SECOND)
+ continue
+
+ hal_last_end = last_speech_timestamp
+
+ for si in range(len(current_segments)):
+ segment = current_segments[si]
+ if not segment["words"]: continue
+
+ # If a hallucination pattern is caught, cut the segment list short and reposition seek
+ if is_segment_anomaly(segment):
+ next_segment = next_words_segment(current_segments[si + 1 :])
+ hal_next_start = next_segment["words"][0]["start"] if next_segment is not None else (time_offset + segment_duration)
+
+ if (
+ segment["start"] - hal_last_end > threshold or segment["start"] < threshold or segment["start"] - time_offset < 2.0
+ ) and (
+ hal_next_start - segment["end"] > threshold or is_segment_anomaly(next_segment) or window_end_time - segment["end"] < 2.0
+ ):
+ seek = round(max(time_offset + 1, segment["start"]) * FRAMES_PER_SECOND)
+ if content_duration - segment["end"] < threshold: seek = content_frames
+
+ current_segments[si:] = []
+ break
+
+ hal_last_end = segment["end"]
+
+ last_word_end = get_end(current_segments)
+ if last_word_end is not None: last_speech_timestamp = last_word_end
+
+ # Filter out and clean empty segments
+ for _, segment in enumerate(current_segments):
+ if segment["start"] == segment["end"] or segment["text"].strip() == "":
+ segment["text"] = ""
+ segment["tokens"] = []
+ segment["words"] = []
+
+ # Append the calculated results to global arrays
+ all_segments.extend([{"id": i, **segment} for i, segment in enumerate(current_segments, start=len(all_segments))])
+ all_tokens.extend([token for segment in current_segments for token in segment["tokens"]])
+
+ # Clear prompt conditioning history if temperature is high to prevent repetitive loops
+ if not condition_on_previous_text or result.temperature > 0.5: prompt_reset_since = len(all_tokens)
+ pbar.update(min(content_frames, seek) - previous_seek)
+
+ return dict(text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]), segments=all_segments, language=language)
+
+def compression_ratio(text):
+ """
+ Calculate the compression ratio of a text string using zlib.
+
+ Used as a heuristic metric to detect repetitive or low-quality generations.
+
+ Args:
+ text (str): The input string to evaluate.
+
+ Returns:
+ float: The ratio of raw byte length to compressed byte length.
+ """
+ text_bytes = text.encode("utf-8")
+ # Ratio > 1.0 means compression successfully reduced the size
+ return len(text_bytes) / len(zlib.compress(text_bytes))
+
+def sinusoids(length, channels, max_timescale=10000):
+ """
+ Generate sinusoidal positional embeddings (used in the audio encoder).
+
+ Args:
+ length (int): Number of position steps (context length).
+ channels (int): Dimensionality of the embedding space.
+ max_timescale (float, optional): Maximum scale factor for frequency. Defaults to 10000.
+
+ Returns:
+ torch.Tensor: A tensor of shape (length, channels) containing sine/cosine waves.
+ """
+
+ assert channels % 2 == 0 # Dimensions must be even to split equally into sin and cos
+ # Compute exponential frequency scaling factors
+ scaled_time = torch.arange(length)[:, np.newaxis] * (-(np.log(max_timescale) / (channels // 2 - 1)) * torch.arange(channels // 2)).exp()[np.newaxis, :]
+ # Concatenate sine and cosine waves along the feature dimension
+ return torch.cat([scaled_time.sin(), scaled_time.cos()], dim=1)
+
+@torch.no_grad()
+def detect_language_function(model, mel, tokenizer = None):
+ """
+ Detect the language of the given audio features (Mel Spectrogram).
+
+ Args:
+ model (nn.Module): The Whisper model instance.
+ mel (torch.Tensor): Audio Mel Spectrogram tensor.
+ tokenizer (Tokenizer, optional): A Whisper Tokenizer instance. Defaults to None.
+
+ Returns:
+ Tuple[torch.Tensor, List[Dict[str, float]]]: Detected language tokens and probability distributions.
+ """
+
+ # Initialize tokenizer if not provided
+ if tokenizer is None: tokenizer = get_tokenizer(model.is_multilingual, num_languages=model.num_languages)
+ if (tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence): raise ValueError("Invalid tokenizer configuration for language detection.")
+
+ single = mel.ndim == 2
+ # Add batch dimension if single instance
+ if single: mel = mel.unsqueeze(0)
+ # Extract audio features if raw Mel spectrogram is passed
+ if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): mel = model.encoder(mel)
+
+ n_audio = mel.shape[0]
+ # Predict logits using the Start-Of-Transcript (SOT) token as prompt context
+ logits = model.logits(torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device), mel)[:, 0]
+
+ # Create a mask to suppress all tokens except explicit language tokens
+ mask = torch.ones(logits.shape[-1], dtype=torch.bool)
+ mask[list(tokenizer.all_language_tokens)] = False
+
+ logits[:, mask] = -np.inf # Set non-language tokens to negative infinity
+
+ # Get the token ID of the highest probability language
+ language_tokens = logits.argmax(dim=-1)
+ # Compute full probability mapping distribution per batch item
+ language_probs = [
+ {
+ c: logits.softmax(dim=-1).cpu()[i, j].item()
+ for j, c in zip(
+ tokenizer.all_language_tokens,
+ tokenizer.all_language_codes
+ )
+ }
+ for i in range(n_audio)
+ ]
+
+ if single:
+ language_tokens = language_tokens[0]
+ language_probs = language_probs[0]
+
+ return language_tokens, language_probs
+
+@lru_cache(maxsize=None)
+def get_tokenizer(multilingual, *, num_languages = 99, language = None, task = None):
+ """
+ Fetch or create a cached Tokenizer instance based on language configurations.
+
+ Args:
+ multilingual (bool): Whether to load the multilingual vocabulary or English-only variant.
+ num_languages (int, optional): Number of languages supported. Defaults to 99.
+ language (str, optional): Target language name or code. Defaults to None.
+ task (str, optional): Task type (e.g., 'transcribe', 'translate'). Defaults to None.
+
+ Returns:
+ Tokenizer: Initialized tokenizer wrapper instance.
+ """
+
+ if language is not None:
+ language = language.lower()
+ if language not in LANGUAGES:
+ if language in TO_LANGUAGE_CODE: language = TO_LANGUAGE_CODE[language]
+ else: raise ValueError(f"Unsupported language: {language}")
+
+ if multilingual:
+ encoding_name = "multilingual"
+ language = language or "en"
+ task = task or "transcribe"
+ else:
+ encoding_name = "gpt2"
+ language = None
+ task = None
+
+ return Tokenizer(
+ encoding_name=encoding_name,
+ num_languages=num_languages,
+ language=language,
+ task=task
+ )
+
+@lru_cache(maxsize=None)
+def get_encoding(name = "gpt2", num_languages = 99):
+ """
+ Load and configure the Byte-Pair Encoding (BPE) structures utilizing tiktoken.
+
+ Args:
+ name (str, optional): Name of the tiktoken tokenizer mapping. Defaults to "gpt2".
+ num_languages (int, optional): Number of language specific tokens to register. Defaults to 99.
+
+ Returns:
+ tiktoken.Encoding: The configured BPE byte encoder structure.
+ """
+
+ vocab_path = os.path.join(configs["speaker_diarization_path"], "assets", f"{name}.tiktoken")
+ # Load core token merge ranks from file
+ ranks = {
+ base64.b64decode(token): int(rank)
+ for token, rank in (
+ line.split()
+ for line in open(vocab_path)
+ if line
+ )
+ }
+
+ n_vocab = len(ranks)
+ special_tokens = {}
+ # Define all structural and special control tokens for the transformer
+ specials = [
+ "<|endoftext|>", "<|startoftranscript|>",
+ *[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]],
+ "<|translate|>",
+ "<|transcribe|>",
+ "<|startoflm|>",
+ "<|startofprev|>",
+ "<|nospeech|>",
+ "<|notimestamps|>",
+ *[f"<|{i * 0.02:.2f}|>" for i in range(1501)] # Special tokens capturing timestamp steps
+ ]
+
+ # Dynamically allocate indices for special control tokens
+ for token in specials:
+ special_tokens[token] = n_vocab
+ n_vocab += 1
+
+ return tiktoken.Encoding(
+ name=os.path.basename(vocab_path),
+ explicit_n_vocab=n_vocab,
+ pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
+ mergeable_ranks=ranks,
+ special_tokens=special_tokens
+ )
+
+class DecodingOptions:
+ """Dataclass configuration containing adjustable options for custom auto-regressive generation."""
+
+ def __init__(
+ self,
+ task = "transcribe",
+ language = None,
+ temperature = 0.0,
+ sample_len = None,
+ best_of = None,
+ beam_size = None,
+ patience = None,
+ length_penalty = None,
+ prompt = None,
+ prefix = None,
+ suppress_tokens = "-1",
+ suppress_blank = True,
+ without_timestamps = False,
+ max_initial_timestamp = 1.0,
+ fp16 = False
+ ):
+ self.task = task
+ self.language = language
+ self.temperature = temperature
+ self.sample_len = sample_len
+ self.best_of = best_of
+ self.beam_size = beam_size
+ self.patience = patience
+ self.length_penalty = length_penalty
+ self.prompt = prompt
+ self.prefix = prefix
+ self.suppress_tokens = suppress_tokens
+ self.suppress_blank = suppress_blank
+ self.without_timestamps = without_timestamps
+ self.max_initial_timestamp = max_initial_timestamp
+ self.fp16 = fp16
+
+@torch.no_grad()
+def decode_function(model, mel, options = DecodingOptions(), **kwargs):
+ """
+ Main decoding utility wrapper to sequence tokens from given processed acoustic features.
+
+ Args:
+ model (nn.Module): The Whisper model block.
+ mel (torch.Tensor): Audio features spectrogram context.
+ options (DecodingOptions, optional): Decoding configuration settings.
+ **kwargs: Arbitrary keyword arguments to override specified options dynamic values.
+
+ Returns:
+ Union[DecodingResult, List[DecodingResult]]: The result object(s) containing text and features.
+ """
+
+ if single := mel.ndim == 2: mel = mel.unsqueeze(0) # Ensure batched representation format
+ if kwargs: options = replace(options, **kwargs) # Override default parameter options safely
+
+ # Instantiate the dedicated core DecodingTask framework
+ result = DecodingTask(model, options).run(mel)
+ return result[0] if single else result
+
+class ModelDimensions:
+ """Structure wrapper explicitly specifying the model configuration sizes."""
+
+ def __init__(
+ self,
+ n_mels,
+ n_audio_ctx,
+ n_audio_state,
+ n_audio_head,
+ n_audio_layer,
+ n_vocab,
+ n_text_ctx,
+ n_text_state,
+ n_text_head,
+ n_text_layer
+ ):
+ self.n_mels = n_mels
+ self.n_audio_ctx = n_audio_ctx
+ self.n_audio_state = n_audio_state
+ self.n_audio_head = n_audio_head
+ self.n_audio_layer = n_audio_layer
+ self.n_vocab = n_vocab
+ self.n_text_ctx = n_text_ctx
+ self.n_text_state = n_text_state
+ self.n_text_head = n_text_head
+ self.n_text_layer = n_text_layer
+
+class Linear(nn.Linear):
+ """Custom Linear layer wrapper executing safe runtime precision casting to match inputs dynamic precision."""
+
+ def forward(
+ self,
+ x
+ ):
+ return F.linear(
+ x,
+ self.weight.to(x.dtype),
+ self.bias.to(x.dtype) if self.bias is not None else None
+ )
+
+class Conv1d(nn.Conv1d):
+ """Custom 1D Convolution wrapper executing dynamic type casting to handle fp16 execution safely."""
+
+ def _conv_forward(
+ self,
+ x,
+ weight,
+ bias
+ ):
+ return super()._conv_forward(
+ x,
+ weight.to(x.dtype),
+ bias.to(x.dtype) if bias is not None else None
+ )
+
+class TextDecoder(nn.Module):
+ """The auto-regressive transformer decoder generating text output sequences based on acoustic contexts."""
+
+ def __init__(
+ self,
+ n_vocab,
+ n_ctx,
+ n_state,
+ n_head,
+ n_layer
+ ):
+ super().__init__()
+ self.token_embedding = nn.Embedding(n_vocab, n_state)
+ self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
+ self.blocks = nn.ModuleList([
+ ResidualAttentionBlock(
+ n_state,
+ n_head,
+ cross_attention=True
+ )
+ for _ in range(n_layer)
+ ])
+ self.ln = Fp32LayerNorm(n_state)
+ # Upper triangular causal matrix to avoid attention lookaheads on generation loops
+ self.register_buffer("mask", torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1), persistent=False)
+
+ def forward(self, x, xa, kv_cache = None):
+ # Calculate generation sequence token offset index from current Key-Value cache
+ offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+ x = (self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]).to(xa.dtype)
+
+ for block in self.blocks:
+ x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
+
+ x = self.ln(x)
+ # Weight-tying projection back to raw token embedding logits
+ return (x @ self.token_embedding.weight.to(x.dtype).transpose(0, 1)).float()
+
+class AudioEncoder(nn.Module):
+ """The convolutional-transformer block processing raw input audio Mel frames into continuous states."""
+
+ def __init__(self, n_mels, n_ctx, n_state, n_head, n_layer):
+ super().__init__()
+ self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
+ self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
+ self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
+ self.blocks = nn.ModuleList([
+ ResidualAttentionBlock(
+ n_state,
+ n_head
+ )
+ for _ in range(n_layer)
+ ])
+ self.ln_post = Fp32LayerNorm(n_state)
+
+ def forward(self, x):
+ # Pass through Conv1D feature extractors with GELU activations
+ x = F.gelu(self.conv2(F.gelu(self.conv1(x)))).permute(0, 2, 1)
+
+ assert x.shape[1:] == self.positional_embedding.shape
+ x = (x + self.positional_embedding).to(x.dtype) # Inject static positional waves
+
+ for block in self.blocks:
+ x = block(x)
+
+ return self.ln_post(x)
+
+class Whisper(nn.Module):
+ """The overarching Sequence-to-Sequence multi-task Whisper pipeline model entity wrapper."""
+
+ def __init__(self, dims):
+ super().__init__()
+ self.dims = dims
+ self.encoder = AudioEncoder(
+ self.dims.n_mels,
+ self.dims.n_audio_ctx,
+ self.dims.n_audio_state,
+ self.dims.n_audio_head,
+ self.dims.n_audio_layer
+ )
+ self.decoder = TextDecoder(
+ self.dims.n_vocab,
+ self.dims.n_text_ctx,
+ self.dims.n_text_state,
+ self.dims.n_text_head,
+ self.dims.n_text_layer
+ )
+ # Initialize cross-attention head alignments for timestamp calculation logic
+ all_heads = torch.zeros(self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool)
+ all_heads[self.dims.n_text_layer // 2 :] = True
+ self.register_buffer("alignment_heads", all_heads if config.device.startswith(("privateuseone", "ocl")) else all_heads.to_sparse(), persistent=False)
+
+ def set_alignment_heads(self, dump):
+ """Decompress and parse custom binary alignment head masks."""
+
+ alignment = torch.from_numpy(
+ np.frombuffer(
+ gzip.decompress(base64.b85decode(dump)), dtype=bool
+ ).copy()
+ ).reshape(self.dims.n_text_layer, self.dims.n_text_head)
+
+ if not config.device.startswith(("privateuseone", "ocl")): alignment = alignment.to_sparse()
+ self.register_buffer("alignment_heads", alignment, persistent=False)
+
+ def embed_audio(self, mel):
+ return self.encoder(mel)
+
+ def logits(self, tokens, audio_features):
+ return self.decoder(tokens, audio_features)
+
+ def forward(self, mel, tokens):
+ return self.decoder(tokens, self.encoder(mel))
+
+ @property
+ def device(self):
+ return next(self.parameters()).device
+
+ @property
+ def is_multilingual(self):
+ return self.dims.n_vocab >= 51865 # Models with vocabulary larger than 51865 contain language tokens
+
+ @property
+ def num_languages(self):
+ return self.dims.n_vocab - 51765 - int(self.is_multilingual)
+
+ def install_kv_cache_hooks(self, cache = None):
+ """
+ Install native PyTorch forward hooks to continuously build and expand the generation KV cache.
+
+ Args:
+ cache (dict, optional): Existing cached tensor collection mappings. Defaults to None.
+
+ Returns:
+ Tuple[dict, list]: The active cache structure and a list of registered hook handles.
+ """
+
+ cache = {**cache} if cache is not None else {}
+ hooks = []
+
+ def save_to_cache(module, _, output):
+ # Concat current output tensors directly into the key-value historical state dictionary
+ cache[module] = output if module not in cache or output.shape[1] > self.dims.n_text_ctx else torch.cat([cache[module], output], dim=1).detach()
+ return cache[module]
+
+ def install_hooks(layer: nn.Module):
+ if isinstance(layer, MultiHeadAttention):
+ hooks.append(layer.key.register_forward_hook(save_to_cache))
+ hooks.append(layer.value.register_forward_hook(save_to_cache))
+
+ self.decoder.apply(install_hooks)
+ return cache, hooks
+
+ # Bind internal core execution strategies explicitly
+ detect_language = detect_language_function
+ transcribe = transcribe_function
+ decode = decode_function
+
+class ResidualAttentionBlock(nn.Module):
+ """A standard Transformer layer module possessing Self-Attention, Optional Cross-Attention, and an MLP block."""
+
+ def __init__(
+ self,
+ n_state,
+ n_head,
+ cross_attention = False
+ ):
+ super().__init__()
+ self.attn = MultiHeadAttention(n_state, n_head)
+ self.attn_ln = Fp32LayerNorm(n_state)
+ self.cross_attn = (MultiHeadAttention(n_state, n_head) if cross_attention else None)
+ self.cross_attn_ln = Fp32LayerNorm(n_state) if cross_attention else None
+
+ n_mlp = n_state * 4
+ self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
+ self.mlp_ln = Fp32LayerNorm(n_state)
+
+ def forward(self, x, xa = None, mask = None, kv_cache = None):
+ # Self Attention over sequence history with residual connection
+ x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
+ # Cross Attention using contextual visual/audio encoder activations (xa)
+ if self.cross_attn: x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
+
+ # Feed Forward Network segment with residual connection
+ return x + self.mlp(self.mlp_ln(x))
+
+class MultiHeadAttention(nn.Module):
+ """Multi-Head Attention framework managing either causal self-attention or cross-attention projections."""
+
+ def __init__(
+ self,
+ n_state,
+ n_head
+ ):
+ super().__init__()
+ self.n_head = n_head
+ self.query = Linear(n_state, n_state)
+ self.key = Linear(n_state, n_state, bias=False)
+ self.value = Linear(n_state, n_state)
+ self.out = Linear(n_state, n_state)
+
+ def forward(
+ self,
+ x,
+ xa = None,
+ mask = None,
+ kv_cache = None
+ ):
+ # Use cross-attention context tensors if provided (xa), otherwise use self sequences (x)
+ k, v = (
+ self.key(x if xa is None else xa),
+ self.value(x if xa is None else xa)
+ ) if kv_cache is None or xa is None or self.key not in kv_cache else (
+ kv_cache[self.key],
+ kv_cache[self.value]
+ )
+
+ wv, qk = self.qkv_attention(self.query(x), k, v, mask)
+ return self.out(wv), qk
+
+ def qkv_attention(self, q, k, v, mask = None):
+ _, n_ctx, n_state = q.shape
+ scale = (n_state // self.n_head) ** -0.25 # Scaling factor calculation step 1
+
+ # Reshape projection blocks to separate attention heads
+ q, k, v = (
+ q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3),
+ k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 1, 3),
+ v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+ )
+
+ # MatMul score computation with scale factor distribution
+ qk = (q * scale) @ (k * scale).transpose(-1, -2)
+ if mask is not None: qk = qk + mask[:n_ctx, :n_ctx]
+ qk = qk.float()
+
+ # Compute Softmax weight probabilities and project values back to full sequence shapes
+ return (F.softmax(qk, dim=-1).to(q.dtype) @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
+
+class SuppressBlank:
+ """Logit filter that suppresses structural whitespace/blank space generations right at the very first token."""
+
+ def __init__(self, tokenizer, sample_begin):
+ self.tokenizer = tokenizer
+ self.sample_begin = sample_begin
+
+ def apply(self, logits, tokens):
+ if tokens.shape[1] == self.sample_begin:
+ # Invalidate raw white space token and End of Transcript sequences instantly on start
+ logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
+
+class SuppressTokens:
+ """Suppresses specific targeted bad or invalid token IDs entirely during text generation."""
+
+ def __init__(self, suppress_tokens):
+ self.suppress_tokens = list(suppress_tokens)
+
+ def apply(self, logits, tokens):
+ logits[:, self.suppress_tokens] = -np.inf
+
+class PyTorchInference:
+ """Standard PyTorch inference runtime implementation preserving and updating historical KV tracking frames."""
+
+ def __init__(self, model, initial_token_length):
+ self.model = model
+ self.initial_token_length = initial_token_length
+ self.kv_cache = {}
+ self.hooks = []
+
+ # Target attention weights across all nested structural layers
+ self.kv_modules = [
+ block.attn.key
+ for block in self.model.decoder.blocks
+ ] + [
+ block.attn.value
+ for block in self.model.decoder.blocks
+ ]
+
+ def logits(self, tokens, audio_features):
+ if not self.kv_cache: self.kv_cache, self.hooks = self.model.install_kv_cache_hooks()
+ # Optimize auto-regressive processing loop: Only pass the newest token if historical context is cached
+ if tokens.shape[-1] > self.initial_token_length: tokens = tokens[:, -1:]
+
+ return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache)
+
+ def cleanup_caching(self):
+ for hook in self.hooks:
+ hook.remove()
+
+ self.kv_cache = {}
+ self.hooks = []
+
+ def rearrange_kv_cache(self, source_indices):
+ """Permute or filter the current cached sequences to align correctly with active beam search indices."""
+
+ if source_indices != list(range(len(source_indices))):
+ for module in self.kv_modules:
+ self.kv_cache[module] = self.kv_cache[module][source_indices].detach()
+
+class MaximumLikelihoodRanker:
+ """Ranker selecting the best sequences based on their accumulated log probabilities with length penalization."""
+
+ def __init__(
+ self,
+ length_penalty
+ ):
+ self.length_penalty = length_penalty
+
+ def rank(self, tokens, sum_logprobs):
+ def scores(logprobs, lengths):
+ result = []
+
+ for logprob, length in zip(logprobs, lengths):
+ result.append(
+ # Standard formula applying length constraints penalties safely
+ logprob / (length if self.length_penalty is None else ((5 + length) / 6) ** self.length_penalty)
+ )
+
+ return result
+
+ return [
+ np.argmax(scores(p, l))
+ for p, l in zip(sum_logprobs, [[len(t) for t in s] for s in tokens])
+ ]
+
+class GreedyDecoder:
+ """Simple greedy/categorical sampler generating the single best tokens sequentially."""
+
+ def __init__(
+ self,
+ temperature,
+ eot
+ ):
+ self.temperature = temperature
+ self.eot = eot
+
+ def update(self, tokens, logits, sum_logprobs):
+ next_tokens = logits.argmax(dim=-1) if self.temperature == 0 else (
+ # Sample dynamically using Categorical distributions based on selected temperature configurations
+ Categorical(
+ logits=(logits / self.temperature).cpu() if config.device.startswith("ocl") else (logits / self.temperature)
+ )
+ ).sample().to(logits.device)
+
+ # Accumulate log probability values for tracking confidence scores
+ logprobs = F.log_softmax(logits.float(), dim=-1)
+ sum_logprobs += logprobs[torch.arange(logprobs.shape[0]), next_tokens] * (tokens[:, -1] != self.eot)
+
+ # Lock sequences that have already hit the End-of-Transcript token
+ next_tokens[tokens[:, -1] == self.eot] = self.eot
+ tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1)
+
+ return tokens, (tokens[:, -1] == self.eot).all()
+
+ def finalize(self, tokens, sum_logprobs):
+ return F.pad(tokens, (0, 1), value=self.eot), sum_logprobs.tolist()
+
+class BeamSearchDecoder:
+ """Advanced search tracker preserving up to K alternative high probability token pathways simultaneously."""
+
+ def __init__(
+ self,
+ beam_size,
+ eot,
+ inference,
+ patience = None
+ ):
+ self.beam_size = beam_size
+ self.eot = eot
+ self.inference = inference
+ self.patience = patience or 1.0
+ self.max_candidates = round(beam_size * self.patience)
+ self.finished_sequences = None
+
+ assert (self.max_candidates > 0)
+
+ def reset(self):
+ self.finished_sequences = None
+
+ def update(self, tokens, logits, sum_logprobs):
+ if tokens.shape[0] % self.beam_size != 0: raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
+
+ n_audio = tokens.shape[0] // self.beam_size
+ if self.finished_sequences is None: self.finished_sequences = [{} for _ in range(n_audio)]
+
+ logprobs = F.log_softmax(logits.float(), dim=-1)
+ next_tokens, source_indices, finished_sequences = [], [], []
+
+ for i in range(n_audio):
+ scores, sources, finished = {}, {}, {}
+
+ # Evaluate options within individual candidate sequence groups
+ for j in range(self.beam_size):
+ idx = i * self.beam_size + j
+ prefix = tokens[idx].tolist()
+ for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)):
+ sequence = tuple(prefix + [token.item()])
+ scores[sequence] = (sum_logprobs[idx] + logprob).item()
+ sources[sequence] = idx
+
+ saved = 0
+ # Harvest best scoring branches and group them respectively
+ for sequence in sorted(scores, key=scores.get, reverse=True):
+ if sequence[-1] == self.eot: finished[sequence] = scores[sequence]
+ else:
+ sum_logprobs[len(next_tokens)] = scores[sequence]
+ next_tokens.append(sequence)
+ source_indices.append(sources[sequence])
+
+ saved += 1
+ if saved == self.beam_size: break
+
+ finished_sequences.append(finished)
+
+ # Mutate the underlying inference cache positions to reflect survival branches
+ self.inference.rearrange_kv_cache(source_indices)
+ assert len(self.finished_sequences) == len(finished_sequences)
+
+ # Save finished candidates to list up to max threshold limits
+ for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences):
+ for seq in sorted(newly_finished, key=newly_finished.get, reverse=True):
+ if len(previously_finished) >= self.max_candidates: break
+ previously_finished[seq] = newly_finished[seq]
+
+ return (
+ torch.tensor(next_tokens, device=tokens.device),
+ all(len(sequences) >= self.max_candidates for sequences in self.finished_sequences)
+ )
+
+ def finalize(self, preceding_tokens, sum_logprobs):
+ sum_logprobs = sum_logprobs.cpu()
+
+ for i, sequences in enumerate(self.finished_sequences):
+ # Fallback handling step: If search completes but lacks finished beams, force-inject fallback records
+ if (len(sequences) < self.beam_size):
+ for j in list(np.argsort(sum_logprobs[i]))[::-1]:
+ sequence = preceding_tokens[i, j].tolist() + [self.eot]
+ sequences[tuple(sequence)] = sum_logprobs[i][j].item()
+ if len(sequences) >= self.beam_size: break
+
+ return (
+ [[torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences],
+ [list(sequences.values()) for sequences in self.finished_sequences]
+ )
+
+class ApplyTimestampRules:
+ """Enforces grammar constraints governing structural timestamp token sequence configurations."""
+
+ def __init__(
+ self,
+ tokenizer,
+ sample_begin,
+ max_initial_timestamp_index
+ ):
+ self.tokenizer = tokenizer
+ self.sample_begin = sample_begin
+ self.max_initial_timestamp_index = max_initial_timestamp_index
+
+ def apply(self, logits, tokens):
+ if self.tokenizer.no_timestamps is not None: logits[:, self.tokenizer.no_timestamps] = -np.inf
+
+ for k in range(tokens.shape[0]):
+ sampled_tokens = tokens[k, self.sample_begin :]
+ seq = [t for t in sampled_tokens.tolist()]
+
+ last_was_timestamp = (len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin)
+ penultimate_was_timestamp = (len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin)
+
+ if last_was_timestamp:
+ if penultimate_was_timestamp: logits[k, self.tokenizer.timestamp_begin :] = -np.inf
+ else: logits[k, : self.tokenizer.eot] = -np.inf
+
+ timestamps = sampled_tokens[sampled_tokens.ge(self.tokenizer.timestamp_begin)]
+
+ if timestamps.numel() > 0:
+ # Ensure timestamp increments monotonically over chronological time steps
+ logits[
+ k,
+ self.tokenizer.timestamp_begin :
+ timestamps[-1] if last_was_timestamp and not penultimate_was_timestamp else (timestamps[-1] + 1)
+ ] = -np.inf
+
+ # Prevent transcription sequences from beginning directly with an empty/unreachable timestamp range
+ if tokens.shape[1] == self.sample_begin:
+ logits[:, : self.tokenizer.timestamp_begin] = -np.inf
+
+ if self.max_initial_timestamp_index is not None:
+ last_allowed = (self.tokenizer.timestamp_begin + self.max_initial_timestamp_index)
+ logits[:, last_allowed + 1 :] = -np.inf
+
+ # Mathematical bias enforcing timestamp generations if their cumulative probability dominates
+ logprobs = F.log_softmax(logits.float(), dim=-1)
+ for k in range(tokens.shape[0]):
+ if logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1) > logprobs[k, : self.tokenizer.timestamp_begin].max():
+ logits[k, : self.tokenizer.timestamp_begin] = -np.inf
+
+class DecodingTask:
+ """The manager orchestrating the complete cross-attention decoding lifecycle."""
+
+ def __init__(
+ self,
+ model,
+ options
+ ):
+ self.model = model
+ language = options.language or "en"
+
+ tokenizer = get_tokenizer(
+ model.is_multilingual,
+ num_languages=model.num_languages,
+ language=language,
+ task=options.task
+ )
+
+ self.tokenizer = tokenizer
+ self.options = self._verify_options(options)
+ self.n_group = options.beam_size or options.best_of or 1
+ self.n_ctx = model.dims.n_text_ctx
+ self.sample_len = options.sample_len or model.dims.n_text_ctx // 2
+ self.sot_sequence = tokenizer.sot_sequence
+
+ if self.options.without_timestamps:
+ self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
+
+ self.initial_tokens = self._get_initial_tokens()
+ self.sample_begin = len(self.initial_tokens)
+ self.sot_index = self.initial_tokens.index(tokenizer.sot)
+
+ self.inference = PyTorchInference(model, len(self.initial_tokens))
+ self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
+ # Dynamically switch between BeamSearchDecoder and GreedyDecoder based on configuration options
+ self.decoder = BeamSearchDecoder(
+ options.beam_size,
+ tokenizer.eot,
+ self.inference,
+ options.patience
+ ) if options.beam_size is not None else GreedyDecoder(
+ options.temperature,
+ tokenizer.eot
+ )
+
+ self.logit_filters = []
+
+ if self.options.suppress_blank:
+ self.logit_filters.append(
+ SuppressBlank(
+ self.tokenizer,
+ self.sample_begin
+ )
+ )
+
+ if self.options.suppress_tokens:
+ self.logit_filters.append(
+ SuppressTokens(
+ self._get_suppress_tokens()
+ )
+ )
+
+ if not options.without_timestamps:
+ max_initial_timestamp_index = None
+
+ if options.max_initial_timestamp:
+ max_initial_timestamp_index = round(
+ self.options.max_initial_timestamp / (CHUNK_LENGTH / model.dims.n_audio_ctx)
+ )
+
+ self.logit_filters.append(
+ ApplyTimestampRules(
+ tokenizer,
+ self.sample_begin,
+ max_initial_timestamp_index
+ )
+ )
+
+ def _verify_options(self, options):
+ """Validate argument combinations to avoid conflicting generation requests."""
+
+ if options.beam_size is not None and options.best_of is not None: raise ValueError("Conflict in options: 'beam_size' and 'best_of' cannot be specified simultaneously.")
+ if options.temperature == 0 and options.best_of is not None: raise ValueError("Invalid configuration: 'best_of' works with sampling and requires temperature > 0.")
+ if options.patience is not None and options.beam_size is None: raise ValueError("Invalid configuration: 'patience' requires 'beam_size' to be set for Beam Search.")
+ if options.length_penalty is not None and not (0 <= options.length_penalty <= 1): raise ValueError(f"Invalid range: 'length_penalty' must be a float between 0 and 1, got {options.length_penalty}.")
+ return options
+
+ def _get_initial_tokens(self):
+ """Assemble prefix prompts and structural start tags into a unified starting sequence context."""
+
+ tokens = list(self.sot_sequence)
+
+ if prefix := self.options.prefix:
+ prefix_tokens = (self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix)
+ if self.sample_len is not None: prefix_tokens = prefix_tokens[-(self.n_ctx // 2 - self.sample_len):]
+ tokens = tokens + prefix_tokens
+
+ if prompt := self.options.prompt:
+ tokens = (
+ [self.tokenizer.sot_prev] +
+ (self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt)[-(self.n_ctx // 2 - 1) :] + tokens
+ )
+
+ return tuple(tokens)
+
+ def _get_suppress_tokens(self):
+ """Collect and group explicit vocabulary token IDs designated to be ignored during inference loops."""
+
+ suppress_tokens = self.options.suppress_tokens
+ if isinstance(suppress_tokens, str): suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
+
+ if -1 in suppress_tokens:
+ suppress_tokens = [t for t in suppress_tokens if t >= 0]
+ suppress_tokens.extend(self.tokenizer.non_speech_tokens)
+ elif suppress_tokens is None or len(suppress_tokens) == 0: suppress_tokens = []
+ else: assert isinstance(suppress_tokens, list)
+
+ suppress_tokens.extend([
+ self.tokenizer.transcribe,
+ self.tokenizer.translate,
+ self.tokenizer.sot,
+ self.tokenizer.sot_prev,
+ self.tokenizer.sot_lm
+ ])
+
+ if self.tokenizer.no_speech is not None: suppress_tokens.append(self.tokenizer.no_speech)
+ return tuple(sorted(set(suppress_tokens)))
+
+ def _get_audio_features(self, mel):
+ """Process and sanitize raw acoustic signals, projecting inputs through the AudioEncoder if necessary."""
+
+ if self.options.fp16: mel = mel.half()
+
+ audio_features = mel if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state) else self.model.encoder(mel)
+ if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32): return TypeError("Mismatched tensor data types during calculation.")
+
+ return audio_features
+
+ def _detect_language(self, audio_features, tokens):
+ """Execute on-the-fly automated identification tracking logic across input components."""
+
+ languages = [self.options.language] * audio_features.shape[0]
+ lang_probs = None
+
+ if self.options.language is None or self.options.task == "lang_id":
+ lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer)
+ languages = [max(probs, key=probs.get) for probs in lang_probs]
+
+ if self.options.language is None: tokens[:, self.sot_index + 1] = lang_tokens
+
+ return languages, lang_probs
+
+ def _main_loop(self, audio_features, tokens):
+ """The core auto-regressive generation execution loop structure."""
+
+ n_batch = tokens.shape[0]
+ sum_logprobs = torch.zeros(n_batch, device=audio_features.device)
+ no_speech_probs = [np.nan] * n_batch
+
+ try:
+ for i in range(self.sample_len):
+ logits = self.inference.logits(tokens, audio_features)
+ # Capture probability scores designating silence/no-speech boundaries on step zero
+ if (i == 0 and self.tokenizer.no_speech is not None):
+ probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1)
+ no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
+
+ logits = logits[:, -1] # Isolate trailing predicted token values
+ for logit_filter in self.logit_filters:
+ logit_filter.apply(logits.cpu() if config.device.startswith("ocl") else logits, tokens)
+
+ tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)
+ if completed or tokens.shape[-1] > self.n_ctx: break
+ finally:
+ self.inference.cleanup_caching() # Remove active PyTorch computation hooks safely
+
+ return tokens, sum_logprobs, no_speech_probs
+
+ @torch.no_grad()
+ def run(self, mel):
+ """
+ Entry point running the end-to-end decoding orchestrations.
+
+ Args:
+ mel (torch.Tensor): Audio Spectrogram inputs.
+
+ Returns:
+ List[DecodingResult]: Collected list containing the structured results data outputs.
+ """
+
+ self.decoder.reset()
+ tokenizer = self.tokenizer
+ n_audio = mel.shape[0]
+
+ audio_features = self._get_audio_features(mel)
+ tokens = torch.tensor([self.initial_tokens]).repeat(n_audio, 1)
+
+ languages, language_probs = self._detect_language(audio_features, tokens)
+ if self.options.task == "lang_id":
+ return [
+ DecodingResult(
+ audio_features=features,
+ language=language,
+ language_probs=probs
+ )
+ for features, language, probs in zip(
+ audio_features,
+ languages,
+ language_probs
+ )
+ ]
+
+ # Duplicate arrays to align with structural beam grouping counts safely
+ tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device)
+ tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens)
+
+ # Slice indices down back to standard un-grouped batch layouts
+ audio_features = audio_features[:: self.n_group]
+ no_speech_probs = no_speech_probs[:: self.n_group]
+
+ assert audio_features.shape[0] == len(no_speech_probs) == n_audio
+
+ # Strip structural pad tokens and resolve optimal paths
+ tokens = tokens.reshape(n_audio, self.n_group, -1)
+ sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
+
+ tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
+ tokens = [[t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens]
+
+ # Use ranker class to fetch the single best survival pathway index
+ selected = self.sequence_ranker.rank(tokens, sum_logprobs)
+ tokens = [t[i].tolist() for i, t in zip(selected, tokens)]
+
+ fields = (
+ [tokenizer.decode(t).strip() for t in tokens],
+ languages,
+ tokens,
+ audio_features,
+ [lp / (len(t) + 1) for t, lp in zip(tokens, [lp[i] for i, lp in zip(selected, sum_logprobs)])],
+ no_speech_probs
+ )
+ if len(set(map(len, fields))) != 1: raise RuntimeError(f"Data mapping error: All processed fields fields must share an identical length layout, but got lengths {[len(f) for f in fields]}.")
+
+ # Build final object collection payloads
+ return [
+ DecodingResult(
+ audio_features=features,
+ language=language,
+ tokens=tokens,
+ text=text,
+ avg_logprob=avg_logprob,
+ no_speech_prob=no_speech_prob,
+ temperature=self.options.temperature,
+ compression_ratio=compression_ratio(text)
+ )
+ for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields)
+ ]
+
+class DecodingResult:
+ """The structured data payload wrapping transcription text segments, token tracks, and validation scores."""
+
+ def __init__(
+ self,
+ audio_features,
+ language,
+ language_probs = None,
+ tokens = None,
+ text = "",
+ avg_logprob = np.nan,
+ no_speech_prob = np.nan,
+ temperature = np.nan,
+ compression_ratio = np.nan
+ ):
+ self.audio_features = audio_features
+ self.language = language
+ self.language_probs = language_probs if language_probs is not None else {}
+ self.tokens = tokens if tokens is not None else []
+ self.text = text
+ self.avg_logprob = avg_logprob
+ self.no_speech_prob = no_speech_prob
+ self.temperature = temperature
+ self.compression_ratio = compression_ratio
+
+class Tokenizer:
+ """
+ A comprehensive tokenizer class for text encoding, decoding, and handling
+ special/control tokens for multilingual and multi-task speech models (similar to Whisper).
+ """
+
+ def __init__(
+ self,
+ encoding_name,
+ num_languages = 2,
+ language = None,
+ task = None,
+ sot_sequence = ()
+ ):
+ """
+ Initializes the Tokenizer with specific encoding settings, language, and task context.
+
+ Args:
+ encoding_name (str): The name of the tokenizer encoding pattern to load.
+ num_languages (int, optional): Maximum number of languages allowed. Defaults to 2.
+ language (str, optional): Target language for text generation. Defaults to None.
+ task (str, optional): The task type ('transcribe' or 'translate'). Defaults to None.
+ sot_sequence (tuple, optional): Initial start of sequence. Defaults to ().
+ """
+
+ # Fetch the core tokenizer based on name and allowed number of languages
+ self.encoding = get_encoding(
+ name=encoding_name,
+ num_languages=num_languages
+ )
+
+ self.num_languages = num_languages
+ self.language = language
+ self.task = task
+ self.sot_sequence = sot_sequence
+ self.special_tokens = {}
+ # Cache all special token string-to-ID mappings from the core encoding
+ for special in self.encoding.special_tokens_set:
+ special_token = self.encoding.encode_single_token(special)
+ self.special_tokens[special] = special_token
+
+ # Construct the Start Of Transcript (SOT) token sequence dynamically
+ sot = self.special_tokens["<|startoftranscript|>"]
+ langs = tuple(LANGUAGES.keys())[: self.num_languages]
+ sot_sequence = [sot]
+
+ # 1. Append language token if a target language is specified
+ if self.language is not None:
+ sot_sequence.append(
+ sot + 1 + langs.index(self.language)
+ )
+
+ # 2. Append task token based on transcription or translation objective
+ if self.task is not None:
+ sot_sequence.append(
+ self.special_tokens[
+ "<|transcribe|>"
+ ] if self.task == "transcribe" else self.special_tokens[
+ "<|translate|>"
+ ]
+ )
+
+ # Finalize the SOT sequence as an immutable tuple
+ self.sot_sequence = tuple(sot_sequence)
+
+ def encode(self, text, **kwargs):
+ """Encodes a string of text into a list of token IDs.
+
+ Args:
+ text (str): Input text string to be encoded.
+ **kwargs: Extra parameters passed to the underlying encoder.
+
+ Returns:
+ list: List of integer token IDs.
+ """
+
+ return self.encoding.encode(text, **kwargs)
+
+ def decode(self, token_ids, **kwargs):
+ """Decodes a list of token IDs back into text, filtering out timestamp tokens.
+
+ Args:
+ token_ids (list): List of integer token IDs to decode.
+ **kwargs: Extra parameters passed to the underlying decoder.
+
+ Returns:
+ str: Decoded string excluding timestamps.
+ """
+
+ # Exclude any tokens that are equal to or greater than the timestamp boundary
+ return self.encoding.decode([
+ t
+ for t in token_ids
+ if t < self.timestamp_begin
+ ], **kwargs)
+
+ def decode_with_timestamps(self, token_ids, **kwargs):
+ """Decodes token IDs into a text string including raw timestamp tags.
+
+ Args:
+ token_ids (list): List of integer token IDs.
+ **kwargs: Extra parameters passed to the underlying decoder.
+
+ Returns:
+ str: Full decoded string representation including all special tags.
+ """
+
+ return self.encoding.decode(token_ids, **kwargs)
+
+ @cached_property
+ def eot(self):
+ return self.encoding.eot_token
+
+ @cached_property
+ def transcribe(self):
+ return self.special_tokens["<|transcribe|>"]
+
+ @cached_property
+ def translate(self):
+ return self.special_tokens["<|translate|>"]
+
+ @cached_property
+ def sot(self):
+ return self.special_tokens["<|startoftranscript|>"]
+
+ @cached_property
+ def sot_lm(self):
+ return self.special_tokens["<|startoflm|>"]
+
+ @cached_property
+ def sot_prev(self):
+ return self.special_tokens["<|startofprev|>"]
+
+ @cached_property
+ def no_speech(self):
+ return self.special_tokens["<|nospeech|>"]
+
+ @cached_property
+ def no_timestamps(self):
+ return self.special_tokens["<|notimestamps|>"]
+
+ @cached_property
+ def timestamp_begin(self):
+ return self.special_tokens["<|0.00|>"]
+
+ @cached_property
+ def language_token(self):
+ """int: Gets the active language token ID. Raises ValueError if language is unset."""
+
+ if self.language is None: raise ValueError("Language is not specified for this tokenizer instance.")
+ return self.to_language_token(self.language)
+
+ def to_language_token(self, language):
+ """
+ Retrieves the explicit token ID for a given language code string.
+
+ Args:
+ language (str): The language identifier (e.g., 'en').
+
+ Returns:
+ int: The corresponding language token ID.
+
+ Raises:
+ KeyError: If the language code is not found in the special tokens dictionary.
+ """
+
+ if token := self.special_tokens.get(f"<|{language}|>", None): return token
+ raise KeyError(f"Language token for '<|{language}|>' not found.")
+
+ @cached_property
+ def all_language_tokens(self):
+ """tuple: A tuple containing all supported language token IDs up to `num_languages`."""
+
+ result = []
+ for token, token_id in self.special_tokens.items():
+ # Extract language name from bracket format "<|en|>" -> "en"
+ if token.strip("<|>") in LANGUAGES: result.append(token_id)
+
+ return tuple(result)[: self.num_languages]
+
+ @cached_property
+ def all_language_codes(self):
+ """tuple: A tuple containing string language codes derived from all valid language tokens."""
+
+ return tuple(self.decode([_l]).strip("<|>") for _l in self.all_language_tokens)
+
+ @cached_property
+ def sot_sequence_including_notimestamps(self):
+ """tuple: The baseline SOT sequence augmented with a trailing `<|notimestamps|>` token."""
+
+ return tuple(list(self.sot_sequence) + [self.no_timestamps])
+
+ @cached_property
+ def non_speech_tokens(self):
+ """
+ tuple: Returns a sorted tuple of token IDs containing punctuation, musical notes, and other non-speech symbols.
+
+ Useful for logit filtering / suppressing gibberish or background noises during inference.
+ """
+
+ symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』')
+ symbols += ("<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split())
+
+ # Isolated musical notation characters
+ miscellaneous = set("♩♪♫♬♭♮♯")
+ # Ensure all misc symbols fall inside specific unicode blocks
+ assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
+
+ # Pre-seed with spaces preceding common hyphen/apostrophe combinations
+ result = {self.encoding.encode(" -")[0], self.encoding.encode(" '")[0]}
+ # Accumulate single token variations or versions prefixed with a whitespace character
+ for symbol in symbols + list(miscellaneous):
+ for tokens in [self.encoding.encode(symbol), self.encoding.encode(" " + symbol)]:
+ if len(tokens) == 1 or symbol in miscellaneous: result.add(tokens[0])
+
+ return tuple(sorted(result))
+
+ def split_to_word_tokens(self, tokens):
+ """
+ Splits a list of token IDs into words based on the tokenizer instance's language.
+
+ Args:
+ tokens (list): List of token IDs to split.
+
+ Returns:
+ tuple: (list of string words, list of lists of token IDs matching each word)
+ """
+
+ # East Asian / Logographic languages do not use spaces as word boundaries
+ if self.language in {"zh", "ja", "th", "lo", "my", "yue"}: return self.split_tokens_on_unicode(tokens)
+ return self.split_tokens_on_spaces(tokens)
+
+ def split_tokens_on_unicode(self, tokens):
+ """
+ Splits tokens incrementally by checking valid UTF-8 boundary reconstructions.
+
+ Prevents broken Unicode multi-byte characters from being isolated during extraction.
+
+ Args:
+ tokens (list): List of token IDs.
+
+ Returns:
+ tuple: (words, word_tokens)
+ """
+
+ replacement_char = "\ufffd" # The standard Unicode Replacement Character used for decoding errors
+
+ words, word_tokens, current_tokens = [], [], []
+ unicode_offset = 0
+
+ for token in tokens:
+ current_tokens.append(token)
+ decoded = self.decode_with_timestamps(current_tokens)
+
+ # If there's no decoding anomaly (replacement char) OR if the original text truly
+ # had a replacement character at this spot, it's safe to separate into a new unit.
+ if (
+ replacement_char not in decoded or
+ self.decode_with_timestamps(tokens)[unicode_offset + decoded.index(replacement_char)] == replacement_char
+ ):
+ words.append(decoded)
+ word_tokens.append(current_tokens)
+ current_tokens = [] # Clear buffer for next token sequence
+ unicode_offset += len(decoded)
+
+ return words, word_tokens
+
+ def split_tokens_on_spaces(self, tokens):
+ """
+ Group subwords together to construct individual whole words by splitting on space occurrences.
+
+ Args:
+ tokens (list): List of token IDs.
+
+ Returns:
+ tuple: (words, word_tokens)
+ """
+
+ # First isolate character/subword tokens securely via unicode boundary checks
+ subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens)
+ words, word_tokens = [], []
+
+ for subword, subword_tokens in zip(subwords, subword_tokens_list):
+ # A subword signifies a completely new word structure if:
+ # 1. It is a special / control token (>= EOT)
+ # 2. It explicitly begins with a whitespace character " "
+ # 3. It is a punctuation token
+ # 4. Or if it's the very first token in the loop
+ if (
+ subword_tokens[0] >= self.eot
+ ) or (
+ subword.startswith(" ")
+ ) or (
+ subword.strip() in string.punctuation
+ ) or len(words) == 0:
+ words.append(subword)
+ word_tokens.append(subword_tokens)
+ else:
+ # Otherwise, append/merge the subword to the previous word index
+ words[-1] = words[-1] + subword
+ word_tokens[-1].extend(subword_tokens)
+
+ return words, word_tokens
\ No newline at end of file
diff --git a/main/library/utils.py b/main/library/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..77b7e579924f54cfc3e21cda21804e101c06facd
--- /dev/null
+++ b/main/library/utils.py
@@ -0,0 +1,480 @@
+import os
+import re
+import sys
+import torch
+import codecs
+import logging
+import warnings
+
+sys.path.append(os.getcwd())
+
+from main.tools import huggingface
+from main.library.backends import directml, opencl
+from main.library.algorithm.faisssearch import IndexWrapper
+from main.app.variables import translations, configs, config, logger, embedders_model
+
+if not config.debug_mode:
+ warnings.filterwarnings("ignore")
+ for l in ["httpx", "httpcore"]:
+ logging.getLogger(l).setLevel(logging.ERROR)
+
+def check_assets(f0_method, hubert, predictor_onnx=False, embedders_mode="fairseq"):
+ """
+ Checks and downloads necessary asset models (predictors and embedders) if they do not exist.
+
+ Args:
+ f0_method (str): The pitch extraction method (e.g., 'rmvpe', 'fcpe', 'crepe', 'hybrid[...]').
+ hubert (str): Name of the Hubert embedder model.
+ predictor_onnx (bool, optional): Whether to use the ONNX version of the predictor. Defaults to False.
+ embedders_mode (str, optional): Mode of the embedder ('fairseq', 'onnx', 'transformers'). Defaults to "fairseq".
+
+ Raises:
+ SystemExit: If assets cannot be fully downloaded after the maximum allowed retry attempts.
+ """
+
+ # Force ONNX mode and 8-bit quantization if specified in global configuration
+ if config.int8: predictor_onnx, embedders_mode = True, "onnx"
+
+ # Decode repository URLs from ROT13 obfuscation
+ predictors_url = codecs.decode(
+ "uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/cerqvpgbef/",
+ "rot13"
+ )
+ embedders_url = codecs.decode(
+ "uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/rzorqqref/",
+ "rot13"
+ )
+
+ def download_predictor(predictor):
+ """Downloads a specific predictor model from HuggingFace if missing."""
+
+ model_path = os.path.join(configs["predictors_path"], predictor)
+
+ if not os.path.exists(model_path):
+ huggingface.HF_download_file(
+ predictors_url + predictor,
+ model_path
+ )
+
+ return os.path.exists(model_path)
+
+ def download_embedder(embedders_mode, hubert):
+ """Downloads a specific embedder model or its components based on the mode."""
+
+ model_path = os.path.join(configs["embedders_path"], hubert)
+
+ # Handle standalone Fairseq or ONNX models
+ if embedders_mode != "transformers" and not os.path.exists(model_path):
+ huggingface.HF_download_file(
+ "".join([
+ embedders_url,
+ "fairseq/" if embedders_mode == "fairseq" else "onnx/",
+ hubert
+ ]),
+ model_path
+ )
+ elif embedders_mode == "transformers": # Handle HuggingFace Transformers models which require weights and configuration files
+ bin_file = os.path.join(model_path, "model.safetensors")
+ config_file = os.path.join(model_path, "config.json")
+
+ os.makedirs(model_path, exist_ok=True)
+
+ if not os.path.exists(bin_file):
+ huggingface.HF_download_file(
+ "".join([embedders_url, "transformers/", hubert, "/model.safetensors"]),
+ bin_file
+ )
+
+ if not os.path.exists(config_file):
+ huggingface.HF_download_file(
+ "".join([embedders_url, "transformers/", hubert, "/config.json"]),
+ config_file
+ )
+
+ return os.path.exists(bin_file) and os.path.exists(config_file)
+
+ return os.path.exists(model_path)
+
+ def get_modelname(f0_method, predictor_onnx=False):
+ """Resolves the exact file name and suffix of the model based on F0 method."""
+
+ suffix = ("-int8.onnx" if config.int8 else ".onnx") if predictor_onnx else (".pt" if "crepe" not in f0_method else ".pth")
+ # Resolve model name based on keyword matching in F0 method string
+ if "rmvpe" in f0_method:
+ modelname = (
+ "hpa-v4" if "v4" in f0_method else (
+ "hpa-rmvpe-76000"
+ if "previous" in f0_method else
+ "hpa-rmvpe-112000"
+ )
+ ) if "hpa" in f0_method else (
+ "rmvpe-mix"
+ if "mix" in f0_method else
+ "rmvpe"
+ )
+ elif "fcpe" in f0_method:
+ modelname = (
+ "fcpe_legacy"
+ if "legacy" in f0_method else
+ "fcpe"
+ ) if "previous" in f0_method or "legacy" in f0_method else "ddsp_200k"
+ elif "crepe" in f0_method:
+ modelname = "crepe_" + f0_method.replace("mangio-", "").split("-")[1]
+ elif "penn" in f0_method:
+ modelname = "fcn"
+ elif "djcm" in f0_method:
+ modelname = "djcm" + ("-svs" if "svs" in f0_method else "")
+ elif "pesto" in f0_method:
+ modelname = "pesto"
+ elif "swift" in f0_method:
+ modelname = "swift"
+ suffix = ("-int8.onnx" if config.int8 else ".onnx")
+ else:
+ return None
+
+ return modelname + suffix
+
+ results = []
+ count = configs.get("num_of_restart", 5)
+ # Retry loop for robust asset downloading
+ for _ in range(count):
+ # Extract and download all sub-methods if hybrid F0 is utilized
+ if "hybrid" in f0_method:
+ methods_str = re.search(r"hybrid\[(.+)\]", f0_method)
+
+ if methods_str:
+ methods = [
+ f0_method.strip()
+ for f0_method in methods_str.group(1).split("+")
+ ]
+
+ for method in methods:
+ modelname = get_modelname(method, predictor_onnx)
+ if modelname is not None: results.append(download_predictor(modelname))
+ else:
+ modelname = get_modelname(f0_method, predictor_onnx)
+ if modelname is not None: results.append(download_predictor(modelname))
+
+ # Format append extensions for Hubert embedder based on quantization and mode
+ if hubert in embedders_model:
+ if config.int8 and embedders_mode == "onnx": hubert += "-int8"
+ if embedders_mode != "transformers": hubert += ".pt" if embedders_mode == "fairseq" else ".onnx"
+
+ results.append(download_embedder(embedders_mode, hubert))
+
+ # Successfully downloaded all targeted assets
+ if all(results): return
+ else: results = []
+
+ # If the block fails to download everything successfully within allowed restarts
+ logger.warning(translations["check_assets_error"].format(count=count))
+ sys.exit(1)
+
+def check_spk_diarization(model_size, speechbrain=True):
+ """
+ Checks and downloads Whisper and SpeechBrain models required for speaker diarization.
+
+ Args:
+ model_size (str, optional): Size name of the Whisper model (e.g., 'tiny', 'base').
+ speechbrain (bool, optional): Whether to download SpeechBrain models. Defaults to True.
+ """
+
+ whisper_model = os.path.join(configs["speaker_diarization_path"], "models", f"{model_size}.pt")
+ # Download Whisper segmenter model if missing
+ if not os.path.exists(whisper_model) and model_size is not None:
+ huggingface.HF_download_file(
+ "".join([
+ codecs.decode(
+ "uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/fcrnxre_qvnevmngvba/",
+ "rot13"
+ ),
+ model_size,
+ ".pt"
+ ]),
+ whisper_model
+ )
+
+ speechbrain_path = os.path.join(configs["speaker_diarization_path"], "models", "speechbrain")
+ if not os.path.exists(speechbrain_path): os.makedirs(speechbrain_path, exist_ok=True)
+ # Download all necessary configuration and checkpoint components for SpeechBrain
+ if speechbrain:
+ for f in [
+ "classifier.ckpt",
+ "config.json",
+ "embedding_model.ckpt",
+ "hyperparams.yaml",
+ "mean_var_norm_emb.ckpt"
+ ]:
+ speechbrain_model = os.path.join(speechbrain_path, f)
+
+ if not os.path.exists(speechbrain_model):
+ huggingface.HF_download_file(
+ codecs.decode(
+ "uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/fcrnxre_qvnevmngvba/fcrrpuoenva/",
+ "rot13"
+ ) + f,
+ speechbrain_model
+ )
+
+def check_upscaler():
+ """Checks and downloads the upscaler model asset if it is not present locally."""
+
+ upscaler_model = os.path.join("assets", "models", "upscalers", "upscalers.pth")
+ if not os.path.exists(upscaler_model):
+ huggingface.HF_download_file(
+ codecs.decode(
+ "uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/hcfpnyref/hcfpnyref.cgu",
+ "rot13"
+ ),
+ upscaler_model
+ )
+
+def load_embedders_model(embedder_model, embedders_mode="fairseq"):
+ """
+ Loads an audio embedder model (Hubert) from disk using the specified mode wrapper.
+
+ Args:
+ embedder_model (str): File path or model name identifier.
+ embedders_mode (str, optional): Loading wrapper type ('fairseq', 'onnx', 'transformers'). Defaults to "fairseq".
+
+ Raises:
+ FileNotFoundError: If the resolved path of the model file does not exist.
+ ValueError: If an unsupported embedder mode option is supplied.
+ RuntimeError: Wrapper initialization or reading error.
+
+ Returns:
+ Any: Initialized wrapper instance of the loaded Hubert embedder model.
+ """
+
+ if config.int8:
+ embedders_mode = "onnx"
+ embedder_model += "-int8"
+
+ # Append standard extension suffixes if missing
+ if embedders_mode == "fairseq" and not embedder_model.endswith(".pt"): embedder_model += ".pt"
+ elif embedders_mode == "onnx" and not embedder_model.endswith(".onnx"): embedder_model += ".onnx"
+
+ # Determine absolute path location
+ if os.path.exists(embedder_model): embedder_model_path = embedder_model
+ else: embedder_model_path = os.path.join(configs["embedders_path"], embedder_model)
+
+ if not os.path.exists(embedder_model_path): raise FileNotFoundError(translations['not_found'].format(name=embedder_model))
+
+ try:
+ # Construct and instantiate model structure according to the specified backend framework
+ if embedders_mode == "fairseq":
+ from main.library.embedders.fairseq import load_model
+
+ hubert_model = load_model(
+ embedder_model_path
+ )
+ elif embedders_mode == "onnx":
+ from main.library.embedders.onnx import HubertModelONNX
+
+ hubert_model = HubertModelONNX(
+ embedder_model_path,
+ config.providers,
+ config.device
+ )
+ elif embedders_mode == "transformers":
+ from main.library.embedders.transformers import HubertModelWithFinalProj
+
+ hubert_model = HubertModelWithFinalProj.from_pretrained(
+ embedder_model_path
+ )
+ else: raise ValueError(translations["option_not_valid"])
+ except Exception as e:
+ raise RuntimeError(translations["read_model_error"].format(e=e))
+
+ return hubert_model
+
+def extract_features(model, feats, version, mix=False, mix_layers=9, mix_ratio=0.5):
+ """
+ Extracts features from the hidden states of an embedder model with multi-layer mixing support.
+
+ Args:
+ model (Any): The instantiated Hubert embedder model.
+ feats (torch.Tensor): Input tensor containing audio features.
+ version (str): Model version identifier ('v1' or 'v2').
+ mix (bool, optional): Whether to mix alternative layer representations. Defaults to False.
+ mix_layers (int, optional): The target layer index to blend when mix is enabled. Defaults to 9.
+ mix_ratio (float, optional): Weight ratio given to the alternate mixed layer. Defaults to 0.5.
+
+ Returns:
+ torch.Tensor: The final projected feature tensor embeddings.
+ """
+
+ # Toggle project status depending on RVC/SVC model architecture version
+ if hasattr(model, "_final_proj"): model._final_proj = version == "v1"
+ # Extract logits from standard target layers (9 for v1, 12 for v2)
+ logits = model.extract_features(feats, output_layer=9 if version == "v1" else 12)[0]
+
+ if mix: # Handle cross-layer representation mixing interpolation
+ mix_logits = model.extract_features(feats, output_layer=mix_layers)[0]
+ logits = mix_ratio * mix_logits + (1 - mix_ratio) * logits
+
+ # Compute optional final projections
+ feats = model.final_proj(logits) if version == "v1" else logits
+ return feats
+
+def clear_gpu_cache():
+ """Clears empty memory blocks across various compute backends (CUDA, XPU, MPS, DirectML, OpenCL)."""
+
+ if config.device.startswith("cuda"): torch.cuda.empty_cache()
+ elif config.device.startswith("xpu"): torch.xpu.empty_cache()
+ elif config.device.startswith("mps"): torch.mps.empty_cache()
+ elif config.device.startswith("privateuseone"): directml.empty_cache()
+ elif config.device.startswith("ocl"): opencl.empty_cache()
+
+def circular_write(new_data, target):
+ """
+ Performs a circular array buffer shift operation, writing new data to the tail.
+
+ Args:
+ new_data (torch.Tensor): The 1D/2D incoming tensor slice to append.
+ target (torch.Tensor): The main persistent tensor buffer.
+
+ Returns:
+ torch.Tensor: Updated main persistent target tensor buffer.
+ """
+
+ offset = new_data.shape[0]
+ # Shift remaining elements leftwards to create buffer room at the end
+ target[: -offset] = target[offset :].detach().clone()
+ # Write fresh chunk data into the freed up index tail slots
+ target[-offset :] = new_data
+
+ return target
+
+def phase_vocoder(a, b, fade_out, fade_in):
+ """
+ Performs a phase vocoder crossfade between two audio segments.
+
+ This function blends segment `a` and segment `b` by aligning their phases
+ in the frequency domain to prevent phase cancellation during the transition,
+ while applying the provided fade-out and fade-in windows.
+
+ Args:
+ a (torch.Tensor): The first audio segment.
+ b (torch.Tensor): The second audio segment.
+ fade_out (torch.Tensor): The fade-out window applied to segment `a`.
+ fade_in (torch.Tensor): The fade-in window applied to segment `b`.
+
+ Returns:
+ torch.Tensor: The crossfaded audio segment.
+ """
+
+ # Compute the analysis window as the geometric mean of fade curves
+ window = (fade_out * fade_in).sqrt()
+ # Transform both windowed segments to the frequency domain (Real FFT)
+ fa = torch.fft.rfft(a * window)
+ fb = torch.fft.rfft(b * window)
+ # Calculate the combined magnitude spectrum
+ absab = fa.abs() + fb.abs()
+ n = a.shape[0]
+
+ # Compensate for the energy of negative frequencies (except DC and Nyquist components)
+ if n % 2 == 0: absab[1:-1] *= 2
+ else: absab[1:] *= 2
+
+ # Extract initial phase and calculate the raw phase difference
+ phia = fa.angle()
+ deltaphase = fb.angle() - phia
+
+ # Reconstruct the signal using a combination of time-domain crossfade
+ # and phase-aligned sinusoidal synthesis (Phase Vocoder)
+ return (
+ a * (fade_out ** 2) + b * (fade_in ** 2) + (
+ absab * (
+ (
+ # Base frequency grid for each bin
+ 2 * torch.pi * torch.arange(n // 2 + 1).to(a) +
+ # Phase unwrapping (wrapping delta phase to the [-pi, pi] range)
+ (deltaphase - 2 * torch.pi * (deltaphase / 2 / torch.pi + 0.5).floor())
+ ) * (torch.arange(n).unsqueeze(-1).to(a) / n) + phia # Continuous phase evolution over time
+ ).cos()
+ ).sum(-1) * window / n # Sum the sinusoidal components (IFFT equivalent) and normalize
+ )
+
+def load_faiss_index(index_path, nprobe=1, cpu_mode=False):
+ """
+ Loads a FAISS index from file path with explicit configurations.
+
+ Args:
+ index_path (str): File system path pointing to the FAISS index file.
+ nprobe (int, optional): Number of cell centroids to query during search. Defaults to 1.
+ cpu_mode (bool, optional): Force index evaluation exclusively on host CPU. Defaults to False.
+
+ Returns:
+ Tuple[IndexWrapper, Any]: Wrapped FAISS structural object instance along with index array tensors.
+ """
+
+ index = IndexWrapper(index_path, nprobe=nprobe, device=config.device, is_half=config.is_half, faiss_cpu=configs.get("faiss_cpu", False) or cpu_mode)
+ big_npy, _ = index.read_index_tensor()
+
+ return index, big_npy
+
+def load_model(model_path, weights_only=True, log_severity_level=3):
+ """
+ Loads an RVC/SVC inference model file from disk supporting PyTorch weights (.pth) and ONNX formats.
+
+ Args:
+ model_path (str): File path containing model structure.
+ weights_only (bool, optional): PyTorch specific safe-unpickling flag. Defaults to True.
+ log_severity_level (int, optional): ONNX initialization logging verbosity. Defaults to 3.
+
+ Returns:
+ Model: Instantiated model wrapper or state dictionary tensor mapping.
+ """
+
+ if not os.path.isfile(model_path): return None
+
+ if model_path.endswith(".pth"):
+ return torch.load(
+ model_path,
+ map_location="cpu",
+ weights_only=weights_only
+ )
+ else:
+ from main.library.onnx.wrapper import ONNXRVC
+
+ return ONNXRVC(
+ model_path,
+ config.providers,
+ log_severity_level=log_severity_level
+ )
+
+def strtobool(value):
+ """Parses a generic string expression to its categorical boolean equivalent (1 or 0).
+
+ Args:
+ value (str): Textual representation flag input.
+
+ Raises:
+ ValueError: If string value does not match any preset boolean maps.
+
+ Returns:
+ int: Returns 1 for positive flags and 0 for negative flags.
+ """
+
+ value = value.lower()
+
+ if value in ('y', 'yes', 't', 'true', 'on', '1'): return 1
+ elif value in ('n', 'no', 'f', 'false', 'off', '0'): return 0
+ else: raise ValueError("Invalid format!")
+
+def check_ffmpeg():
+ """Downloads static standard ffmpeg and ffprobe binary utilities on Windows if missing."""
+
+ try:
+ if not sys.platform == "win32": return
+ ffmpeg_url = codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/sszcrt/", "rot13")
+
+ if not os.path.exists("ffmpeg.exe"): huggingface.HF_download_file(ffmpeg_url + "ffmpeg.exe", output_path="ffmpeg.exe")
+ if not os.path.exists("ffprobe.exe"): huggingface.HF_download_file(ffmpeg_url + "ffprobe.exe", output_path="ffprobe.exe")
+ except:
+ logger.error(translations["ffmpeg_error"])
+ return
+
+# Trigger conditional lazy execution sequence checking on script initialization
+if configs.get("ffmpeg_download", True): check_ffmpeg()
\ No newline at end of file
diff --git a/main/library/uvr5_lib/common_separator.py b/main/library/uvr5_lib/common_separator.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7b8eff54b345aaf3c7ea28fb08abb60647bfbf7
--- /dev/null
+++ b/main/library/uvr5_lib/common_separator.py
@@ -0,0 +1,204 @@
+import os
+import sys
+import librosa
+
+import numpy as np
+import soundfile as sf
+
+sys.path.append(os.getcwd())
+
+from main.library.uvr5_lib.spec_utils import normalize
+
+class CommonSeparator:
+ """
+ A foundational separator class responsible for managing configurations,
+ audio I/O operations, stem mapping, and post-processing tasks for audio source separation.
+ """
+
+ # Audio stem type constants
+ VOCAL_STEM = "Vocals"
+ INST_STEM = "Instrumental"
+ OTHER_STEM = "Other"
+ BASS_STEM = "Bass"
+ DRUM_STEM = "Drums"
+ GUITAR_STEM = "Guitar"
+ PIANO_STEM = "Piano"
+ SYNTH_STEM = "Synthesizer"
+ STRINGS_STEM = "Strings"
+ WOODWINDS_STEM = "Woodwinds"
+ BRASS_STEM = "Brass"
+ WIND_INST_STEM = "Wind Inst"
+ PRIMARY_STEM = "Primary Stem"
+ SECONDARY_STEM = "Secondary Stem"
+ LEAD_VOCAL_STEM = "lead_only"
+ BV_VOCAL_STEM = "backing_only"
+ NO_STEM = "No "
+
+ # Maps a primary stem to its logical complementary/secondary counterpart
+ STEM_PAIR_MAPPER = {
+ VOCAL_STEM: INST_STEM,
+ INST_STEM: VOCAL_STEM,
+ LEAD_VOCAL_STEM: BV_VOCAL_STEM,
+ BV_VOCAL_STEM: LEAD_VOCAL_STEM,
+ PRIMARY_STEM: SECONDARY_STEM
+ }
+
+ # Stems that are not categorized as traditional accompaniment
+ NON_ACCOM_STEMS = (
+ VOCAL_STEM,
+ OTHER_STEM,
+ BASS_STEM,
+ DRUM_STEM,
+ GUITAR_STEM,
+ PIANO_STEM,
+ SYNTH_STEM,
+ STRINGS_STEM,
+ WOODWINDS_STEM,
+ BRASS_STEM,
+ WIND_INST_STEM
+ )
+
+ def __init__(self, config):
+ """
+ Initializes the CommonSeparator with configuration parameters.
+
+ Args:
+ config (dict): Configuration dictionary containing hardware settings,
+ model paths, and audio processing hyperparameters.
+ """
+
+ # Logging and Hardware Execution Targets
+ self.logger = config.get("logger")
+ self.onnx_execution_provider = config.get("onnx_execution_provider")
+ self.torch_device = config.get("torch_device")
+ # Audio Processing & Post-Processing Hyperparameters
+ self.normalization_threshold = config.get("normalization_threshold")
+ self.invert_using_spec = config.get("invert_using_spec")
+ self.enable_denoise = config.get("enable_denoise")
+ self.output_format = config.get("output_format")
+ self.sample_rate = config.get("sample_rate")
+ # Model Metadata and File Paths
+ self.model_name = config.get("model_name")
+ self.model_path = config.get("model_path")
+ self.model_data = config.get("model_data")
+ # Target Output Directory
+ self.output_dir = config.get("output_dir")
+ # Track active stems for the current task
+ self.secondary_stem_name = None
+ self.primary_stem_name = None
+
+ # Attempt to extract target stems from model training metadata if available
+ if "training" in self.model_data and "instruments" in self.model_data["training"]:
+ instruments = self.model_data["training"]["instruments"]
+ # If a second instrument exists, use it; otherwise, dynamically find the secondary pair
+ if instruments: self.primary_stem_name, self.secondary_stem_name = instruments[0], instruments[1] if len(instruments) > 1 else self.secondary_stem(self.primary_stem_name)
+
+ # Fallback: Extract stem configuration from general keys if training data is missing
+ if self.primary_stem_name is None:
+ self.primary_stem_name = self.model_data.get("primary_stem", "Vocals")
+ self.secondary_stem_name = self.secondary_stem(self.primary_stem_name)
+
+ # File-specific state variables (Reset per audio file)
+ self.secondary_stem_output_path = None
+ self.primary_stem_output_path = None
+ self.secondary_source = None
+ self.audio_file_path = None
+ self.audio_file_base = None
+ self.primary_source = None
+
+ def secondary_stem(self, primary_stem):
+ """
+ Determines the appropriate secondary stem name based on the primary stem name.
+
+ Args:
+ primary_stem (str): The name of the primary source stem.
+
+ Returns:
+ str: The corresponding secondary stem name.
+ """
+
+ primary_stem = primary_stem if primary_stem else self.NO_STEM
+ return self.STEM_PAIR_MAPPER[primary_stem] if primary_stem in self.STEM_PAIR_MAPPER else primary_stem.replace(self.NO_STEM, "") if self.NO_STEM in primary_stem else f"{self.NO_STEM}{primary_stem}"
+
+ def separate(self, audio_file_path):
+ """
+ Placeholder method for the core audio separation logic.
+ Must be implemented by subclasses.
+
+ Args:
+ audio_file_path (str): Path to the input audio file.
+ """
+
+ pass
+
+ def final_process(self, stem_path, source, stem_name):
+ """
+ Executes final saving procedures on an isolated source stem.
+
+ Args:
+ stem_path (str): Relative destination filename/path for the stem.
+ source (np.ndarray): Waveform data array of the processed audio stem.
+ stem_name (str): Key identifying name for the dictionary output.
+
+ Returns:
+ dict: Map matching the stem identifier name to its audio array.
+ """
+
+ self.write_audio_soundfile(stem_path, source)
+ return {stem_name: source}
+
+ def prepare_mix(self, mix):
+ """
+ Loads and prepares input audio into a uniform format ready for model processing.
+
+ Args:
+ mix (str or np.ndarray): File path string to audio or pre-loaded numpy array.
+
+ Returns:
+ np.ndarray: A Fortran-contiguous stereo audio waveform array.
+ """
+
+ # Load file path into numpy array via librosa if string is passed
+ if not isinstance(mix, np.ndarray): mix, _ = librosa.load(mix, mono=False, sr=self.sample_rate)
+ else: mix = mix.T # Transpose if already a numpy array to align axis formatting
+
+ # Convert mono audio (1D) to a stereo format (2D Fortran array)
+ if mix.ndim == 1: mix = np.asfortranarray([mix, mix])
+ return mix
+
+ def write_audio_soundfile(self, stem_path, stem_source):
+ """
+ Normalizes, prepares memory layout, and exports the audio stem to disk.
+
+ Args:
+ stem_path (str): Target output filename or path.
+ stem_source (np.ndarray): Audio data array to write out.
+ """
+
+ # Peak normalization based on configuration threshold
+ stem_source = normalize(wave=stem_source, max_peak=self.normalization_threshold)
+ # Skip export if the audio track contains near-absolute silence
+ if np.max(np.abs(stem_source)) < 1e-6: return
+
+ # Ensure output directory exists before writing file
+ if self.output_dir:
+ os.makedirs(self.output_dir, exist_ok=True)
+ stem_path = os.path.join(self.output_dir, stem_path)
+
+ # Convert multi-channel Fortran-contiguous arrays to standard C-contiguous
+ # structure to guarantee full library compatibility with soundfile.write
+ if stem_source.shape[1] == 2 and stem_source.flags["F_CONTIGUOUS"]: stem_source = np.ascontiguousarray(stem_source)
+ sf.write(stem_path, stem_source, self.sample_rate)
+
+ def clear_file_specific_paths(self):
+ """
+ Resets instance variables linked to individual files to avoid memory
+ leaks or data cross-contamination during batch processing loops.
+ """
+
+ self.secondary_stem_output_path = None
+ self.primary_stem_output_path = None
+ self.secondary_source = None
+ self.audio_file_path = None
+ self.audio_file_base = None
+ self.primary_source = None
\ No newline at end of file
diff --git a/main/library/uvr5_lib/demucs/apply.py b/main/library/uvr5_lib/demucs/apply.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d9c60d38bddbbdebe6d3287fae8414f2d5e13b8
--- /dev/null
+++ b/main/library/uvr5_lib/demucs/apply.py
@@ -0,0 +1,304 @@
+import os
+import sys
+import tqdm
+import torch
+import random
+import concurrent.futures
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config
+
+def center_trim(tensor, reference):
+ """
+ Trim the input tensor along its last dimension to match the size of a reference.
+
+ Args:
+ tensor (torch.Tensor): The tensor to be trimmed.
+ reference (Union[torch.Tensor, int]): A reference tensor or an integer
+ representing the target size for the last dimension.
+
+ Returns:
+ torch.Tensor: The center-trimmed tensor.
+
+ Raises:
+ ValueError: If the tensor size is smaller than the reference size.
+ """
+
+ # Get the target size from either the reference tensor's last dimension or directly from an integer
+ ref_size = reference.size(-1) if isinstance(reference, torch.Tensor) else reference
+ delta = tensor.size(-1) - ref_size
+
+ if delta < 0: raise ValueError("Tensor size is smaller than the reference size, cannot trim.")
+
+ # If there is a difference, perform symmetric center cropping
+ if delta: tensor = tensor[..., delta // 2 : -(delta - delta // 2)]
+
+ return tensor
+
+class DummyPoolExecutor:
+ """
+ A fallback single-threaded executor mimicking concurrent.futures.ThreadPoolExecutor.
+ Useful when running tasks sequentially without multithreading overhead (e.g., on GPU).
+ """
+
+ class DummyResult:
+ """A wrapper class to simulate a Future object."""
+
+ def __init__(
+ self,
+ func,
+ *args,
+ **kwargs
+ ):
+ self.func = func
+ self.args = args
+ self.kwargs = kwargs
+
+ def result(self):
+ """Execute the function and return the actual result synchronously."""
+
+ return self.func(
+ *self.args,
+ **self.kwargs
+ )
+
+ def __init__(self, workers=0):
+ pass
+
+ def submit(self, func, *args, **kwargs):
+ """Submit a task to be wrapped inside DummyResult."""
+
+ return DummyPoolExecutor.DummyResult(func, *args, **kwargs)
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, exc_type, exc_value, exc_tb):
+ return
+
+class TensorChunk:
+ """
+ Represents a specific chunk/slice of a torch.Tensor without duplicating memory,
+ supporting custom zero-padding mechanisms.
+ """
+
+ def __init__(
+ self,
+ tensor,
+ offset=0,
+ length=None
+ ):
+ """
+ Initializes the TensorChunk.
+
+ Args:
+ tensor (Union[torch.Tensor, TensorChunk]): The source tensor or another TensorChunk.
+ offset (int): Starting position inside the source tensor. Defaults to 0.
+ length (Optional[int]): Length of the chunk. Defaults to the remaining length.
+ """
+
+ total_length = tensor.shape[-1]
+ assert offset >= 0
+ assert offset < total_length
+ # If length is not specified, take all remaining elements
+ length = total_length - offset if length is None else min(total_length - offset, length)
+
+ # Unpack nested TensorChunks to reference the original underlying tensor directly
+ if isinstance(tensor, TensorChunk):
+ self.tensor = tensor.tensor
+ self.offset = offset + tensor.offset
+ else:
+ self.tensor = tensor
+ self.offset = offset
+
+ self.length = length
+ self.device = tensor.device
+
+ @property
+ def shape(self):
+ """Returns the shape of the chunk, overriding the final dimension with the chunk length."""
+
+ shape = list(self.tensor.shape)
+ shape[-1] = self.length
+ return shape
+
+ def padded(self, target_length):
+ """
+ Extract the chunk and pad it symmetrically with zeros if it exceeds bounds.
+
+ Args:
+ target_length (int): The expected output length after padding.
+
+ Returns:
+ torch.Tensor: The padded chunk tensor.
+ """
+
+ delta = target_length - self.length
+ total_length = self.tensor.shape[-1]
+ assert delta >= 0
+ # Calculate ideal start and end indices for center padding
+ start = self.offset - delta // 2
+ end = start + target_length
+ # Constrain boundaries to avoid IndexError
+ correct_start = max(0, start)
+ correct_end = min(total_length, end)
+ # Calculate required padding for out-of-bound segments
+ pad_left = correct_start - start
+ pad_right = end - correct_end
+
+ # Slice the real data and apply zero-padding to both ends
+ out = torch.nn.functional.pad(
+ self.tensor[..., correct_start:correct_end],
+ (pad_left, pad_right)
+ )
+
+ assert out.shape[-1] == target_length
+ return out
+
+def tensor_chunk(tensor_or_chunk):
+ """Ensures the input is wrapped in a TensorChunk object."""
+
+ if isinstance(tensor_or_chunk, TensorChunk): return tensor_or_chunk
+ else:
+ assert isinstance(tensor_or_chunk, torch.Tensor)
+ return TensorChunk(tensor_or_chunk)
+
+def apply_model(
+ model,
+ mix,
+ shifts=1,
+ split=True,
+ overlap=0.25,
+ transition_power=1.0,
+ static_shifts=1,
+ set_progress_bar=None,
+ device=None,
+ progress=False,
+ num_workers=0,
+ pool=None,
+ hybrid = False,
+ weights = None
+):
+ """
+ Apply a separation model to a mixture audio tensor with advanced processing techniques
+ including model ensembling (hybrid mode), random time shifts, and overlapped chunk splitting.
+
+ Args:
+ model (Any): Single model instance or a list of models (if hybrid=True).
+ mix (torch.Tensor): Input audio tensor of shape (batch, channels, length).
+ shifts (int): Number of random time shifts to smooth out boundary artifacts.
+ split (bool): If True, process audio in overlapping segments.
+ overlap (float): Overlap ratio between consecutive chunks [0, 1).
+ transition_power (float): Power factor for cross-fading window weights.
+ static_shifts (int): Multiplier used solely for tracking progress tracking.
+ set_progress_bar (Optional[Callable]): Progress bar update callback.
+ device (Optional[Union[str, torch.device]]): Target device (CPU/CUDA).
+ progress (bool): If True, display a local tqdm progress bar.
+ num_workers (int): Number of worker threads for CPU parallel execution.
+ pool (Optional[Any]): Custom thread pool executor.
+ hybrid (bool): If True, treats `model` as an ensemble list and blends outputs.
+ weights (Optional[List[List[float]]]): Blend weights for each source in hybrid mode.
+
+ Returns:
+ torch.Tensor: Separated sources tensor.
+ """
+
+ global fut_length, bag_num, prog_bar
+
+ # Setup execution device and fallback worker pool
+ device = mix.device if device is None else torch.device(device)
+ if pool is None: pool = concurrent.futures.ThreadPoolExecutor(num_workers) if num_workers > 0 and device.type == "cpu" else DummyPoolExecutor()
+ # Pack basic configuration parameters for recursive calls
+ kwargs = {"shifts": shifts, "split": split, "overlap": overlap, "transition_power": transition_power, "progress": progress, "device": device, "pool": pool, "set_progress_bar": set_progress_bar, "static_shifts": static_shifts}
+
+ # MODE 1: HYybrid Ensemble Processing
+ if hybrid:
+ estimates, fut_length, prog_bar, current_model = 0, 0, 0, 0
+ totals = [0] * len(model[0].sources)
+ bag_num = len(model)
+
+ # Iterate over each sub-model in the ensemble
+ for sub_model, weight in zip(model, weights):
+ sub_model.to(device).eval()
+ if config.is_half: sub_model = sub_model.half()
+
+ fut_length += fut_length
+ current_model += 1
+ # Recursively call apply_model for the single sub-model instance
+ out = apply_model(sub_model, mix, **kwargs)
+
+ # Apply weighted combination for each separated stem (source)
+ for k, inst_weight in enumerate(weight):
+ out[:, k, :, :] *= inst_weight
+ totals[k] += inst_weight
+
+ estimates += out
+ del out
+
+ # Normalize the accumulated estimates by total assigned weights
+ for k in range(estimates.shape[1]):
+ estimates[:, k, :, :] /= totals[k]
+
+ return estimates
+
+ assert transition_power >= 1
+ batch, channels, length = mix.shape
+
+ # MODE 2: Random Time Shifting (Trick To Improve Quality)
+ if shifts:
+ kwargs["shifts"] = 0 # Disable nested shift looping in recursion
+ max_shift = int(0.5 * model.samplerate)
+ padded_mix = tensor_chunk(mix).padded(length + 2 * max_shift)
+ out = 0
+
+ # Run inference multiple times with random offsets and average the results
+ for _ in range(shifts):
+ offset = random.randint(0, max_shift)
+ out += apply_model(model, TensorChunk(padded_mix, offset, length + max_shift - offset), **kwargs)[..., max_shift - offset :]
+
+ out /= shifts
+ return out
+ elif split: # MODE 3: Overlapping Chunk Spitting (For Long Audio)
+ kwargs["split"] = False # Avoid redundant splitting in nested calls
+ out = torch.zeros(batch, len(model.sources), channels, length, device=mix.device)
+ sum_weight = torch.zeros(length, device=mix.device)
+ segment = int(model.samplerate * model.segment)
+
+ # Create a linear triangular/trapezoidal fade-in and fade-out cross-fading window
+ weight = torch.cat([torch.arange(1, segment // 2 + 1, device=device), torch.arange(segment - segment // 2, 0, -1, device=device)])
+ assert len(weight) == segment
+ weight = (weight / weight.max()) ** transition_power
+ futures = []
+
+ # Submit overlapping audio slices into the execution pool
+ for offset in range(0, length, int((1 - overlap) * segment)):
+ futures.append((pool.submit(apply_model, model, TensorChunk(mix, offset, segment), **kwargs), offset))
+ offset += segment
+
+ if progress: futures = tqdm.tqdm(futures)
+ # Gather processed chunks and reconstruct the full signal using overlap-add
+ for future, offset in futures:
+ if set_progress_bar:
+ fut_length = len(futures) * bag_num * static_shifts
+ prog_bar += 1
+ set_progress_bar(0.1, (0.8 / fut_length * prog_bar))
+
+ chunk_out = future.result()
+ chunk_length = chunk_out.shape[-1]
+
+ # Accumulate the weighted chunks into the output buffer
+ out[..., offset : offset + segment] += (weight[:chunk_length].to(device) * chunk_out).to(mix.device)
+ sum_weight[offset : offset + segment] += weight[:chunk_length].to(mix.device)
+
+ # Normalize by accumulated window weights to prevent volume distortion
+ assert sum_weight.min() > 0
+ out /= sum_weight
+ return out
+ else: # MODE 4: Base Model Inference (Direct Inference On Ram Chunk)
+ with torch.no_grad():
+ # Pad valid length if required by convolutional/architecture constraints
+ out = model(tensor_chunk(mix).padded(model.valid_length(length) if hasattr(model, "valid_length") else length).to(device))
+
+ # Trim extra padding introduced by the model architecture back to original length
+ return center_trim(out, length)
\ No newline at end of file
diff --git a/main/library/uvr5_lib/demucs/hdemucs.py b/main/library/uvr5_lib/demucs/hdemucs.py
new file mode 100644
index 0000000000000000000000000000000000000000..815771906afdd0737042278fdfac62f44719145c
--- /dev/null
+++ b/main/library/uvr5_lib/demucs/hdemucs.py
@@ -0,0 +1,1379 @@
+import os
+import sys
+import math
+import torch
+
+from torch import nn
+from copy import deepcopy
+
+from torch.nn import functional as F
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config
+from main.library.algorithm.commons import capture_init, rescale_module
+from main.library.algorithm.normalization import Fp32GroupNorm, LayerScale
+
+def spectro(x, n_fft=512, hop_length=None, pad=0):
+ """
+ Compute the Short-Time Fourier Transform (STFT) of an audio signal.
+
+ Args:
+ x (torch.Tensor): Input audio tensor with shape (*other, length).
+ n_fft (int): Number of FFT bins. Defaults to 512.
+ hop_length (Optional[int]): Stride between STFT windows. Defaults to n_fft // 4.
+ pad (int): Factor to increase n_fft size via padding. Defaults to 0.
+
+ Returns:
+ torch.Tensor: Complex spectrogram tensor with shape (*other, freqs, frames).
+ """
+
+ *other, length = x.shape
+ x = x.reshape(-1, length) # Flatten batch/channel dimensions for PyTorch STFT compatibility
+ device_type = x.device.type
+
+ # Fallback to CPU if running on unsupported hardware architectures
+ is_other_gpu = not device_type in ["cuda", "xpu", "cpu"]
+ if is_other_gpu: x = x.cpu()
+
+ z = torch.stft(
+ x,
+ n_fft=n_fft * (1 + pad),
+ hop_length=hop_length or n_fft // 4,
+ window=torch.hann_window(n_fft).to(x),
+ win_length=n_fft,
+ normalized=True,
+ center=True,
+ return_complex=True,
+ pad_mode="reflect"
+ )
+
+ _, freqs, frame = z.shape
+ # Reshape back to the original nested dimension structure
+ return z.view(*other, freqs, frame)
+
+def ispectro(z, hop_length=None, length=None, pad=0):
+ """
+ Compute the Inverse Short-Time Fourier Transform (iSTFT) to reconstruct audio.
+
+ Args:
+ z (torch.Tensor): Complex spectrogram with shape (*other, freqs, frames).
+ hop_length (Optional[int]): Stride between windows. Defaults to n_fft // 4.
+ length (Optional[int]): Target output signal length. Defaults to None.
+ pad (int): Padding factor matching the initial `spectro` call. Defaults to 0.
+
+ Returns:
+ torch.Tensor: Reconstructed time-domain audio tensor with shape (*other, length).
+ """
+
+ *other, freqs, frames = z.shape
+ n_fft = 2 * freqs - 2 # Deduce original FFT window size from Hermitian symmetry
+ z = z.view(-1, freqs, frames)
+
+ win_length = n_fft // (1 + pad)
+ device_type = z.device.type
+
+ is_other_gpu = not device_type in ["cuda", "xpu", "cpu"]
+ if is_other_gpu: z = z.cpu()
+
+ x = torch.istft(
+ z,
+ n_fft,
+ hop_length,
+ window=torch.hann_window(win_length).to(z.real),
+ win_length=win_length,
+ normalized=True,
+ length=length,
+ center=True
+ )
+
+ _, length = x.shape
+ return x.view(*other, length)
+
+def atan2(y, x):
+ """
+ Element-wise multi-quadrant inverse tangent function mapping angles to (-pi, pi].
+
+ Args:
+ y (torch.Tensor): Y-coordinate (Sine/Imaginary component).
+ x (torch.Tensor): X-coordinate (Cosine/Real component).
+
+ Returns:
+ torch.Tensor: Computed angle phase tensor.
+ """
+
+ pi = 2 * torch.tensor(1.0).asin()
+ # Safeguard division by zero by shifting absolute zeros
+ x += ((x == 0) & (y == 0)) * 1.0
+ out = (y / x).atan()
+ # Quad-based phase unwrapping offsets
+ out += ((y >= 0) & (x < 0)) * pi
+ out -= ((y < 0) & (x < 0)) * pi
+ out *= 1 - ((y > 0) & (x == 0)) * 1.0
+ out += ((y > 0) & (x == 0)) * (pi / 2)
+ out *= 1 - ((y < 0) & (x == 0)) * 1.0
+ out += ((y < 0) & (x == 0)) * (-pi / 2)
+ return out
+
+def _norm(x):
+ """Compute the squared absolute magnitude of complex values represented as [..., 2]."""
+
+ return x[..., 0].abs() ** 2 + x[..., 1].abs() ** 2
+
+def _mul_add(a, b, out = None):
+ """Perform complex multiplication followed by accumulator addition: out += a * b."""
+
+ target_shape = torch.Size([max(sa, sb) for (sa, sb) in zip(a.shape, b.shape)])
+
+ if out is None or out.shape != target_shape:
+ out = torch.zeros(
+ target_shape,
+ dtype=a.dtype,
+ device=a.device
+ )
+
+ # Standard algebra for complex numbers
+ if out is a:
+ real_a = a[..., 0]
+ out[..., 0] = out[..., 0] + (real_a * b[..., 0] - a[..., 1] * b[..., 1])
+ out[..., 1] = out[..., 1] + (real_a * b[..., 1] + a[..., 1] * b[..., 0])
+ else:
+ out[..., 0] = out[..., 0] + (a[..., 0] * b[..., 0] - a[..., 1] * b[..., 1])
+ out[..., 1] = out[..., 1] + (a[..., 0] * b[..., 1] + a[..., 1] * b[..., 0])
+
+ return out
+
+def _mul(a, b, out = None):
+ """Perform pure complex multiplication: out = a * b."""
+
+ target_shape = torch.Size([max(sa, sb) for (sa, sb) in zip(a.shape, b.shape)])
+
+ if out is None or out.shape != target_shape:
+ out = torch.zeros(
+ target_shape,
+ dtype=a.dtype,
+ device=a.device
+ )
+
+ if out is a:
+ real_a = a[..., 0]
+ out[..., 0] = real_a * b[..., 0] - a[..., 1] * b[..., 1]
+ out[..., 1] = real_a * b[..., 1] + a[..., 1] * b[..., 0]
+ else:
+ out[..., 0] = a[..., 0] * b[..., 0] - a[..., 1] * b[..., 1]
+ out[..., 1] = a[..., 0] * b[..., 1] + a[..., 1] * b[..., 0]
+
+ return out
+
+def _inv(z, out = None):
+ """Compute the multiplicative inverse of complex values: out = 1 / z."""
+
+ ez = _norm(z)
+ if out is None or out.shape != z.shape: out = torch.zeros_like(z)
+
+ out[..., 0] = z[..., 0] / ez
+ out[..., 1] = -z[..., 1] / ez
+
+ return out
+
+def _conj(z, out = None):
+ """Compute the complex conjugate: out = real - i * imag."""
+
+ if out is None or out.shape != z.shape: out = torch.zeros_like(z)
+
+ out[..., 0] = z[..., 0]
+ out[..., 1] = -z[..., 1]
+
+ return out
+
+def _invert(M, out = None):
+ """
+ Invert small 1x1 or 2x2 complex spatial covariance matrices.
+
+ Args:
+ M (torch.Tensor): Complex matrix tensor with channel dimensions on self.shape[-2].
+ out (Optional[torch.Tensor]): Target allocation buffer.
+
+ Returns:
+ torch.Tensor: Inverted matrix tensor.
+ """
+
+ nb_channels = M.shape[-2]
+ if out is None or out.shape != M.shape: out = torch.empty_like(M)
+
+ if nb_channels == 1: out = _inv(M, out)
+ elif nb_channels == 2:
+ # Compute analytical matrix determinant using complex operations
+ det = _mul(M[..., 0, 0, :], M[..., 1, 1, :])
+ det = det - _mul(M[..., 0, 1, :], M[..., 1, 0, :])
+ invDet = _inv(det)
+ # Cramer's Rule for matrix inversion: adj(A) / det(A)
+ out[..., 0, 0, :] = _mul(invDet, M[..., 1, 1, :], out[..., 0, 0, :])
+ out[..., 1, 0, :] = _mul(-invDet, M[..., 1, 0, :], out[..., 1, 0, :])
+ out[..., 0, 1, :] = _mul(-invDet, M[..., 0, 1, :], out[..., 0, 1, :])
+ out[..., 1, 1, :] = _mul(invDet, M[..., 0, 0, :], out[..., 1, 1, :])
+ else: raise Exception("Unsupported channel dimension size for direct matrix inversion.")
+ return out
+
+def expectation_maximization(y, x, iterations = 2, eps = 1e-10, batch_size = 200):
+ """
+ Expectation-Maximization (EM) algorithm to optimize multichannel Wiener filters.
+
+ Args:
+ y (torch.Tensor): Estimated sources STFT with shape (nb_frames, nb_bins, 2, nb_channels, nb_sources).
+ x (torch.Tensor): Mixture STFT with shape (nb_frames, nb_bins, nb_channels, 2).
+ iterations (int): Total refinement steps. Defaults to 2.
+ eps (float): Numerical regularization constant. Defaults to 1e-10.
+ batch_size (int): Max frame segment processed concurrently. Defaults to 200.
+
+ Returns:
+ Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]: Refined sources, source variances, and covariance matrices.
+ """
+
+ (nb_frames, nb_bins, nb_channels) = x.shape[:-1]
+ nb_sources = y.shape[-1]
+
+ # Initialize a diagonal identity matrix used for covariance regularization
+ regularization = torch.cat((
+ torch.eye(nb_channels, dtype=x.dtype, device=x.device)[..., None],
+ torch.zeros((nb_channels, nb_channels, 1), dtype=x.dtype, device=x.device)
+ ), dim=2)
+
+ regularization = (
+ torch.as_tensor(eps)
+ ).sqrt() * (
+ regularization[None, None, ...].expand((-1, nb_bins, -1, -1, -1))
+ )
+
+ R = [
+ torch.zeros((nb_bins, nb_channels, nb_channels, 2), dtype=x.dtype, device=x.device)
+ for _ in range(nb_sources)
+ ]
+
+ weight = torch.zeros((nb_bins,), dtype=x.dtype, device=x.device)
+ v = torch.zeros((nb_frames, nb_bins, nb_sources), dtype=x.dtype, device=x.device)
+
+ # Main iterative EM optimization loop
+ for _ in range(iterations):
+ # 1. Expectation Step (E-step): Estimate source power spectral density variances
+ v = torch.mean(y[..., 0, :].abs() ** 2 + y[..., 1, :].abs() ** 2, dim=-2)
+ for j in range(nb_sources):
+ R[j] = torch.tensor(0.0, device=x.device)
+
+ weight = torch.tensor(eps, device=x.device)
+ pos = 0
+ batch_size = batch_size if batch_size else nb_frames
+ # Chunk loop processing to minimize VRAM saturation
+ while pos < nb_frames:
+ t = torch.arange(pos, min(nb_frames, pos + batch_size))
+ pos = int(t[-1]) + 1
+
+ R[j] = R[j] + _covariance(y[t, ..., j]).sum(dim=0)
+ weight = weight + v[t, ..., j].sum(dim=0)
+
+ # Normalize structural covariance fields
+ R[j] = R[j] / weight[..., None, None, None]
+ weight = torch.zeros_like(weight)
+
+ # 2. Maximization Step (M-step): Reconstruct source tensors using updated Wiener gains
+ if y.requires_grad: y = y.clone()
+
+ pos = 0
+
+ while pos < nb_frames:
+ t = torch.arange(pos, min(nb_frames, pos + batch_size))
+ pos = int(t[-1]) + 1
+
+ y[t, ...] = torch.tensor(0.0, device=x.device, dtype=x.dtype)
+
+ Cxx = regularization
+
+ for j in range(nb_sources):
+ Cxx = Cxx + (v[t, ..., j, None, None, None] * R[j][None, ...].clone())
+
+ inv_Cxx = _invert(Cxx)
+
+ for j in range(nb_sources):
+ gain = torch.zeros_like(inv_Cxx)
+
+ indices = torch.cartesian_prod(
+ torch.arange(nb_channels),
+ torch.arange(nb_channels),
+ torch.arange(nb_channels)
+ )
+
+ # Multichannel matrix-multiply to derive optimal filter gains
+ for index in indices:
+ gain[:, :, index[0], index[1], :] = _mul_add(
+ R[j][None, :, index[0], index[2], :].clone(),
+ inv_Cxx[:, :, index[2], index[1], :],
+ gain[:, :, index[0], index[1], :]
+ )
+
+ gain = gain * v[t, ..., None, None, None, j]
+ # Map filtering gain matrices against original mixture paths
+ for i in range(nb_channels):
+ y[t, ..., j] = _mul_add(
+ gain[..., i, :],
+ x[t, ..., i, None, :],
+ y[t, ..., j]
+ )
+
+ return y, v, R
+
+def wiener(
+ targets_spectrograms,
+ mix_stft,
+ iterations = 1,
+ softmask = False,
+ residual = False,
+ scale_factor = 10.0,
+ eps = 1e-10
+):
+ """
+ Apply multi-channel Wiener filtering to map magnitude spectrogram predictions back into complex signals.
+
+ Args:
+ targets_spectrograms (torch.Tensor): Predicted real magnitudes for target stems.
+ mix_stft (torch.Tensor): Complex STFT reference of the original mixed track.
+ iterations (int): EM optimization count. Defaults to 1.
+ softmask (bool): If True, falls back to raw ratio soft-masking.
+ residual (bool): If True, captures unallocated power into an explicit extra source.
+ scale_factor (float): Dynamic range scaling factor to maintain numerical stability.
+ eps (float): Regularization epsilon.
+
+ Returns:
+ torch.Tensor: Complex separation source tracks.
+ """
+
+ if softmask:
+ # Standard soft-masking: Multiply input complex track by proportional ratios
+ y = mix_stft[..., None] * (
+ targets_spectrograms / (
+ eps + targets_spectrograms.sum(dim=-1, keepdim=True).to(mix_stft.dtype)
+ )
+ )[..., None, :]
+ else:
+ # Phase-aware Initialization: Extract mixed phase vectors to seed initial targets
+ angle = atan2(mix_stft[..., 1], mix_stft[..., 0])[..., None]
+ nb_sources = targets_spectrograms.shape[-1]
+
+ y = torch.zeros(mix_stft.shape + (nb_sources,), dtype=mix_stft.dtype, device=mix_stft.device)
+ y[..., 0, :] = targets_spectrograms * angle.cos()
+ y[..., 1, :] = targets_spectrograms * angle.sin()
+
+ if residual: y = torch.cat([y, mix_stft[..., None] - y.sum(dim=-1, keepdim=True)], dim=-1) # Allocate any missing mixture energy into a residual destination slice
+ if iterations == 0: return y
+
+ # Rescale components to prevent underflow/overflow during multi-channel covariance estimations
+ max_abs = torch.as_tensor(
+ 1.0,
+ dtype=mix_stft.dtype,
+ device=mix_stft.device
+ ).max(_norm(mix_stft).sqrt().max() / scale_factor)
+
+ mix_stft = mix_stft / max_abs
+ y = y / max_abs
+ y = expectation_maximization(y, mix_stft, iterations, eps=eps)[0]
+ y = y * max_abs # Restore scaling profiles
+
+ return y
+
+def _covariance(y_j):
+ """Compute local channel-wise spatial covariance matrices."""
+
+ (nb_frames, nb_bins, nb_channels) = y_j.shape[:-1]
+
+ Cj = torch.zeros(
+ (nb_frames, nb_bins, nb_channels, nb_channels, 2),
+ dtype=y_j.dtype,
+ device=y_j.device
+ )
+
+ indices = torch.cartesian_prod(
+ torch.arange(nb_channels),
+ torch.arange(nb_channels)
+ )
+
+ # Cross-multiply target paths by complex conjugates
+ for index in indices:
+ Cj[:, :, index[0], index[1], :] = _mul_add(
+ y_j[:, :, index[0], :],
+ _conj(y_j[:, :, index[1], :]),
+ Cj[:, :, index[0], index[1], :]
+ )
+
+ return Cj
+
+def pad1d(x, paddings, mode = "constant", value = 0.0):
+ """
+ Extended 1D padding function with support for reflection on sequences shorter than the padding length.
+
+ Args:
+ x (torch.Tensor): Input tensor.
+ paddings (Tuple[int, int]): Left and right padding amounts.
+ mode (str): Padding mode ('constant', 'reflect', etc.). Defaults to "constant".
+ value (float): Quantization value for constant fills. Defaults to 0.0.
+
+ Returns:
+ torch.Tensor: Padded tensor.
+ """
+
+ x0 = x
+ length = x.shape[-1]
+ padding_left, padding_right = paddings
+
+ if mode == "reflect":
+ max_pad = max(padding_left, padding_right)
+ # If the input sequence is shorter than padding bounds, pre-pad to avoid standard PyTorch crashes
+ if length <= max_pad:
+ extra_pad = max_pad - length + 1
+ extra_pad_right = min(padding_right, extra_pad)
+ extra_pad_left = extra_pad - extra_pad_right
+ paddings = (padding_left - extra_pad_left, padding_right - extra_pad_right)
+ x = F.pad(x, (extra_pad_left, extra_pad_right))
+
+ out = F.pad(x, paddings, mode, value)
+
+ assert out.shape[-1] == length + padding_left + padding_right
+ assert (out[..., padding_left : padding_left + length] == x0).all()
+ return out
+
+def unfold(a, kernel_size, stride):
+ """
+ Extract overlapping windows from a 1D sequence tensor using stride view manipulation.
+
+ Args:
+ a (torch.Tensor): Target array tensor with shape (*shape, length).
+ kernel_size (int): Output frame window length.
+ stride (int): Frame stepping distance.
+
+ Returns:
+ torch.Tensor: Strided unfolded block view.
+ """
+
+ *shape, length = a.shape
+ n_frames = math.ceil(length / stride)
+ # Pad tail entries to fit matching window multiples cleanly
+ a = F.pad(a, (0, (n_frames - 1) * stride + kernel_size - length))
+ strides = list(a.stride())
+
+ assert strides[-1] == 1
+ # Use as_strided to create memory views without raw tensor allocation overheads
+ return a.as_strided([*shape, n_frames, kernel_size], strides[:-1] + [stride, 1])
+
+class LocalState(nn.Module):
+ """
+ Local Attention Layer enhanced with periodic frequency kernels and decay masks
+ to track harmonic musical context over time.
+ """
+
+ def __init__(
+ self,
+ channels,
+ heads = 4,
+ nfreqs = 0,
+ ndecay = 4
+ ):
+ super().__init__()
+ assert channels % heads == 0, (channels, heads)
+ self.heads = heads
+ self.nfreqs = nfreqs
+ self.ndecay = ndecay
+ # Query, Key, Content feature linear mappings
+ self.content = nn.Conv1d(channels, channels, 1)
+ self.query = nn.Conv1d(channels, channels, 1)
+ self.key = nn.Conv1d(channels, channels, 1)
+
+ if nfreqs: self.query_freqs = nn.Conv1d(channels, heads * nfreqs, 1)
+
+ if ndecay:
+ self.query_decay = nn.Conv1d(channels, heads * ndecay, 1)
+ self.query_decay.weight.data *= 0.01
+ assert self.query_decay.bias is not None
+ self.query_decay.bias.data[:] = -2
+
+ self.proj = nn.Conv1d(channels + heads * nfreqs, channels, 1)
+
+ def forward(self, x):
+ B, _, T = x.shape
+
+ # Construct localized relative position index deltas
+ indexes = torch.arange(T, device=x.device, dtype=x.dtype)
+ delta = indexes[:, None] - indexes[None, :]
+ keys = self.key(x).view(B, self.heads, -1, T)
+ # Calculate standard scaled dot-product attention scores
+ dots = torch.einsum("bhct,bhcs->bhts", keys, self.query(x).view(B, self.heads, -1, T))
+ dots /= keys.shape[2] ** 0.5
+
+ # Incorporate explicit cos-based frequency period biases (for musical pitch/rhythm alignment)
+ if self.nfreqs:
+ periods = torch.arange(1, self.nfreqs + 1, device=x.device, dtype=x.dtype)
+ freq_kernel = (2 * math.pi * delta / periods.view(-1, 1, 1)).cos()
+ dots += torch.einsum("fts,bhfs->bhts", freq_kernel, self.query_freqs(x).view(B, self.heads, -1, T) / self.nfreqs ** 0.5)
+
+ # Inject relative distance decay parameters to privilege closer context frames
+ if self.ndecay: dots += torch.einsum("fts,bhfs->bhts", -torch.arange(1, self.ndecay + 1, device=x.device, dtype=x.dtype).view(-1, 1, 1) * delta.abs() / self.ndecay ** 0.5, self.query_decay(x).view(B, self.heads, -1, T).sigmoid() / 2)
+ # Mask out exact self-attention correlations
+ dots.masked_fill_(torch.eye(T, device=dots.device, dtype=torch.bool), -100)
+
+ weights = dots.softmax(dim=2)
+ result = torch.einsum("bhts,bhct->bhcs", weights, self.content(x).view(B, self.heads, -1, T))
+
+ if self.nfreqs: result = torch.cat([result, torch.einsum("bhts,fts->bhfs", weights, freq_kernel)], 2)
+ return x + self.proj(result.reshape(B, -1, T))
+
+class BLSTM(nn.Module):
+ """
+ Bidirectional LSTM module featuring chunk-based window processing
+ for managing long sequence inputs efficiently.
+ """
+
+ def __init__(
+ self,
+ dim,
+ layers=1,
+ max_steps=None,
+ skip=False
+ ):
+ super().__init__()
+ assert max_steps is None or max_steps % 4 == 0
+ self.max_steps = max_steps
+ self.lstm = nn.LSTM(
+ bidirectional=True,
+ num_layers=layers,
+ hidden_size=dim,
+ input_size=dim
+ )
+ self.linear = nn.Linear(2 * dim, dim)
+ self.skip = skip
+
+ def forward(self, x):
+ B, C, T = x.shape
+ framed = False
+ y = x
+
+ # Chunk the sequence if length exceeds max_steps bounds to keep memory footprint bounded
+ if self.max_steps is not None and T > self.max_steps:
+ width = self.max_steps
+ stride = width // 2
+ frames = unfold(x, width, stride)
+ nframes = frames.shape[2]
+ framed = True
+ x = frames.permute(0, 2, 1, 3).reshape(-1, C, width)
+
+ # Run Bidirectional Recurrent Layer
+ x = self.linear(self.lstm(x.permute(2, 0, 1))[0]).permute(1, 2, 0)
+
+ # Stitch window slices back together into a continuous timeline array
+ if framed:
+ frames = x.reshape(B, -1, C, width)
+ limit = stride // 2
+ out = []
+
+ for k in range(nframes):
+ if k == 0: out.append(frames[:, k, :, :-limit])
+ elif k == nframes - 1: out.append(frames[:, k, :, limit:])
+ else: out.append(frames[:, k, :, limit:-limit])
+
+ x = torch.cat(out, -1)[..., :T]
+
+ if self.skip: x = x + y
+ return x
+
+class DConv(nn.Module):
+ """
+ Dilated Convolution Block stacking groups of increasing dilation layers,
+ with options for embedded attention or LSTM layers.
+ """
+
+ def __init__(
+ self,
+ channels,
+ compress = 4,
+ depth = 2,
+ init = 1e-4,
+ norm=True,
+ attn=False,
+ heads=4,
+ ndecay=4,
+ lstm=False,
+ gelu=True,
+ kernel=3,
+ dilate=True
+ ):
+ super().__init__()
+ assert kernel % 2 == 1
+ self.channels = channels
+ self.compress = compress
+ self.depth = abs(depth)
+ dilate = depth > 0
+ norm_fn = lambda d: nn.Identity()
+ if norm: norm_fn = lambda d: Fp32GroupNorm(1, d)
+ hidden = int(channels / compress)
+ act = nn.GELU if gelu else nn.ReLU
+ self.layers = nn.ModuleList([])
+
+ # Sequentially construct layers with exponentially scaling dilation steps
+ for d in range(self.depth):
+ dilation = 2**d if dilate else 1
+ padding = dilation * (kernel // 2)
+
+ mods = [
+ nn.Conv1d(
+ channels,
+ hidden,
+ kernel,
+ dilation=dilation,
+ padding=padding
+ ),
+ norm_fn(hidden),
+ act(),
+ nn.Conv1d(hidden, 2 * channels, 1),
+ norm_fn(2 * channels),
+ nn.GLU(1), # Gated Linear Unit down-samples dimension back to original
+ LayerScale(channels, init)
+ ]
+
+ # Optional structural injects inside block depths
+ if attn: mods.insert(3, LocalState(hidden, heads=heads, ndecay=ndecay))
+ if lstm: mods.insert(3, BLSTM(hidden, layers=2, max_steps=200, skip=True))
+
+ layer = nn.Sequential(*mods)
+ self.layers.append(layer)
+
+ def forward(self, x):
+ # Evaluate standard residual skip loops
+ for layer in self.layers:
+ x = x + layer(x)
+
+ return x
+
+class ScaledEmbedding(nn.Module):
+ """
+ Custom wrapper around standard nn.Embedding scaled by a constant factor
+ and featuring optional cumulative smoothing adjustments.
+ """
+
+ def __init__(
+ self,
+ num_embeddings,
+ embedding_dim,
+ scale = 10.0,
+ smooth=False
+ ):
+ super().__init__()
+ self.embedding = nn.Embedding(
+ num_embeddings,
+ embedding_dim
+ )
+
+ if smooth:
+ # Build running aggregate smooth views to steady target embedding steps
+ weight = self.embedding.weight.data.cumsum(dim=0)
+ weight = weight / torch.arange(1, num_embeddings + 1).to(weight).sqrt()[:, None]
+ self.embedding.weight.data[:] = weight
+
+ self.embedding.weight.data /= scale
+ self.scale = scale
+
+ @property
+ def weight(self):
+ """Returns the scaled version of the embedding weights matrix."""
+
+ return self.embedding.weight * self.scale
+
+ def forward(self, x):
+ return self.embedding(x) * self.scale
+
+class HEncLayer(nn.Module):
+ """
+ Hybrid Encoder Layer supporting either 1D time-domain or 2D frequency-domain convolutions.
+ Integrates an optional DConv (dilated residual block) and a 1x1 GLU rewrite layer.
+ """
+
+ def __init__(
+ self,
+ chin,
+ chout,
+ kernel_size=8,
+ stride=4,
+ norm_groups=1,
+ empty=False,
+ freq=True,
+ dconv=True,
+ norm=True,
+ context=0,
+ dconv_kw={},
+ pad=True,
+ rewrite=True
+ ):
+ super().__init__()
+ norm_fn = lambda d: nn.Identity()
+ if norm: norm_fn = lambda d: Fp32GroupNorm(norm_groups, d)
+ pad = kernel_size // 4 if pad else 0
+
+ klass = nn.Conv1d
+ self.freq = freq
+ self.kernel_size = kernel_size
+ self.stride = stride
+ self.empty = empty
+ self.norm = norm
+ self.pad = pad
+
+ # Adapting to 2D Spatial Convolutions if processing Spectrogram branches
+ if freq:
+ kernel_size = [kernel_size, 1]
+ stride = [stride, 1]
+ pad = [pad, 0]
+ klass = nn.Conv2d
+
+ self.conv = klass(chin, chout, kernel_size, stride, pad)
+ if self.empty: return
+
+ self.norm1 = norm_fn(chout)
+ self.rewrite = None
+ # Optional Context Rewrite block utilizing Gated Linear Units (GLU)
+ if rewrite:
+ self.rewrite = klass(chout, 2 * chout, 1 + 2 * context, 1, context)
+ self.norm2 = norm_fn(2 * chout)
+
+ self.dconv = None
+ if dconv: self.dconv = DConv(chout, **dconv_kw)
+
+ def forward(self, x, inject=None):
+ # Flatten frequency dimension if a 1D time-domain layer receives 4D spectrogram data
+ if not self.freq and x.dim() == 4:
+ B, C, Fr, T = x.shape
+ x = x.view(B, -1, T)
+
+ # Pad right tail symmetrically if input lengths do not align with convolution strides
+ if not self.freq:
+ le = x.shape[-1]
+ if not le % self.stride == 0: x = F.pad(x, (0, self.stride - (le % self.stride)))
+
+ y = self.conv(x)
+ if self.empty: return y
+
+ # Cross-layer injection mechanism from corresponding time/frequency branch
+ if inject is not None:
+ assert inject.shape[-1] == y.shape[-1], (inject.shape, y.shape)
+
+ if inject.dim() == 3 and y.dim() == 4: inject = inject[:, :, None]
+ y = y + inject
+
+ y = F.gelu(self.norm1(y))
+ # Apply localized multi-channel dilated convolutions
+ if self.dconv:
+ if self.freq:
+ B, C, Fr, T = y.shape
+ y = y.permute(0, 2, 1, 3).reshape(-1, C, T)
+
+ y = self.dconv(y)
+ if self.freq: y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)
+
+ if self.rewrite:
+ z = self.norm2(self.rewrite(y))
+ z = F.glu(z, dim=1) # Channels halved due to GLU gating multiplication
+ else: z = y
+
+ return z
+
+class MultiWrap(nn.Module):
+ """
+ Wrapper module splitting a single convolution tier into specialized parallel operations,
+ each optimized for specific frequency sub-bands.
+ """
+
+ def __init__(
+ self,
+ layer,
+ split_ratios
+ ):
+ super().__init__()
+ self.split_ratios = split_ratios
+ self.layers = nn.ModuleList()
+ self.conv = isinstance(layer, HEncLayer)
+ assert not layer.norm
+ assert layer.freq
+ assert layer.pad
+
+ if not self.conv: assert not layer.context_freq
+ # Duplicate base tier configurations for each separate frequency slice
+ for _ in range(len(split_ratios) + 1):
+ lay = deepcopy(layer)
+
+ if self.conv: lay.conv.padding = (0, 0)
+ else: lay.pad = False
+
+ # Reset internal weights to maintain structural uniqueness
+ for m in lay.modules():
+ if hasattr(m, "reset_parameters"): m.reset_parameters()
+
+ self.layers.append(lay)
+
+ def forward(self, x, skip=None, length=None):
+ _, _, Fr, _ = x.shape
+ ratios = list(self.split_ratios) + [1]
+ start = 0
+ outs = []
+
+ for ratio, layer in zip(ratios, self.layers):
+ if self.conv:
+ # Execution logic for Encoder layers
+ pad = layer.kernel_size // 4
+
+ if ratio == 1:
+ limit = Fr
+ frames = -1
+ else:
+ limit = int(round(Fr * ratio))
+ le = limit - start
+
+ if start == 0: le += pad
+
+ frames = round((le - layer.kernel_size) / layer.stride + 1)
+ limit = start + (frames - 1) * layer.stride + layer.kernel_size
+
+ if start == 0: limit -= pad
+
+ assert limit - start > 0, (limit, start)
+ assert limit <= Fr, (limit, Fr)
+
+ # Segment input along frequency bin limits
+ y = x[:, :, start:limit, :]
+
+ if start == 0: y = F.pad(y, (0, 0, pad, 0))
+ if ratio == 1: y = F.pad(y, (0, 0, 0, pad))
+
+ outs.append(layer(y))
+ start = limit - layer.kernel_size + layer.stride
+ else:
+ # Execution logic for Decoder layers (incorporating overlap-add operations)
+ limit = Fr if ratio == 1 else int(round(Fr * ratio))
+
+ last = layer.last
+ layer.last = True
+
+ y = x[:, :, start:limit]
+ s = skip[:, :, start:limit]
+ out, _ = layer(y, s, None)
+ # Smoothly blend sub-band boundary overlaps using accumulated residuals
+ if outs:
+ outs[-1][:, :, -layer.stride :] += out[:, :, : layer.stride] - layer.conv_tr.bias.view(1, -1, 1, 1)
+ out = out[:, :, layer.stride :]
+
+ if ratio == 1: out = out[:, :, : -layer.stride // 2, :]
+ if start == 0: out = out[:, :, layer.stride // 2 :, :]
+
+ outs.append(out)
+ layer.last = last
+ start = limit
+
+ out = torch.cat(outs, dim=2)
+ if not self.conv and not last: out = F.gelu(out)
+
+ if self.conv: return out
+ else: return out, None
+
+class HDecLayer(nn.Module):
+ """
+ Hybrid Decoder Layer mirroring HEncLayer. Rebuilds signals using Transposed 1D or 2D Convolutions,
+ incorporating U-Net skip-connection aggregations.
+ """
+
+ def __init__(
+ self,
+ chin,
+ chout,
+ last=False,
+ kernel_size=8,
+ stride=4,
+ norm_groups=1,
+ empty=False,
+ freq=True,
+ dconv=True,
+ norm=True,
+ context=1,
+ dconv_kw={},
+ pad=True,
+ context_freq=True,
+ rewrite=True
+ ):
+ super().__init__()
+ norm_fn = lambda d: nn.Identity()
+
+ if norm: norm_fn = lambda d: Fp32GroupNorm(norm_groups, d)
+ pad = kernel_size // 4 if pad else 0
+
+ self.pad = pad
+ self.last = last
+ self.freq = freq
+ self.chin = chin
+ self.empty = empty
+ self.stride = stride
+ self.kernel_size = kernel_size
+ self.norm = norm
+ self.context_freq = context_freq
+ klass = nn.Conv1d
+ klass_tr = nn.ConvTranspose1d
+
+ if freq:
+ kernel_size = [kernel_size, 1]
+ stride = [stride, 1]
+ klass = nn.Conv2d
+ klass_tr = nn.ConvTranspose2d
+
+ self.conv_tr = klass_tr(chin, chout, kernel_size, stride)
+ self.norm2 = norm_fn(chout)
+
+ if self.empty: return
+ self.rewrite = None
+
+ if rewrite:
+ if context_freq: self.rewrite = klass(chin, 2 * chin, 1 + 2 * context, 1, context)
+ else: self.rewrite = klass(chin, 2 * chin, [1, 1 + 2 * context], 1, [0, context])
+
+ self.norm1 = norm_fn(2 * chin)
+
+ self.dconv = None
+ if dconv: self.dconv = DConv(chin, **dconv_kw)
+
+ def forward(self, x, skip, length):
+ if self.freq and x.dim() == 3:
+ B, C, T = x.shape
+ x = x.view(B, self.chin, -1, T)
+
+ if not self.empty:
+ # U-Net structural shortcut fusion step
+ x = x + skip
+
+ y = F.glu(self.norm1(self.rewrite(x)), dim=1) if self.rewrite else x
+
+ if self.dconv:
+ if self.freq:
+ B, C, Fr, T = y.shape
+ y = y.permute(0, 2, 1, 3).reshape(-1, C, T)
+
+ y = self.dconv(y)
+ if self.freq: y = y.view(B, Fr, C, T).permute(0, 2, 1, 3)
+ else:
+ y = x
+ assert skip is None
+
+ z = self.norm2(self.conv_tr(y))
+ # Remove outer convolution boundary paddings
+ if self.freq:
+ if self.pad: z = z[..., self.pad : -self.pad, :]
+ else:
+ z = z[..., self.pad : self.pad + length]
+ assert z.shape[-1] == length, (z.shape[-1], length)
+
+ if not self.last: z = F.gelu(z)
+ return z, y
+
+class HDemucs(nn.Module):
+ """
+ Hybrid Demucs v4 Deep Audio Source Separation Model.
+ Processes audio concurrently via multi-channel time networks and complex spectrogram masks.
+ """
+
+ @capture_init
+ def __init__(
+ self,
+ sources,
+ audio_channels=2,
+ channels=48,
+ channels_time=None,
+ growth=2, nfft=4096,
+ wiener_iters=0,
+ end_iters=0,
+ wiener_residual=False,
+ cac=True,
+ depth=6,
+ rewrite=True,
+ hybrid=True,
+ hybrid_old=False,
+ multi_freqs=None,
+ multi_freqs_depth=2,
+ freq_emb=0.2,
+ emb_scale=10,
+ emb_smooth=True,
+ kernel_size=8,
+ time_stride=2,
+ stride=4,
+ context=1,
+ context_enc=0,
+ norm_starts=4,
+ norm_groups=4,
+ dconv_mode=1,
+ dconv_depth=2,
+ dconv_comp=4,
+ dconv_attn=4,
+ dconv_lstm=4,
+ dconv_init=1e-4,
+ rescale=0.1,
+ samplerate=44100,
+ segment=4 * 10
+ ):
+ super().__init__()
+ self.cac = cac
+ self.wiener_residual = wiener_residual
+ self.audio_channels = audio_channels
+ self.sources = sources
+ self.kernel_size = kernel_size
+ self.context = context
+ self.stride = stride
+ self.depth = depth
+ self.channels = channels
+ self.samplerate = samplerate
+ self.segment = segment
+ self.nfft = nfft
+ self.hop_length = nfft // 4
+ self.wiener_iters = wiener_iters
+ self.end_iters = end_iters
+ self.freq_emb = None
+ self.hybrid = hybrid
+ self.hybrid_old = hybrid_old
+ if hybrid_old: assert hybrid
+ if hybrid: assert wiener_iters == end_iters
+ self.encoder = nn.ModuleList()
+ self.decoder = nn.ModuleList()
+
+ if hybrid:
+ self.tencoder = nn.ModuleList()
+ self.tdecoder = nn.ModuleList()
+
+ chin = audio_channels
+ chin_z = chin
+
+ if self.cac: chin_z *= 2 # Complex-As-Channels expands depth dimension by two
+
+ chout = channels_time or channels
+ chout_z = channels
+ freqs = nfft // 2
+
+ # Core iterative U-Net deep stack generation loop
+ for index in range(depth):
+ lstm = index >= dconv_lstm
+ attn = index >= dconv_attn
+ norm = index >= norm_starts
+ freq = freqs > 1
+ stri = stride
+ ker = kernel_size
+
+ # Fallback to temporal settings once frequency channels contract to 1
+ if not freq:
+ assert freqs == 1
+
+ ker = time_stride * 2
+ stri = time_stride
+
+ pad = True
+ last_freq = False
+
+ if freq and freqs <= kernel_size:
+ ker = freqs
+ pad = False
+ last_freq = True
+
+ kw = {
+ "kernel_size": ker,
+ "stride": stri,
+ "freq": freq,
+ "pad": pad,
+ "norm": norm,
+ "rewrite": rewrite,
+ "norm_groups": norm_groups,
+ "dconv_kw": {
+ "lstm": lstm,
+ "attn": attn,
+ "depth": dconv_depth,
+ "compress": dconv_comp,
+ "init": dconv_init,
+ "gelu": True
+ },
+ }
+
+ kwt = dict(kw)
+ kwt["freq"] = 0
+ kwt["kernel_size"] = kernel_size
+ kwt["stride"] = stride
+ kwt["pad"] = True
+ kw_dec = dict(kw)
+
+ multi = False
+
+ if multi_freqs and index < multi_freqs_depth:
+ multi = True
+ kw_dec["context_freq"] = False
+
+ if last_freq:
+ chout_z = max(chout, chout_z)
+ chout = chout_z
+
+ enc = HEncLayer(
+ chin_z,
+ chout_z,
+ dconv=dconv_mode & 1,
+ context=context_enc,
+ **kw
+ )
+
+ if hybrid and freq:
+ tenc = HEncLayer(
+ chin,
+ chout,
+ dconv=dconv_mode & 1,
+ context=context_enc,
+ empty=last_freq,
+ **kwt
+ )
+ self.tencoder.append(tenc)
+
+ if multi:
+ enc = MultiWrap(
+ enc,
+ multi_freqs
+ )
+
+ self.encoder.append(enc)
+ # Re-scale target tracking channels to match the expanded sources count after the input layer
+ if index == 0:
+ chin = self.audio_channels * len(self.sources)
+ chin_z = chin
+
+ if self.cac: chin_z *= 2
+
+ dec = HDecLayer(
+ chout_z,
+ chin_z,
+ dconv=dconv_mode & 2,
+ last=index == 0,
+ context=context,
+ **kw_dec
+ )
+
+ if multi:
+ dec = MultiWrap(
+ dec,
+ multi_freqs
+ )
+
+ if hybrid and freq:
+ tdec = HDecLayer(
+ chout, chin,
+ dconv=dconv_mode & 2,
+ empty=last_freq,
+ last=index == 0,
+ context=context,
+ **kwt
+ )
+
+ self.tdecoder.insert(0, tdec)
+
+ self.decoder.insert(0, dec)
+ chin = chout
+ chin_z = chout_z
+ chout = int(growth * chout)
+ chout_z = int(growth * chout_z)
+
+ if freq:
+ if freqs <= kernel_size: freqs = 1
+ else: freqs //= stride
+
+ # Inject explicit learned embeddings for absolute frequency tracking
+ if index == 0 and freq_emb:
+ self.freq_emb = ScaledEmbedding(
+ freqs,
+ chin_z,
+ smooth=emb_smooth,
+ scale=emb_scale
+ )
+
+ self.freq_emb_scale = freq_emb
+
+ if rescale: rescale_module(self, reference=rescale)
+ self.dtype = torch.float16 if config.is_half else torch.float32
+
+ def _spec(self, x):
+ """Compute the padded complex forward spectrogram."""
+
+ if self.hybrid:
+ assert self.hop_length == self.nfft // 4
+ le = int(math.ceil(x.shape[-1] / self.hop_length))
+ pad = self.hop_length // 2 * 3
+ x = pad1d(x, (pad, pad + le * self.hop_length - x.shape[-1]), mode="reflect" if not self.hybrid_old else "constant")
+
+ z = spectro(x, self.nfft, self.hop_length)[..., :-1, :]
+ if self.hybrid:
+ assert z.shape[-1] == le + 4, (z.shape, x.shape, le)
+ z = z[..., 2 : 2 + le]
+
+ return z
+
+ def _ispec(self, z, length=None, scale=0):
+ """Reconstruct time-domain signal waveforms using inverse spectrogram paths."""
+
+ hl = self.hop_length // (4 ** scale)
+ z = F.pad(z, (0, 0, 0, 1))
+
+ if self.hybrid:
+ z = F.pad(z, (2, 2))
+ pad = hl // 2 * 3
+
+ x = ispectro(z, hl, length=hl * int(math.ceil(length / hl)) + 2 * pad if not self.hybrid_old else hl * int(math.ceil(length / hl)))
+ x = x[..., pad : pad + length] if not self.hybrid_old else x[..., :length]
+ else: x = ispectro(z, hl, length)
+
+ return x
+
+ def _magnitude(self, z):
+ """Extract magnitude fields or unroll real/imaginary complex vectors into structural channels."""
+
+ if self.cac:
+ B, C, Fr, T = z.shape
+ m = torch.view_as_real(z).permute(0, 1, 4, 2, 3)
+ m = m.reshape(B, C * 2, Fr, T)
+ else: m = z.abs()
+
+ return m
+
+ def _mask(self, z, m):
+ """Execute phase-based masking reconstruction or apply EM Wiener multi-channel optimization."""
+
+ niters = self.wiener_iters
+ if self.cac:
+ B, S, _, Fr, T = m.shape
+ out = m.view(B, S, -1, 2, Fr, T).permute(0, 1, 2, 4, 5, 3)
+ out = torch.view_as_complex(out.contiguous())
+ return out
+
+ if self.training: niters = self.end_iters
+
+ if niters < 0:
+ z = z[:, None]
+ return z / (1e-8 + z.abs()) * m
+ else: return self._wiener(m, z, niters)
+
+ def _wiener(self, mag_out, mix_stft, niters):
+ """Divide long timelines into frames to securely compute windowed multichannel Wiener algorithms."""
+
+ init = mix_stft.dtype
+ wiener_win_len = 300
+ residual = self.wiener_residual
+ B, S, C, Fq, T = mag_out.shape
+ mag_out = mag_out.permute(0, 4, 3, 2, 1)
+ mix_stft = torch.view_as_real(mix_stft.permute(0, 3, 2, 1))
+ outs = []
+
+ for sample in range(B):
+ pos = 0
+ out = []
+
+ for pos in range(0, T, wiener_win_len):
+ frame = slice(pos, pos + wiener_win_len)
+ z_out = wiener(mag_out[sample, frame], mix_stft[sample, frame], niters, residual=residual)
+ out.append(z_out.transpose(-1, -2))
+
+ outs.append(torch.cat(out, dim=0))
+
+ out = torch.view_as_complex(torch.stack(outs, 0))
+ out = out.permute(0, 4, 3, 2, 1).contiguous()
+
+ if residual: out = out[:, :-1]
+ assert list(out.shape) == [B, S, C, Fq, T]
+ return out.to(init)
+
+ def forward(self, mix):
+ length = mix.shape[-1]
+
+ # Step 1: Forward transformation to get target feature maps
+ z = self._spec(mix)
+ mag = self._magnitude(z).to(mix.device)
+ x = mag
+
+ B, _, Fq, T = x.shape
+ # Normalize spectrogram attributes using standard z-scoring
+ mean = x.mean(dim=(1, 2, 3), keepdim=True)
+ std = x.std(dim=(1, 2, 3), keepdim=True)
+ x = (x - mean) / (1e-5 + std)
+
+ if self.hybrid:
+ xt = mix
+ meant = xt.mean(dim=(1, 2), keepdim=True)
+ stdt = xt.std(dim=(1, 2), keepdim=True)
+ xt = (xt - meant) / (1e-5 + stdt)
+
+ saved, saved_t, lengths, lengths_t = [], [], [], []
+ # Step 2: Processing the downsampling encoder path
+ for idx, encode in enumerate(self.encoder):
+ if config.is_half: encode = encode.float()
+
+ lengths.append(x.shape[-1])
+ inject = None
+
+ if self.hybrid and idx < len(self.tencoder):
+ lengths_t.append(xt.shape[-1])
+ tenc = self.tencoder[idx]
+
+ if config.is_half: tenc = tenc.float()
+ xt = tenc(xt.float()).to(self.dtype)
+
+ if not tenc.empty: saved_t.append(xt)
+ else: inject = xt
+
+ x = encode(x.float(), inject)
+ # Apply frequency absolute learned coordinate embeddings
+ if idx == 0 and self.freq_emb is not None:
+ frs = torch.arange(x.shape[-2], device=x.device)
+ emb = self.freq_emb(frs).t()[None, :, :, None].expand_as(x)
+ x = x + self.freq_emb_scale * emb
+
+ x = x.to(self.dtype)
+ saved.append(x)
+
+ # Clear bottleneck representations before initializing decoding aggregations
+ x = torch.zeros_like(x)
+ if self.hybrid: xt = torch.zeros_like(x)
+
+ # Step 3: Processing the upsampling decoder path (U-Net link restoration)
+ for idx, decode in enumerate(self.decoder):
+ x, pre = decode(x, saved.pop(-1), lengths.pop(-1))
+ if self.hybrid: offset = self.depth - len(self.tdecoder)
+
+ if self.hybrid and idx >= offset:
+ tdec = self.tdecoder[idx - offset]
+ length_t = lengths_t.pop(-1)
+
+ if tdec.empty:
+ assert pre.shape[2] == 1, pre.shape
+ pre = pre[:, :, 0]
+
+ xt, _ = tdec(pre.to(self.dtype), None, length_t)
+ else:
+ xt, _ = tdec(xt.to(self.dtype), saved_t.pop(-1), length_t)
+
+ assert len(saved) == 0
+ assert len(lengths_t) == 0
+ assert len(saved_t) == 0
+
+ # Step 4: Rescaling variables and inverse mapping target tracks back into audio waveforms
+ S = len(self.sources)
+ x = x.view(B, S, -1, Fq, T)
+ x = x * std[:, None] + mean[:, None] # Revert spectrogram z-scoring
+ device_type = x.device.type
+ device_load = f"{device_type}:{x.device.index}" if not device_type == "mps" else device_type
+ x_is_other_gpu = not device_type in ["cuda", "xpu", "cpu"]
+ if x_is_other_gpu: x = x.cpu()
+ zout = self._mask(z, x.float())
+ x = self._ispec(zout, length)
+ if x_is_other_gpu: x = x.to(device_load)
+
+ # Concurrently reconstruct the final time-domain waveforms
+ if self.hybrid:
+ xt = xt.view(B, S, -1, length)
+ xt = xt * stdt[:, None] + meant[:, None]
+ x = xt + x # Add time-domain outputs and inverse spectrogram tracks together
+
+ return x
\ No newline at end of file
diff --git a/main/library/uvr5_lib/demucs/htdemucs.py b/main/library/uvr5_lib/demucs/htdemucs.py
new file mode 100644
index 0000000000000000000000000000000000000000..14addebde6458726b943e55a5ea14b1e85abe9dc
--- /dev/null
+++ b/main/library/uvr5_lib/demucs/htdemucs.py
@@ -0,0 +1,1203 @@
+import os
+import sys
+import math
+import torch
+import random
+
+import numpy as np
+
+from torch import nn
+from einops import rearrange
+from fractions import Fraction
+from torch.nn import functional as F
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations, config
+from main.library.algorithm.commons import capture_init, rescale_module
+from main.library.algorithm.normalization import Fp32LayerNorm, Fp32GroupNormTranspose, LayerScale
+from main.library.uvr5_lib.demucs.hdemucs import pad1d, spectro, ispectro, wiener, ScaledEmbedding, HEncLayer, MultiWrap, HDecLayer
+
+def create_sin_embedding(
+ length,
+ dim,
+ shift = 0,
+ device="cpu",
+ max_period=10000
+):
+ """
+ Generates standard 1D sinusoidal positional embeddings.
+
+ Args:
+ length (int): Temporal or sequence length.
+ dim (int): Embedding dimension size (must be even).
+ shift (int, optional): Initial position index shift offset. Defaults to 0.
+ device (str, optional): Target execution device. Defaults to "cpu".
+ max_period (int, optional): Controls the maximum wave period. Defaults to 10000.
+
+ Returns:
+ torch.Tensor: Sinusoidal positional embeddings of shape [length, 1, dim].
+ """
+
+ assert dim % 2 == 0
+ # Shape: [length, 1, 1] - represents sequence indices with the optional shift applied
+ pos = shift + torch.arange(length, device=device).view(-1, 1, 1)
+ half_dim = dim // 2
+ # Shape: [1, 1, half_dim] - index increments for the frequency calculations
+ adim = torch.arange(dim // 2, device=device).view(1, 1, -1)
+
+ # Compute phase: pos / (max_period ** frequencies)
+ phase = pos / (
+ max_period ** ((
+ adim.to(torch.float32) / torch.tensor(half_dim - 1, dtype=torch.float32, device=device)
+ ) if str(device).startswith("ocl") else ( # Edge-case handling for OpenCL (ocl) targets requiring explicit casting to fp32
+ adim / (half_dim - 1)
+ ))
+ )
+
+ # Concatenate cosines and sines along the hidden dimension axis
+ return torch.cat([phase.cos(), phase.sin()], dim=-1)
+
+def create_2d_sin_embedding(
+ d_model,
+ height,
+ width,
+ device="cpu",
+ max_period=10000
+):
+ """
+ Generates 2D sinusoidal positional embeddings for spatial grids or spectrograms.
+
+ Args:
+ d_model (int): Full feature dimension (must be divisible by 4).
+ height (int): Height dimension size (e.g., frequency bins).
+ width (int): Width dimension size (e.g., time bins).
+ device (str, optional): Target execution device. Defaults to "cpu".
+ max_period (int, optional): Controls the maximum wave period. Defaults to 10000.
+
+ Raises:
+ ValueError: If d_model is not divisible by 4.
+
+ Returns:
+ torch.Tensor: 2D positional embedding tensor of shape [1, d_model, height, width].
+ """
+
+ if d_model % 4 != 0: raise ValueError("d_model must be divisible by 4 for 2D sinusoidal embeddings.")
+
+ pe = torch.zeros(d_model, height, width)
+ d_model = int(d_model / 2) # Divide space equally between height and width embeddings
+
+ # Exponentially spaced division term for frequencies
+ div_term = (torch.arange(0.0, d_model, 2) * -(math.log(max_period) / d_model)).exp()
+ # Position mappings for width and height axes
+ pos_w = torch.arange(0.0, width).unsqueeze(1)
+ pos_h = torch.arange(0.0, height).unsqueeze(1)
+
+ # Assign sine/cosine frequencies independently across spatial grids
+ pe[0:d_model:2, :, :] = (pos_w * div_term).sin().transpose(0, 1).unsqueeze(1).repeat(1, height, 1)
+ pe[1:d_model:2, :, :] = (pos_w * div_term).cos().transpose(0, 1).unsqueeze(1).repeat(1, height, 1)
+ pe[d_model::2, :, :] = (pos_h * div_term).sin().transpose(0, 1).unsqueeze(2).repeat(1, 1, width)
+ pe[d_model + 1 :: 2, :, :] = (pos_h * div_term).cos().transpose(0, 1).unsqueeze(2).repeat(1, 1, width)
+
+ # Add batch dimension and move to target hardware
+ return pe[None, :].to(device)
+
+def create_sin_embedding_cape(
+ length,
+ dim,
+ batch_size,
+ mean_normalize,
+ augment,
+ max_global_shift = 0.0,
+ max_local_shift = 0.0,
+ max_scale = 1.0,
+ device = "cpu",
+ max_period = 10000.0
+):
+ """
+ Generates Continuous Augmented Positional Embeddings (CAPE).
+ Applies global/local shifts and scaling scaling rules for domain augmentation.
+
+ Args:
+ length (int): Sequence length.
+ dim (int): Hidden embedding dimension (must be even).
+ batch_size (int): Current batch size dimension.
+ mean_normalize (bool): If True, centers positions around zero mean.
+ augment (bool): Toggles active random augmentation shifts and scaling.
+ max_global_shift (float, optional): Maximum uniform global position shift. Defaults to 0.0.
+ max_local_shift (float, optional): Maximum point-wise structural variance shift. Defaults to 0.0.
+ max_scale (float, optional): Scaling limit boundaries. Defaults to 1.0.
+ device (str, optional): Target device context. Defaults to "cpu".
+ max_period (float, optional): Underlying sinusoidal period. Defaults to 10000.0.
+
+ Returns:
+ torch.Tensor: Generated CAPE positional tensor of shape [length, batch_size, dim].
+ """
+
+ assert dim % 2 == 0
+ # Base sequence coordinates
+ pos = 1.0 * torch.arange(length).view(-1, 1, 1)
+ pos = pos.repeat(1, batch_size, 1)
+
+ if mean_normalize: pos -= torch.nanmean(pos, dim=0, keepdim=True)
+ # Apply data augmentation policies if flagged and training
+ if augment:
+ # Shift entire sequence systematically across the batch uniform domain
+ delta = np.random.uniform(
+ -max_global_shift,
+ +max_global_shift,
+ size=[1, batch_size, 1]
+ )
+ # Point-wise noise injections per sequence timestep
+ delta_local = np.random.uniform(
+ -max_local_shift,
+ +max_local_shift,
+ size=[length, batch_size, 1]
+ )
+ # Frequency scale factors tracking geometric deviations
+ log_lambdas = np.random.uniform(
+ -np.log(max_scale),
+ +np.log(max_scale),
+ size=[1, batch_size, 1]
+ )
+
+ pos = (pos + delta + delta_local) * np.exp(log_lambdas)
+
+ pos = pos.to(device)
+ half_dim = dim // 2
+
+ adim = torch.arange(dim // 2, device=device).view(1, 1, -1)
+ phase = pos / (
+ max_period ** ((
+ adim.to(torch.float32) / torch.tensor(half_dim - 1, dtype=torch.float32, device=device)
+ ) if str(device).startswith("ocl") else ( # Process phase steps tracking architectural targets
+ adim / (half_dim - 1)
+ ))
+ )
+
+ return torch.cat([phase.cos(), phase.sin()], dim=-1).float()
+
+class MyTransformerEncoderLayer(nn.TransformerEncoderLayer):
+ """Custom Transformer Encoder block supporting Group Normalization and LayerScale."""
+
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation=F.relu,
+ group_norm=0,
+ norm_first=False,
+ norm_out=False,
+ layer_norm_eps=1e-5,
+ layer_scale=False,
+ init_values=1e-4,
+ device=None,
+ dtype=None,
+ sparse=False,
+ mask_type="diag",
+ mask_random_seed=42,
+ sparse_attn_window=500,
+ global_window=50,
+ auto_sparsity=False,
+ sparsity=0.95,
+ batch_first=False
+ ):
+ factory_kwargs = {
+ "device": device,
+ "dtype": dtype
+ }
+
+ super().__init__(
+ d_model=d_model,
+ nhead=nhead,
+ dim_feedforward=dim_feedforward,
+ dropout=dropout,
+ activation=activation,
+ layer_norm_eps=layer_norm_eps,
+ batch_first=batch_first,
+ norm_first=norm_first,
+ device=device,
+ dtype=dtype
+ )
+
+ self.auto_sparsity = auto_sparsity
+ # Override standard LayerNorm modules with GroupNorm wrappers if requested
+ if group_norm:
+ self.norm1 = Fp32GroupNormTranspose(
+ int(group_norm),
+ d_model,
+ eps=layer_norm_eps,
+ **factory_kwargs
+ )
+
+ self.norm2 = Fp32GroupNormTranspose(
+ int(group_norm),
+ d_model,
+ eps=layer_norm_eps,
+ **factory_kwargs
+ )
+
+ # Optional output normalization layout for Pre-LN normalization paths
+ self.norm_out = None
+ if self.norm_first & norm_out:
+ self.norm_out = Fp32GroupNormTranspose(
+ num_groups=int(norm_out),
+ num_channels=d_model
+ )
+
+ # Initialize LayerScale coefficients to stabilize deep network residual streams
+ self.gamma_1 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()
+ self.gamma_2 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()
+
+ def forward(self, src, src_mask=None, src_key_padding_mask=None):
+ """Forward pass through the custom encoder block.
+
+ Args:
+ src (torch.Tensor): Input sequence tensor.
+ src_mask (Optional[torch.Tensor]): Mask for attention coefficients.
+ src_key_padding_mask (Optional[torch.Tensor]): Key padding mask.
+ """
+
+ # Dynamic precision conversion based on global configurations
+ x = src.half() if config.is_half else src
+
+ if self.norm_first:
+ # Pre-LN forward path configuration
+ x = x + self.gamma_1(
+ self._sa_block(self.norm1(x), src_mask, src_key_padding_mask)
+ )
+
+ x = x + self.gamma_2(
+ self._ff_block(self.norm2(x))
+ )
+
+ if self.norm_out: x = self.norm_out(x)
+ else:
+ # Post-LN forward path configuration
+ x = self.norm1(
+ x + self.gamma_1(
+ self._sa_block(x, src_mask, src_key_padding_mask)
+ )
+ )
+
+ x = self.norm2(
+ x + self.gamma_2(
+ self._ff_block(x)
+ )
+ )
+
+ return x
+
+class CrossTransformerEncoder(nn.Module):
+ """Bidirectional Transformer pipeline routing cross-attention fields."""
+
+ def __init__(
+ self,
+ dim,
+ emb = "sin",
+ hidden_scale = 4.0,
+ num_heads = 8,
+ num_layers = 6,
+ cross_first = False,
+ dropout = 0.0,
+ max_positions = 1000,
+ norm_in = True,
+ norm_in_group = False,
+ group_norm = False,
+ norm_first = False,
+ norm_out = False,
+ max_period = 10000.0,
+ weight_decay = 0.0,
+ lr = None,
+ layer_scale = False,
+ gelu = True,
+ sin_random_shift = 0,
+ weight_pos_embed = 1.0,
+ cape_mean_normalize = True,
+ cape_augment = True,
+ cape_glob_loc_scale = [5000.0, 1.0, 1.4],
+ sparse_self_attn = False,
+ sparse_cross_attn = False,
+ mask_type = "diag",
+ mask_random_seed = 42,
+ sparse_attn_window = 500,
+ global_window = 50,
+ auto_sparsity = False,
+ sparsity = 0.95
+ ):
+ super().__init__()
+ assert dim % num_heads == 0
+ hidden_dim = int(dim * hidden_scale)
+ self.num_layers = num_layers
+ self.classic_parity = int(cross_first) # Toggles alternating order of layer processing paths
+ self.emb = emb
+ self.max_period = max_period
+ self.weight_decay = weight_decay
+ self.weight_pos_embed = weight_pos_embed
+ self.sin_random_shift = sin_random_shift
+
+ if emb == "cape":
+ self.cape_mean_normalize = cape_mean_normalize
+ self.cape_augment = cape_augment
+ self.cape_glob_loc_scale = cape_glob_loc_scale
+
+ if emb == "scaled":
+ self.position_embeddings = ScaledEmbedding(
+ max_positions,
+ dim,
+ scale=0.2
+ )
+
+ self.lr = lr
+ activation = F.gelu if gelu else F.relu
+
+ # Configure input initialization layer normalization blocks
+ if norm_in:
+ self.norm_in = Fp32LayerNorm(dim)
+ self.norm_in_t = Fp32LayerNorm(dim)
+ elif norm_in_group:
+ self.norm_in = Fp32GroupNormTranspose(int(norm_in_group), dim)
+ self.norm_in_t = Fp32GroupNormTranspose(int(norm_in_group), dim)
+ else:
+ self.norm_in = nn.Identity()
+ self.norm_in_t = nn.Identity()
+
+ self.layers = nn.ModuleList()
+ self.layers_t = nn.ModuleList()
+
+ kwargs_common = {
+ "d_model": dim,
+ "nhead": num_heads,
+ "dim_feedforward": hidden_dim,
+ "dropout": dropout,
+ "activation": activation,
+ "group_norm": group_norm,
+ "norm_first": norm_first,
+ "norm_out": norm_out,
+ "layer_scale": layer_scale,
+ "mask_type": mask_type,
+ "mask_random_seed": mask_random_seed,
+ "sparse_attn_window": sparse_attn_window,
+ "global_window": global_window,
+ "sparsity": sparsity,
+ "auto_sparsity": auto_sparsity,
+ "batch_first": True,
+ }
+
+ # Differentiate configurations based on sparsity choices
+ kwargs_classic_encoder = dict(kwargs_common)
+ kwargs_classic_encoder.update({"sparse": sparse_self_attn})
+ kwargs_cross_encoder = dict(kwargs_common)
+ kwargs_cross_encoder.update({"sparse": sparse_cross_attn})
+
+ # Alternately assemble Self-Attention layers and Cross-Attention blocks
+ for idx in range(num_layers):
+ if idx % 2 == self.classic_parity:
+ self.layers.append(
+ MyTransformerEncoderLayer(
+ **kwargs_classic_encoder
+ )
+ )
+
+ self.layers_t.append(
+ MyTransformerEncoderLayer(
+ **kwargs_classic_encoder
+ )
+ )
+ else:
+ self.layers.append(
+ CrossTransformerEncoderLayer(
+ **kwargs_cross_encoder
+ )
+ )
+
+ self.layers_t.append(
+ CrossTransformerEncoderLayer(
+ **kwargs_cross_encoder
+ )
+ )
+
+ def forward(self, x, xt):
+ """
+ Executes forward cross-transformer processing for multidimensional inputs.
+
+ Args:
+ x (torch.Tensor): 2D spatial/spectrogram input.
+ xt (torch.Tensor): 1D sequential/audio frame input.
+ """
+
+ B, C, Fr, T1 = x.shape
+
+ pos_emb_2d = create_2d_sin_embedding(C, Fr, T1, x.device, self.max_period)
+ pos_emb_2d = rearrange(pos_emb_2d, "b c fr t1 -> b (t1 fr) c")
+
+ x = rearrange(x, "b c fr t1 -> b (t1 fr) c")
+ x = self.norm_in(x)
+ x = x + self.weight_pos_embed * pos_emb_2d
+
+ B, C, T2 = xt.shape
+ xt = rearrange(xt, "b c t2 -> b t2 c")
+
+ pos_emb = self._get_pos_embedding(T2, B, C, x.device)
+ pos_emb = rearrange(pos_emb, "t2 b c -> b t2 c")
+
+ xt = self.norm_in_t(xt)
+ xt = xt + self.weight_pos_embed * pos_emb
+ # Sequentially Forward Through the Stacked Pipeline Layers
+ for idx in range(self.num_layers):
+ if idx % 2 == self.classic_parity:
+ # Independent Self-Attention Processing Blocks
+ x = self.layers[idx](x)
+ xt = self.layers_t[idx](xt)
+ else:
+ # Intertwined Cross-Attention Routing Blocks
+ old_x = x
+ x = self.layers[idx](x, xt)
+ xt = self.layers_t[idx](xt, old_x)
+
+ x = rearrange(x, "b (t1 fr) c -> b c fr t1", t1=T1)
+ xt = rearrange(xt, "b t2 c -> b c t2")
+ return x, xt
+
+ def _get_pos_embedding(self, T, B, C, device):
+ """Dispatches and extracts proper structural positional embeddings based on config flags."""
+
+ if self.emb == "sin":
+ shift = random.randrange(self.sin_random_shift + 1)
+
+ pos_emb = create_sin_embedding(
+ T,
+ C,
+ shift=shift,
+ device=device,
+ max_period=self.max_period
+ )
+ elif self.emb == "cape":
+ if self.training:
+ pos_emb = create_sin_embedding_cape(
+ T,
+ C,
+ B,
+ device=device,
+ max_period=self.max_period,
+ mean_normalize=self.cape_mean_normalize,
+ augment=self.cape_augment,
+ max_global_shift=self.cape_glob_loc_scale[0],
+ max_local_shift=self.cape_glob_loc_scale[1],
+ max_scale=self.cape_glob_loc_scale[2]
+ )
+ else:
+ pos_emb = create_sin_embedding_cape(
+ T,
+ C,
+ B,
+ device=device,
+ max_period=self.max_period,
+ mean_normalize=self.cape_mean_normalize,
+ augment=False
+ )
+ elif self.emb == "scaled":
+ pos = torch.arange(T, device=device)
+ pos_emb = self.position_embeddings(pos)[:, None]
+
+ return pos_emb
+
+ def make_optim_group(self):
+ """Constructs an optimization parameter dictionary detailing explicit weight decay metrics."""
+
+ group = {"params": list(self.parameters()), "weight_decay": self.weight_decay}
+ if self.lr is not None: group["lr"] = self.lr
+ return group
+
+class CrossTransformerEncoderLayer(nn.Module):
+ """Dedicated layer for cross-attention mechanisms mapping query and key/value blocks."""
+
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dim_feedforward = 2048,
+ dropout = 0.1,
+ activation=F.relu,
+ layer_norm_eps = 1e-5,
+ layer_scale = False,
+ init_values = 1e-4,
+ norm_first = False,
+ group_norm = False,
+ norm_out = False,
+ sparse=False,
+ mask_type="diag",
+ mask_random_seed=42,
+ sparse_attn_window=500,
+ global_window=50,
+ sparsity=0.95,
+ auto_sparsity=None,
+ device=None,
+ dtype=None,
+ batch_first=False
+ ):
+ super().__init__()
+ self.auto_sparsity = auto_sparsity
+ factory_kwargs = {
+ "device": device,
+ "dtype": dtype
+ }
+
+ self.cross_attn = nn.MultiheadAttention(
+ d_model,
+ nhead,
+ dropout=dropout,
+ batch_first=batch_first
+ )
+
+ self.linear1 = nn.Linear(
+ d_model,
+ dim_feedforward,
+ **factory_kwargs
+ )
+
+ self.norm_first = norm_first
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(
+ dim_feedforward,
+ d_model,
+ **factory_kwargs
+ )
+
+ # Configure requested layer-normalization schemes
+ if group_norm:
+ self.norm1 = Fp32GroupNormTranspose(
+ int(group_norm),
+ d_model,
+ eps=layer_norm_eps,
+ **factory_kwargs
+ )
+
+ self.norm2 = Fp32GroupNormTranspose(
+ int(group_norm),
+ d_model,
+ eps=layer_norm_eps,
+ **factory_kwargs
+ )
+
+ self.norm3 = Fp32GroupNormTranspose(
+ int(group_norm),
+ d_model,
+ eps=layer_norm_eps,
+ **factory_kwargs
+ )
+ else:
+ self.norm1 = Fp32LayerNorm(
+ d_model,
+ eps=layer_norm_eps,
+ **factory_kwargs
+ )
+
+ self.norm2 = Fp32LayerNorm(
+ d_model,
+ eps=layer_norm_eps,
+ **factory_kwargs
+ )
+
+ self.norm3 = Fp32LayerNorm(
+ d_model,
+ eps=layer_norm_eps,
+ **factory_kwargs
+ )
+
+ self.norm_out = None
+ if self.norm_first & norm_out:
+ self.norm_out = Fp32GroupNormTranspose(
+ num_groups=int(norm_out),
+ num_channels=d_model
+ )
+
+ # Optional stabilization multipliers
+ self.gamma_1 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()
+ self.gamma_2 = LayerScale(d_model, init_values, True) if layer_scale else nn.Identity()
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
+
+ # Activation checking routing logic
+ if isinstance(activation, str): self.activation = self._get_activation_fn(activation)
+ else: self.activation = activation
+
+ def forward(self, q, k, mask=None):
+ """
+ Executes cross-attention querying between independent contexts.
+
+ Args:
+ q (torch.Tensor): Query source matrix.
+ k (torch.Tensor): Key and value paired feature repository.
+ mask (Optional[torch.Tensor], optional): Attention block mask matrices. Defaults to None.
+ """
+
+ if self.norm_first:
+ x = q + self.gamma_1(self._ca_block(self.norm1(q), self.norm2(k), mask))
+ x = x + self.gamma_2(self._ff_block(self.norm3(x)))
+
+ if self.norm_out: x = self.norm_out(x)
+ else:
+ x = self.norm1(q + self.gamma_1(self._ca_block(q, k, mask)))
+ x = self.norm2(x + self.gamma_2(self._ff_block(x)))
+
+ return x
+
+ def _ca_block(self, q, k, attn_mask=None):
+ """Executes inner Multihead Cross-Attention mechanism."""
+
+ x = self.cross_attn(q, k, k, attn_mask=attn_mask, need_weights=False)[0]
+ return self.dropout1(x)
+
+ def _ff_block(self, x):
+ """Executes inner Feed-Forward Layer processing sequence."""
+
+ x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+ return self.dropout2(x)
+
+ def _get_activation_fn(self, activation):
+ """Resolves structural activation reference functions from local literal strings."""
+
+ if activation == "relu": return F.relu
+ elif activation == "gelu": return F.gelu
+ raise RuntimeError(translations["activation"].format(activation=activation))
+
+class HTDemucs(nn.Module):
+ """
+ Hybrid Transformer Demucs (HTDemucs) audio source separation network.
+
+ This model processes audio simultaneously across two branches:
+ 1. A spectral branch that operates on STFT magnitude features.
+ 2. A temporal branch that operates on raw 1D time-domain signals.
+
+ The branches are intertwined at their bottleneck via a hybrid Cross-Transformer network.
+ """
+
+ @capture_init
+ def __init__(
+ self,
+ sources,
+ audio_channels=2,
+ channels=48,
+ channels_time=None,
+ growth=2,
+ nfft=4096,
+ wiener_iters=0,
+ end_iters=0,
+ wiener_residual=False,
+ cac=True,
+ depth=4,
+ rewrite=True,
+ multi_freqs=None,
+ multi_freqs_depth=3,
+ freq_emb=0.2,
+ emb_scale=10,
+ emb_smooth=True,
+ kernel_size=8,
+ time_stride=2,
+ stride=4,
+ context=1,
+ context_enc=0,
+ norm_starts=4,
+ norm_groups=4,
+ dconv_mode=1,
+ dconv_depth=2,
+ dconv_comp=8,
+ dconv_init=1e-3,
+ bottom_channels=0,
+ t_layers=5,
+ t_emb="sin",
+ t_hidden_scale=4.0,
+ t_heads=8,
+ t_dropout=0.0,
+ t_max_positions=10000,
+ t_norm_in=True,
+ t_norm_in_group=False,
+ t_group_norm=False,
+ t_norm_first=True,
+ t_norm_out=True,
+ t_max_period=10000.0,
+ t_weight_decay=0.0,
+ t_lr=None,
+ t_layer_scale=True,
+ t_gelu=True,
+ t_weight_pos_embed=1.0,
+ t_sin_random_shift=0,
+ t_cape_mean_normalize=True,
+ t_cape_augment=True,
+ t_cape_glob_loc_scale=[5000.0, 1.0, 1.4],
+ t_sparse_self_attn=False,
+ t_sparse_cross_attn=False,
+ t_mask_type="diag",
+ t_mask_random_seed=42,
+ t_sparse_attn_window=500,
+ t_global_window=100,
+ t_sparsity=0.95,
+ t_auto_sparsity=False,
+ t_cross_first=False,
+ rescale=0.1,
+ samplerate=44100,
+ segment=4 * 10,
+ use_train_segment=True
+ ):
+ super().__init__()
+ self.cac = cac
+ self.wiener_residual = wiener_residual
+ self.audio_channels = audio_channels
+ self.sources = sources
+ self.kernel_size = kernel_size
+ self.context = context
+ self.stride = stride
+ self.depth = depth
+ self.bottom_channels = bottom_channels
+ self.channels = channels
+ self.samplerate = samplerate
+ self.segment = segment
+ self.use_train_segment = use_train_segment
+ self.nfft = nfft
+ self.hop_length = nfft // 4
+ self.wiener_iters = wiener_iters
+ self.end_iters = end_iters
+ self.freq_emb = None
+ assert wiener_iters == end_iters
+ # Initialize parallel layer tracking blocks
+ self.encoder = nn.ModuleList() # Spectrogram Encoder
+ self.decoder = nn.ModuleList() # Spectrogram Decoder
+ self.tencoder = nn.ModuleList() # Time-domain Encoder
+ self.tdecoder = nn.ModuleList() # Time-domain Decoder
+ chin = audio_channels
+ chin_z = chin
+ if self.cac: chin_z *= 2 # Real and imaginary components concatenated as extra channels
+ chout = channels_time or channels
+ chout_z = channels
+ freqs = nfft // 2
+
+ # Stack Encoder and Decoder Layers
+ for index in range(depth):
+ norm = index >= norm_starts
+ freq = freqs > 1
+ stri = stride
+ ker = kernel_size
+ # If downsampling hits the minimum frequency limit, fallback to 1D time parameters
+ if not freq:
+ assert freqs == 1
+ ker = time_stride * 2
+ stri = time_stride
+
+ pad = True
+ last_freq = False
+ # If remaining frequency bins are smaller than kernel size, collapse them without padding
+ if freq and freqs <= kernel_size:
+ ker = freqs
+ pad = False
+ last_freq = True
+
+ kw = {
+ "kernel_size": ker,
+ "stride": stri,
+ "freq": freq,
+ "pad": pad,
+ "norm": norm,
+ "rewrite": rewrite,
+ "norm_groups": norm_groups,
+ "dconv_kw": {
+ "depth": dconv_depth,
+ "compress": dconv_comp,
+ "init": dconv_init,
+ "gelu": True
+ },
+ }
+
+ # Copy parameters to build the 1D time-domain version
+ kwt = dict(kw)
+ kwt["freq"] = 0
+ kwt["kernel_size"] = kernel_size
+ kwt["stride"] = stride
+ kwt["pad"] = True
+ kw_dec = dict(kw)
+ multi = False
+
+ # Enable multi-frequency wrappers at initial outer layers
+ if multi_freqs and index < multi_freqs_depth:
+ multi = True
+ kw_dec["context_freq"] = False
+
+ if last_freq:
+ chout_z = max(chout, chout_z)
+ chout = chout_z
+
+ # Instantiate and store encoder blocks
+ enc = HEncLayer(
+ chin_z,
+ chout_z,
+ dconv=dconv_mode & 1,
+ context=context_enc,
+ **kw
+ )
+
+ if freq:
+ tenc = HEncLayer(
+ chin,
+ chout,
+ dconv=dconv_mode & 1,
+ context=context_enc,
+ empty=last_freq,
+ **kwt
+ )
+
+ self.tencoder.append(tenc)
+
+ if multi:
+ enc = MultiWrap(
+ enc,
+ multi_freqs
+ )
+
+ self.encoder.append(enc)
+ # Switch internal mapping rules after processing the first target layout level
+ if index == 0:
+ chin = self.audio_channels * len(self.sources)
+ chin_z = chin
+ if self.cac: chin_z *= 2
+
+ # Instantiate and append decoder blocks
+ dec = HDecLayer(
+ chout_z,
+ chin_z,
+ dconv=dconv_mode & 2,
+ last=index == 0,
+ context=context,
+ **kw_dec
+ )
+
+ if multi:
+ dec = MultiWrap(
+ dec,
+ multi_freqs
+ )
+
+ if freq:
+ tdec = HDecLayer(
+ chout,
+ chin,
+ dconv=dconv_mode & 2,
+ empty=last_freq,
+ last=index == 0,
+ context=context,
+ **kwt
+ )
+
+ self.tdecoder.insert(0, tdec) # Inverse order to follow decoding upsampling paths
+
+ self.decoder.insert(0, dec)
+ # Progress dimension scales geometrically
+ chin = chout
+ chin_z = chout_z
+ chout = int(growth * chout)
+ chout_z = int(growth * chout_z)
+
+ if freq:
+ if freqs <= kernel_size: freqs = 1
+ else: freqs //= stride
+
+ # Instantiate absolute frequency positional embeddings if requested
+ if index == 0 and freq_emb:
+ self.freq_emb = ScaledEmbedding(
+ freqs,
+ chin_z,
+ smooth=emb_smooth,
+ scale=emb_scale
+ )
+
+ self.freq_emb_scale = freq_emb
+
+ # Rescale initialization weights to stabilize training streams
+ if rescale: rescale_module(self, reference=rescale)
+ transformer_channels = channels * growth ** (depth - 1)
+
+ # Instantiate Linear/Conv Bottleneck Samplers
+ if bottom_channels:
+ self.channel_upsampler = nn.Conv1d(
+ transformer_channels,
+ bottom_channels,
+ 1
+ )
+
+ self.channel_downsampler = nn.Conv1d(
+ bottom_channels,
+ transformer_channels,
+ 1
+ )
+
+ self.channel_upsampler_t = nn.Conv1d(
+ transformer_channels,
+ bottom_channels,
+ 1
+ )
+
+ self.channel_downsampler_t = nn.Conv1d(
+ bottom_channels,
+ transformer_channels,
+ 1
+ )
+
+ transformer_channels = bottom_channels
+
+ # Cross-Transformer Initialization Block
+ if t_layers > 0:
+ self.crosstransformer = CrossTransformerEncoder(
+ dim=transformer_channels,
+ emb=t_emb,
+ hidden_scale=t_hidden_scale,
+ num_heads=t_heads,
+ num_layers=t_layers,
+ cross_first=t_cross_first,
+ dropout=t_dropout,
+ max_positions=t_max_positions,
+ norm_in=t_norm_in,
+ norm_in_group=t_norm_in_group,
+ group_norm=t_group_norm,
+ norm_first=t_norm_first,
+ norm_out=t_norm_out,
+ max_period=t_max_period,
+ weight_decay=t_weight_decay,
+ lr=t_lr,
+ layer_scale=t_layer_scale,
+ gelu=t_gelu,
+ sin_random_shift=t_sin_random_shift,
+ weight_pos_embed=t_weight_pos_embed,
+ cape_mean_normalize=t_cape_mean_normalize,
+ cape_augment=t_cape_augment,
+ cape_glob_loc_scale=t_cape_glob_loc_scale,
+ sparse_self_attn=t_sparse_self_attn,
+ sparse_cross_attn=t_sparse_cross_attn,
+ mask_type=t_mask_type,
+ mask_random_seed=t_mask_random_seed,
+ sparse_attn_window=t_sparse_attn_window,
+ global_window=t_global_window,
+ sparsity=t_sparsity,
+ auto_sparsity=t_auto_sparsity
+ )
+ else: self.crosstransformer = None
+
+ self.dtype = torch.float16 if config.is_half else torch.float32
+
+ def _spec(self, x):
+ """Computes the Short-Time Fourier Transform (STFT) with custom padding configurations."""
+
+ assert self.hop_length == self.nfft // 4
+ le = int(math.ceil(x.shape[-1] / self.hop_length))
+ pad = self.hop_length // 2 * 3
+
+ # Apply symmetric reflection padding across 1D boundaries
+ x = pad1d(x, (pad, pad + le * self.hop_length - x.shape[-1]), mode="reflect")
+ z = spectro(x, self.nfft, self.hop_length)[..., :-1, :]
+ assert z.shape[-1] == le + 4, (z.shape, x.shape, le)
+
+ return z[..., 2 : 2 + le]
+
+ def _ispec(self, z, length=None, scale=0):
+ """Computes the Inverse Short-Time Fourier Transform (iSTFT) to reconstruct audio waveforms."""
+
+ z = F.pad(F.pad(z, (0, 0, 0, 1)), (2, 2))
+ hl = self.hop_length // (4 ** scale)
+ pad = hl // 2 * 3
+
+ return ispectro(z, hl, length=hl * int(math.ceil(length / hl)) + 2 * pad)[..., pad : pad + length]
+
+ def _magnitude(self, z):
+ """Converts raw STFT tensors to either standard magnitudes or concatenated real/imag arrays."""
+
+ if self.cac:
+ B, C, Fr, T = z.shape
+ # Map complex data into stacked [B, C * 2, Fr, T] channels
+ m = torch.view_as_real(z).permute(0, 1, 4, 2, 3).reshape(B, C * 2, Fr, T)
+ else: m = z.abs()
+
+ return m
+
+ def _mask(self, z, m):
+ """Applies decoded masks back to STFT matrices using standard multiplication or Wiener filtering."""
+
+ niters = self.wiener_iters
+ if self.cac:
+ B, S, _, Fr, T = m.shape
+ # Reconstruct structural complex layouts out of stacked mask fields
+ return torch.view_as_complex(m.view(B, S, -1, 2, Fr, T).permute(0, 1, 2, 4, 5, 3).contiguous())
+
+ if self.training: niters = self.end_iters
+
+ if niters < 0:
+ z = z[:, None]
+ return z / (1e-8 + z.abs()) * m
+ else: return self._wiener(m, z, niters)
+
+ def _wiener(self, mag_out, mix_stft, niters):
+ """Executes windowed multi-channel Wiener filtering over the target mixture."""
+
+ init = mix_stft.dtype
+ B, S, C, Fq, T = mag_out.shape
+ mag_out = mag_out.permute(0, 4, 3, 2, 1)
+ mix_stft = torch.view_as_real(mix_stft.permute(0, 3, 2, 1))
+
+ outs = []
+
+ for sample in range(B):
+ pos = 0
+ out = []
+ # Memory safety mapping chunked frames through the Wiener algorithm
+ for pos in range(0, T, 300):
+ frame = slice(pos, pos + 300)
+ out.append(wiener(mag_out[sample, frame], mix_stft[sample, frame], niters, residual=self.wiener_residual).transpose(-1, -2))
+
+ outs.append(torch.cat(out, dim=0))
+
+ out = torch.view_as_complex(torch.stack(outs, 0)).permute(0, 4, 3, 2, 1).contiguous()
+ if self.wiener_residual: out = out[:, :-1]
+
+ assert list(out.shape) == [B, S, C, Fq, T]
+ return out.to(init)
+
+ def valid_length(self, length):
+ """Validates input sequence lengths against the configured training chunk boundaries."""
+
+ if not self.use_train_segment: return length
+ training_length = int(self.segment * self.samplerate)
+ if training_length < length: raise ValueError(translations["length_or_training_length"].format(length=length, training_length=training_length))
+
+ return training_length
+
+ def forward(self, mix):
+ """
+ Executes forward multi-domain feature separation.
+
+ Args:
+ mix (torch.Tensor): Audio mixture input tensor.
+
+ Returns:
+ torch.Tensor: Separated sources tensor.
+ """
+
+ length = mix.shape[-1]
+ length_pre_pad = None
+
+ # Align Audio Segments via Truncation or Padding
+ if self.use_train_segment:
+ if self.training: self.segment = Fraction(mix.shape[-1], self.samplerate)
+ else:
+ training_length = int(self.segment * self.samplerate)
+
+ if mix.shape[-1] < training_length:
+ length_pre_pad = mix.shape[-1]
+ mix = F.pad(mix, (0, training_length - length_pre_pad))
+
+ # Compute STFT Magnitudes
+ z = self._spec(mix)
+ mag = self._magnitude(z).to(mix.device)
+ x = mag
+
+ B, _, Fq, T = x.shape
+ # Normalize spectral inputs
+ mean = x.mean(dim=(1, 2, 3), keepdim=True)
+ std = x.std(dim=(1, 2, 3), keepdim=True)
+ x = (x - mean) / (1e-5 + std)
+ # Normalize raw temporal inputs
+ xt = mix
+ meant = xt.mean(dim=(1, 2), keepdim=True)
+ stdt = xt.std(dim=(1, 2), keepdim=True)
+ xt = (xt - meant) / (1e-5 + stdt)
+
+ saved, saved_t, lengths, lengths_t = [], [], [], []
+ # Forward Path Through the Multi-Branch Encoder Stack
+ for idx, encode in enumerate(self.encoder):
+ if config.is_half: encode = encode.float()
+
+ lengths.append(x.shape[-1])
+ inject = None
+ # Process parallel temporal components if the timeline layer exists
+ if idx < len(self.tencoder):
+ lengths_t.append(xt.shape[-1])
+ tenc = self.tencoder[idx]
+
+ if config.is_half: tenc = tenc.float()
+ xt = tenc(xt.float()).to(self.dtype)
+
+ if not tenc.empty: saved_t.append(xt)
+ else: inject = xt # Capture injection hook for the spectral encoder path
+
+ x = encode(x.float(), inject)
+ # Apply frequency absolute embeddings to the outermost layer context
+ if idx == 0 and self.freq_emb is not None:
+ frs = torch.arange(x.shape[-2], device=x.device)
+ emb = self.freq_emb(frs).t()[None, :, :, None].expand_as(x)
+ x = x + self.freq_emb_scale * emb
+
+ x = x.to(self.dtype)
+ saved.append(x)
+
+ # Bottleneck Cross-Transformer Alignment Block
+ if self.crosstransformer:
+ if self.bottom_channels:
+ _, _, f, _ = x.shape
+ x = rearrange(x, "b c f t-> b c (f t)")
+ x = self.channel_upsampler(x)
+ x = rearrange(x, "b c (f t)-> b c f t", f=f)
+ xt = self.channel_upsampler_t(xt)
+
+ x, xt = self.crosstransformer(x, xt)
+
+ if self.bottom_channels:
+ x = rearrange(x, "b c f t-> b c (f t)")
+ x = self.channel_downsampler(x)
+ x = rearrange(x, "b c (f t)-> b c f t", f=f)
+ xt = self.channel_downsampler_t(xt)
+
+ # Backward Path Through the Multi-Branch Decoder Stack
+ for idx, decode in enumerate(self.decoder):
+ x, pre = decode(x, saved.pop(-1), lengths.pop(-1))
+ offset = self.depth - len(self.tdecoder)
+
+ if idx >= offset:
+ tdec = self.tdecoder[idx - offset]
+ length_t = lengths_t.pop(-1)
+
+ if tdec.empty:
+ assert pre.shape[2] == 1, pre.shape
+ pre = pre[:, :, 0]
+
+ xt, _ = tdec(pre.to(self.dtype), None, length_t)
+ else:
+ xt, _ = tdec(xt.to(self.dtype), saved_t.pop(-1), length_t)
+
+ assert len(saved) == 0
+ assert len(lengths_t) == 0
+ assert len(saved_t) == 0
+
+ # Rescale, Inverse Transform, and Reconstruct Waveforms
+ S = len(self.sources)
+ x = x.view(B, S, -1, Fq, T)
+ x = x * std[:, None] + mean[:, None]
+ # Safe execution offload tracking unsupported custom GPU acceleration kernels
+ device_type = x.device.type
+ device_load = f"{device_type}:{x.device.index}" if not device_type == "mps" else device_type
+ x_is_other_gpu = not device_type in ["cuda", "xpu", "cpu"]
+ if x_is_other_gpu: x = x.cpu()
+ zout = self._mask(z, x.float())
+
+ if self.use_train_segment: x = self._ispec(zout, length) if self.training else self._ispec(zout, training_length)
+ else: x = self._ispec(zout, length)
+
+ if x_is_other_gpu: x = x.to(device_load)
+
+ if self.use_train_segment: xt = xt.view(B, S, -1, length) if self.training else xt.view(B, S, -1, training_length)
+ else: xt = xt.view(B, S, -1, length)
+
+ # Denormalize time outputs and sum residual features together
+ xt = xt * stdt[:, None] + meant[:, None]
+ x = xt + x
+
+ # Remove temporary segment expansion padding blocks
+ if length_pre_pad: x = x[..., :length_pre_pad]
+ return x
\ No newline at end of file
diff --git a/main/library/uvr5_lib/demucs_separator.py b/main/library/uvr5_lib/demucs_separator.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1c8d86610160d8befc3324c66c3712bc405d564
--- /dev/null
+++ b/main/library/uvr5_lib/demucs_separator.py
@@ -0,0 +1,187 @@
+import os
+import sys
+import torch
+import warnings
+import contextlib
+
+import numpy as np
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config
+from main.library.utils import clear_gpu_cache
+from main.library.uvr5_lib.demucs import apply
+from main.library.uvr5_lib import common_separator
+from main.library.uvr5_lib.demucs.hdemucs import HDemucs
+from main.library.uvr5_lib.demucs.htdemucs import HTDemucs
+
+if not config.debug_mode: warnings.filterwarnings("ignore")
+
+DEMUCS_4_SOURCE_MAPPER = {
+ common_separator.CommonSeparator.BASS_STEM: 0,
+ common_separator.CommonSeparator.DRUM_STEM: 1,
+ common_separator.CommonSeparator.OTHER_STEM: 2,
+ common_separator.CommonSeparator.VOCAL_STEM: 3
+}
+
+def load_model(modelname, model_path, segment_size = None):
+ """
+ Loads and initializes a Demucs neural network model (HDemucs or HTDemucs) from a file checkpoint.
+
+ Args:
+ modelname (str): Name/type of the model (e.g., starts with 'hdemucs' or 'htdemucs_ft').
+ model_path (str): File system path to the torch checkpoint (.pth / .ckpt).
+ segment_size (str or int, optional): Segment length parameter. Defaults to None ("Default").
+
+ Returns:
+ tuple: (model, hybrid)
+ - model (torch.nn.Module or torch.nn.ModuleList): The initialized torch model(s).
+ - hybrid (bool): True if the architecture uses a FT fine-tuned ensemble model array.
+ """
+
+ # Conditionally suppress torch load alerts if debug mode is disabled
+ with warnings.catch_warnings() if not config.debug_mode else contextlib.nullcontext():
+ if not config.debug_mode: warnings.simplefilter("ignore")
+ package = torch.load(model_path, map_location="cpu", weights_only=True)
+
+ # Select architecture implementation based on prefix match
+ demucs_fn = HDemucs if modelname.startswith("hdemucs") else HTDemucs
+ hybrid = False
+
+ # Check if the model is an ensemble fine-tuned model (contains 4 individual sub-models)
+ if modelname.startswith("htdemucs_ft"):
+ model_list = []
+ for i in range(4):
+ model = demucs_fn(**package[f"kwargs-{i}"])
+ model.load_state_dict(package[f"state-{i}"])
+ # Set custom segment size or fall back to default architecture value
+ model.segment = None if segment_size == "Default" or segment_size is None else int(segment_size)
+ model_list.append(model)
+
+ model = torch.nn.ModuleList(model_list)
+ hybrid = True
+ else:
+ # Standard standalone model checkpoint
+ model = demucs_fn(**package["kwargs"])
+ model.load_state_dict(package["state"])
+ model.segment = None if segment_size == "Default" or segment_size is None else int(segment_size)
+
+ return model, hybrid
+
+class DemucsSeparator(common_separator.CommonSeparator):
+ """
+ Audio separator specialized for Meta's Demucs (Hybrid / Hybrid Transformer) architectures.
+ Inherits global I/O and normalization utilities from CommonSeparator.
+ """
+
+ def __init__(self, common_config, arch_config):
+ """
+ Initializes Demucs architecture configurations, loads parameters, and moves weights to device.
+
+ Args:
+ common_config (dict): Core separation pipeline configurations.
+ arch_config (dict): Hyperparameters specific to Demucs inference (shifts, overlaps, etc.).
+ """
+
+ super().__init__(config=common_config)
+ # Demucs-specific inference settings
+ self.segment_size = arch_config.get("segment_size", "Default")
+ self.shifts = arch_config.get("shifts", 2)
+ self.overlap = arch_config.get("overlap", 0.25)
+ self.segments_enabled = arch_config.get("segments_enabled", True)
+ self.demucs_source_map = DEMUCS_4_SOURCE_MAPPER
+ # Force fallback to CPU if specified in global application configurations
+ if config.configs.get("demucs_cpu_mode", False): self.torch_device = torch.device("cpu")
+ # Default weights configuration used during prediction blending
+ self.weights = [[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]
+ # Instantiate model structure and populate weights
+ self.model, self.hybrid = load_model(self.model_name, self.model_path, self.segment_size)
+ self.model.to(self.torch_device).eval()
+ # Optimize memory usage with FP16 precision if enabled globally
+ if config.is_half: self.model.half()
+
+ def cleanup(self):
+ """
+ Deletes the model from application memory and forces an explicit cache flush on the GPU.
+ """
+
+ del self.model
+ self.model = None
+ clear_gpu_cache()
+
+ def separate(self, audio_file_path):
+ """
+ Executes source separation on an audio file, maps output layers, and saves individual stems.
+
+ Args:
+ audio_file_path (str): Input file path of the source mix.
+
+ Returns:
+ list: List of created output file paths.
+ """
+
+ source = self.demix_demucs(audio_file_path)
+ source_length = len(source)
+ output_files = []
+
+ # Adapt source mapping dictionary based on the number of stems returned by the model
+ if source_length == 2: self.demucs_source_map = {common_separator.CommonSeparator.INST_STEM: 0, common_separator.CommonSeparator.VOCAL_STEM: 1}
+ elif source_length == 6: self.demucs_source_map = {common_separator.CommonSeparator.BASS_STEM: 0, common_separator.CommonSeparator.DRUM_STEM: 1, common_separator.CommonSeparator.OTHER_STEM: 2, common_separator.CommonSeparator.VOCAL_STEM: 3, common_separator.CommonSeparator.GUITAR_STEM: 4, common_separator.CommonSeparator.PIANO_STEM: 5}
+ else: self.demucs_source_map = DEMUCS_4_SOURCE_MAPPER
+
+ # Export each mapped audio stem to disk
+ for stem_name, stem_value in self.demucs_source_map.items():
+ # Build clean out-path string: base_name_(StemName)_ModelName.ext
+ stem_path = os.path.join(f"{os.path.splitext(os.path.basename(audio_file_path))[0]}_({stem_name})_{self.model_name}.{self.output_format.lower()}")
+ # Transpose array back (Channels x Samples -> Samples x Channels) for saving
+ self.final_process(stem_path, source[stem_value].T, stem_name)
+ output_files.append(stem_path)
+
+ # Free GPU resource footprint
+ self.cleanup()
+ return output_files
+
+ def demix_demucs(self, audio_file_path):
+ """
+ Prepares raw audio tensors, standardizes signals, and infers isolated multi-stem sources.
+
+ Args:
+ audio_file_path (str): Target mix sound track location.
+
+ Returns:
+ np.ndarray: Matrix of separated audio sources.
+ """
+
+ processed = {}
+ # Load file and initialize as single-precision PyTorch tensor
+ mix = torch.tensor(self.prepare_mix(audio_file_path), dtype=torch.float32)
+ ref = mix.mean(0)
+
+ # Normalize variance and mean values across signal matrices
+ mix = (mix - ref.mean()) / ref.std()
+ mix_infer = mix
+
+ # Disable autograd engine calculation for inference performance gains
+ with torch.inference_mode():
+ sources = apply.apply_model(
+ model=self.model,
+ mix=mix_infer[None], # Add batch dimension
+ shifts=self.shifts,
+ split=self.segments_enabled,
+ overlap=self.overlap,
+ static_shifts=max(self.shifts, 1),
+ set_progress_bar=None,
+ device=self.torch_device,
+ progress=True,
+ hybrid=self.hybrid,
+ weights=self.weights
+ )[0] # Extract result from single batch element
+
+ # Reverse scaling normalization transformations and copy array to host RAM
+ sources = (sources * ref.std() + ref.mean()).cpu().numpy()
+ # Swap internal audio channel arrangements to match canonical targets if required
+ sources[[0, 1]] = sources[[1, 0]]
+ processed[mix] = sources[:, :, 0:None].copy()
+
+ # Concatenate and compile dictionary into unified stacked numpy array tracks
+ return np.concatenate([s[:, :, 0:None] for s in list(processed.values())], axis=-1)
\ No newline at end of file
diff --git a/main/library/uvr5_lib/mdx_separator.py b/main/library/uvr5_lib/mdx_separator.py
new file mode 100644
index 0000000000000000000000000000000000000000..00452014e2d64de1fbbf2e4a37e60bf442e25e88
--- /dev/null
+++ b/main/library/uvr5_lib/mdx_separator.py
@@ -0,0 +1,351 @@
+import os
+import sys
+import onnx
+import torch
+import platform
+import warnings
+import onnx2torch
+
+import numpy as np
+import onnxruntime as ort
+
+from tqdm import tqdm
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import config
+from main.library.uvr5_lib import spec_utils
+from main.library.uvr5_lib.common_separator import CommonSeparator
+
+if not config.debug_mode: warnings.filterwarnings("ignore")
+
+class MDXSeparator(CommonSeparator):
+ """
+ Audio separator specialized for MDX (Multi-band Demixing) models.
+ Supports native ONNX Runtime inference using optimized IO-bindings or
+ fallback dynamic graph execution using onnx2torch wrappers.
+ """
+
+ def __init__(self, common_config, arch_config):
+ """
+ Initializes the MDX model runtime environment, parsing windowing specifications,
+ Fourier transform targets, and establishing either an ONNX or PyTorch session.
+
+ Args:
+ common_config (dict): Core configuration values passed to the base separator.
+ arch_config (dict): Architecture-specific hyperparameters for MDX block processing.
+ """
+
+ super().__init__(config=common_config)
+ # Load hyperparameter constraints from the structural config dictionary
+ self.enable_denoise = arch_config.get("enable_denoise")
+ self.segment_size = arch_config.get("segment_size")
+ self.batch_size = arch_config.get("batch_size", 1)
+ self.hop_length = arch_config.get("hop_length")
+ self.overlap = arch_config.get("overlap")
+ # Load specific metadata parameters packed inside model definitions
+ self.config_yaml = self.model_data.get("config_yaml", None)
+ self.n_fft = self.model_data["mdx_n_fft_scale_set"]
+ self.dim_t = 2 ** self.model_data["mdx_dim_t_set"]
+ self.compensate = self.model_data["compensate"]
+ self.dim_f = self.model_data["mdx_dim_f_set"]
+ # Optimized Pipeline: Direct ONNX execution via shared hardware pointer memory map
+ if self.segment_size == self.dim_t:
+ ort_session_options = ort.SessionOptions()
+ ort_session_options.log_severity_level = 3 # Keep logging minimal (errors only)
+
+ # Instantiate the execution session mapping to user hardware targets
+ ort_inference_session = ort.InferenceSession(self.model_path, providers=self.onnx_execution_provider, sess_options=ort_session_options)
+ device = "cuda" if self.onnx_execution_provider[0][0].startswith("CUDA") else "cpu"
+
+ def run_io(spek):
+ """
+ Binds tensor data pointers directly to ONNX runtime buffers to eliminate CPU/GPU transfer overhead.
+ """
+
+ spek = spek.float().contiguous()
+ device_idx = spek.device.index or 0
+
+ # Instantiate and allocate bindings matching the active context device
+ io_binding = ort_inference_session.io_binding()
+ io_binding.bind_input(name="input", device_type=device, device_id=device_idx, element_type=np.float32, shape=tuple(spek.shape), buffer_ptr=spek.data_ptr())
+ io_binding.bind_output(name="output", device_type=device, device_id=device_idx)
+ # Execute engine evaluation directly on target hardware buffer
+ ort_inference_session.run_with_iobinding(io_binding)
+ return io_binding.get_outputs()[0].numpy()
+
+ # Dynamic assignment of the execution lambda depending on hardware compatibility
+ self.model_run = lambda spek: run_io(spek) if self.onnx_execution_provider[0][0].startswith(("CUDA", "CPU")) else ort_inference_session.run(None, {"input": spek.cpu().numpy()})[0]
+ else:
+ # Fallback Pipeline: Convert ONNX graphs to native PyTorch modules for flexible segment window slicing
+ self.model_run = onnx2torch.convert(onnx.load(self.model_path)) if platform.system() == 'Windows' else onnx2torch.convert(self.model_path)
+ self.model_run.to(self.torch_device).eval()
+
+ # Placeholders for STFT parameter mappings
+ self.trim = 0
+ self.n_bins = 0
+ self.gen_size = 0
+ self.chunk_size = 0
+
+ self.stft = None
+ self.primary_source = None
+ self.audio_file_path = None
+ self.audio_file_base = None
+ self.secondary_source = None
+
+ def separate(self, audio_file_path):
+ """
+ Executes structural source separation on an target audio file using MDX parameters.
+
+ Args:
+ audio_file_path (str): Target filesystem location of the audio file to split.
+
+ Returns:
+ list: Paths to the exported primary and secondary stem audio tracks.
+ """
+
+ self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0]
+ self.audio_file_path = audio_file_path
+ # Normalize whole track mix to prevent quantization noise artifacts
+ mix = spec_utils.normalize(wave=self.prepare_mix(self.audio_file_path), max_peak=self.normalization_threshold)
+ # Infer target sources and rescale values relative to original waveform amplitude limits
+ source = self.demix(mix) * np.abs(mix).max()
+ output_files = []
+
+ if not isinstance(self.primary_source, np.ndarray): self.primary_source = source.T
+ if not isinstance(self.secondary_source, np.ndarray):
+ if self.invert_using_spec: self.secondary_source = spec_utils.invert_stem(self.demix(mix, is_match_mix=True), self.primary_source * self.compensate) # Frequency-domain algorithmic inversion matching phase properties
+ else: self.secondary_source = (-self.primary_source * self.compensate) + mix.T # Standard time-domain phase subtraction method
+
+ # Compose output filenames incorporating track metadata definitions
+ self.secondary_stem_output_path = os.path.join(f"{self.audio_file_base}_({self.secondary_stem_name})_{self.model_name}.{self.output_format.lower()}")
+ self.primary_stem_output_path = os.path.join(f"{self.audio_file_base}_({self.primary_stem_name})_{self.model_name}.{self.output_format.lower()}")
+
+ # Run file-saving IO loops
+ self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)
+ self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)
+
+ output_files.append(self.secondary_stem_output_path)
+ output_files.append(self.primary_stem_output_path)
+
+ return output_files
+
+ def initialize_model_settings(self):
+ """
+ Calculates window sizes, processing chunk layouts, and initializes the internal STFT helper module.
+ """
+
+ self.n_bins = self.n_fft // 2 + 1
+ self.trim = self.n_fft // 2
+ self.chunk_size = self.hop_length * (self.segment_size - 1)
+ self.gen_size = self.chunk_size - 2 * self.trim
+ self.stft = STFT(self.n_fft, self.hop_length, self.dim_f, self.torch_device)
+
+ def demix(self, mix, is_match_mix=False):
+ """
+ Splits full waveforms using an overlapping chunk overlap loop method to run inference.
+
+ Args:
+ mix (np.ndarray): Target audio waveform track matrix.
+ is_match_mix (bool, optional): Override parameter to lock window sizes during phase matching steps.
+
+ Returns:
+ np.ndarray: Evaluated target audio waveform array.
+ """
+
+ self.initialize_model_settings()
+ tar_waves_list = []
+
+ # Configure overlapping window geometries
+ if is_match_mix:
+ chunk_size = self.hop_length * (self.segment_size - 1)
+ overlap = 0.02
+ else:
+ chunk_size = self.chunk_size
+ overlap = self.overlap
+
+ gen_size = chunk_size - 2 * self.trim
+ # Zero-pad outer boundaries of the audio array block to handle edge window fading smoothly
+ mixture = np.concatenate((np.zeros((2, self.trim), dtype=np.float32), mix, np.zeros((2, gen_size + self.trim - ((mix.shape[-1]) % gen_size)), dtype=np.float32)), 1)
+ step = int((1 - overlap) * chunk_size)
+
+ # Allocate matching memory matrices to compute overlapped window reconstructions
+ result = np.zeros((1, 2, mixture.shape[-1]), dtype=np.float32)
+ divider = np.zeros((1, 2, mixture.shape[-1]), dtype=np.float32)
+ total = 0
+
+ # Segment iteration loop over audio length slices
+ for i in tqdm(range(0, mixture.shape[-1], step), ncols=100, unit="f"):
+ total += 1
+ start = i
+ end = min(i + chunk_size, mixture.shape[-1])
+
+ chunk_size_actual = end - start
+ window = None
+
+ # Calculate a Hanning cross-fade window function to avoid audio clicking artifacts at block edges
+ if overlap != 0: window = np.tile(np.hanning(chunk_size_actual)[None, None, :], (1, 2, 1))
+ mix_part = mixture[:, start:end]
+ # Zero-pad incomplete trailing context frames
+ if end != i + chunk_size: mix_part = np.concatenate((mix_part, np.zeros((2, (i + chunk_size) - end), dtype=np.float32)), axis=-1)
+ mix_waves = torch.from_numpy(np.array([mix_part])).float().to(self.torch_device).split(self.batch_size)
+
+ with torch.no_grad():
+ batches_processed = 0
+
+ for mix_wave in mix_waves:
+ batches_processed += 1
+ tar_waves = self.run_model(mix_wave, is_match_mix=is_match_mix)
+
+ # Accumulate calculations scaled relative to localized window fading weight properties
+ if window is not None:
+ tar_waves[..., :chunk_size_actual] *= window
+ divider[..., start:end] += window
+ else: divider[..., start:end] += 1
+
+ result[..., start:end] += tar_waves[..., : end - start]
+
+ # Normalize cumulative multi-pass windows, strip bounding pad blocks, and crop to exact sample length
+ tar_waves_list.append(result / (divider + 1e-8))
+ source = np.concatenate(np.vstack(tar_waves_list)[:, :, self.trim : -self.trim], axis=-1)[:, : mix.shape[-1]][:, 0:None]
+
+ return source
+
+ def run_model(self, mix, is_match_mix=False):
+ """
+ Converts waveforms to spectrogram representations, passes them to target execution engines,
+ and applies denoising.
+
+ Args:
+ mix (torch.Tensor): Pytorch signal batch tensor to process.
+ is_match_mix (bool, optional): Skip execution flag during phase inversion alignment.
+
+ Returns:
+ np.ndarray: Reconstructed source audio waveform track.
+ """
+
+ spek = self.stft(mix.to(self.torch_device))
+ # Zero-out ultra-low frequency sub-bass noise components to clean processing artifacts
+ spek[:, :, :3, :] *= 0
+
+ if is_match_mix: spec_pred = spek.cpu().numpy()
+ else: spec_pred = ((self.model_run(-spek) * -0.5) + (self.model_run(spek) * 0.5)) if self.enable_denoise else self.model_run(spek) # Denoise trick: Evaluate raw signals alongside phase-inverted counterparts to balance distortion artifacts
+
+ # Reconstruct output tracking matrices back into time-domain waveforms
+ result = self.stft.inverse((torch.from_numpy(spec_pred) if spec_pred.dtype == np.float32 else spec_pred).to(self.torch_device)).cpu().detach().numpy()
+ return result
+
+class STFT:
+ """
+ An optimized Short-Time Fourier Transform (STFT) wrapper handling multi-channel structures,
+ frequency padding tricks, and hardware target device shifts.
+ """
+
+ def __init__(self, n_fft, hop_length, dim_f, device):
+ """
+ Initializes windowing parameters and pre-allocates standard Hann window curves on hardware.
+
+ Args:
+ n_fft (int): Size of Fourier Transform context windows.
+ hop_length (int): Frame shift distance stepping properties.
+ dim_f (int): Target maximum frequency bin to slice during execution.
+ device (torch.device): Host execution target for tensor shifts.
+ """
+
+ self.n_fft = n_fft
+ self.hop_length = hop_length
+ self.dim_f = dim_f
+ self.device = device
+ self.hann_window = torch.hann_window(window_length=self.n_fft, periodic=True)
+
+ def __call__(self, input_tensor):
+ """
+ Executes a forward STFT, merging stereo data dimensions into target model layout shapes.
+
+ Args:
+ input_tensor (torch.Tensor): Raw 1D/2D audio signal matrices.
+
+ Returns:
+ torch.Tensor: Permuted spectrogram tensor.
+ """
+
+ # Fall back to CPU processing if handling custom non-standard accelerators
+ is_non_standard_device = not input_tensor.device.type in ["cuda", "xpu", "cpu"]
+ if is_non_standard_device: input_tensor = input_tensor.cpu()
+
+ batch_dimensions = input_tensor.shape[:-2]
+ channel_dim, time_dim = input_tensor.shape[-2:]
+
+ # Extract complex spectrogram frames and convert real/imaginary components into explicit tensor axes
+ stft_output = torch.view_as_real(torch.stft(input_tensor.reshape([-1, time_dim]), n_fft=self.n_fft, hop_length=self.hop_length, window=self.hann_window.to(input_tensor.device), center=True, return_complex=True)).permute([0, 3, 1, 2])
+ # Interleave channel and real/imag dimensions to meet MDX expected model structural shape constraints
+ final_output = stft_output.reshape([*batch_dimensions, channel_dim, 2, -1, stft_output.shape[-1]]).reshape([*batch_dimensions, channel_dim * 2, -1, stft_output.shape[-1]])
+
+ if is_non_standard_device: final_output = final_output.to(self.device)
+ return final_output[..., : self.dim_f, :]
+
+ def pad_frequency_dimension(self, input_tensor, batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins):
+ """
+ Pads stripped high-frequency spectrogram matrices with zeros back to standard size for correct inverse parsing.
+ """
+
+ return torch.cat([input_tensor, torch.zeros([*batch_dimensions, channel_dim, num_freq_bins - freq_dim, time_dim]).to(input_tensor.device)], -2)
+
+ def calculate_inverse_dimensions(self, input_tensor):
+ """
+ Decomposes dimensions and computes required window shapes to perform an Inverse Short-Time Fourier Transform.
+ """
+
+ channel_dim, freq_dim, time_dim = input_tensor.shape[-3:]
+
+ return input_tensor.shape[:-3], channel_dim, freq_dim, time_dim, self.n_fft // 2 + 1
+
+ def prepare_for_istft(self, padded_tensor, batch_dimensions, channel_dim, num_freq_bins, time_dim):
+ """
+ Reconstructs interleaved real/imaginary feature matrices back into native torch-compliant complex number arrays.
+ """
+
+ permuted_tensor = padded_tensor.reshape([*batch_dimensions, channel_dim // 2, 2, num_freq_bins, time_dim]).reshape([-1, 2, num_freq_bins, time_dim]).permute([0, 2, 3, 1])
+
+ return permuted_tensor[..., 0] + permuted_tensor[..., 1] * 1.0j
+
+ def inverse(self, input_tensor):
+ """
+ Transforms a multi-channel spectrogram tensor back into a standard time-domain waveform track.
+
+ Args:
+ input_tensor (torch.Tensor): Processed source spectrogram tensor.
+
+ Returns:
+ torch.Tensor: Reconstructed time-domain audio waveform track.
+ """
+
+ is_non_standard_device = not input_tensor.device.type in ["cuda", "xpu", "cpu"]
+ if is_non_standard_device: input_tensor = input_tensor.cpu()
+
+ # Unpack dimensional geometry configurations
+ batch_dimensions, channel_dim, freq_dim, time_dim, num_freq_bins = self.calculate_inverse_dimensions(input_tensor)
+ # Execute padding, combine complex values, and run the native torch ISTFT operation
+ final_output = torch.istft(
+ self.prepare_for_istft(
+ self.pad_frequency_dimension(
+ input_tensor,
+ batch_dimensions,
+ channel_dim,
+ freq_dim,
+ time_dim,
+ num_freq_bins
+ ),
+ batch_dimensions,
+ channel_dim,
+ num_freq_bins,
+ time_dim
+ ),
+ n_fft=self.n_fft,
+ hop_length=self.hop_length,
+ window=self.hann_window.to(input_tensor.device),
+ center=True
+ ).reshape([*batch_dimensions, 2, -1])
+
+ if is_non_standard_device: final_output = final_output.to(self.device)
+ return final_output
\ No newline at end of file
diff --git a/main/library/uvr5_lib/separator.py b/main/library/uvr5_lib/separator.py
new file mode 100644
index 0000000000000000000000000000000000000000..e87e1f295254b939cb093a3f1c2f90d018403e6e
--- /dev/null
+++ b/main/library/uvr5_lib/separator.py
@@ -0,0 +1,282 @@
+import os
+import sys
+import time
+import torch
+import codecs
+import hashlib
+import requests
+import warnings
+
+from importlib import import_module
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+
+from main.library.utils import clear_gpu_cache
+from main.tools.huggingface import HF_download_file
+from main.app.variables import config, translations, configs
+
+if not config.debug_mode: warnings.filterwarnings("ignore")
+
+class Separator:
+ """
+ The orchestrator class for Ultimate Vocal Remover v5 (UVR5) inference pipelines.
+ Handles dynamic model downloads, architecture switching (MDX, Demucs, VR),
+ weight checksum validation, and file processing orchestration.
+ """
+
+ def __init__(
+ self,
+ logger,
+ model_file_dir=config.configs["uvr5_path"],
+ output_dir=None,
+ output_format="wav",
+ normalization_threshold=0.9,
+ sample_rate=44100,
+ mdx_params={
+ "hop_length": 1024,
+ "segment_size": 256,
+ "overlap": 0.25,
+ "batch_size": 1,
+ "enable_denoise": False
+ },
+ demucs_params={
+ "segment_size": "Default",
+ "shifts": 2,
+ "overlap": 0.25,
+ "segments_enabled": True
+ },
+ vr_params={
+ "batch_size": 1,
+ "window_size": 512,
+ "aggression": 5,
+ "enable_tta": False,
+ "enable_post_process": False,
+ "post_process_threshold": 0.2,
+ "high_end_process": False
+ }
+ ):
+ """
+ Initializes runtime parameters, cross-validates thresholds, and maps model-specific hyperparameters.
+
+ Args:
+ logger: System logger instance for tracking process messages.
+ model_file_dir (str): Folder where model weights are stored or downloaded.
+ output_dir (str, optional): Target directory for saving split tracks. Defaults to current directory.
+ output_format (str): Audio container format extension (e.g., 'wav', 'mp3'). Defaults to "wav".
+ normalization_threshold (float): Amplitude limit peak scaling factor. Defaults to 0.9.
+ sample_rate (int): Target sampling frequency rate. Defaults to 44100.
+ mdx_params (dict): Processing hyperparameters for MDX models.
+ demucs_params (dict): Processing hyperparameters for Demucs architectures.
+ vr_params (dict): Processing hyperparameters for VR (Vocal Remover) models.
+
+ Raises:
+ ValueError: If `normalization_threshold` falls outside the allowed range (0, 1].
+ """
+
+ self.logger = logger
+ self.logger.info(translations["separator_info"].format(output_dir=output_dir, output_format=output_format))
+ # Establish and verify filesystem storage spaces
+ self.output_dir = output_dir if output_dir is not None else now_dir
+ self.model_file_dir = model_file_dir
+ os.makedirs(self.output_dir, exist_ok=True)
+ os.makedirs(self.model_file_dir, exist_ok=True)
+
+ self.output_format = output_format if output_format is not None else "wav"
+ self.normalization_threshold = normalization_threshold
+ self.sample_rate = int(sample_rate)
+ # Enforce valid range constraints on audio peak limits
+ if normalization_threshold <= 0 or normalization_threshold > 1: raise ValueError("Normalization threshold must be between 0 and 1 (exclusive of 0).")
+
+ # Map execution params to their respective structural categories
+ self.arch_specific_params = {"MDX": mdx_params, "Demucs": demucs_params, "VR": vr_params}
+ self.torch_device = torch.device(config.device)
+ self.onnx_execution_provider = config.providers
+ self.model_instance = None
+
+ def download_file_if_not_exists(self, url, output_path):
+ """
+ Downloads files via the HuggingFace bridge API if not present in the local file system.
+
+ Args:
+ url (str): Remote file URL.
+ output_path (str): Target filesystem download path.
+ """
+
+ if os.path.isfile(output_path): return
+ HF_download_file(url, output_path)
+
+ def list_supported_model_files(self):
+ """
+ Fetches the remote model repository inventory map. Obfuscated URLs are decoded via ROT13.
+
+ Returns:
+ dict: Structured directory categorization map containing model filenames.
+ """
+
+ # Remote repository JSON schema index file (Decoded using ROT13 cipher swap)
+ response = requests.get(
+ codecs.decode(
+ "uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/enj/znva/wfba/hie_zbqryf.wfba",
+ "rot13"
+ )
+ )
+
+ response.raise_for_status()
+ model_downloads_list = response.json()
+
+ return {
+ "MDX": {
+ **model_downloads_list["mdx_download_list"],
+ **model_downloads_list["mdx_download_vip_list"]
+ },
+ "Demucs": {
+ **model_downloads_list["demucs_download_list"]
+ },
+ "VR": {
+ **model_downloads_list["vr_download_list"]
+ }
+ }
+
+ def download_model_files(self, model_filename):
+ """
+ Identifies, down-links, and verifies target model weights across available remote categories.
+
+ Args:
+ model_filename (str): Base filename of the target weights checkpoint.
+
+ Returns:
+ tuple: (model_type, model_path)
+ - model_type (str): Identified architecture family ('MDX', 'Demucs', or 'VR').
+ - model_path (str): Absolute local filesystem path to the downloaded weights file.
+
+ Raises:
+ ValueError: If the model file name cannot be matched against remote inventory catalogs.
+ """
+
+ model_repo = codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/erfbyir/znva/hie5_zbqryf", "rot13")
+ model_path = os.path.join(self.model_file_dir, model_filename)
+
+ # Traverse model indexes to pinpoint the matching download endpoint
+ for model_type, model_list in self.list_supported_model_files().items():
+ for _, files in model_list.items():
+ # Handling standalone model file items
+ if isinstance(files, str) and files == model_filename:
+ # Fallback trial sequence to catch models across varying repository endpoints
+ try:
+ self.download_file_if_not_exists(f"{model_repo}/MDX/{model_filename}", model_path)
+ except:
+ try:
+ self.download_file_if_not_exists(f"{model_repo}/VR/{model_filename}", model_path)
+ except:
+ self.download_file_if_not_exists(f"{model_repo}/Demucs/{model_filename}", model_path)
+
+ return model_type, model_path
+ elif isinstance(files, dict) and any(model_filename in (k, v) for k, v in files.items()): # Handling multi-file compound structures (e.g., Demucs collections)
+ for _, file_val in files.items():
+ self.download_file_if_not_exists(f"{model_repo}/Demucs/{file_val}", os.path.join(self.model_file_dir, file_val))
+
+ return model_type, model_path
+
+ raise ValueError(f"Model file '{model_filename}' not found in remote database index records.")
+
+ def load_model_data(self, model_path = None, model_type = None):
+ """
+ Computes partial/full file checksum digests to fetch structural metadata definitions from the server.
+
+ Args:
+ model_path (str, optional): Target model weights path.
+ model_type (str, optional): Architecture type string.
+
+ Returns:
+ dict: Model parsing parameters extracted from the server index database.
+
+ Raises:
+ ValueError: If hash mappings are missing or configuration parameters are invalid.
+ """
+
+ # Demucs handles configurations natively via package attributes
+ if model_type == "Demucs": model_data = {}
+ elif model_path is not None:
+ try:
+ with open(model_path, "rb") as f:
+ # Quick Hash Optimization: Read and parse only the trailing block segment
+ f.seek(-10000 * 1024, 2)
+ model_hash = hashlib.md5(f.read()).hexdigest()
+ except IOError:
+ # Fallback: Parse the full file stream if segment-seeking fails
+ model_hash = hashlib.md5(open(model_path, "rb").read()).hexdigest()
+
+ # Retrieve architectural details map from database server indices
+ response = requests.get(codecs.decode("uggcf://uhttvatsnpr.pb/NauC/Ivrganzrfr-EIP-Cebwrpg/enj/znva/wfba/zbqry_qngn.wfba", "rot13"))
+ response.raise_for_status()
+ model_data_object = response.json()
+
+ if model_hash in model_data_object: model_data = model_data_object[model_hash]
+ else: raise ValueError("Model signature verification failed. Weights are unknown or corrupted.")
+ else: raise ValueError("Invalid parameters provided for metadata loading configurations.")
+
+ return model_data
+
+ def load_model(self, model_filename):
+ """
+ Downloads structural assets, extracts structural configuration logs, and dynamically
+ instantiates the proper processing class subclass.
+
+ Args:
+ model_filename (str): Name of the model file to activate.
+
+ Raises:
+ ValueError: If the detected model type configuration lacks internal runner code support.
+ """
+
+ self.logger.info(translations["loading_model"].format(model_filename=model_filename))
+ model_type, model_path = self.download_model_files(model_filename)
+
+ # Mapping names to their respective module and class identifiers
+ separator_classes = {"MDX": "mdx_separator.MDXSeparator", "Demucs": "demucs_separator.DemucsSeparator", "VR": "vr_separator.VRSeparator"}
+ # Consolidate standard runtime configurations
+ common_params = {
+ "logger": self.logger,
+ "torch_device": self.torch_device,
+ "onnx_execution_provider": self.onnx_execution_provider,
+ "model_name": model_filename.split(".")[0],
+ "model_path": model_path,
+ "model_data": self.load_model_data(model_path, model_type),
+ "output_format": self.output_format,
+ "output_dir": self.output_dir,
+ "normalization_threshold": self.normalization_threshold,
+ "invert_using_spec": configs.get("invert_using_spec", False),
+ "sample_rate": self.sample_rate
+ }
+
+ if model_type not in self.arch_specific_params or model_type not in separator_classes:
+ raise ValueError(translations["model_type_not_support"].format(model_type=model_type))
+
+ # Dynamically import module and initialize class instantiations at runtime
+ module_name, class_name = separator_classes[model_type].split(".")
+ separator_class = getattr(import_module(f"main.library.uvr5_lib.{module_name}"), class_name)
+ # Instantiate active runtime context interface class
+ self.model_instance = separator_class(common_config=common_params, arch_config=self.arch_specific_params[model_type])
+
+ def separate(self, audio_file_path):
+ """
+ Triggers the underlying model extraction pipeline on the target audio track.
+
+ Args:
+ audio_file_path (str): Target mixed track file path destination.
+
+ Returns:
+ list: Generated target stems output path destinations.
+ """
+
+ self.logger.info(f"{translations['starting_separator']}: {audio_file_path}")
+ # Execute processing pipeline within the allocated instance class
+ output_files = self.model_instance.separate(audio_file_path)
+
+ # Clear specific variables and flush memory pipelines to prepare for subsequent batches
+ self.model_instance.clear_file_specific_paths()
+ clear_gpu_cache()
+
+ self.logger.info(translations["separator_success_3"])
+ return output_files
\ No newline at end of file
diff --git a/main/library/uvr5_lib/spec_utils.py b/main/library/uvr5_lib/spec_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..51f2b3e506254151057ec3b2736048f8306312f7
--- /dev/null
+++ b/main/library/uvr5_lib/spec_utils.py
@@ -0,0 +1,658 @@
+import os
+import sys
+import librosa
+import platform
+
+import numpy as np
+import soundfile as sf
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations, logger
+
+wav_resolution = ("polyphase" if platform.processor() == "arm" or "arm" in platform.platform() else "sinc_fastest") if platform.system() == "Darwin" else "sinc_fastest"
+
+def crop_center(h1, h2):
+ """
+ Crops the time dimension (width) of tensor h1 to precisely match the size of tensor h2.
+
+ Args:
+ h1 (torch.Tensor): Source tensor to be cropped, typically with a larger time dimension.
+ h2 (torch.Tensor): Target reference tensor providing the destination shape bounds.
+
+ Returns:
+ torch.Tensor: Center-cropped slice of the h1 tensor.
+
+ Raises:
+ ValueError: If the time dimension of h1 is smaller than h2.
+ """
+
+ h1_shape = h1.size()
+ h2_shape = h2.size()
+
+ if h1_shape[3] == h2_shape[3]: return h1
+ elif h1_shape[3] < h2_shape[3]: raise ValueError("Source tensor dimensions cannot be smaller than target reference bounds.")
+
+ # Calculate symmetrical slicing boundary markers
+ s_time = (h1_shape[3] - h2_shape[3]) // 2
+
+ h1 = h1[:, :, :, s_time:s_time + h2_shape[3]]
+ return h1
+
+def preprocess(X_spec):
+ """
+ Decomposes a complex-valued spectrogram matrix into its magnitude and phase components.
+
+ Args:
+ X_spec (np.ndarray): Complex spectrogram array.
+
+ Returns:
+ tuple: (magnitude, phase) as numpy float arrays.
+ """
+
+ return np.abs(X_spec), np.angle(X_spec)
+
+def make_padding(width, cropsize, offset):
+ """
+ Calculates symmetrical window border padding layouts for block-based inference overlapping loops.
+
+ Args:
+ width (int): Total length of the audio track in frames.
+ cropsize (int): Standard block execution segment width window size.
+ offset (int): Trim overhead factor to strip edge window fading artifacts.
+
+ Returns:
+ tuple: (left_offset, total_padding_needed, region_of_interest_size)
+ """
+
+ roi_size = cropsize - offset * 2
+
+ if roi_size == 0: roi_size = cropsize
+ return offset, roi_size - (width % roi_size) + offset, roi_size
+
+def normalize(wave, max_peak=1.0):
+ """
+ Rescales a waveform linearly so that its absolute peak matches the target maximum ceiling.
+
+ Args:
+ wave (np.ndarray): Input audio waveform array.
+ max_peak (float): Absolute ceiling limit for amplitude normalization. Defaults to 1.0.
+
+ Returns:
+ np.ndarray: Amplitude-normalized audio wave matrix.
+ """
+
+ maxv = np.abs(wave).max()
+
+ if maxv > max_peak: wave *= max_peak / maxv
+ return wave
+
+def write_array_to_mem(audio_data, subtype):
+ """
+ Encodes raw numpy audio signal waveforms directly into an in-memory WAV byte buffer stream.
+
+ Args:
+ audio_data (np.ndarray): Target raw numpy audio signal data.
+ subtype (str): Soundfile encoding bit-depth subtype specification (e.g., 'PCM_16', 'FLOAT').
+
+ Returns:
+ io.BytesIO or object: Seeked in-memory byte buffer containing the encoded file content, or the original object if not a numpy array.
+ """
+
+ if isinstance(audio_data, np.ndarray):
+ import io
+
+ audio_buffer = io.BytesIO()
+ sf.write(audio_buffer, audio_data, 44100, subtype=subtype, format="WAV")
+
+ audio_buffer.seek(0) # Rewind pointer stream to start index position
+ return audio_buffer
+ else: return audio_data
+
+def reduce_vocal_aggressively(X, y, softmask):
+ """
+ Suppresses remaining vocal bleed or transient leakage from an isolated accompaniment spectrogram.
+
+ Args:
+ X (np.ndarray): Original full mixture track spectrogram matrix.
+ y (np.ndarray): Separated instrumental/accompaniment track spectrogram.
+ softmask (float): Masking weight ratio modifier scaling factor.
+
+ Returns:
+ np.ndarray: Filtered spectrogram matrix with reduced bleed artifacts.
+ """
+
+ y_mag_tmp = np.abs(y)
+ v_mag_tmp = np.abs(X - y)
+
+ # Apply weighted error compensation mask to cancel phase overlaps
+ return np.clip(y_mag_tmp - v_mag_tmp * (v_mag_tmp > y_mag_tmp) * softmask, 0, np.inf) * np.exp(1.0j * np.angle(y))
+
+def merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):
+ """
+ Smoothes out localized mask disruptions to eliminate isolated clicking/chirping audio artifacts.
+
+ Args:
+ y_mask (np.ndarray): Original execution binary or soft tracking matrix.
+ thres (float): Energy threshold filter level to detect active frame areas. Defaults to 0.01.
+ min_range (int): Minimum contiguous frame duration to qualify as a valid mask area. Defaults to 64.
+ fade_size (int): Symmetrical cross-fade sample length step sizes. Defaults to 32.
+
+ Returns:
+ np.ndarray: Cleaned artifact-free separation mask tracking matrix.
+ """
+
+ mask = y_mask
+
+ try:
+ if min_range < fade_size * 2: raise ValueError("Minimum range parameter must comfortably wrap double the cross-fade step lengths.")
+
+ # Identify frames where the isolation performance exceeds mask tolerance thresholds
+ idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]
+ start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
+ end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
+ artifact_idx = np.where(end_idx - start_idx > min_range)[0]
+ weight = np.zeros_like(y_mask)
+
+ if len(artifact_idx) > 0:
+ start_idx = start_idx[artifact_idx]
+ end_idx = end_idx[artifact_idx]
+ old_e = None
+
+ # Calculate window slopes to patch transient spikes smoothly
+ for s, e in zip(start_idx, end_idx):
+ if old_e is not None and s - old_e < fade_size: s = old_e - fade_size * 2
+
+ if s != 0: weight[:, :, s : s + fade_size] = np.linspace(0, 1, fade_size)
+ else: s -= fade_size
+
+ if e != y_mask.shape[2]: weight[:, :, e - fade_size : e] = np.linspace(1, 0, fade_size)
+ else: e += fade_size
+
+ weight[:, :, s + fade_size : e - fade_size] = 1
+ old_e = e
+
+ v_mask = 1 - y_mask
+ y_mask += weight * v_mask
+ mask = y_mask
+ except Exception as e:
+ import traceback
+ logger.error(f'{translations["not_success"]} {type(e).__name__}: {e}\n{traceback.format_exc()}')
+
+ return mask
+
+def convert_channels(spec, mp, band):
+ """
+ Converts spatial audio channels (Stereo vs Mid-Side matrix formulations) based on model band rules.
+
+ Args:
+ spec (np.ndarray): Multi-channel input spectrogram array.
+ mp (object): Model parameter storage abstraction configuration instance.
+ band (int): Identifier tag mapping specific target frequency bands.
+
+ Returns:
+ np.ndarray: Spatial channel-reconfigured Fortran-contiguous array layout.
+ """
+
+ cc = mp.param["band"][str(band)].get("convert_channels")
+
+ if "mid_side_c" == cc:
+ spec_left = np.add(spec[0], spec[1] * 0.25)
+ spec_right = np.subtract(spec[1], spec[0] * 0.25)
+ elif "mid_side" == cc:
+ spec_left = np.add(spec[0], spec[1]) / 2
+ spec_right = np.subtract(spec[0], spec[1])
+ elif "stereo_n" == cc:
+ spec_left = np.add(spec[0], spec[1] * 0.25) / 0.9375
+ spec_right = np.add(spec[1], spec[0] * 0.25) / 0.9375
+ else: return spec
+
+ return np.asfortranarray([spec_left, spec_right])
+
+def combine_spectrograms(specs, mp, is_v51_model=False):
+ """
+ Merges diverse multi-band spectrogram chunks into a single unified master full-band tracking array.
+
+ Args:
+ specs (dict): Dictionary mapping band index tags to their isolated partial spectrogram matrices.
+ mp (object): Model parameters class instance containing structural frequency definitions.
+ is_v51_model (bool): Flag defining whether processing rules use newer UVR v5.1 architecture schemas.
+
+ Returns:
+ np.ndarray: Recombined master full-band spectrogram matrix.
+ """
+
+ l = min([specs[i].shape[2] for i in specs])
+ spec_c = np.zeros(shape=(2, mp.param["bins"] + 1, l), dtype=np.complex64)
+ offset = 0
+ bands_n = len(mp.param["band"])
+
+ # Stitch target bands across designated frequency bin ranges
+ for d in range(1, bands_n + 1):
+ h = mp.param["band"][str(d)]["crop_stop"] - mp.param["band"][str(d)]["crop_start"]
+ spec_c[:, offset : offset + h, :l] = specs[d][:, mp.param["band"][str(d)]["crop_start"] : mp.param["band"][str(d)]["crop_stop"], :l]
+ offset += h
+
+ if offset > mp.param["bins"]: raise ValueError("Assembled frequency bin height exceeds system maximum definition targets.")
+ # Apply low-pass anti-aliasing filters across matching edge joints
+ if mp.param["pre_filter_start"] > 0:
+ if is_v51_model: spec_c *= get_lp_filter_mask(spec_c.shape[1], mp.param["pre_filter_start"], mp.param["pre_filter_stop"])
+ else:
+ if bands_n == 1: spec_c = fft_lp_filter(spec_c, mp.param["pre_filter_start"], mp.param["pre_filter_stop"])
+ else:
+ import math
+ gp = 1
+
+ # Apply a gradual logarithmic filter slope across crossover regions
+ for b in range(mp.param["pre_filter_start"] + 1, mp.param["pre_filter_stop"]):
+ g = math.pow(10, -(b - mp.param["pre_filter_start"]) * (3.5 - gp) / 20.0)
+ gp = g
+ spec_c[:, b, :] *= g
+
+ return np.asfortranarray(spec_c)
+
+def wave_to_spectrogram(wave, hop_length, n_fft, mp, band, is_v51_model=False):
+ """
+ Converts time-domain wave signals into multi-channel complex short-time Fourier transform frames.
+
+ Args:
+ wave (np.ndarray): Target input audio waveform array.
+ hop_length (int): Distance stepping parameters between processing context frames.
+ n_fft (int): Total size length window of Fourier transform bins.
+ mp (object): Model metadata parameters configuration map.
+ band (int): Identifier index mapping active target processing tracks.
+ is_v51_model (bool): Flag defining whether processing rules use UVR v5.1 architecture schemas.
+
+ Returns:
+ np.ndarray: Evaluated complex spectrogram matrix array.
+ """
+
+ if wave.ndim == 1: wave = np.asfortranarray([wave, wave])
+ # Apply spatial pre-processing channel conversions depending on version standards
+ if not is_v51_model:
+ if mp.param["reverse"]:
+ wave_left = np.flip(np.asfortranarray(wave[0]))
+ wave_right = np.flip(np.asfortranarray(wave[1]))
+ elif mp.param["mid_side"]:
+ wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
+ wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
+ elif mp.param["mid_side_b2"]:
+ wave_left = np.asfortranarray(np.add(wave[1], wave[0] * 0.5))
+ wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * 0.5))
+ else:
+ wave_left = np.asfortranarray(wave[0])
+ wave_right = np.asfortranarray(wave[1])
+ else:
+ wave_left = np.asfortranarray(wave[0])
+ wave_right = np.asfortranarray(wave[1])
+
+ spec_left = librosa.stft(wave_left, n_fft=n_fft, hop_length=hop_length)
+ spec_right = librosa.stft(wave_right, n_fft=n_fft, hop_length=hop_length)
+
+ spec = np.asfortranarray([spec_left, spec_right])
+
+ if is_v51_model: spec = convert_channels(spec, mp, band)
+ return spec
+
+def spectrogram_to_wave(spec, hop_length=1024, mp={}, band=0, is_v51_model=True):
+ """
+ Transforms multi-channel complex spectrogram matrices back into standard time-domain waveform tracks.
+
+ Args:
+ spec (np.ndarray): Processed complex target spectrogram arrays.
+ hop_length (int): Distance stepping parameters between processing context frames. Defaults to 1024.
+ mp (dict): Configuration parameter storage dictionary.
+ band (int): Active band index tracker identifier. Defaults to 0.
+ is_v51_model (bool): Flag defining whether processing rules use UVR v5.1 architecture schemas.
+
+ Returns:
+ np.ndarray: Reconstructed time-domain audio track waveform matrix.
+ """
+
+ spec_left = np.asfortranarray(spec[0])
+ spec_right = np.asfortranarray(spec[1])
+
+ wave_left = librosa.istft(spec_left, hop_length=hop_length)
+ wave_right = librosa.istft(spec_right, hop_length=hop_length)
+
+ # Perform channel decoding matrix expansions based on architectural configurations
+ if is_v51_model:
+ cc = mp.param["band"][str(band)].get("convert_channels")
+
+ if "mid_side_c" == cc:
+ return np.asfortranarray([
+ np.subtract(wave_left / 1.0625, wave_right / 4.25),
+ np.add(wave_right / 1.0625, wave_left / 4.25)
+ ])
+ elif "mid_side" == cc:
+ return np.asfortranarray([
+ np.add(wave_left, wave_right / 2),
+ np.subtract(wave_left, wave_right / 2)
+ ])
+ elif "stereo_n" == cc:
+ return np.asfortranarray([
+ np.subtract(wave_left, wave_right * 0.25),
+ np.subtract(wave_right, wave_left * 0.25)
+ ])
+ else:
+ if mp.param["reverse"]:
+ return np.asfortranarray([
+ np.flip(wave_left),
+ np.flip(wave_right)
+ ])
+ elif mp.param["mid_side"]:
+ return np.asfortranarray([
+ np.add(wave_left, wave_right / 2),
+ np.subtract(wave_left, wave_right / 2)
+ ])
+ elif mp.param["mid_side_b2"]:
+ return np.asfortranarray([
+ np.add(wave_right / 1.25, 0.4 * wave_left),
+ np.subtract(wave_left / 1.25, 0.4 * wave_right)
+ ])
+
+ return np.asfortranarray([wave_left, wave_right])
+
+def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None, is_v51_model=False):
+ """
+ Decomposes an integrated full master spectrogram tracking array back into its multi-band
+ sub-components and executes individual inverse STFT operations with appropriate cross-resampling.
+
+ Args:
+ spec_m (np.ndarray): Assembled master full-band spectrogram matrix.
+ mp (object): Model parameter storage abstraction instance.
+ extra_bins_h (int, optional): Spatial allocation height limits for ultra-high frequency padding.
+ extra_bins (np.ndarray, optional): Reference matrices containing ultra-high frequency spectrogram padding.
+ is_v51_model (bool): Flag defining whether processing rules use UVR v5.1 architecture schemas.
+
+ Returns:
+ np.ndarray: Recombined time-domain audio waveform track array.
+ """
+
+ bands_n = len(mp.param["band"])
+ offset = 0
+
+ for d in range(1, bands_n + 1):
+ bp = mp.param["band"][str(d)]
+ spec_s = np.zeros(shape=(2, bp["n_fft"] // 2 + 1, spec_m.shape[2]), dtype=complex)
+ h = bp["crop_stop"] - bp["crop_start"]
+ spec_s[:, bp["crop_start"] : bp["crop_stop"], :] = spec_m[:, offset : offset + h, :]
+ offset += h
+
+ if d == bands_n:
+ # Inject ultra-high frequency details from the original mix to preserve high-end clarity
+ if extra_bins_h:
+ max_bin = bp["n_fft"] // 2
+ spec_s[:, max_bin - extra_bins_h : max_bin, :] = extra_bins[:, :extra_bins_h, :]
+
+ # Apply high-pass crossover isolation filtering configurations
+ if bp["hpf_start"] > 0:
+ if is_v51_model: spec_s *= get_hp_filter_mask(spec_s.shape[1], bp["hpf_start"], bp["hpf_stop"] - 1)
+ else: spec_s = fft_hp_filter(spec_s, bp["hpf_start"], bp["hpf_stop"] - 1)
+
+ # Reconstruct the waveform, accumulation-blending multi-band layers together
+ wave = (
+ spectrogram_to_wave(
+ spec_s,
+ bp["hl"],
+ mp,
+ d,
+ is_v51_model
+ )
+ ) if bands_n == 1 else (
+ np.add(
+ wave,
+ spectrogram_to_wave(
+ spec_s,
+ bp["hl"],
+ mp,
+ d,
+ is_v51_model
+ )
+ )
+ )
+ else:
+ sr = mp.param["band"][str(d + 1)]["sr"]
+
+ if d == 1:
+ if is_v51_model:
+ spec_s *= get_lp_filter_mask(
+ spec_s.shape[1],
+ bp["lpf_start"],
+ bp["lpf_stop"]
+ )
+ else:
+ spec_s = fft_lp_filter(
+ spec_s,
+ bp["lpf_start"],
+ bp["lpf_stop"]
+ )
+
+ try:
+ # Execute high-quality resampling matching subsequent band processing sample rates
+ wave = librosa.resample(
+ spectrogram_to_wave(
+ spec_s,
+ bp["hl"],
+ mp,
+ d,
+ is_v51_model
+ ),
+ orig_sr=bp["sr"],
+ target_sr=sr,
+ res_type="soxr_vhq"
+ )
+ except ValueError as e:
+ logger.error(f"{translations['resample_error']}: {e}")
+ logger.error(f"{translations['shapes']} Spec_s: {spec_s.shape}, SR: {sr}, {translations['wav_resolution']}: {wav_resolution}")
+ else:
+ if is_v51_model:
+ spec_s *= get_hp_filter_mask(
+ spec_s.shape[1],
+ bp["hpf_start"],
+ bp["hpf_stop"] - 1
+ )
+
+ spec_s *= get_lp_filter_mask(
+ spec_s.shape[1],
+ bp["lpf_start"],
+ bp["lpf_stop"]
+ )
+ else:
+ spec_s = fft_hp_filter(
+ spec_s,
+ bp["hpf_start"],
+ bp["hpf_stop"] - 1
+ )
+
+ spec_s = fft_lp_filter(
+ spec_s,
+ bp["lpf_start"],
+ bp["lpf_stop"]
+ )
+
+ try:
+ wave = librosa.resample(
+ np.add(wave, spectrogram_to_wave(spec_s, bp["hl"], mp, d, is_v51_model)),
+ orig_sr=bp["sr"],
+ target_sr=sr,
+ res_type="soxr_vhq"
+ )
+ except ValueError as e:
+ logger.error(f"{translations['resample_error']}: {e}")
+ logger.error(f"{translations['shapes']} Spec_s: {spec_s.shape}, SR: {sr}, {translations['wav_resolution']}: {wav_resolution}")
+
+ return wave
+
+def get_lp_filter_mask(n_bins, bin_start, bin_stop):
+ """
+ Generates a low-pass linear-slope frequency masking filter array.
+ """
+
+ return np.concatenate([
+ np.ones((bin_start - 1, 1)),
+ np.linspace(1, 0, bin_stop - bin_start + 1)[:, None],
+ np.zeros((n_bins - bin_stop, 1))
+ ], axis=0)
+
+def get_hp_filter_mask(n_bins, bin_start, bin_stop):
+ """
+ Generates a high-pass linear-slope frequency masking filter array.
+ """
+
+ return np.concatenate([
+ np.zeros((bin_stop + 1, 1)),
+ np.linspace(0, 1, 1 + bin_start - bin_stop)[:, None],
+ np.ones((n_bins - bin_start - 2, 1))
+ ], axis=0)
+
+def fft_lp_filter(spec, bin_start, bin_stop):
+ """
+ Applies an in-place low-pass attenuation filter across designated spectrogram frequency ranges.
+ """
+
+ g = 1.0
+
+ for b in range(bin_start, bin_stop):
+ g -= 1 / (bin_stop - bin_start)
+ spec[:, b, :] = g * spec[:, b, :]
+
+ spec[:, bin_stop:, :] *= 0
+ return spec
+
+def fft_hp_filter(spec, bin_start, bin_stop):
+ """
+ Applies an in-place high-pass attenuation filter across designated spectrogram frequency ranges.
+ """
+
+ g = 1.0
+
+ for b in range(bin_start, bin_stop, -1):
+ g -= 1 / (bin_start - bin_stop)
+ spec[:, b, :] = g * spec[:, b, :]
+
+ spec[:, 0 : bin_stop + 1, :] *= 0
+ return spec
+
+def mirroring(a, spec_m, input_high_end, mp):
+ """
+ Reconstructs missing ultra-high frequencies by mirroring lower frequency bands
+ to prevent low-pass muffled sound artifacts.
+
+ Args:
+ a (str): Mirroring algorithm style keyword strategy selector ('mirroring' or 'mirroring2').
+ spec_m (np.ndarray): Assembled master full-band spectrogram matrix.
+ input_high_end (np.ndarray): Target high-frequency spectrogram matrix layer to reconstruct.
+ mp (object): Model parameter storage abstraction metadata configurations.
+
+ Returns:
+ np.ndarray: Spectrogram matrix block augmented with mirrored high-end frequency components.
+ """
+
+ if "mirroring" == a:
+ mirror = np.flip(
+ np.abs(spec_m[:, mp.param["pre_filter_start"] - 10 - input_high_end.shape[1] : mp.param["pre_filter_start"] - 10, :]), 1
+ ) * np.exp(1.0j * np.angle(input_high_end))
+
+ return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)
+
+ if "mirroring2" == a:
+ mi = np.multiply(
+ np.flip(
+ np.abs(spec_m[:, mp.param["pre_filter_start"] - 10 - input_high_end.shape[1] : mp.param["pre_filter_start"] - 10, :]), 1
+ ), input_high_end * 1.7
+ )
+
+ return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
+
+def adjust_aggr(mask, is_non_accom_stem, aggressiveness):
+ """
+ Adjusts the separation mask dynamically according to non-linear model isolation aggressiveness constraints.
+
+ Args:
+ mask (np.ndarray): Target mask tracking matrix array.
+ is_non_accom_stem (bool): Identifies if processing targets vocal/isolated foreground stems.
+ aggressiveness (dict): Parameter configuration properties dictating amplification factor thresholds.
+
+ Returns:
+ np.ndarray: Modified power-scaled separation mask array.
+ """
+
+ aggr = aggressiveness["value"] * 2
+
+ if aggr != 0:
+ if is_non_accom_stem: aggr = 1 - aggr
+ if np.any(aggr > 10) or np.any(aggr < -10): logger.warning(f"{translations['warnings']}: {aggr}")
+
+ aggr = [aggr, aggr]
+
+ if aggressiveness["aggr_correction"] is not None:
+ aggr[0] += aggressiveness["aggr_correction"]["left"]
+ aggr[1] += aggressiveness["aggr_correction"]["right"]
+
+ # Apply exponential mathematical warping across defined frequency cutoff bins
+ for ch in range(2):
+ mask[ch, : aggressiveness["split_bin"]] = np.power(mask[ch, : aggressiveness["split_bin"]], 1 + aggr[ch] / 3)
+ mask[ch, aggressiveness["split_bin"] :] = np.power(mask[ch, aggressiveness["split_bin"] :], 1 + aggr[ch])
+
+ return mask
+
+def spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024):
+ """
+ Standard fallback function executing standalone Inverse STFT wave generation without model parameter maps.
+ """
+
+ wave = librosa.istft(spec, n_fft=n_fft, hop_length=hop_length)
+ if wave.ndim == 1: wave = np.asfortranarray([wave, wave])
+
+ return wave
+
+def wave_to_spectrogram_no_mp(wave):
+ """
+ Standard fallback function executing standalone Forward STFT tracking without model parameter maps.
+ """
+
+ spec = librosa.stft(wave, n_fft=2048, hop_length=1024)
+
+ if spec.ndim == 1: spec = np.asfortranarray([spec, spec])
+ return spec
+
+def invert_audio(specs, invert_p=True):
+ """
+ Computes a phase-aware spectral subtraction mask between a mix and a target stem to isolate the remaining tracks.
+
+ Args:
+ specs (list): A list containing exactly two elements: `[mixture_spectrogram, isolated_stem_spectrogram]`.
+ invert_p (bool, optional): Activates precise phase-aware matrix inversion math. Defaults to True.
+
+ Returns:
+ np.ndarray: Complex spectrogram matrix representing the isolated, inverted signal.
+ """
+
+ ln = min([specs[0].shape[2], specs[1].shape[2]])
+ specs[0] = specs[0][:, :, :ln]
+ specs[1] = specs[1][:, :, :ln]
+
+ if invert_p:
+ X_mag, y_mag = np.abs(specs[0]), np.abs(specs[1])
+ # Execute absolute destructive phase negation cancellation equations
+ v_spec = specs[1] - np.where(X_mag >= y_mag, X_mag, y_mag) * np.exp(1.0j * np.angle(specs[0]))
+ else:
+ specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
+ v_spec = specs[0] - specs[1]
+
+ return v_spec
+
+def invert_stem(mixture, stem):
+ """
+ Performs standard time-domain phase inversion subtraction to derive an operational secondary track.
+
+ Args:
+ mixture (np.ndarray): Original raw master mixed audio wave track.
+ stem (np.ndarray): Isolated primary target audio wave track (e.g., vocal stem).
+
+ Returns:
+ np.ndarray: Reconstructed secondary audio wave track matrix (e.g., background instrumental track).
+ """
+
+ return -spectrogram_to_wave_no_mp(invert_audio([wave_to_spectrogram_no_mp(mixture), wave_to_spectrogram_no_mp(stem)])).T
\ No newline at end of file
diff --git a/main/library/uvr5_lib/vr_network/layers.py b/main/library/uvr5_lib/vr_network/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f0ca1c1cff4a44ded1cafb783a4058b812cb4c5
--- /dev/null
+++ b/main/library/uvr5_lib/vr_network/layers.py
@@ -0,0 +1,415 @@
+import os
+import sys
+import torch
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+sys.path.append(os.getcwd())
+
+from main.library.uvr5_lib import spec_utils
+
+class Conv2DBNActiv(nn.Module):
+ """
+ A sequential block consisting of a 2D Convolution, 2D Batch Normalization,
+ and an Activation function.
+ """
+
+ def __init__(
+ self,
+ nin,
+ nout,
+ ksize=3,
+ stride=1,
+ pad=1,
+ dilation=1,
+ activ=nn.ReLU
+ ):
+ """
+ Args:
+ nin (int): Number of input channels.
+ nout (int): Number of output channels.
+ ksize (int, optional): Kernel size. Defaults to 3.
+ stride (int, optional): Stride size. Defaults to 1.
+ pad (int, optional): Padding size. Defaults to 1.
+ dilation (int, optional): Dilation rate. Defaults to 1.
+ activ (nn.Module, optional): Activation function class. Defaults to nn.ReLU.
+ """
+
+ super(Conv2DBNActiv, self).__init__()
+ self.conv = nn.Sequential(
+ # Bias is set to False because BatchNorm follows immediately
+ nn.Conv2d(
+ nin,
+ nout,
+ kernel_size=ksize,
+ stride=stride,
+ padding=pad,
+ dilation=dilation,
+ bias=False
+ ),
+ nn.BatchNorm2d(nout),
+ activ()
+ )
+
+ def __call__(self, input_tensor):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Input tensor.
+
+ Returns:
+ torch.Tensor: Normalized and activated feature maps.
+ """
+
+ return self.conv(input_tensor)
+
+class SeperableConv2DBNActiv(nn.Module):
+ """
+ A Depthwise Separable 2D Convolutional block followed by Batch Normalization
+ and an Activation function to reduce computational complexity.
+ """
+
+ def __init__(
+ self,
+ nin,
+ nout,
+ ksize=3,
+ stride=1,
+ pad=1,
+ dilation=1,
+ activ=nn.ReLU
+ ):
+ """
+ Args:
+ nin (int): Number of input channels.
+ nout (int): Number of output channels.
+ ksize (int, optional): Kernel size. Defaults to 3.
+ stride (int, optional): Stride size. Defaults to 1.
+ pad (int, optional): Padding size. Defaults to 1.
+ dilation (int, optional): Dilation rate. Defaults to 1.
+ activ (Type[nn.Module], optional): Activation function class. Defaults to nn.ReLU.
+ """
+
+ super(SeperableConv2DBNActiv, self).__init__()
+ self.conv = nn.Sequential(
+ # Depthwise Convolution: filters are applied to each channel independently (groups=nin)
+ nn.Conv2d(
+ nin,
+ nin,
+ kernel_size=ksize,
+ stride=stride,
+ padding=pad,
+ dilation=dilation,
+ groups=nin,
+ bias=False,
+ ),
+ # Pointwise Convolution: combines channels linearly using a 1x1 kernel
+ nn.Conv2d(
+ nin,
+ nout,
+ kernel_size=1,
+ bias=False,
+ ),
+ nn.BatchNorm2d(nout),
+ activ(),
+ )
+
+ def __call__(self, input_tensor):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Input tensor.
+
+ Returns:
+ torch.Tensor: Extracted features.
+ """
+
+ return self.conv(input_tensor)
+
+class Encoder(nn.Module):
+ """
+ Encoder block comprising two successive Conv2DBNActiv layers.
+ It extracts features and optionally applies downsampling via stride.
+ """
+
+ def __init__(
+ self,
+ nin,
+ nout,
+ ksize=3,
+ stride=1,
+ pad=1,
+ activ=nn.LeakyReLU
+ ):
+ """
+ Args:
+ nin (int): Number of input channels.
+ nout (int): Number of output channels.
+ ksize (int, optional): Kernel size. Defaults to 3.
+ stride (int, optional): Downsampling stride size. Defaults to 1.
+ pad (int, optional): Padding size. Defaults to 1.
+ activ (Type[nn.Module], optional): Activation function class. Defaults to nn.LeakyReLU.
+ """
+
+ super(Encoder, self).__init__()
+ # First layer preserves the original spatial scale (stride=1) and changes channel capacity
+ self.conv1 = Conv2DBNActiv(
+ nin,
+ nout,
+ ksize,
+ 1,
+ pad,
+ activ=activ
+ )
+ # Second layer handles the structural downsampling via the provided stride parameter
+ self.conv2 = Conv2DBNActiv(
+ nout,
+ nout,
+ ksize,
+ stride,
+ pad,
+ activ=activ
+ )
+
+ def __call__(self, input_tensor):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Input feature maps.
+
+ Returns:
+ Tuple[torch.Tensor, torch.Tensor]:
+ - hidden: Downsampled features for the deeper layers.
+ - skip: Higher-resolution features saved for skip connections in the decoder.
+ """
+
+ skip = self.conv1(input_tensor)
+ hidden = self.conv2(skip)
+
+ return hidden, skip
+
+class Decoder(nn.Module):
+ """
+ Decoder block that upsamples the incoming features, optionally
+ concatenates skip connections from the encoder, and applies convolutions.
+ """
+
+ def __init__(
+ self,
+ nin,
+ nout,
+ ksize=3,
+ stride=1,
+ pad=1,
+ activ=nn.ReLU,
+ dropout=False
+ ):
+ """
+ Args:
+ nin (int): Number of combined input channels (including skip connections).
+ nout (int): Number of output channels.
+ ksize (int, optional): Kernel size. Defaults to 3.
+ stride (int, optional): Stride size. Defaults to 1.
+ pad (int, optional): Padding size. Defaults to 1.
+ activ (Type[nn.Module], optional): Activation function class. Defaults to nn.ReLU.
+ dropout (bool, optional): Whether to use 2D spatial dropout. Defaults to False.
+ """
+
+ super(Decoder, self).__init__()
+ self.dropout = nn.Dropout2d(0.1) if dropout else None
+ self.conv = Conv2DBNActiv(
+ nin,
+ nout,
+ ksize,
+ 1,
+ pad,
+ activ=activ
+ )
+
+ def __call__(self, input_tensor, skip=None):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Lower resolution deep feature map.
+ skip (torch.Tensor, optional): Encoder skip feature map for fusion. Defaults to None.
+
+ Returns:
+ torch.Tensor: Upsampled and merged feature map.
+ """
+
+ # Spatially upsample the low-resolution feature map by a factor of 2
+ input_tensor = F.interpolate(
+ input_tensor,
+ scale_factor=2,
+ mode="bilinear",
+ align_corners=True
+ )
+
+ # Merge structural information from the encoder if provided
+ if skip is not None:
+ # Crop encoder features to perfectly match upsampled tensor dimensions
+ skip = spec_utils.crop_center(skip, input_tensor)
+ # Concatenate features along the channel dimension (dim=1)
+ input_tensor = torch.cat([input_tensor, skip], dim=1)
+
+ output_tensor = self.conv(input_tensor)
+ if self.dropout is not None:
+ output_tensor = self.dropout(output_tensor)
+
+ return output_tensor
+
+class ASPPModule(nn.Module):
+ """
+ Atrous Spatial Pyramid Pooling (ASPP) Module with variable architecture support.
+ Captures multi-scale multi-context features using parallel dilated convolutions.
+ """
+
+ def __init__(self, nn_architecture, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
+ """
+ Args:
+ nn_architecture (int): Identifier code representing specific architectural configurations.
+ nin (int): Number of input channels.
+ nout (int): Number of output channels from the bottleneck layer.
+ dilations (Tuple[int, int, int], optional): Dilation rates for parallel branches. Defaults to (4, 8, 16).
+ activ (Type[nn.Module], optional): Activation function class. Defaults to nn.ReLU.
+ """
+
+ super(ASPPModule, self).__init__()
+ self.nn_architecture = nn_architecture
+ # Hardcoded ID groupings for dynamic architectural adaptations
+ self.six_layer = [129605]
+ self.seven_layer = [537238, 537227, 33966]
+
+ # Template for additional high-dilation parallel branches
+ extra_conv = SeperableConv2DBNActiv(
+ nin,
+ nin,
+ 3,
+ 1,
+ dilations[2],
+ dilations[2],
+ activ=activ
+ )
+
+ # Branch 1: Image-level global pooling context
+ self.conv1 = nn.Sequential(
+ nn.AdaptiveAvgPool2d((1, None)),
+ Conv2DBNActiv(
+ nin,
+ nin,
+ 1,
+ 1,
+ 0,
+ activ=activ
+ )
+ )
+ # Branch 2: Standard 1x1 Convolution
+ self.conv2 = Conv2DBNActiv(
+ nin,
+ nin,
+ 1,
+ 1,
+ 0,
+ activ=activ
+ )
+ # Branch 3: Dilated Depthwise Separable 3x3 Conv (Dilation Rate 1)
+ self.conv3 = SeperableConv2DBNActiv(
+ nin,
+ nin,
+ 3,
+ 1,
+ dilations[0],
+ dilations[0],
+ activ=activ
+ )
+ # Branch 4: Dilated Depthwise Separable 3x3 Conv (Dilation Rate 2)
+ self.conv4 = SeperableConv2DBNActiv(
+ nin,
+ nin,
+ 3,
+ 1,
+ dilations[1],
+ dilations[1],
+ activ=activ
+ )
+ # Branch 5: Dilated Depthwise Separable 3x3 Conv (Dilation Rate 3)
+ self.conv5 = SeperableConv2DBNActiv(
+ nin,
+ nin,
+ 3,
+ 1,
+ dilations[2],
+ dilations[2],
+ activ=activ
+ )
+
+ # Dynamically append extra parallel branches based on architecture presets
+ if self.nn_architecture in self.six_layer:
+ self.conv6 = extra_conv
+ nin_x = 6 # 6 features to concatenate
+ elif self.nn_architecture in self.seven_layer:
+ self.conv6 = extra_conv
+ self.conv7 = extra_conv
+ nin_x = 7 # 7 features to concatenate
+ else:
+ nin_x = 5 # Standard 5 baseline branches
+
+ # Fuses concatenated multi-scale channels back to the requested `nout` shape
+ self.bottleneck = nn.Sequential(
+ Conv2DBNActiv(
+ nin * nin_x,
+ nout,
+ 1,
+ 1,
+ 0,
+ activ=activ
+ ),
+ nn.Dropout2d(0.1)
+ )
+
+ def forward(self, input_tensor):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Feature map tensor.
+
+ Returns:
+ torch.Tensor: Context-aware aggregated representation.
+ """
+
+ _, _, h, w = input_tensor.size()
+ # Rescale the global average pooled branch back to match spatial dimensions
+ feat1 = F.interpolate(
+ self.conv1(input_tensor),
+ size=(h, w),
+ mode="bilinear",
+ align_corners=True
+ )
+
+ # Execute baseline branches
+ feat2 = self.conv2(input_tensor)
+ feat3 = self.conv3(input_tensor)
+ feat4 = self.conv4(input_tensor)
+ feat5 = self.conv5(input_tensor)
+
+ # Execute conditional branches and concatenate along channel dimension (dim=1)
+ if self.nn_architecture in self.six_layer:
+ feat6 = self.conv6(input_tensor)
+ out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1)
+ elif self.nn_architecture in self.seven_layer:
+ feat6 = self.conv6(input_tensor)
+ feat7 = self.conv7(input_tensor)
+
+ out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
+ else:
+ out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
+
+ # Apply spatial fusion and dropout
+ bottleneck_output = self.bottleneck(out)
+ return bottleneck_output
\ No newline at end of file
diff --git a/main/library/uvr5_lib/vr_network/layers_new.py b/main/library/uvr5_lib/vr_network/layers_new.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c3f77b79e0888ae217f52d17946f37d9297ba4f
--- /dev/null
+++ b/main/library/uvr5_lib/vr_network/layers_new.py
@@ -0,0 +1,384 @@
+import os
+import sys
+import torch
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+sys.path.append(os.getcwd())
+
+from main.library.uvr5_lib import spec_utils
+
+class Conv2DBNActiv(nn.Module):
+ """
+ A sequential block consisting of a 2D Convolution, 2D Batch Normalization,
+ and an Activation function.
+ """
+
+ def __init__(
+ self,
+ nin,
+ nout,
+ ksize=3,
+ stride=1,
+ pad=1,
+ dilation=1,
+ activ=nn.ReLU
+ ):
+ """
+ Args:
+ nin (int): Number of input channels.
+ nout (int): Number of output channels.
+ ksize (int, optional): Kernel size. Defaults to 3.
+ stride (int, optional): Stride size. Defaults to 1.
+ pad (int, optional): Padding size. Defaults to 1.
+ dilation (int, optional): Dilation rate. Defaults to 1.
+ activ (nn.Module, optional): Activation function class. Defaults to nn.ReLU.
+ """
+
+ super(Conv2DBNActiv, self).__init__()
+ self.conv = nn.Sequential(
+ # Bias is set to False because BatchNorm follows immediately
+ nn.Conv2d(
+ nin,
+ nout,
+ kernel_size=ksize,
+ stride=stride,
+ padding=pad,
+ dilation=dilation,
+ bias=False
+ ),
+ nn.BatchNorm2d(nout),
+ activ()
+ )
+
+ def __call__(self, input_tensor):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Input tensor.
+
+ Returns:
+ torch.Tensor: Normalized and activated feature maps.
+ """
+
+ return self.conv(input_tensor)
+
+class Encoder(nn.Module):
+ """
+ Encoder block composed of two successive Conv2DBNActiv layers.
+ Typically used to downsample and extract features in a U-Net architecture.
+ """
+
+ def __init__(
+ self,
+ nin,
+ nout,
+ ksize=3,
+ stride=1,
+ pad=1,
+ activ=nn.LeakyReLU
+ ):
+ """
+ Args:
+ nin (int): Number of input channels.
+ nout (int): Number of output channels.
+ ksize (int, optional): Kernel size. Defaults to 3.
+ stride (int, optional): Stride size. Defaults to 1.
+ pad (int, optional): Padding size. Defaults to 1.
+ activ (nn.Module, optional): Activation function class. Defaults to nn.LeakyReLU.
+ """
+
+ super(Encoder, self).__init__()
+ # First layer handles downsampling/channel changes via defined stride
+ self.conv1 = Conv2DBNActiv(
+ nin,
+ nout,
+ ksize,
+ stride,
+ pad,
+ activ=activ
+ )
+ # Second layer refines the features with a fixed stride of 1
+ self.conv2 = Conv2DBNActiv(
+ nout,
+ nout,
+ ksize,
+ 1,
+ pad,
+ activ=activ
+ )
+
+ def __call__(self, input_tensor):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Input tensor.
+
+ Returns:
+ torch.Tensor: Encoded feature maps.
+ """
+
+ hidden = self.conv1(input_tensor)
+ hidden = self.conv2(hidden)
+
+ return hidden
+
+class Decoder(nn.Module):
+ """
+ Decoder block that upsamples the input tensor, aligns/concatenates
+ it with skip connections, and processes it through a Conv2DBNActiv layer.
+ """
+
+ def __init__(
+ self,
+ nin,
+ nout,
+ ksize=3,
+ stride=1,
+ pad=1,
+ activ=nn.ReLU,
+ dropout=False
+ ):
+ """
+ Args:
+ nin (int): Number of input channels (including skip connections if any).
+ nout (int): Number of output channels.
+ ksize (int, optional): Kernel size. Defaults to 3.
+ stride (int, optional): Stride size. Defaults to 1.
+ pad (int, optional): Padding size. Defaults to 1.
+ activ (nn.Module, optional): Activation function class. Defaults to nn.ReLU.
+ dropout (bool, optional): Whether to apply 2D Dropout. Defaults to False.
+ """
+
+ super(Decoder, self).__init__()
+ self.dropout = nn.Dropout2d(0.1) if dropout else None
+ self.conv1 = Conv2DBNActiv(
+ nin,
+ nout,
+ ksize,
+ 1,
+ pad,
+ activ=activ
+ )
+
+ def __call__(self, input_tensor, skip=None):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Low-resolution bottleneck features.
+ skip (torch.Tensor, optional): High-resolution skip connection tensor from the encoder.
+
+ Returns:
+ torch.Tensor: Decoded upsampled feature maps.
+ """
+
+ # Upsample the lower resolution input tensor spatially by a factor of 2
+ input_tensor = F.interpolate(
+ input_tensor,
+ scale_factor=2,
+ mode="bilinear",
+ align_corners=True
+ )
+
+ # Handle encoder-decoder skip connections
+ if skip is not None:
+ # Crop the skip connection tensor to match the spatial size of the upsampled input tensor
+ skip = spec_utils.crop_center(skip, input_tensor)
+ # Concatenate along the channel dimension (dim=1)
+ input_tensor = torch.cat([input_tensor, skip], dim=1)
+
+ hidden = self.conv1(input_tensor)
+
+ if self.dropout is not None:
+ hidden = self.dropout(hidden)
+
+ return hidden
+
+class ASPPModule(nn.Module):
+ """
+ Atrous Spatial Pyramid Pooling (ASPP) Module.
+ Captures multi-scale contextual information using parallel dilated convolutions.
+ """
+
+ def __init__(
+ self,
+ nin,
+ nout,
+ dilations=(4, 8, 12),
+ activ=nn.ReLU,
+ dropout=False
+ ):
+ """
+ Args:
+ nin (int): Number of input channels.
+ nout (int): Number of output channels for each parallel branch.
+ dilations (tuple, optional): Dilation rates for the three dilated branches. Defaults to (4, 8, 12).
+ activ (nn.Module, optional): Activation function class. Defaults to nn.ReLU.
+ dropout (bool, optional): Whether to apply 2D Dropout. Defaults to False.
+ """
+
+ super(ASPPModule, self).__init__()
+ # Branch 1: Global Context (Image-level pooling followed by 1x1 Conv)
+ self.conv1 = nn.Sequential(
+ nn.AdaptiveAvgPool2d((1, None)),
+ Conv2DBNActiv(
+ nin,
+ nout,
+ 1,
+ 1,
+ 0,
+ activ=activ
+ )
+ )
+
+ # Branch 2: Standard 1x1 Convolution
+ self.conv2 = Conv2DBNActiv(
+ nin,
+ nout,
+ 1,
+ 1,
+ 0,
+ activ=activ
+ )
+
+ # Branch 3: 3x3 Convolution with first dilation rate
+ self.conv3 = Conv2DBNActiv(
+ nin,
+ nout,
+ 3,
+ 1,
+ dilations[0],
+ dilations[0],
+ activ=activ
+ )
+
+ # Branch 4: 3x3 Convolution with second dilation rate
+ self.conv4 = Conv2DBNActiv(
+ nin,
+ nout,
+ 3,
+ 1,
+ dilations[1],
+ dilations[1],
+ activ=activ
+ )
+
+ # Branch 5: 3x3 Convolution with third dilation rate
+ self.conv5 = Conv2DBNActiv(
+ nin,
+ nout,
+ 3,
+ 1,
+ dilations[2],
+ dilations[2],
+ activ=activ
+ )
+
+ # Bottleneck layer to fuse features from all 5 parallel branches (nout * 5 channels)
+ self.bottleneck = Conv2DBNActiv(
+ nout * 5,
+ nout,
+ 1,
+ 1,
+ 0,
+ activ=activ
+ )
+
+ self.dropout = nn.Dropout2d(0.1) if dropout else None
+
+ def forward(self, input_tensor):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Input feature maps.
+
+ Returns:
+ torch.Tensor: Multi-scale aggregated feature maps.
+ """
+
+ _, _, h, w = input_tensor.size()
+ # Concatenate features from all 5 scales along the channel dimension
+ out = self.bottleneck(
+ torch.cat((
+ # Upsample the global pooling branch back to original spatial dimensions
+ F.interpolate(
+ self.conv1(input_tensor),
+ size=(h, w),
+ mode="bilinear",
+ align_corners=True
+ ),
+ self.conv2(input_tensor),
+ self.conv3(input_tensor),
+ self.conv4(input_tensor),
+ self.conv5(input_tensor)
+ ), dim=1))
+
+ if self.dropout is not None:
+ out = self.dropout(out)
+
+ return out
+
+class LSTMModule(nn.Module):
+ """
+ A hybrid Recurrent-Convolutional module.
+ Squeezes multi-channel feature maps into a 1D sequence per frame,
+ processes it using a Bi-directional LSTM, and projects it back with a Dense layer.
+ """
+
+ def __init__(
+ self,
+ nin_conv,
+ nin_lstm,
+ nout_lstm
+ ):
+ """
+ Args:
+ nin_conv (int): Number of input channels for the initial 2D convolution.
+ nin_lstm (int): Input size (feature dimension) for the LSTM layer.
+ nout_lstm (int): Total output size for the LSTM layer (split equally if bidirectional).
+ """
+
+ super(LSTMModule, self).__init__()
+ # Compresses the input feature channels down to 1 channel
+ self.conv = Conv2DBNActiv(
+ nin_conv,
+ 1,
+ 1,
+ 1,
+ 0
+ )
+ # Sequence modeling layer over time/frequency frames
+ self.lstm = nn.LSTM(
+ input_size=nin_lstm,
+ hidden_size=nout_lstm // 2,
+ bidirectional=True
+ )
+ # Dense linear projections with Batch Normalization and ReLU activation
+ self.dense = nn.Sequential(
+ nn.Linear(nout_lstm, nin_lstm),
+ nn.BatchNorm1d(nin_lstm),
+ nn.ReLU()
+ )
+
+ def forward(self, input_tensor):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Input tensor.
+
+ Returns:
+ torch.Tensor: Sequence-processed tensor restored back to 4D shape.
+ """
+
+ N, _, nbins, nframes = input_tensor.size()
+
+ hidden, _ = self.lstm(self.conv(input_tensor)[:, 0].permute(2, 0, 1))
+ hidden = self.dense(hidden.reshape(-1, hidden.size()[-1])).reshape(nframes, N, 1, nbins)
+
+ return hidden.permute(1, 2, 3, 0)
\ No newline at end of file
diff --git a/main/library/uvr5_lib/vr_network/model_param_init.py b/main/library/uvr5_lib/vr_network/model_param_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..86418af74d377cc2b4b6b69d8d15dfcfd30725b9
--- /dev/null
+++ b/main/library/uvr5_lib/vr_network/model_param_init.py
@@ -0,0 +1,56 @@
+import json
+import struct
+
+class ModelParameters(object):
+ """
+ Handles loading, parsing, and normalizing model configurations from
+ either binary pickle files (.bin) or standard JSON files.
+ """
+
+ def __init__(
+ self,
+ config_path="",
+ key_in_bin=None
+ ):
+ """
+ Args:
+ config_path (str, optional): Path to the configuration file (.json or .bin). Defaults to "".
+ key_in_bin (Optional[str], optional): The specific key to extract if loading from a .bin pickle file. Defaults to None.
+ """
+
+ # Determine the file type by checking the extension
+ if config_path.endswith(".bin"):
+ target_content = None
+ # Load binary configurations using pickle
+ with open(config_path, "rb") as f:
+ while 1:
+ key_len_bytes = f.read(4)
+ if not key_len_bytes: break
+
+ current = f.read(struct.unpack("I", key_len_bytes)[0]).decode('utf-8')
+ data_len = struct.unpack("I", f.read(4))[0]
+
+ if current == key_in_bin:
+ target_content = f.read(data_len)
+ break
+ else:
+ f.seek(data_len, 1)
+
+ if target_content is None:
+ raise KeyError(f"Configuration key '{key_in_bin}' not found inside safe binary database.")
+
+ self.param = json.loads(target_content.decode('utf-8'))
+ else:
+ # Load standard textual configurations using json
+ with open(config_path, "r", encoding="utf-8") as f:
+ # Use the custom int_keys hook to ensure valid integer key lookups
+ self.param = json.loads(f.read())
+
+ # Ensure essential boolean stereo/processing flags exist; default to False if missing
+ for k in ["mid_side", "mid_side_b", "mid_side_b2", "stereo_w", "stereo_n", "reverse"]:
+ if k not in self.param:
+ self.param[k] = False
+
+ # Normalize key naming conventions: mirror 'n_bins' value over to 'bins' if it exists
+ if "n_bins" in self.param:
+ self.param["bins"] = self.param["n_bins"]
\ No newline at end of file
diff --git a/main/library/uvr5_lib/vr_network/nets.py b/main/library/uvr5_lib/vr_network/nets.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca59a108046ac66b107fe12aacc1c945f01a64c1
--- /dev/null
+++ b/main/library/uvr5_lib/vr_network/nets.py
@@ -0,0 +1,368 @@
+import os
+import sys
+import torch
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+sys.path.append(os.getcwd())
+
+from main.library.uvr5_lib.vr_network import layers
+
+class BaseASPPNet(nn.Module):
+ """
+ U-Net style sub-network backbone integrated with an Atrous Spatial Pyramid Pooling
+ (ASPP) module. Dynamically allocates an extra depth layer for deeper model configurations.
+ """
+
+ def __init__(
+ self,
+ nn_architecture,
+ nin,
+ ch,
+ dilations=(4, 8, 16)
+ ):
+ """
+ Args:
+ nn_architecture (int): Configuration ID determining the architectural path.
+ nin (int): Number of input channels.
+ ch (int): Base channel depth multiplier.
+ dilations (Tuple[int, int, int], optional): ASPP module dilation rates. Defaults to (4, 8, 16).
+ """
+
+ super(BaseASPPNet, self).__init__()
+ self.nn_architecture = nn_architecture
+
+ # Baseline Encoder contracting path (Downsampling)
+ self.enc1 = layers.Encoder(
+ nin,
+ ch,
+ 3,
+ 2,
+ 1
+ )
+ self.enc2 = layers.Encoder(
+ ch,
+ ch * 2,
+ 3,
+ 2,
+ 1
+ )
+ self.enc3 = layers.Encoder(
+ ch * 2,
+ ch * 4,
+ 3,
+ 2,
+ 1
+ )
+ self.enc4 = layers.Encoder(
+ ch * 4,
+ ch * 8,
+ 3,
+ 2,
+ 1
+ )
+
+ # Dynamic expansion branch based on architecture key
+ if self.nn_architecture == 129605:
+ self.enc5 = layers.Encoder(
+ ch * 8,
+ ch * 16,
+ 3,
+ 2,
+ 1
+ )
+ self.aspp = layers.ASPPModule(
+ nn_architecture,
+ ch * 16,
+ ch * 32,
+ dilations
+ )
+ self.dec5 = layers.Decoder(
+ ch * (16 + 32),
+ ch * 16,
+ 3,
+ 1,
+ 1
+ )
+ else:
+ self.aspp = layers.ASPPModule(
+ nn_architecture,
+ ch * 8,
+ ch * 16,
+ dilations
+ )
+
+ # Baseline Decoder expanding path (Upsampling)
+ self.dec4 = layers.Decoder(
+ ch * (8 + 16),
+ ch * 8,
+ 3,
+ 1,
+ 1
+ )
+ self.dec3 = layers.Decoder(
+ ch * (4 + 8),
+ ch * 4,
+ 3,
+ 1,
+ 1
+ )
+ self.dec2 = layers.Decoder(
+ ch * (2 + 4),
+ ch * 2,
+ 3,
+ 1,
+ 1
+ )
+ self.dec1 = layers.Decoder(
+ ch * (1 + 2),
+ ch,
+ 3,
+ 1,
+ 1
+ )
+
+ def __call__(self, input_tensor):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Input feature maps.
+
+ Returns:
+ torch.Tensor: Reconstructed full-resolution feature maps.
+ """
+
+ # Execute downsampling encoder sequence and store intermediate states for skip connections
+ hidden_state, encoder_output1 = self.enc1(input_tensor)
+ hidden_state, encoder_output2 = self.enc2(hidden_state)
+ hidden_state, encoder_output3 = self.enc3(hidden_state)
+ hidden_state, encoder_output4 = self.enc4(hidden_state)
+
+ # Apply multi-scale context bottleneck according to preset configuration depth
+ if self.nn_architecture == 129605:
+ hidden_state, encoder_output5 = self.enc5(hidden_state)
+ hidden_state = self.dec5(self.aspp(hidden_state), encoder_output5)
+ else:
+ hidden_state = self.aspp(hidden_state)
+
+ # Execute upsampling decoder sequence utilizing skip connections
+ hidden_state = self.dec1(
+ self.dec2(
+ self.dec3(self.dec4(hidden_state, encoder_output4), encoder_output3),
+ encoder_output2
+ ),
+ encoder_output1
+ )
+ return hidden_state
+
+def determine_model_capacity(n_fft_bins, nn_architecture):
+ """
+ Factory function that retrieves hyperparameters based on model capacity profiles,
+ instantiates, and returns a CascadedASPPNet instance.
+
+ Args:
+ n_fft_bins (int): The number of raw frequency bins from the FFT process.
+ nn_architecture (int): Configuration ID flag identifying capacity constraints.
+
+ Raises:
+ ValueError: If an unexpected or unknown architecture ID is provided.
+
+ Returns:
+ nn.Module: Configured CascadedASPPNet model instance ready for weight loading or training.
+ """
+
+ # Architecture capacity categories
+ sp_model_arch = [31191, 33966, 129605]
+ hp_model_arch = [123821, 123812]
+ hp2_model_arch = [537238, 537227]
+
+ if nn_architecture in sp_model_arch:
+ # Standard Profile structural tuples
+ model_capacity_data = [
+ (2, 16),
+ (2, 16),
+ (18, 8, 1, 1, 0),
+ (8, 16),
+ (34, 16, 1, 1, 0),
+ (16, 32),
+ (32, 2, 1),
+ (16, 2, 1),
+ (16, 2, 1)
+ ]
+
+ if nn_architecture in hp_model_arch:
+ # High Profile structural tuples
+ model_capacity_data = [
+ (2, 32),
+ (2, 32),
+ (34, 16, 1, 1, 0),
+ (16, 32),
+ (66, 32, 1, 1, 0),
+ (32, 64),
+ (64, 2, 1),
+ (32, 2, 1),
+ (32, 2, 1)
+ ]
+
+ if nn_architecture in hp2_model_arch:
+ # High Profile 2 structural tuples
+ model_capacity_data = [
+ (2, 64),
+ (2, 64),
+ (66, 32, 1, 1, 0),
+ (32, 64),
+ (130, 64, 1, 1, 0),
+ (64, 128),
+ (128, 2, 1),
+ (64, 2, 1),
+ (64, 2, 1)
+ ]
+
+ # Initialize the target network wrapper mapping selected structural data configurations
+ model = CascadedASPPNet(
+ n_fft_bins,
+ model_capacity_data,
+ nn_architecture
+ )
+
+ return model
+
+class CascadedASPPNet(nn.Module):
+ """
+ Multi-stage cascaded separation architecture using parallel ASPP modules.
+ Splits audio into low/high sub-bands before fusing them progressively
+ into full-band resolution estimators.
+ """
+
+ def __init__(
+ self,
+ n_fft,
+ model_capacity_data,
+ nn_architecture
+ ):
+ """
+ Args:
+ n_fft (int): Total FFT size determining spectrogram bin width.
+ model_capacity_data (List[Tuple]): Hyperparameters unpacking structure for layer dims.
+ nn_architecture (int): Configuration token for BaseASPPNet variants.
+ """
+
+ super(CascadedASPPNet, self).__init__()
+ # STAGE 1: Independent Sub-Band Processing Nets
+ self.stg1_low_band_net = BaseASPPNet(
+ nn_architecture,
+ *model_capacity_data[0]
+ )
+ self.stg1_high_band_net = BaseASPPNet(
+ nn_architecture,
+ *model_capacity_data[1]
+ )
+ # STAGE 2: Channel Dimensionality Bridge and Full-Band Fusion Net
+ self.stg2_bridge = layers.Conv2DBNActiv(*model_capacity_data[2])
+ self.stg2_full_band_net = BaseASPPNet(
+ nn_architecture,
+ *model_capacity_data[3]
+ )
+ # STAGE 3: Final Dense Aggregation Bridge and Full-Band Net
+ self.stg3_bridge = layers.Conv2DBNActiv(*model_capacity_data[4])
+ self.stg3_full_band_net = BaseASPPNet(
+ nn_architecture,
+ *model_capacity_data[5]
+ )
+ # Projection Heads
+ self.out = nn.Conv2d(
+ *model_capacity_data[6],
+ bias=False
+ )
+ self.aux1_out = nn.Conv2d(
+ *model_capacity_data[7],
+ bias=False
+ )
+ self.aux2_out = nn.Conv2d(
+ *model_capacity_data[8],
+ bias=False
+ )
+ # Boundary calculations and padding configuration constants
+ self.max_bin = n_fft // 2
+ self.output_bin = n_fft // 2 + 1
+ self.offset = 128
+
+ def forward(self, input_tensor):
+ """
+ Forward pass. Computes cascading target sub-band components and recovers shapes via padding.
+
+ Args:
+ input_tensor (torch.Tensor): Audio mixture spectrogram tensor of shape (N, 2, Bins, Frames).
+
+ Returns:
+ Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
+ In evaluation mode: Primary isolated signal tensor.
+ In training mode: Tuple containing masked target signal and two multi-stage auxiliary predictions.
+ """
+
+ # Maintain an isolated detached pointer of the reference mix spectrogram
+ mix = input_tensor.detach()
+ # Slice down to target structural maximum frequency limits
+ input_tensor = input_tensor.clone()
+ input_tensor = input_tensor[:, :, : self.max_bin]
+ # Calculate midpoint split threshold across frequency axis
+ bandwidth = input_tensor.size()[2] // 2
+ # Stage 1: Process independent sub-bands, then concat along frequency axis (dim=2)
+ aux1 = torch.cat([
+ self.stg1_low_band_net(input_tensor[:, :, :bandwidth]),
+ self.stg1_high_band_net(input_tensor[:, :, bandwidth:])
+ ], dim=2)
+ # Stage 2: Concatenate initial mix with stage 1 features along channels (dim=1)
+ hidden_state = torch.cat([input_tensor, aux1], dim=1)
+ aux2 = self.stg2_full_band_net(self.stg2_bridge(hidden_state))
+ # Stage 3: Merge cumulative multi-stage features for comprehensive full-band inference
+ hidden_state = torch.cat([input_tensor, aux1, aux2], dim=1)
+ hidden_state = self.stg3_full_band_net(self.stg3_bridge(hidden_state))
+ # Project output estimation and scale back to original STFT footprint via edge replication
+ mask = self.out(hidden_state).sigmoid()
+ mask = F.pad(
+ input=mask,
+ pad=(0, 0, 0, self.output_bin - mask.size()[2]),
+ mode="replicate"
+ )
+
+ if self.training:
+ # Format and pad Stage 1 auxiliary output profiles for optimization tracking
+ aux1 = self.aux1_out(aux1).sigmoid()
+ aux1 = F.pad(
+ input=aux1,
+ pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
+ mode="replicate"
+ )
+ # Format and pad Stage 2 auxiliary output profiles for optimization tracking
+ aux2 = self.aux2_out(aux2).sigmoid()
+ aux2 = F.pad(
+ input=aux2,
+ pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
+ mode="replicate"
+ )
+
+ # Return element-wise masked component segments multiplied directly by the original mix
+ return mask * mix, aux1 * mix, aux2 * mix
+ else:
+ return mask
+
+ def predict_mask(self, input_tensor):
+ """
+ Helper method that invokes the forward pass and clips frame overlap boundary margins.
+
+ Args:
+ input_tensor (torch.Tensor): Audio mix input spectrogram.
+
+ Returns:
+ torch.Tensor: Cropped evaluation isolation filter mask.
+ """
+
+ mask = self.forward(input_tensor)
+ # Remove sliding window frame overlap margins along the time dimension if configured
+ if self.offset > 0:
+ mask = mask[:, :, :, self.offset : -self.offset]
+
+ return mask
\ No newline at end of file
diff --git a/main/library/uvr5_lib/vr_network/nets_new.py b/main/library/uvr5_lib/vr_network/nets_new.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3b8ef94b3c806a76f08a377528d30e9e641228c
--- /dev/null
+++ b/main/library/uvr5_lib/vr_network/nets_new.py
@@ -0,0 +1,347 @@
+import os
+import sys
+import torch
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+sys.path.append(os.getcwd())
+
+from main.library.uvr5_lib.vr_network import layers_new as layers
+
+class BaseNet(nn.Module):
+ """
+ Standard U-Net backbone integrated with Atrous Spatial Pyramid Pooling (ASPP)
+ and a Bi-directional LSTM layer for progressive spatial-temporal audio features.
+ """
+
+ def __init__(
+ self,
+ nin,
+ nout,
+ nin_lstm,
+ nout_lstm,
+ dilations=((4, 2), (8, 4), (12, 6))
+ ):
+ """
+ Args:
+ nin (int): Number of input channels.
+ nout (int): Baseline output channel scale for the first encoder.
+ nin_lstm (int): Frequency bin feature sequence length/size for the LSTM.
+ nout_lstm (int): Total hidden size of the bidirectional LSTM layer.
+ dilations (Tuple, optional): Dilation configurations for the ASPP module branches.
+ """
+
+ super(BaseNet, self).__init__()
+ # Encoder Layers
+ self.enc1 = layers.Conv2DBNActiv(
+ nin,
+ nout,
+ 3,
+ 1,
+ 1
+ )
+ self.enc2 = layers.Encoder(
+ nout,
+ nout * 2,
+ 3,
+ 2,
+ 1
+ )
+ self.enc3 = layers.Encoder(
+ nout * 2,
+ nout * 4,
+ 3,
+ 2,
+ 1
+ )
+ self.enc4 = layers.Encoder(
+ nout * 4,
+ nout * 6,
+ 3,
+ 2,
+ 1
+ )
+ self.enc5 = layers.Encoder(
+ nout * 6,
+ nout * 8,
+ 3,
+ 2,
+ 1
+ )
+ # Bottleneck Multi-Scale Context Aggregator
+ self.aspp = layers.ASPPModule(
+ nout * 8,
+ nout * 8,
+ dilations,
+ dropout=True
+ )
+ # Decoder Layers
+ # nin format: current upsampled channels + skip connection channel width
+ self.dec4 = layers.Decoder(
+ nout * (6 + 8),
+ nout * 6,
+ 3,
+ 1,
+ 1
+ )
+ self.dec3 = layers.Decoder(
+ nout * (4 + 6),
+ nout * 4,
+ 3,
+ 1,
+ 1
+ )
+ self.dec2 = layers.Decoder(
+ nout * (2 + 4),
+ nout * 2,
+ 3,
+ 1,
+ 1
+ )
+ # Recurrent Temporal Processing Refinement
+ self.lstm_dec2 = layers.LSTMModule(
+ nout * 2,
+ nin_lstm,
+ nout_lstm
+ )
+ # Final decoder joins standard features, LSTM temporal features, and skip connections (+1 channel)
+ self.dec1 = layers.Decoder(
+ nout * (1 + 2) + 1,
+ nout * 1,
+ 3,
+ 1,
+ 1
+ )
+
+ def __call__(self, input_tensor):
+ """
+ Forward pass.
+
+ Args:
+ input_tensor (torch.Tensor): Input audio spectrogram maps.
+
+ Returns:
+ torch.Tensor: Refined and decoded audio representations.
+ """
+
+ # Execute hierarchical contracting path (Encoder)
+ encoded1 = self.enc1(input_tensor)
+ encoded2 = self.enc2(encoded1)
+ encoded3 = self.enc3(encoded2)
+ encoded4 = self.enc4(encoded3)
+
+ # Bottom layer extraction followed by progressive upsampling and skip-connections matching
+ bottleneck = self.dec2(
+ self.dec3(
+ self.dec4(
+ self.aspp(self.enc5(encoded4)),
+ encoded4
+ ),
+ encoded3
+ ),
+ encoded2
+ )
+ # Concatenate spatial layout maps with 1D sequential temporal features from the LSTM
+ bottleneck = self.dec1(
+ torch.cat([
+ bottleneck,
+ self.lstm_dec2(bottleneck)
+ ], dim=1),
+ encoded1
+ )
+
+ return bottleneck
+
+class CascadedNet(nn.Module):
+ """
+ Cascaded Multi-Band Architecture designed for full audio separation tasks.
+ Splits the spectrogram into low/high sub-bands, processes them across stages,
+ and predicts an ideal separation filter/mask.
+ """
+
+ def __init__(
+ self,
+ n_fft,
+ nn_arch_size=51000,
+ nout=32,
+ nout_lstm=128
+ ):
+ """
+ Args:
+ n_fft (int): Fast Fourier Transform (FFT) frequency window footprint.
+ nn_arch_size (int, optional): Preset capacity key dictating filter depth limits.
+ nout (int, optional): Core feature channels size. Defaults to 32.
+ nout_lstm (int, optional): Inner hidden sequence limit sizes for recurrent blocks.
+ """
+
+ super(CascadedNet, self).__init__()
+ # Determine internal frequency dimensions ignoring the complex symmetric conjugate block
+ self.max_bin = n_fft // 2
+ self.output_bin = n_fft // 2 + 1
+ self.nin_lstm = self.max_bin // 2
+ self.offset = 64 # Overlap padding boundary crop parameter
+ # Dynamic structural sizing check based on architecture footprint definitions
+ nout = 64 if nn_arch_size == 218409 else nout
+
+ # STAGE 1: Independent Sub-Band Nets
+ self.stg1_low_band_net = nn.Sequential(
+ BaseNet(
+ 2,
+ nout // 2,
+ self.nin_lstm // 2,
+ nout_lstm
+ ),
+ layers.Conv2DBNActiv(
+ nout // 2,
+ nout // 4,
+ 1,
+ 1,
+ 0
+ )
+ )
+
+ self.stg1_high_band_net = BaseNet(
+ 2,
+ nout // 4,
+ self.nin_lstm // 2,
+ nout_lstm // 2
+ )
+
+ # STAGE 2: Dense-Linked Sub-Band Nets
+ self.stg2_low_band_net = nn.Sequential(
+ BaseNet(
+ nout // 4 + 2,
+ nout,
+ self.nin_lstm // 2, nout_lstm
+ ),
+ layers.Conv2DBNActiv(
+ nout,
+ nout // 2,
+ 1,
+ 1,
+ 0
+ )
+ )
+
+ self.stg2_high_band_net = BaseNet(
+ nout // 4 + 2,
+ nout // 2,
+ self.nin_lstm // 2,
+ nout_lstm // 2
+ )
+
+ # STAGE 3: Full-Band Cross-Attention / Aggregation Net
+ self.stg3_full_band_net = BaseNet(
+ 3 * nout // 4 + 2,
+ nout,
+ self.nin_lstm,
+ nout_lstm
+ )
+
+ # Linear Projection Output Probes
+ self.out = nn.Conv2d(
+ nout,
+ 2,
+ 1,
+ bias=False
+ )
+ self.aux_out = nn.Conv2d(
+ 3 * nout // 4,
+ 2,
+ 1,
+ bias=False
+ )
+
+ def forward(self, input_tensor):
+ """
+ Forward pass. Splits, predicts masks hierarchically, and maps results to original sizes.
+
+ Args:
+ input_tensor (torch.Tensor): Audio spectrogram batch of size.
+
+ Returns:
+ Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+ Primary mask, plus auxiliary mask if module is set to training mode.
+ """
+
+ # Crop to standard max frequency limits
+ input_tensor = input_tensor[:, :, : self.max_bin]
+ bandw = input_tensor.size()[2] // 2
+
+ # Squeeze split into distinct low-band and high-band modules
+ l1_in = input_tensor[:, :, :bandw]
+ h1_in = input_tensor[:, :, bandw:]
+
+ # Execute Stage 1 processing
+ l1 = self.stg1_low_band_net(l1_in)
+ h1 = self.stg1_high_band_net(h1_in)
+ aux1 = torch.cat([l1, h1], dim=2)
+
+ # Cascade features down to Stage 2 inputs using dense connections
+ l2_in = torch.cat([l1_in, l1], dim=1)
+ h2_in = torch.cat([h1_in, h1], dim=1)
+
+ # Execute Stage 2 processing
+ l2 = self.stg2_low_band_net(l2_in)
+ h2 = self.stg2_high_band_net(h2_in)
+
+ aux2 = torch.cat([l2, h2], dim=2)
+ # Fuse Stage 1, Stage 2, and initial inputs together for Full-Band global resolution
+ f3_in = torch.cat([input_tensor, aux1, aux2], dim=1)
+ f3 = self.stg3_full_band_net(f3_in)
+
+ # Compute target isolation filter mask [bounded 0.0 to 1.0]
+ mask = self.out(f3).sigmoid()
+ # Pad back to match original input frequency output bin lengths using edge replication
+ mask = F.pad(input=mask, pad=(0, 0, 0, self.output_bin - mask.size()[2]), mode="replicate")
+
+ if self.training:
+ # Generate intermediate auxiliary mask profiles for training optimization constraints
+ aux = self.aux_out(torch.cat([aux1, aux2], dim=1)).sigmoid()
+ aux = F.pad(input=aux, pad=(0, 0, 0, self.output_bin - aux.size()[2]), mode="replicate")
+
+ return mask, aux
+ else:
+ return mask
+
+ def predict_mask(self, input_tensor):
+ """
+ Generates and trims the separation mask by slicing window padding offsets.
+
+ Args:
+ input_tensor (torch.Tensor): Audio spectrogram tensor.
+
+ Returns:
+ torch.Tensor: Trimmed audio mask.
+ """
+
+ mask = self.forward(input_tensor)
+ # Slice off overlapping contextual boundaries along the time dimension if offset is active
+ if self.offset > 0:
+ mask = mask[:, :, :, self.offset : -self.offset]
+ assert mask.size()[3] > 0
+
+ return mask
+
+ def predict(self, input_tensor):
+ """
+ Extracts targeted source signal magnitude values from the audio mix.
+
+ Args:
+ input_tensor (torch.Tensor): Audio input mix spectrogram.
+
+ Returns:
+ torch.Tensor: Isolated target source magnitude spectrogram.
+ """
+
+ mask = self.forward(input_tensor)
+ # Apply element-wise multiplication to mask out unwanted frequency components
+ pred_mag = input_tensor * mask
+
+ # Remove temporary window overlap boundaries along the time dimension
+ if self.offset > 0:
+ pred_mag = pred_mag[:, :, :, self.offset : -self.offset]
+ assert pred_mag.size()[3] > 0
+
+ return pred_mag
\ No newline at end of file
diff --git a/main/library/uvr5_lib/vr_separator.py b/main/library/uvr5_lib/vr_separator.py
new file mode 100644
index 0000000000000000000000000000000000000000..6124defe91d96311ea8d0a13731342f40d2e4f14
--- /dev/null
+++ b/main/library/uvr5_lib/vr_separator.py
@@ -0,0 +1,253 @@
+import os
+import sys
+import math
+import torch
+import librosa
+import audioread
+
+import numpy as np
+
+from tqdm import tqdm
+
+sys.path.append(os.getcwd())
+
+from main.library.uvr5_lib import spec_utils
+from main.app.variables import configs, config
+from main.library.uvr5_lib.vr_network import nets
+from main.library.uvr5_lib.vr_network import nets_new
+from main.library.uvr5_lib.common_separator import CommonSeparator
+from main.library.uvr5_lib.vr_network.model_param_init import ModelParameters
+
+class VRSeparator(CommonSeparator):
+ """
+ Inference runner for the VR (Vocal Remover) Architecture models within UVR5.
+ Handles multi-band processing, dynamic neural network layer capacity allocation,
+ Test-Time Augmentation (TTA), and high-end frequency reconstruction.
+ """
+
+ def __init__(self, common_config, arch_config):
+ """
+ Initializes the VR model architecture, determines channel configurations,
+ and maps training hyper-parameters from binary parameter graphs.
+
+ Args:
+ common_config (dict): Global shared parameter mappings from the controller.
+ arch_config (dict): Architecture-specific configuration keys for VR processing.
+ """
+
+ super().__init__(config=common_config)
+ self.model_capacity = (32, 128) # Default baseline channel dimensions (nout, nout_lstm)
+ self.is_vr_51_model = False
+ self.input_high_end_h = None
+ self.input_high_end = None
+
+ # Verify if model metadata contains modern UVR v5.1 feature keys
+ if "nout" in self.model_data.keys() and "nout_lstm" in self.model_data.keys():
+ self.model_capacity = (self.model_data["nout"], self.model_data["nout_lstm"])
+ self.is_vr_51_model = True
+
+ # Parse structural parameter guidelines from local binary map
+ self.model_params = ModelParameters(os.path.join(configs["binary_path"], "vr_params.bin"), f"{self.model_data['vr_model_param']}.json")
+ self.aggressiveness = {"value": float(int(arch_config.get("aggression", 5)) / 100), "split_bin": self.model_params.param["band"]["1"]["crop_stop"], "aggr_correction": self.model_params.param.get("aggr_correction")}
+ self.post_process_threshold = arch_config.get("post_process_threshold", 0.2)
+ # Bind structural audio runtime thresholds
+ self.enable_post_process = arch_config.get("enable_post_process", False)
+ self.model_samplerate = self.sample_rate = self.model_params.param["sr"]
+ self.high_end_process = arch_config.get("high_end_process", False)
+ self.enable_tta = arch_config.get("enable_tta", False)
+ self.window_size = arch_config.get("window_size", 512)
+ self.batch_size = arch_config.get("batch_size", 1)
+ # Match model precision standards against hardware constraints
+ self.dtype = torch.float16 if config.is_half else torch.float32
+ # Reverse-engineer the target network capacity by scanning total filesystem byte size
+ nn_arch_size = min([31191, 33966, 56817, 123821, 123812, 129605, 218409, 537238, 537227], key=lambda x: abs(x - math.ceil(os.stat(self.model_path).st_size / 1024)))
+ if nn_arch_size in [56817, 218409] or self.is_vr_51_model:
+ self.model_run = nets_new.CascadedNet(self.model_params.param["bins"] * 2, nn_arch_size, nout=self.model_capacity[0], nout_lstm=self.model_capacity[1])
+ self.is_vr_51_model = True
+ else: self.model_run = nets.determine_model_capacity(self.model_params.param["bins"] * 2, nn_arch_size)
+
+ # Inject checkpoint state keys into instance memory mappings
+ self.model_run.load_state_dict(torch.load(self.model_path, map_location="cpu", weights_only=True))
+ self.model_run.to(self.torch_device).to(self.dtype).eval()
+
+ def separate(self, audio_file_path):
+ """
+ Orchestrates full VR pipeline separation, mapping raw mixed streams to distinct
+ primary and secondary stem audio outputs.
+
+ Args:
+ audio_file_path (str): Target filesystem file location of the source audio.
+
+ Returns:
+ list: Paths to the isolated generated audio track stems.
+ """
+
+ self.audio_file_base = os.path.splitext(os.path.basename(audio_file_path))[0]
+ self.primary_source, self.secondary_source = None, None
+ self.audio_file_path = audio_file_path
+ output_files = []
+
+ # Execute core neural forward propagation loop
+ y_spec, v_spec = self.inference_vr(self.loading_mix(), self.torch_device)
+
+ # Convert the secondary mask track back to a standard time-domain waveform
+ if not isinstance(self.secondary_source, np.ndarray):
+ self.secondary_source = self.spec_to_wav(np.nan_to_num(v_spec, nan=0.0, posinf=0.0, neginf=0.0)).T
+ if not self.model_samplerate == 44100: self.secondary_source = librosa.resample(self.secondary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T
+
+ # Convert the primary mask track back to a standard time-domain waveform
+ if not isinstance(self.primary_source, np.ndarray):
+ self.primary_source = self.spec_to_wav(np.nan_to_num(y_spec, nan=0.0, posinf=0.0, neginf=0.0)).T
+ if not self.model_samplerate == 44100: self.primary_source = librosa.resample(self.primary_source.T, orig_sr=self.model_samplerate, target_sr=44100).T
+
+ # Establish standardized nomenclature file naming hierarchies
+ self.secondary_stem_output_path = os.path.join(f"{self.audio_file_base}_({self.secondary_stem_name})_{self.model_name}.{self.output_format.lower()}")
+ self.primary_stem_output_path = os.path.join(f"{self.audio_file_base}_({self.primary_stem_name})_{self.model_name}.{self.output_format.lower()}")
+
+ # Export raw numpy data arrays out into actual file storage layouts
+ self.final_process(self.secondary_stem_output_path, self.secondary_source, self.secondary_stem_name)
+ self.final_process(self.primary_stem_output_path, self.primary_source, self.primary_stem_name)
+
+ output_files.append(self.secondary_stem_output_path)
+ output_files.append(self.primary_stem_output_path)
+
+ return output_files
+
+ def loading_mix(self):
+ """
+ Loads the source audio track, slices it across variable model frequency bands,
+ and extracts an integrated combined full master spectrogram.
+
+ Returns:
+ np.ndarray: Integrated multi-band complex master spectrogram layout.
+ """
+
+ X_wave, X_spec_s = {}, {}
+ bands_n = len(self.model_params.param["band"])
+
+ audio_file = spec_utils.write_array_to_mem(self.audio_file_path, subtype="PCM_16")
+ is_mp3 = audio_file.endswith(".mp3") if isinstance(audio_file, str) else False
+
+ # Loop through available target parameters in reverse order (High frequency bands down to base bands)
+ for d in tqdm(range(bands_n, 0, -1)):
+ bp = self.model_params.param["band"][str(d)]
+
+ if d == bands_n:
+ # Initialize base track extraction from target physical source file
+ X_wave[d], _ = librosa.load(audio_file, sr=bp["sr"], mono=False, dtype=np.float32, res_type="soxr_vhq")
+ X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp["hl"], bp["n_fft"], self.model_params, band=d, is_v51_model=self.is_vr_51_model)
+
+ # Execute dynamic file error recovery routine if the stream returns blank data vectors
+ if not np.any(X_wave[d]) and is_mp3: X_wave[d] = rerun_mp3(audio_file, bp["sr"])
+ if X_wave[d].ndim == 1: X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
+ else:
+ # Down-sample high-band audio components to map down to lower band structural rules
+ X_wave[d] = librosa.resample(X_wave[d + 1], orig_sr=self.model_params.param["band"][str(d + 1)]["sr"], target_sr=bp["sr"], res_type="soxr_vhq")
+ X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp["hl"], bp["n_fft"], self.model_params, band=d, is_v51_model=self.is_vr_51_model)
+
+ # Preserve top-end high-frequency components for post-inference mirroring
+ if d == bands_n and self.high_end_process:
+ self.input_high_end_h = (bp["n_fft"] // 2 - bp["crop_stop"]) + (self.model_params.param["pre_filter_stop"] - self.model_params.param["pre_filter_start"])
+ self.input_high_end = X_spec_s[d][:, bp["n_fft"] // 2 - self.input_high_end_h : bp["n_fft"] // 2, :]
+
+ X_spec = spec_utils.combine_spectrograms(X_spec_s, self.model_params, is_v51_model=self.is_vr_51_model)
+ del X_wave, X_spec_s, audio_file
+ return X_spec
+
+ def inference_vr(self, X_spec, device):
+ """
+ Executes sliding-window neural inference across the combined spectrogram magnitude graph.
+
+ Args:
+ X_spec (np.ndarray): Complex mixture spectrogram.
+ device (torch.device): Device target allocation instance (CPU or CUDA).
+
+ Returns:
+ tuple: (primary_complex_spectrogram, secondary_complex_spectrogram)
+ """
+
+ def _execute(X_mag_pad, roi_size):
+ X_dataset = []
+ patches = (X_mag_pad.shape[2] - 2 * self.model_run.offset) // roi_size
+
+ # Slice the full padded spectrogram into regular chunk subsets
+ for i in tqdm(range(patches)):
+ start = i * roi_size
+ X_dataset.append(X_mag_pad[:, :, start : start + self.window_size])
+
+ X_dataset = np.asarray(X_dataset)
+ # Pass sliced patches through the neural network to construct the prediction mask
+ with torch.no_grad():
+ mask = [np.concatenate(self.model_run.predict_mask(torch.from_numpy(X_dataset[i : i + self.batch_size]).to(device).to(self.dtype)).detach().cpu().numpy(), axis=2) for i in tqdm(range(0, patches, self.batch_size))]
+ if len(mask) == 0: raise ValueError("Neural framework execution yielded empty mask frames.")
+ mask = np.concatenate(mask, axis=2)
+
+ return mask
+
+ def postprocess(mask, X_mag, X_phase):
+ is_non_accom_stem = False
+ for stem in CommonSeparator.NON_ACCOM_STEMS:
+ if stem == self.primary_stem_name: is_non_accom_stem = True
+
+ # Scale mask tracking values against aggressiveness parameters
+ mask = spec_utils.adjust_aggr(mask, is_non_accom_stem, self.aggressiveness)
+ if self.enable_post_process: mask = spec_utils.merge_artifacts(mask, thres=self.post_process_threshold)
+
+ # Apply softmask ratios back onto phase fields to isolate tracks
+ return mask * X_mag * np.exp(1.0j * X_phase), (1 - mask) * X_mag * np.exp(1.0j * X_phase)
+
+ X_mag, X_phase = spec_utils.preprocess(X_spec)
+ n_frame = X_mag.shape[2]
+ # Pad boundaries to handle neural network convolutional offset trimming lengths
+ pad_l, pad_r, roi_size = spec_utils.make_padding(n_frame, self.window_size, self.model_run.offset)
+ X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
+ mask = _execute(X_mag_pad / X_mag_pad.max(), roi_size)
+
+ # Handle Test-Time Augmentation (TTA) using half-window phase shifts
+ if self.enable_tta:
+ pad_l += roi_size // 2
+ pad_r += roi_size // 2
+ X_mag_pad = np.pad(X_mag, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
+
+ # Average standard and phase-shifted predictions to reduce transient artifacts
+ mask = (mask[:, :, :n_frame] + _execute(X_mag_pad / X_mag_pad.max(), roi_size)[:, :, roi_size // 2 :][:, :, :n_frame]) * 0.5
+ else:
+ mask = mask[:, :, :n_frame]
+
+ return postprocess(mask, X_mag, X_phase)
+
+ def spec_to_wav(self, spec):
+ """
+ Converts the processed spectrogram back to a time-domain wave signal.
+ Reconstructs high frequencies via lower frequency mirroring if enabled.
+
+ Args:
+ spec (np.ndarray): Target complex spectrogram array.
+
+ Returns:
+ np.ndarray: Reassembled audio waveform signal matrix.
+ """
+
+ return (
+ spec_utils.cmb_spectrogram_to_wave(spec, self.model_params, self.input_high_end_h, spec_utils.mirroring("mirroring", spec, self.input_high_end, self.model_params), is_v51_model=self.is_vr_51_model)
+ if self.high_end_process and isinstance(self.input_high_end, np.ndarray) and self.input_high_end_h else
+ spec_utils.cmb_spectrogram_to_wave(spec, self.model_params, is_v51_model=self.is_vr_51_model)
+ )
+
+def rerun_mp3(audio_file, sample_rate=44100):
+ """
+ Fallback recovery reader that forces alternative librosa streaming
+ loops if an MP3 file returns null data vectors during initial parsing.
+
+ Args:
+ audio_file (str): Target physical file path location on disk.
+ sample_rate (int): Target resampling frequency bound parameters. Defaults to 44100.
+
+ Returns:
+ np.ndarray: Fully recovered raw multi-channel audio data waveform matrix.
+ """
+
+ with audioread.audio_open(audio_file) as f:
+ track_length = int(f.duration)
+
+ return librosa.load(audio_file, duration=track_length, mono=False, sr=sample_rate)[0]
\ No newline at end of file
diff --git a/main/tools/analyzer.py b/main/tools/analyzer.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c4f656b42f31a3b8b185b250e60a876e18c70d4
--- /dev/null
+++ b/main/tools/analyzer.py
@@ -0,0 +1,165 @@
+import os
+import gc
+import sys
+import librosa
+import librosa.display
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations
+
+def calculate_features(y, sr):
+ """
+ Calculates various audio features including STFT, duration, and spectral features.
+
+ Args:
+ y (np.ndarray): Audio time series.
+ sr (int): Sampling rate of the audio.
+
+ Returns:
+ Tuple[np.ndarray, float, np.ndarray, np.ndarray, np.ndarray]: A tuple
+ containing:
+ - stft (np.ndarray): Absolute Short-Time Fourier Transform.
+ - duration (float): Duration of the audio in seconds.
+ - centroid (np.ndarray): Spectral centroid.
+ - bandwidth (np.ndarray): Spectral bandwidth.
+ - rolloff (np.ndarray): Spectral rolloff.
+ """
+
+ # Compute the Short-Time Fourier Transform (STFT) and get its magnitude
+ stft = np.abs(librosa.stft(y))
+ return (
+ stft,
+ librosa.get_duration(y=y, sr=sr),
+ # Extract spectral features from the STFT magnitude
+ librosa.feature.spectral_centroid(S=stft, sr=sr)[0],
+ librosa.feature.spectral_bandwidth(S=stft, sr=sr)[0],
+ librosa.feature.spectral_rolloff(S=stft, sr=sr)[0]
+ )
+
+def plot_title(title):
+ """
+ Sets a global bold title for the current matplotlib figure.
+
+ Args:
+ title (str): The text to display as the main title.
+ """
+
+ plt.suptitle(title, fontsize=16, fontweight="bold")
+
+def plot_spectrogram(sr, stft, duration, cmap="inferno"):
+ """
+ Plots the spectrogram of the audio signal.
+
+ Args:
+ sr (int): Sampling rate of the audio.
+ stft (np.ndarray): Magnitude of the STFT.
+ duration (float): Duration of the audio in seconds.
+ cmap (str, optional): Colormap for the spectrogram plot. Defaults to "inferno".
+ """
+
+ # Define position in a 3x1 grid subplot
+ plt.subplot(3, 1, 1)
+ # Convert amplitude to decibels and display as an image
+ # Frequency axis is converted to kHz (sr / 1000)
+ plt.imshow(librosa.amplitude_to_db(stft, ref=np.max), origin="lower", extent=[0, duration, 0, sr / 1000], aspect="auto", cmap=cmap)
+ # Add a colorbar indicating dB levels
+ plt.colorbar(format="%+2.0f dB")
+ # Set localized labels and title from translations dictionary
+ plt.xlabel(translations["times"])
+ plt.ylabel(translations["frequencykhz"])
+ plt.title(translations["spectrogram"])
+
+def plot_waveform(y, sr):
+ """
+ Plots the time-domain waveform of the audio signal.
+
+ Args:
+ y (np.ndarray): Audio time series.
+ sr (int): Sampling rate of the audio.
+ """
+
+ # Define position in a 3x1 grid subplot
+ plt.subplot(3, 1, 2)
+ # Use librosa's optimized waveform display helper
+ librosa.display.waveshow(y, sr=sr)
+ plt.xlabel(translations["times"])
+ plt.ylabel(translations["amplitude"])
+ plt.title(translations["waveform"])
+
+def plot_features(times, cent, bw, rolloff):
+ """
+ Plots spectral features (centroid, bandwidth, rolloff) over time.
+
+ Args:
+ times (np.ndarray): Array of time boundaries for each frame.
+ cent (np.ndarray): Spectral centroid values.
+ bw (np.ndarray): Spectral bandwidth values.
+ rolloff (np.ndarray): Spectral rolloff values.
+ """
+
+ # Define position in a 3x1 grid subplot
+ plt.subplot(3, 1, 3)
+ plt.plot(times, cent, label=translations["centroid"], color="b")
+ plt.plot(times, bw, label=translations["bandwidth"], color="g")
+ plt.plot(times, rolloff, label=translations["rolloff"], color="r")
+ # Set localized labels, title, and display the legend
+ plt.xlabel(translations["times"])
+ plt.title(translations["features"])
+ plt.legend()
+
+def analyze_audio(audio_file):
+ """Performs comprehensive audio analysis, generates visualization plots, and saves the output image.
+
+ Args:
+ audio_file (str): Path to the target audio file.
+
+ Returns:
+ Tuple[str, str]: A tuple containing:
+ - audio_info (str): A formatted summary of the audio properties.
+ - save_plot_path (str): The file path where the analysis plot was
+ saved.
+ """
+
+ # Define the output directory and path for the analysis plot
+ save_plot_path = os.path.join("assets", "audio_analysis.png")
+
+ # Load audio file (sr=None preserves native sampling rate)
+ y, sr = librosa.load(audio_file, sr=None)
+ # Extract all necessary audio features
+ stft, duration, cent, bw, rolloff = calculate_features(y, sr)
+
+ # Initialize a clean matplotlib figure size
+ plt.figure(figsize=(12, 10))
+
+ # Generate all subplots
+ plot_title(translations["analysis"])
+ plot_spectrogram(sr, stft, duration)
+ plot_waveform(y, sr)
+ plot_features(librosa.times_like(cent), cent, bw, rolloff)
+
+ # Adjust subplot parameters to give specified padding
+ plt.tight_layout()
+
+ # Save the visualization to disk if path is provided
+ if save_plot_path: plt.savefig(save_plot_path, bbox_inches="tight", dpi=300)
+ plt.close()
+
+ # Construct the localized summary string
+ audio_info = translations["analysis_info"].format(
+ sr=sr,
+ # Format the duration string dynamically based on length (seconds vs minutes)
+ duration=(str(round(duration, 2)) + translations["seconds"] if duration < 60 else str(round(duration / 60, 2)) + translations["minutes"]),
+ length=len(y),
+ bit=librosa.get_samplerate(audio_file),
+ channel=translations["mono"] if y.ndim == 1 else translations["stereo"]
+ )
+
+ # Clean up large memory-consuming objects explicitly
+ del y, sr, stft, duration, cent, bw, rolloff
+ gc.collect()
+
+ return audio_info, save_plot_path
\ No newline at end of file
diff --git a/main/tools/export_svc.py b/main/tools/export_svc.py
new file mode 100644
index 0000000000000000000000000000000000000000..36c0b6c3ed612c80d9bd6b31ef7f8bc203fc2e16
--- /dev/null
+++ b/main/tools/export_svc.py
@@ -0,0 +1,111 @@
+import os
+import gc
+import sys
+import json
+import torch
+import hashlib
+import datetime
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations
+
+def svc_converter(model_path, config_path, output_path, is_half=False):
+ """
+ Converts a So-VITS-SVC checkpoint and its configuration into a unified deployable model format.
+
+ Args:
+ model_path (str): Path to the source PyTorch model file (.pth).
+ config_path (str): Path to the JSON configuration file.
+ output_path (str): Target path where the converted model will be saved.
+ is_half (bool, optional): If True, converts model weights to FP16
+ (half precision). Defaults to False.
+
+ Returns:
+ str: The output file path if successful, or an error message from
+ translations if conversion fails.
+ """
+
+ try:
+ model_dict, weight = {}, {}
+ # Load the source PyTorch model onto CPU (using weights_only=True for security)
+ model = torch.load(model_path, map_location="cpu", weights_only=True)
+
+ # Validate that the loaded file contains a valid SVC model structure
+ if "model" not in model:
+ return translations["not_svc"]
+
+ # Process and filter the state dictionary weights
+ for key, value in model["model"].items():
+ if torch.is_tensor(value): # Cast precision based on the is_half flag
+ value = value.half() if is_half else value.float()
+
+ # Map legacy or specific encoder keys to the standardized layout
+ if key.startswith("enc_p.enc_."): weight[key.replace("enc_p.enc_.", "enc_p.encoder.")] = value
+ elif key.startswith("f0_decoder."): continue # Exclude f0_decoder weights as they are handled differently during inference
+ else: weight[key] = value
+
+ # Read and parse the JSON configuration parameters securely
+ config = json.load(open(config_path, "r", encoding="utf-8"))
+
+ # Structure the target model metadata and weights dictionary
+ model_dict["weight"] = weight
+ model_dict["config"] = [
+ config["data"]["filter_length"] // 2 + 1,
+ config["train"]["segment_size"] / config["data"]["hop_length"],
+ config["model"]["inter_channels"],
+ config["model"]["hidden_channels"],
+ config["model"]["filter_channels"],
+ config["model"]["n_heads"],
+ config["model"]["n_layers"],
+ config["model"]["kernel_size"],
+ config["model"]["p_dropout"],
+ config["model"]["resblock"],
+ config["model"]["resblock_kernel_sizes"],
+ config["model"]["resblock_dilation_sizes"],
+ config["model"]["upsample_rates"],
+ config["model"]["upsample_initial_channel"],
+ config["model"]["upsample_kernel_sizes"],
+ config["model"]["n_speakers"],
+ config["model"]["gin_channels"],
+ config["data"]["sampling_rate"]
+ ]
+
+ # Extract training iteration/epoch info (fallback to 0 if not present)
+ epoch = model.get("iteration", 0)
+
+ # Try to extract the step count from the filename if it follows the "G_.pth" pattern
+ try:
+ step = (
+ int(os.path.basename(model_path).replace("G_", "").replace(".pth", ""))
+ ) if model_path.startswith("G_") and model_path.endswith(".pth") else epoch
+ except:
+ step = epoch
+
+ # Populate model metadata fields
+ model_dict["epoch"] = f"{epoch}epoch"
+ model_dict["step"] = step
+ model_dict["sr"] = config["data"]["sampling_rate"]
+ model_dict["f0"] = True
+ # Determine the model version based on Content Vector / SSL dimensions
+ model_dict["version"] = "v2" if config["model"]["ssl_dim"] == 768 else "v1"
+ model_dict["creation_date"] = datetime.datetime.now().isoformat()
+ # Generate a unique hash identity for this specific model package
+ model_dict["model_hash"] = hashlib.sha256(f"{str(weight)} {epoch} {step} {datetime.datetime.now().isoformat()}".encode()).hexdigest()
+ model_dict["model_name"] = os.path.basename(output_path).replace(".pth", "")
+ model_dict["author"] = ""
+ model_dict["vocoder"] = "Default"
+ model_dict["speakers_id"] = config["model"]["n_speakers"]
+ model_dict["architecture"] = "SVC"
+
+ # Save the structured package back to disk
+ torch.save(model_dict, output_path)
+
+ # Explicitly clean up heavy tensors and dictionaries to free up RAM
+ del model, model_dict, config, weight
+ gc.collect()
+
+ return output_path
+ except:
+ # Fallback error handling if any unexpected file I/O or parsing error occurs
+ return translations["not_svc"]
\ No newline at end of file
diff --git a/main/tools/gdown.py b/main/tools/gdown.py
new file mode 100644
index 0000000000000000000000000000000000000000..9da4cbc14eef82bdf60622299d6827810e9e7c69
--- /dev/null
+++ b/main/tools/gdown.py
@@ -0,0 +1,249 @@
+import os
+import re
+import sys
+import json
+import tqdm
+import codecs
+import tempfile
+import requests
+
+from urllib.parse import urlparse, parse_qs, unquote
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations
+
+def parse_url(url):
+ """
+ Parses a Google Drive URL to extract the unique file ID and identify download links.
+
+ Args:
+ url (str): The Google Drive or Docs URL to parse.
+
+ Returns:
+ Tuple[Optional[str], bool]: A tuple containing:
+ - file_id (str or None): The extracted Google Drive file ID, or None
+ if not found.
+ - is_download_link (bool): True if the URL path ends with '/uc'
+ (direct download link).
+ """
+
+ parsed = urlparse(url)
+ is_download_link = parsed.path.endswith("/uc")
+
+ # Guard clause: Ensure the domain belongs to Google Drive or Docs
+ if not parsed.hostname in ("drive.google.com", "docs.google.com"): return None, is_download_link
+ # Strategy 1: Attempt to extract ID from query parameters (e.g., ?id=...)
+ file_id = parse_qs(parsed.query).get("id", [None])[0]
+
+ # Strategy 2: Match predefined URL patterns within the path via regex if query ID is missing
+ if file_id is None:
+ for pattern in (
+ r"^/file/d/(.*?)/(edit|view)$",
+ r"^/file/u/[0-9]+/d/(.*?)/(edit|view)$",
+ r"^/document/d/(.*?)/(edit|htmlview|view)$",
+ r"^/document/u/[0-9]+/d/(.*?)/(edit|htmlview|view)$",
+ r"^/presentation/d/(.*?)/(edit|htmlview|view)$",
+ r"^/presentation/u/[0-9]+/d/(.*?)/(edit|htmlview|view)$",
+ r"^/spreadsheets/d/(.*?)/(edit|htmlview|view)$",
+ r"^/spreadsheets/u/[0-9]+/d/(.*?)/(edit|htmlview|view)$"
+ ):
+ match = re.match(pattern, parsed.path)
+
+ if match:
+ file_id = match.group(1)
+ break
+
+ return file_id, is_download_link
+
+def get_url_from_gdrive_confirmation(contents):
+ """
+ Extracts the actual download URL from Google Drive's large file virus scan warning page.
+
+ Args:
+ contents (str): The HTML source text of the confirmation page.
+
+ Returns:
+ str: The resolved, un-obfuscated download URL.
+
+ Raises:
+ Exception: If a Google Drive quota error occurs or extraction fails.
+ """
+
+ for pattern in (
+ r'href="(\/uc\?export=download[^"]+)',
+ r'href="/open\?id=([^"]+)"',
+ r'"downloadUrl":"([^"]+)'
+ ):
+ match = re.search(pattern, contents)
+
+ if match:
+ url = match.group(1)
+ # Case 1: URL is embedded within standard open link format
+ if pattern == r'href="/open\?id=([^"]+)"':
+ url = (
+ # Obfuscation workaround: decode target base domain via ROT13
+ codecs.decode("uggcf://qevir.hfrepbagrag.tbbtyr.pbz/qbjaybnq?vq=", "rot13") +
+ url +
+ "&confirm=t&uuid=" +
+ re.search(r' (.*)', contents)
+ if match: raise Exception(match.group(1))
+
+ raise Exception(translations["gdown_error"])
+
+def _get_session(use_cookies, return_cookies_file=False):
+ """
+ Initializes a network request session with browser User-Agent and handles cookie persistence.
+
+ Args:
+ use_cookies (bool): Whether to look up and load stored session cookies.
+ return_cookies_file (bool, optional): If True, returns a tuple including
+ the cookies file path. Defaults to False.
+
+ Returns:
+ Union[requests.Session, Tuple[requests.Session, str]]: The configured
+ Session object, or a tuple of (Session, file_path).
+ """
+
+ sess = requests.session()
+ sess.headers.update({"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6)"})
+ # Resolve platform-independent cache directory for session cookies
+ cookies_file = os.path.join(os.path.expanduser("~"), ".cache/gdown/cookies.json")
+
+ # Load previously exported cookies if authorized
+ if os.path.exists(cookies_file) and use_cookies:
+ with open(cookies_file) as f:
+ for k, v in json.load(f):
+ sess.cookies[k] = v
+
+ return (sess, cookies_file) if return_cookies_file else sess
+
+def gdown_download(url=None, output=None):
+ """
+ Downloads a file from Google Drive using public download protocols, bypassing large file warnings.
+
+ Args:
+ url (str, optional): The target Google Drive URL or file link. Defaults to None.
+ output (str, optional): Path or directory where the final file should be written. Defaults to None (current folder).
+
+ Returns:
+ Optional[str]: The absolute or relative string path to the successfully downloaded file, or None if parsing fails.
+
+ Raises:
+ ValueError: If url argument is missing.
+ Exception: For continuous network download failures.
+ """
+
+ file_id = None
+ if url is None: raise ValueError(translations["gdown_value_error"])
+
+ # Step 1: Pre-process URL structures to isolate raw ID tokens
+ if "/file/d/" in url:
+ file_id = url.split("/d/")[1].split("/")[0]
+ elif "open?id=" in url:
+ file_id = url.split("open?id=")[1].split("/")[0]
+ elif "/download?id=" in url:
+ file_id = url.split("/download?id=")[1].split("&")[0]
+
+ if file_id:
+ # Reconstruct standard direct stream endpoint
+ url = f"{codecs.decode('uggcf://qevir.tbbtyr.pbz/hp?vq=', 'rot13')}{file_id}"
+ url_origin = url
+
+ sess, cookies_file = _get_session(use_cookies=True, return_cookies_file=True)
+ gdrive_file_id, is_gdrive_download_link = parse_url(url)
+
+ if gdrive_file_id:
+ url = f"{codecs.decode('uggcf://qevir.tbbtyr.pbz/hp?vq=', 'rot13')}{gdrive_file_id}"
+ url_origin = url
+ is_gdrive_download_link = True
+
+ # Step 2: Handle redirection and large file landing confirmations
+ while 1:
+ res = sess.get(url, stream=True, verify=True)
+ # Retry with alternate viewer schema if the direct backend hits a 500 error
+ if url == url_origin and res.status_code == 500:
+ url = f"{codecs.decode('uggcf://qevir.tbbtyr.pbz/bcra?vq=', 'rot13')}{gdrive_file_id}"
+ continue
+
+ # Cache the successful session cookies locally (excluding temporary warning tokens)
+ os.makedirs(os.path.dirname(cookies_file), exist_ok=True)
+ with open(cookies_file, "w") as f:
+ json.dump(
+ [(k, v) for k, v in sess.cookies.items() if not k.startswith("download_warning_")],
+ f,
+ indent=2
+ )
+
+ # If content header indicates an incoming file stream, proceed to download stage
+ if ("Content-Disposition" in res.headers) or (not (gdrive_file_id and is_gdrive_download_link)): break
+
+ # If intercepted by confirmation page, extract the hidden download link and loop again
+ try:
+ url = get_url_from_gdrive_confirmation(res.text)
+ except Exception as e:
+ raise Exception(e)
+
+ # Step 3: Determine file name from server context or fallback
+ if gdrive_file_id and is_gdrive_download_link:
+ content_disposition = unquote(res.headers["Content-Disposition"])
+
+ filename_from_url = (
+ re.search(r"filename\*=UTF-8''(.*)", content_disposition) or re.search(r'filename=["\']?(.*?)["\']?$', content_disposition)
+ ).group(1).replace(os.path.sep, "_")
+ else:
+ filename_from_url = os.path.basename(url)
+
+ # Step 4: Prepare localized output destinations and partial cache files
+ output = os.path.join(output or ".", filename_from_url)
+ tmp_file = tempfile.mktemp(suffix=tempfile.template, prefix=os.path.basename(output), dir=os.path.dirname(output))
+ f = open(tmp_file, "ab")
+
+ # Step 5: Execute Range HTTP requests if a partial download signature already exists
+ if tmp_file is not None and f.tell() != 0:
+ res = sess.get(
+ url,
+ headers={
+ "Range": f"bytes={f.tell()}-"
+ },
+ stream=True,
+ verify=True
+ )
+
+ # Step 6: Stream chunk iterations directly into the disk file using a tqdm progress bar
+ try:
+ with tqdm.tqdm(
+ desc=os.path.basename(output),
+ total=int(res.headers.get("Content-Length", 0)),
+ ncols=100,
+ unit="byte"
+ ) as pbar:
+ for chunk in res.iter_content(chunk_size=512 * 1024):
+ f.write(chunk)
+ pbar.update(len(chunk))
+
+ pbar.close()
+ if tmp_file: f.close()
+ finally:
+ # Atomic swap operation: replace or finalize target file destination
+ os.rename(tmp_file, output)
+ sess.close()
+
+ return output
+
+ return None
\ No newline at end of file
diff --git a/main/tools/huggingface.py b/main/tools/huggingface.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4b33a7bab789d614243514f3aebeb922d1dc4da
--- /dev/null
+++ b/main/tools/huggingface.py
@@ -0,0 +1,70 @@
+import os
+import tqdm
+import requests
+
+try:
+ import wget
+except:
+ wget = None
+
+def HF_download_file(url, output_path=None):
+ """
+ Downloads a file from Hugging Face, resolving blob links to raw file paths.
+
+ Args:
+ url (str): The Hugging Face file URL (supports standard or blob links).
+ output_path (str, optional): Target file path or directory. If None,
+ saves to the current working directory using the file's original name.
+ Defaults to None.
+
+ Returns:
+ str: The absolute or relative path to the downloaded file.
+
+ Raises:
+ ValueError: If requests fails to download the file (non-200 status
+ code).
+ """
+
+ # Step 1: Normalize Hugging Face URL structure to get the direct payload download link
+ url = url.replace("/blob/", "/resolve/").replace("?download=true", "").strip()
+ # Step 2: Dynamically calculate the safe local destination path
+ output_path = (
+ os.path.basename(url)
+ ) if output_path is None else (
+ os.path.join(output_path, os.path.basename(url)) if os.path.isdir(output_path) else output_path
+ )
+
+ # Step 3: Execute download operation
+ if wget != None:
+ # Strategy A: Use optimized module fallback if available
+ wget.download(
+ url,
+ out=output_path
+ )
+ else:
+ # Strategy B: Stream download natively via standard HTTP chunk retrieval
+ response = requests.get(url, stream=True, timeout=300)
+
+ if response.status_code == 200:
+ # Initialize terminal status progress bar tracker
+ progress_bar = tqdm.tqdm(
+ total=int(response.headers.get("content-length", 0)),
+ desc=os.path.basename(url),
+ ncols=100,
+ unit="byte",
+ leave=False
+ )
+
+ # Iteratively write data chunks directly to disk
+ with open(output_path, "wb") as f:
+ # Read chunks up to 10MB in memory buffer sequentially
+ for chunk in response.iter_content(chunk_size=10 * 1024 * 1024):
+ progress_bar.update(len(chunk))
+ f.write(chunk)
+
+ progress_bar.close()
+ else:
+ # Raise an operational exception if server returns an error code
+ raise ValueError(f"Failed to download file, server responded with status code: {response.status_code}")
+
+ return output_path
\ No newline at end of file
diff --git a/main/tools/mediafire.py b/main/tools/mediafire.py
new file mode 100644
index 0000000000000000000000000000000000000000..534fde513866a4d1b75506ecda6d051d4b5bbfed
--- /dev/null
+++ b/main/tools/mediafire.py
@@ -0,0 +1,67 @@
+import os
+import sys
+import requests
+
+from bs4 import BeautifulSoup
+
+def Mediafire_Download(url, output=None, filename=None):
+ """
+ Parses a MediaFire landing page to extract and stream the direct file download.
+
+ Args:
+ url (str): The MediaFire sharing URL.
+ output (str, optional): The directory where the file should be saved.
+ Defaults to the directory of the current script file.
+ filename (str, optional): The custom name for the downloaded file.
+ Defaults to the parsed folder name string from the URL.
+
+ Returns:
+ str: The full absolute or relative file path to the downloaded payload.
+
+ Raises:
+ RuntimeError: If scraping the download token fails or any network and
+ I/O error occurs.
+ """
+
+ # Step 1: Assign default values for local storage targets if not provided
+ if not filename: filename = url.split('/')[-2]
+ if not output: output = os.path.dirname(os.path.realpath(__file__))
+
+ output_file = os.path.join(output, filename)
+ # Step 2: Initialize a structured requests session with a browser User-Agent header
+ sess = requests.session()
+ sess.headers.update({"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6)"})
+
+ try:
+ # Step 3: Fetch landing page and parse the HTML DOM to extract the raw target anchor link
+ bs4 = BeautifulSoup(
+ sess.get(url).content,
+ "html.parser"
+ ).find(id="downloadButton").get("href")
+
+ # Step 4: Stream the binary payload using chunk iterations
+ with requests.get(
+ bs4,
+ stream=True
+ ) as r:
+ r.raise_for_status()
+
+ with open(output_file, "wb") as f:
+ # Resolve content size metadata safely (fallback to 0 if missing)
+ total_length = int(r.headers.get('content-length'))
+ download_progress = 0
+ # Read streamed buffer bytes progressively
+ for chunk in r.iter_content(chunk_size=1024):
+ download_progress += len(chunk)
+ f.write(chunk)
+
+ # Step 5: Render terminal-friendly download status calculations dynamically
+ stdout = f"\r[{filename}]: {int(100 * download_progress / total_length)}% ({round(download_progress / 1024 / 1024, 2)}mb/{round(total_length / 1024 / 1024, 2)}mb)"
+ # Write inline output log directly to system stream
+ sys.stdout.write(stdout)
+ sys.stdout.flush()
+
+ sys.stdout.write("\n")
+ return output_file
+ except Exception as e:
+ raise RuntimeError(f"MediaFire Download operational failure: {e}")
\ No newline at end of file
diff --git a/main/tools/meganz.py b/main/tools/meganz.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a4a47e5072209fc8a0773a9101f3c75377b7727
--- /dev/null
+++ b/main/tools/meganz.py
@@ -0,0 +1,291 @@
+import os
+import re
+import sys
+import json
+import tqdm
+import codecs
+import random
+import base64
+import struct
+import shutil
+import requests
+import tempfile
+
+from Crypto.Cipher import AES
+from Crypto.Util import Counter
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import translations
+
+def makebyte(x):
+ """
+ Encodes a string using Latin-1 to map it straight into raw byte buffers.
+
+ Args:
+ x (str): Input string to encode.
+
+ Returns:
+ bytes: Raw bytes encoded via Latin-1.
+ """
+
+ return codecs.latin_1_encode(x)[0]
+
+def a32_to_str(a):
+ """
+ Converts an array of 32-bit unsigned integers into a big-endian byte string.
+
+ Args:
+ a (Union[List[int], Tuple[int, ...]]): Iterable collection of 32-bit integers.
+
+ Returns:
+ bytes: Big-endian formatted binary byte layout.
+ """
+
+ return struct.pack('>%dI' % len(a), *a)
+
+def get_chunks(size):
+ """
+ Generates sequential file chunk boundaries based on MEGA's progressive streaming algorithm.
+
+ Args:
+ size (int): Total size of the file package payload.
+
+ Yields:
+ Generator[Tuple[int, int], None, None]: Tuples containing (chunk_start_offset, chunk_size).
+ """
+
+ p, s = 0, 0x20000 # Start offset, dynamic initial chunk chunking window (128KB)
+
+ while p + s < size:
+ yield(p, s)
+ p += s
+ # Incrementally expand buffer windows up to 1MB bounds to scale connection bandwidth
+ if s < 0x100000: s += 0x20000
+
+ yield (p, size - p)
+
+def aes_cbc_decrypt(data, key):
+ """
+ Decrypts binary data using the AES-CBC block cipher protocol with a null Initialization Vector.
+
+ Args:
+ data (bytes): Ciphertext sequence block.
+ key (bytes): Private symmetric encryption key payload.
+
+ Returns:
+ bytes: Decrypted plaintext payload byte array.
+ """
+
+ aes_cipher = AES.new(key, AES.MODE_CBC, makebyte('\0' * 16))
+
+ return aes_cipher.decrypt(data)
+
+def decrypt_attr(attr, key):
+ """
+ Decrypts and parses MEGA node serialization objects into standard attribute dictionaries.
+
+ Args:
+ attr (bytes): Raw obfuscated attributes block.
+ key (Union[List[int], Tuple[int, ...]]): Extracted 32-bit symmetric decryption key parts.
+
+ Returns:
+ Union[Dict[str, Any], bool]: Decoded JSON attribute map if successful, else False.
+ """
+
+ attr = codecs.latin_1_decode(aes_cbc_decrypt(attr, a32_to_str(key)))[0].rstrip('\0')
+
+ return json.loads(attr[4:]) if attr[:6] == 'MEGA{"' else False
+
+def _api_request(data):
+ """
+ Dispatches a payload request packet directly to the MEGA decentralized CS gateway infrastructure.
+
+ Args:
+ data (Union[Dict[str, Any], List[Dict[str, Any]]]): Raw request command objects or list of actions.
+
+ Returns:
+ Dict[str, Any]: Parsed backend server confirmation message map.
+
+ Raises:
+ Exception: If the server passes back a negative application exception error integer.
+ """
+
+ sequence_num = random.randint(0, 0xFFFFFFFF)
+ params = {'id': sequence_num}
+
+ sequence_num += 1
+ if not isinstance(data, list): data = [data]
+
+ response = requests.post(
+ '{0}://g.api.{1}/cs'.format('https', 'mega.co.nz'),
+ params=params,
+ data=json.dumps(data),
+ timeout=160
+ )
+
+ json_resp = json.loads(response.text)
+ if isinstance(json_resp, int): raise Exception(json_resp)
+
+ return json_resp[0]
+
+def base64_url_decode(data):
+ """
+ Decodes non-standard variants of Base64 strings safely with padding restorations.
+
+ Args:
+ data (str): Unpadded, safe url token Base64 string.
+
+ Returns:
+ bytes: Resolved binary byte payload array.
+ """
+
+ data += '=='[(2 - len(data) * 3) % 4:]
+
+ for search, replace in (('-', '+'), ('_', '/'), (',', '')):
+ data = data.replace(search, replace)
+
+ return base64.b64decode(data)
+
+def str_to_a32(b):
+ """
+ Unpacks alphanumeric or byte strings out into groups of 32-bit unsigned integers.
+
+ Args:
+ b (Union[str, bytes]): Plain text or byte array sequence.
+
+ Returns:
+ Tuple[int, ...]: Unpacked Big-Endian structured 32-bit integer array.
+ """
+
+ if isinstance(b, str): b = makebyte(b)
+ if len(b) % 4: b += b'\0' * (4 - len(b) % 4)
+
+ return struct.unpack('>%dI' % (len(b) / 4), b)
+
+def base64_to_a32(s):
+ """
+ Wraps URL-safe Base64 token translation directly into parsed 32-bit unsigned integer arrays.
+
+ Args:
+ s (str): Obfuscated source Base64 asset token string.
+
+ Returns:
+ Tuple[int, ...]: Transformed 32-bit data block maps.
+ """
+
+ return str_to_a32(
+ base64_url_decode(s)
+ )
+
+def mega_download_file(file_handle, file_key, dest_path=None):
+ """
+ Downloads, decrypts, and verifies file entities mapped out inside the MEGA storage grids.
+
+ Args:
+ file_handle (str): Target unique resource node reference token identifier.
+ file_key (str): base64 key parameter string linked to metadata node definitions.
+ dest_path (str, optional): Safe destination folder reference path. Defaults to None.
+
+ Returns:
+ str: Absolute or localized target landing path of the decrypted asset file.
+
+ Raises:
+ Exception: If connection node parameters fail access authorizations.
+ ValueError: If file verification calculations fail internal MAC parity validation checks.
+ """
+
+ # Parse out cryptographic descriptors from base64 arrays
+ file_key = base64_to_a32(file_key)
+ file_data = _api_request({'a': 'g', 'g': 1, 'p': file_handle})
+
+ # Isolate sub-key transformations using XOR operations over the key array layout split
+ k = (
+ file_key[0] ^ file_key[4],
+ file_key[1] ^ file_key[5],
+ file_key[2] ^ file_key[6],
+ file_key[3] ^ file_key[7]
+ )
+
+ iv = file_key[4:6] + (0, 0)
+ if 'g' not in file_data: raise Exception(translations["file_not_access"])
+
+ file_size = file_data['s']
+ attribs = decrypt_attr(base64_url_decode(file_data['at']), k)
+ # Initialize standard multi-stream response handles to temporary cache endpoints
+ input_file = requests.get(file_data['g'], stream=True).raw
+ temp_output_file = tempfile.NamedTemporaryFile(mode='w+b', prefix='megapy_', delete=False)
+
+ k_str = a32_to_str(k)
+ aes = AES.new(
+ k_str,
+ AES.MODE_CTR,
+ counter=Counter.new(
+ 128,
+ initial_value=(
+ (iv[0] << 32) + iv[1]
+ ) << 64
+ )
+ )
+
+ # Prepare Message Authentication Code (MAC) verifying structures
+ mac_str = b'\0' * 16
+ mac_encryptor = AES.new(k_str, AES.MODE_CBC, mac_str)
+ iv_str = a32_to_str([iv[0], iv[1], iv[0], iv[1]])
+
+ # Read fragments sequentially to stream large items comfortably within RAM allocations
+ with tqdm.tqdm(total=file_size, ncols=100, unit="byte") as pbar:
+ for _, chunk_size in get_chunks(file_size):
+ chunk = aes.decrypt(input_file.read(chunk_size))
+ temp_output_file.write(chunk)
+
+ pbar.update(len(chunk))
+ encryptor = AES.new(k_str, AES.MODE_CBC, iv_str)
+
+ for i in range(0, len(chunk) - 16, 16):
+ block = chunk[i:i + 16]
+ encryptor.encrypt(block)
+
+ i = (i + 16) if file_size > 16 else 0
+ block = chunk[i:i + 16]
+
+ if len(block) % 16: block += b'\0' * (16 - (len(block) % 16))
+ mac_str = mac_encryptor.encrypt(encryptor.encrypt(block))
+
+ file_mac = str_to_a32(mac_str)
+ temp_output_file.close()
+
+ # Step 4: Validate computed MAC integrity signatures with data structure headers
+ if (file_mac[0] ^ file_mac[1], file_mac[2] ^ file_mac[3]) != file_key[6:8]: raise ValueError(translations["mac_not_match"])
+
+ # Finalize target directory bindings and execute clean cache swaps
+ file_path = os.path.join(dest_path, attribs['n'])
+ if os.path.exists(file_path): os.remove(file_path)
+
+ shutil.move(temp_output_file.name, file_path)
+ return file_path
+
+def mega_download_url(url, dest_path=None):
+ """
+ Normalizes and routes public MEGA uniform resource locator parameters down to target extractors.
+
+ Args:
+ url (str): Target web link parameter addressing shared MEGA file instances.
+ dest_path (str, optional): Target localized disk landing directory destination. Defaults to None.
+
+ Returns:
+ str: Absolute or relative output location referencing the downloaded file object.
+
+ Raises:
+ Exception: If URL format layout structure misses expected validation criteria.
+ """
+
+ if '/file/' in url:
+ url = url.replace(' ', '')
+ # Isolate node handle structures via standard boundaries regex matches
+ file_id = re.findall(r'\W\w\w\w\w\w\w\w\w\W', url)[0][1:-1]
+ path = f'{file_id}!{url[re.search(file_id, url).end() + 1:]}'.split('!')
+ elif '!' in url: path = re.findall(r'/#!(.*)', url)[0].split('!')
+ else: raise Exception(translations["missing_url"])
+
+ return mega_download_file(path[0], path[1], dest_path)
\ No newline at end of file
diff --git a/main/tools/pixeldrain.py b/main/tools/pixeldrain.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5cd70d2895504453fd569fa67113968a8a773e9
--- /dev/null
+++ b/main/tools/pixeldrain.py
@@ -0,0 +1,42 @@
+import os
+import requests
+
+def pixeldrain(url, output_dir):
+ """
+ Downloads a file from Pixeldrain using its public API backend.
+
+ Args:
+ url (str): The public sharing URL of the Pixeldrain file (e.g.,
+ https://pixeldrain.com/u/file_id).
+ output_dir (str): The local directory path where the downloaded file
+ should be stored.
+
+ Returns:
+ Optional[str]: The absolute or relative path to the downloaded file if
+ successful, or None if the server returns a non-200 status code.
+
+ Raises:
+ RuntimeError: If any internal network connection or I/O processing operation fails.
+ """
+
+ try:
+ # Step 1: Extract the unique file ID from the URL and query the direct file API endpoint
+ response = requests.get(f"https://pixeldrain.com/api/file/{url.split('pixeldrain.com/u/')[1]}")
+
+ if response.status_code == 200:
+ # Build the clean target storage path
+ file_path = os.path.join(
+ output_dir,
+ # Step 2: Parse out the original filename from the 'Content-Disposition' header safely
+ response.headers.get("Content-Disposition").split("filename=")[-1].strip('";')
+ )
+
+ # Step 3: Write the binary stream bytes directly into the local file
+ with open(file_path, "wb") as newfile:
+ newfile.write(response.content)
+
+ return file_path
+ # Return None explicitly if the resource is unavailable or has expired
+ return None
+ except Exception as e:
+ raise RuntimeError(e)
\ No newline at end of file
diff --git a/main/tools/sortedcontainers.py b/main/tools/sortedcontainers.py
new file mode 100644
index 0000000000000000000000000000000000000000..6310fceb1d5e3a48b690ffbed13d1ec181c4e2b2
--- /dev/null
+++ b/main/tools/sortedcontainers.py
@@ -0,0 +1,917 @@
+import os
+import sys
+import math
+import bisect
+import operator
+import collections.abc
+
+from _thread import get_ident
+from functools import reduce, wraps
+from itertools import chain, repeat, starmap
+
+sys.path.append(os.getcwd())
+
+from main.app.variables import logger
+
+def recursive_repr(func):
+ """
+ Decorator to prevent infinite recursion in __repr__ methods.
+
+ If a container references itself, this replaces the recursive call with '...'.
+ """
+
+ repr_running = set()
+
+ @wraps(func)
+ def wrapper(self):
+ key = id(self), get_ident()
+ if key in repr_running: return '...'
+
+ repr_running.add(key)
+
+ try:
+ return func(self)
+ finally:
+ repr_running.discard(key)
+
+ return wrapper
+
+class SortedList(collections.abc.MutableSequence):
+ """A mutable sequence that automatically maintains its elements in sorted order."""
+
+ def __init__(self, iterable=None, load=1000):
+ """
+ Initialize the SortedList.
+
+ Args:
+ iterable: An optional iterable of items to initialize the list with.
+ load: The load factor controlling the maximum sublist size before splitting.
+ """
+
+ self._len, self._maxes, self._lists, self._index = 0, [], [], []
+ self._load, self._twice, self._half = load, load * 2, load >> 1
+ if iterable is not None: self.update(iterable)
+
+ def clear(self):
+ """Remove all elements from the SortedList."""
+
+ self._len = 0
+
+ del self._maxes[:]
+ del self._lists[:]
+ del self._index[:]
+
+ def add(self, val):
+ """
+ Insert a new element into the SortedList maintaining the sorted order.
+
+ Args:
+ val: The value to be added.
+ """
+
+ _maxes, _lists = self._maxes, self._lists
+
+ if _maxes:
+ # Find the appropriate sublist index based on maximum sublist values
+ pos = bisect.bisect_right(_maxes, val)
+
+ if pos == len(_maxes):
+ # Element is larger than all current elements; append to the last sublist
+ pos -= 1
+ _maxes[pos] = val
+ _lists[pos].append(val)
+ else:
+ # Insert inside the designated sublist maintaining order
+ bisect.insort(_lists[pos], val)
+
+ self._expand(pos)
+ else:
+ # First element initialization
+ _maxes.append(val)
+ _lists.append([val])
+
+ self._len += 1
+
+ def _expand(self, pos):
+ """
+ Split a sublist if it exceeds the load limit threshold.
+
+ Args:
+ pos: Position index of the sublist to balance.
+ """
+
+ _lists, _index = self._lists, self._index
+ # Check if the sublist size violates the load factor ceiling
+ if len(_lists[pos]) > self._twice:
+ _maxes, _load = self._maxes, self._load
+ half = _lists[pos][_load:]
+ # Split the heavy sublist into two equal halves
+ _lists[pos] = _lists[pos][:_load]
+ _maxes[pos] = _lists[pos][-1]
+
+ _maxes.insert(pos + 1, half[-1])
+ _lists.insert(pos + 1, half)
+ # Invalidate index tree since structure changed
+ del _index[:]
+ else:
+ # Update positional index counters if tree index is active
+ if len(_index) > 0:
+ child = self._offset + pos
+
+ while child > 0:
+ _index[child] += 1
+ child = (child - 1) >> 1
+
+ _index[0] += 1
+
+ def update(self, iterable):
+ """
+ Batch insert multiple values from an iterable into the SortedList.
+
+ Args:
+ iterable: Collection of elements to be merged.
+ """
+
+ _maxes, _lists = self._maxes, self._lists
+ values = sorted(iterable)
+
+ if _maxes:
+ # If incoming dataset is large, merge entirely and rebuild to minimize overhead
+ if len(values) * 4 >= self._len:
+ values.extend(chain.from_iterable(_lists))
+ values.sort()
+ self.clear()
+ else:
+ # Step-by-step element injection for smaller datasets
+ _add = self.add
+
+ for val in values:
+ _add(val)
+
+ return
+
+ # Distribute sorted items into fresh sublists based on load factor chunking
+ _load, _index = self._load, self._index
+ _lists.extend(values[pos:(pos + _load)] for pos in range(0, len(values), _load))
+ _maxes.extend(sublist[-1] for sublist in _lists)
+
+ self._len = len(values)
+ del _index[:]
+
+ def __contains__(self, val):
+ """Check if a value exists in the SortedList."""
+ _maxes = self._maxes
+ if not _maxes: return False
+
+ # Locate candidate sublist via binary search
+ pos = bisect.bisect_left(_maxes, val)
+ if pos == len(_maxes): return False
+
+ # Query sublist for presence of element
+ _lists = self._lists
+ idx = bisect.bisect_left(_lists[pos], val)
+ return _lists[pos][idx] == val
+
+ def discard(self, val):
+ """Remove a value if it is present in the container."""
+
+ _maxes = self._maxes
+
+ if not _maxes: return
+ pos = bisect.bisect_left(_maxes, val)
+
+ if pos == len(_maxes): return
+ _lists = self._lists
+
+ idx = bisect.bisect_left(_lists[pos], val)
+ if _lists[pos][idx] == val: self._delete(pos, idx)
+
+ def remove(self, val):
+ """
+ Remove a value from the container; raises ValueError if not found.
+
+ Args:
+ val: Element to eliminate.
+ """
+
+ _maxes = self._maxes
+ if not _maxes: raise ValueError(f"remove(x): x not in SortedList (list is empty)")
+
+ pos = bisect.bisect_left(_maxes, val)
+ if pos == len(_maxes): raise ValueError(f"remove(x): x not in SortedList (value exceeds max bound)")
+
+ _lists = self._lists
+ idx = bisect.bisect_left(_lists[pos], val)
+
+ if _lists[pos][idx] == val: self._delete(pos, idx)
+ else: raise ValueError(f"remove(x): x not in SortedList")
+
+ def _delete(self, pos, idx):
+ """
+ Internal operation to delete an element at a specific sublist coordinates.
+
+ Args:
+ pos: Sublist container index.
+ idx: Local list index element.
+ """
+
+ _maxes, _lists, _index = self._maxes, self._lists, self._index
+
+ lists_pos = _lists[pos]
+ del lists_pos[idx]
+
+ self._len -= 1
+ len_lists_pos = len(lists_pos)
+ # Check if sublist size remains stable within bounds
+ if len_lists_pos > self._half:
+ _maxes[pos] = lists_pos[-1]
+ # Cascade decrements up the positional tree index
+ if len(_index) > 0:
+ child = self._offset + pos
+
+ while child > 0:
+ _index[child] -= 1
+ child = (child - 1) >> 1
+
+ _index[0] -= 1
+ elif len(_lists) > 1:
+ # Merge shallow sublist with adjacent neighbors to maintain density
+ if pos == 0: pos += 1
+
+ prev = pos - 1
+ _lists[prev].extend(_lists[pos])
+ _maxes[prev] = _lists[prev][-1]
+
+ del _maxes[pos]
+ del _lists[pos]
+ del _index[:]
+
+ self._expand(prev)
+ elif len_lists_pos: _maxes[pos] = lists_pos[-1]
+ else:
+ del _maxes[pos]
+ del _lists[pos]
+ del _index[:]
+
+ def _loc(self, pos, idx):
+ """
+ Convert a localized (sublist, element) coordinate to a global linear index.
+
+ Args:
+ pos: Sublist target offset.
+ idx: Localized array element position.
+ """
+
+ if pos == 0: return idx
+
+ _index = self._index
+ if len(_index) == 0: self._build_index()
+
+ total = 0
+ pos += self._offset
+ # Traverse upwards through tree structure accumulators
+ while pos:
+ if not (pos & 1): total += _index[pos - 1]
+ pos = (pos - 1) >> 1
+
+ return total + idx
+
+ def _pos(self, idx):
+ """
+ Convert a global sequence index into localized (sublist, element) index.
+
+ Args:
+ idx: Global positional index integer.
+ """
+
+ _len, _lists = self._len, self._lists
+ # Standardizing negative indexing parameters
+ if idx < 0:
+ last_len = len(_lists[-1])
+ if (-idx) <= last_len: return len(_lists) - 1, last_len + idx
+
+ idx += _len
+ if idx < 0: raise IndexError(f"SortedList index out of range (negative index overflow: {idx - _len})")
+ elif idx >= _len: raise IndexError(f"SortedList index out of range: {idx}")
+ # Short-circuit optimize mapping targets landing in the primary sublist
+ if idx < len(_lists[0]): return 0, idx
+
+ _index = self._index
+ if len(_index) == 0: self._build_index()
+ # Binary search downwards through the segmented tracking index
+ pos = 0
+ len_index = len(_index)
+ child = (pos << 1) + 1
+
+ while child < len_index:
+ index_child = _index[child]
+
+ if idx < index_child: pos = child
+ else:
+ idx -= index_child
+ pos = child + 1
+
+ child = (pos << 1) + 1
+
+ return (pos - self._offset, idx)
+
+ def _build_index(self):
+ """Construct the structural indexing tree layout mapping offsets to segment counts."""
+
+ row0 = list(map(len, self._lists))
+
+ if len(row0) == 1:
+ self._index[:] = row0
+ self._offset = 0
+ return
+
+ head = iter(row0)
+ tail = iter(head)
+ row1 = list(starmap(operator.add, zip(head, tail)))
+
+ if len(row0) & 1: row1.append(row0[-1])
+
+ if len(row1) == 1:
+ self._index[:] = row1 + row0
+ self._offset = 1
+ return
+
+ size = 2 ** (int(math.log(len(row1) - 1, 2)) + 1)
+ row1.extend(repeat(0, size - len(row1)))
+ tree = [row0, row1]
+ # Generate aggregated index level structures
+ while len(tree[-1]) > 1:
+ head = iter(tree[-1])
+ tail = iter(head)
+
+ row = list(starmap(operator.add, zip(head, tail)))
+ tree.append(row)
+
+ reduce(operator.iadd, reversed(tree), self._index)
+ self._offset = size * 2 - 1
+
+ def _slice(self, slc):
+ """
+ Normalize Python slicing specifications matching current state limitations.
+
+ Args:
+ slc: Native slice configuration.
+ """
+
+ start, stop, step = slc.start, slc.stop, slc.step
+ if step == 0: raise ValueError("slice step cannot be zero")
+
+ if step is None: step = 1
+
+ if step > 0:
+ if start is None: start = 0
+ if stop is None: stop = len(self)
+ elif stop < 0: stop += len(self)
+ else:
+ if start is None: start = len(self) - 1
+ if stop is None: stop = -1
+ elif stop < 0: stop += len(self)
+
+ if start < 0: start += len(self)
+
+ if step > 0:
+ if start < 0: start = 0
+ elif start > len(self): start = len(self)
+ if stop < 0: stop = 0
+ elif stop > len(self): stop = len(self)
+ else:
+ if start < 0: start = -1
+ elif start >= len(self): start = len(self) - 1
+ if stop < 0: stop = -1
+ elif stop > len(self): stop = len(self)
+
+ return start, stop, step
+
+ def __delitem__(self, idx):
+ """Remove element or slice from container at designated reference index."""
+
+ if isinstance(idx, slice):
+ start, stop, step = self._slice(idx)
+ # Heavy structural truncation optimize: rewrite container instead of individual deletes
+ if ((step == 1) and (start < stop) and ((stop - start) * 8 >= self._len)):
+ values = self[:start]
+ if stop < self._len: values += self[stop:]
+
+ self.clear()
+ self.update(values)
+
+ return
+
+ indices = range(start, stop, step)
+ if step > 0: indices = reversed(indices)
+ _pos, _delete = self._pos, self._delete
+
+ for index in indices:
+ pos, idx = _pos(index)
+ _delete(pos, idx)
+ else:
+ pos, idx = self._pos(idx)
+ self._delete(pos, idx)
+
+ def __getitem__(self, idx):
+ """Retrieve element or slice segment based on requested index mapping."""
+
+ _lists = self._lists
+
+ if isinstance(idx, slice):
+ start, stop, step = self._slice(idx)
+ if step == 1 and start < stop:
+ if start == 0 and stop == self._len: return self.as_list()
+ start_pos, start_idx = self._pos(start)
+
+ if stop == self._len:
+ stop_pos = len(_lists) - 1
+ stop_idx = len(_lists[stop_pos])
+ else: stop_pos, stop_idx = self._pos(stop)
+
+ if start_pos == stop_pos: return _lists[start_pos][start_idx:stop_idx]
+
+ # Stitch slices traversing boundary lines together
+ prefix = _lists[start_pos][start_idx:]
+ middle = _lists[(start_pos + 1):stop_pos]
+
+ result = reduce(operator.iadd, middle, prefix)
+ result += _lists[stop_pos][:stop_idx]
+
+ return result
+
+ if step == -1 and start > stop:
+ result = self[(stop + 1):(start + 1)]
+ result.reverse()
+ return result
+
+ indices = range(start, stop, step)
+ return list(self[index] for index in indices)
+ else:
+ pos, idx = self._pos(idx)
+ return _lists[pos][idx]
+
+ def _check_order(self, idx, val):
+ """
+ Verify sorting property guarantees are maintained around a specific index index position.
+
+ Args:
+ idx: Positional index verified.
+ val: Evaluation entity tracking candidates.
+ """
+
+ _lists, _len = self._lists, self._len
+ pos, loc = self._pos(idx)
+ if idx < 0: idx += _len
+
+ # Verify left neighbor relation integrity
+ if idx > 0:
+ idx_prev = loc - 1
+ pos_prev = pos
+
+ if idx_prev < 0:
+ pos_prev -= 1
+ idx_prev = len(_lists[pos_prev]) - 1
+
+ if _lists[pos_prev][idx_prev] > val: raise ValueError(f"Order violation: element at index {idx} ({val}) cannot be less than index {idx - 1} ({_lists[pos_prev][idx_prev]})")
+
+ # Verify right neighbor relation integrity
+ if idx < (_len - 1):
+ idx_next = loc + 1
+ pos_next = pos
+
+ if idx_next == len(_lists[pos_next]):
+ pos_next += 1
+ idx_next = 0
+
+ if _lists[pos_next][idx_next] < val: raise ValueError(f"Order violation: element at index {idx} ({val}) cannot be greater than index {idx + 1} ({_lists[pos_next][idx_next]})")
+
+ def __setitem__(self, index, value):
+ """Modify sequence items while strictly maintaining ordering layout rules."""
+
+ _maxes, _lists, _pos = self._maxes, self._lists, self._pos
+ _check_order = self._check_order
+
+ if isinstance(index, slice):
+ start, stop, step = self._slice(index)
+ indices = range(start, stop, step)
+
+ if step != 1:
+ if not hasattr(value, '__len__'): value = list(value)
+
+ indices = list(indices)
+ if len(value) != len(indices): raise ValueError(f"attempt to assign sequence of size {len(value)} to extended slice of size {len(indices)}")
+
+ log = []
+ _append = log.append
+ # Stage modification targets transactions
+ for idx, val in zip(indices, value):
+ pos, loc = _pos(idx)
+ _append((idx, _lists[pos][loc], val))
+
+ _lists[pos][loc] = val
+ if len(_lists[pos]) == (loc + 1): _maxes[pos] = val
+
+ # Rollback operations if monotonic ordering contracts break
+ try:
+ for idx, oldval, newval in log:
+ _check_order(idx, newval)
+ except ValueError:
+ for idx, oldval, newval in log:
+ pos, loc = _pos(idx)
+ _lists[pos][loc] = oldval
+
+ if len(_lists[pos]) == (loc + 1): _maxes[pos] = oldval
+
+ raise
+ else:
+ # Slice modifications requiring sequence rebuilding properties
+ if not hasattr(value, '__getitem__'): value = list(value)
+ ordered = all(value[pos - 1] <= value[pos] for pos in range(1, len(value)))
+ if not ordered: raise ValueError("Assigned slice values must be sorted monotonically")
+
+ if start == 0 or len(value) == 0: pass
+ else:
+ if self[start - 1] > value[0]: raise ValueError(f"Slice value start boundary violation: sequence element at {start - 1} ({self[start - 1]}) > new element ({value[0]})")
+
+ if stop == len(self) or len(value) == 0: pass
+ else:
+ if self[stop] < value[-1]: raise ValueError(f"Slice value end boundary violation: sequence element at {stop} ({self[stop]}) < new element ({value[-1]})")
+
+ del self[index]
+ _insert = self.insert
+
+ for idx, val in enumerate(value):
+ _insert(start + idx, val)
+ else:
+ pos, loc = _pos(index)
+ _check_order(index, value)
+
+ _lists[pos][loc] = value
+ if len(_lists[pos]) == (loc + 1): _maxes[pos] = value
+
+ def __iter__(self):
+ """Return an iterator over the SortedList."""
+
+ return chain.from_iterable(self._lists)
+
+ def __reversed__(self):
+ """Return a reverse iterator over the SortedList."""
+
+ _lists = self._lists
+ start = len(_lists) - 1
+
+ iterable = (reversed(_lists[pos]) for pos in range(start, -1, -1))
+ return chain.from_iterable(iterable)
+
+ def __len__(self):
+ """Return the total number of elements in the SortedList."""
+
+ return self._len
+
+ def bisect_left(self, val):
+ """Find the index where val should be inserted to maintain sorted order (leftmost)."""
+
+ _maxes = self._maxes
+ if not _maxes: return 0
+
+ pos = bisect.bisect_left(_maxes, val)
+ if pos == len(_maxes): return self._len
+
+ idx = bisect.bisect_left(self._lists[pos], val)
+ return self._loc(pos, idx)
+
+ def bisect(self, val):
+ """Alias for bisect_right."""
+
+ return self.bisect_right(val)
+
+ def bisect_right(self, val):
+ """Find the index where val should be inserted to maintain sorted order (rightmost)."""
+
+ _maxes = self._maxes
+ if not _maxes: return 0
+
+ pos = bisect.bisect_right(_maxes, val)
+ if pos == len(_maxes): return self._len
+ idx = bisect.bisect_right(self._lists[pos], val)
+
+ return self._loc(pos, idx)
+
+ def count(self, val):
+ """Return the number of occurrences of a value."""
+
+ _maxes = self._maxes
+ if not _maxes: return 0
+
+ pos_left = bisect.bisect_left(_maxes, val)
+ if pos_left == len(_maxes): return 0
+
+ _lists = self._lists
+ idx_left = bisect.bisect_left(_lists[pos_left], val)
+ pos_right = bisect.bisect_right(_maxes, val)
+
+ if pos_right == len(_maxes): return self._len - self._loc(pos_left, idx_left)
+ idx_right = bisect.bisect_right(_lists[pos_right], val)
+
+ if pos_left == pos_right:
+ return idx_right - idx_left
+
+ right = self._loc(pos_right, idx_right)
+ left = self._loc(pos_left, idx_left)
+
+ return right - left
+
+ def copy(self):
+ """Return a shallow copy of the SortedList."""
+
+ return SortedList(self, load=self._load)
+
+ def __copy__(self):
+ """Return a shallow copy of the SortedList."""
+
+ return self.copy()
+
+ def append(self, val):
+ """Append a value; raises ValueError if it breaks sorted order restrictions."""
+
+ _maxes, _lists = self._maxes, self._lists
+
+ if not _maxes:
+ _maxes.append(val)
+ _lists.append([val])
+
+ self._len = 1
+ return
+
+ pos = len(_lists) - 1
+ if val < _lists[pos][-1]: raise ValueError(f"append(x): x ({val}) cannot be less than the largest element ({_lists[pos][-1]})")
+
+ _maxes[pos] = val
+ _lists[pos].append(val)
+
+ self._len += 1
+ self._expand(pos)
+
+ def extend(self, values):
+ """Extend list elements; raises ValueError if incoming data breaks sorted properties."""
+
+ _maxes, _lists, _load = self._maxes, self._lists, self._load
+
+ if not isinstance(values, list):
+ values = list(values)
+
+ if any(values[pos - 1] > values[pos] for pos in range(1, len(values))):
+ raise ValueError("extend(iterable): items in iterable must be sorted monotonically")
+
+ offset = 0
+
+ if _maxes:
+ if values[0] < _lists[-1][-1]: raise ValueError(f"extend(iterable): first item ({values[0]}) cannot be less than the largest container element ({_lists[-1][-1]})")
+
+ if len(_lists[-1]) < self._half:
+ _lists[-1].extend(values[:_load])
+ _maxes[-1] = _lists[-1][-1]
+ offset = _load
+
+ len_lists = len(_lists)
+
+ for idx in range(offset, len(values), _load):
+ _lists.append(values[idx:(idx + _load)])
+ _maxes.append(_lists[-1][-1])
+
+ _index = self._index
+
+ if len_lists == len(_lists):
+ len_index = len(_index)
+
+ if len_index > 0:
+ len_values = len(values)
+ child = len_index - 1
+
+ while child:
+ _index[child] += len_values
+ child = (child - 1) >> 1
+
+ _index[0] += len_values
+ else: del self._index[:]
+
+ self._len += len(values)
+
+ def insert(self, idx, val):
+ """Insert a value at a numerical position; raises ValueError if order is compromised."""
+
+ _maxes, _lists, _len = self._maxes, self._lists, self._len
+
+ if idx < 0: idx += _len
+ if idx < 0: idx = 0
+ if idx > _len: idx = _len
+
+ if not _maxes:
+ _maxes.append(val)
+ _lists.append([val])
+
+ self._len = 1
+ return
+
+ if idx == 0:
+ if val > _lists[0][0]: raise ValueError(f"insert(0, x): x ({val}) cannot be greater than the first element ({_lists[0][0]})")
+ else:
+ _lists[0].insert(0, val)
+ self._expand(0)
+
+ self._len += 1
+ return
+
+ if idx == _len:
+ pos = len(_lists) - 1
+
+ if _lists[pos][-1] > val: raise ValueError(f"insert({_len}, x): x ({val}) cannot be less than the last element ({_lists[pos][-1]})")
+ else:
+ _lists[pos].append(val)
+ _maxes[pos] = _lists[pos][-1]
+
+ self._expand(pos)
+ self._len += 1
+ return
+
+ pos, idx = self._pos(idx)
+ idx_before = idx - 1
+
+ if idx_before < 0:
+ pos_before = pos - 1
+ idx_before = len(_lists[pos_before]) - 1
+ else: pos_before = pos
+
+ before = _lists[pos_before][idx_before]
+
+ if before <= val <= _lists[pos][idx]:
+ _lists[pos].insert(idx, val)
+ self._expand(pos)
+ self._len += 1
+ else: raise ValueError(f"insert({idx}, x): x ({val}) violates sorted properties bounds")
+
+ def pop(self, idx=-1):
+ """Remove and return item at index (default last element)."""
+
+ if (idx < 0 and -idx > self._len) or (idx >= self._len): raise IndexError(f"pop index out of range: {idx}")
+
+ pos, idx = self._pos(idx)
+ val = self._lists[pos][idx]
+ self._delete(pos, idx)
+
+ return val
+
+ def index(self, val, start=None, stop=None):
+ """Return first index index occurrence of value within target slice bounds."""
+
+ _len, _maxes = self._len, self._maxes
+ if not _maxes: raise ValueError(f"index(x): x not in SortedList (list is empty)")
+
+ if start is None: start = 0
+ if start < 0: start += _len
+ if start < 0: start = 0
+ if stop is None: stop = _len
+ if stop < 0: stop += _len
+ if stop > _len: stop = _len
+
+ if stop <= start: raise ValueError(f"index(x): valid slice range empty (start={start} >= stop={stop})")
+
+ stop -= 1
+ pos_left = bisect.bisect_left(_maxes, val)
+ if pos_left == len(_maxes): raise ValueError(f"index(x): x not in SortedList (exceeds maximum elements)")
+
+ _lists = self._lists
+ idx_left = bisect.bisect_left(_lists[pos_left], val)
+
+ if _lists[pos_left][idx_left] != val: raise ValueError(f"index(x): x not in SortedList")
+ left = self._loc(pos_left, idx_left)
+
+ if start <= left:
+ if left <= stop: return left
+ else:
+ right = self.bisect_right(val) - 1
+ if start <= right: return start
+
+ raise ValueError(f"index(x): x found at index {left}, but it is out of the specified bounds [{start}:{stop+1}]")
+
+ def as_list(self):
+ """Flatten segmented representation returning objects in a single continuous layout list."""
+
+ return reduce(operator.iadd, self._lists, [])
+
+ def __add__(self, that):
+ """Concatenate SortedList with an external sequence, producing a new SortedList instance."""
+
+ values = self.as_list()
+ values.extend(that)
+
+ return SortedList(values)
+
+ def __iadd__(self, that):
+ """Inplace addition expansion sequence updates."""
+
+ self.update(that)
+ return self
+
+ def __mul__(self, that):
+ """Multiply SortedList items generating duplicated sequence sets into a new object instance."""
+
+ values = self.as_list() * that
+ return SortedList(values)
+
+ def __imul__(self, that):
+ """Inplace multiplication allocation update processing."""
+
+ values = self.as_list() * that
+ self.clear()
+ self.update(values)
+ return self
+
+ def __eq__(self, that):
+ """Check if structural sequence elements are strictly identical."""
+
+ return ((self._len == len(that)) and all(lhs == rhs for lhs, rhs in zip(self, that)))
+
+ def __ne__(self, that):
+ """Check if structural sequence elements are non-identical."""
+
+ return ((self._len != len(that)) or any(lhs != rhs for lhs, rhs in zip(self, that)))
+
+ def __lt__(self, that):
+ """Less than inequality comparator checks."""
+
+ return ((self._len <= len(that)) and all(lhs < rhs for lhs, rhs in zip(self, that)))
+
+ def __le__(self, that):
+ """Less-or-equal inequality comparator checks."""
+
+ return ((self._len <= len(that)) and all(lhs <= rhs for lhs, rhs in zip(self, that)))
+
+ def __gt__(self, that):
+ """Greater than inequality comparator checks."""
+
+ return ((self._len >= len(that)) and all(lhs > rhs for lhs, rhs in zip(self, that)))
+
+ def __ge__(self, that):
+ """Greater-or-equal inequality comparator checks."""
+
+ return ((self._len >= len(that)) and all(lhs >= rhs for lhs, rhs in zip(self, that)))
+
+ @recursive_repr
+ def __repr__(self):
+ """Return a string representation of the object."""
+
+ return '{0}({1})'.format(self.__class__.__name__, repr(self.as_list()))
+
+ def _check(self):
+ """Perform comprehensive integrity checks on the internal state structure of the SortedList."""
+
+ try:
+ assert self._load >= 4
+ assert self._half == (self._load >> 1)
+ assert self._twice == (self._load * 2)
+
+ if self._maxes == []:
+ assert self._lists == []
+ return
+
+ assert len(self._maxes) > 0 and len(self._lists) > 0
+ assert all(sublist[pos - 1] <= sublist[pos] for sublist in self._lists for pos in range(1, len(sublist)))
+
+ for pos in range(1, len(self._lists)):
+ assert self._lists[pos - 1][-1] <= self._lists[pos][0]
+
+ assert len(self._maxes) == len(self._lists)
+ assert all(self._maxes[pos] == self._lists[pos][-1] for pos in range(len(self._maxes)))
+ assert all(len(sublist) <= self._twice for sublist in self._lists)
+ assert all(len(self._lists[pos]) >= self._half for pos in range(0, len(self._lists) - 1))
+ assert self._len == sum(len(sublist) for sublist in self._lists)
+
+ if len(self._index):
+ assert len(self._index) == self._offset + len(self._lists)
+ assert self._len == self._index[0]
+
+ def test_offset_pos(pos):
+ from_index = self._index[self._offset + pos]
+ return from_index == len(self._lists[pos])
+
+ assert all(test_offset_pos(pos) for pos in range(len(self._lists)))
+
+ for pos in range(self._offset):
+ child = (pos << 1) + 1
+
+ if self._index[pos] == 0: assert child >= len(self._index)
+ elif child + 1 == len(self._index): assert self._index[pos] == self._index[child]
+ else: assert self._index[pos] == (self._index[child] + self._index[child + 1])
+ except:
+ import traceback
+
+ # Log execution traceback state logs for analysis
+ logger.error(traceback.format_exc())
+ logger.debug(self._len, self._load, self._half, self._twice)
+ logger.debug(self._index)
+ logger.debug(self._maxes)
+ logger.debug(self._lists)
+
+ raise
\ No newline at end of file
diff --git a/packages.txt b/packages.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d414f583486984c729845c58d07c98d7bce4b73d
--- /dev/null
+++ b/packages.txt
@@ -0,0 +1,2 @@
+portaudio19-dev
+libportaudio2
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d876e066881e5232bd752c27e167d50886d4a1f
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,62 @@
+# Platform-specific requirements section
+pip>=23.3
+wheel
+omegaconf>=2.0.6
+setuptools==81.0.0
+webrtcvad-wheels>=2.0.14
+
+onnxruntime; sys_platform == 'darwin'
+onnxruntime-gpu==1.22.0; sys_platform != 'darwin'
+
+# Core dependencies
+PyYAML>=6.0
+tiktoken
+hyperpyyaml
+torch>=2.3.1
+tqdm>=4.63.1
+torchvision>=0.18.1
+torchaudio>=2.3.1
+torchcodec==0.9.1
+
+faiss-cpu==1.7.3; python_version < "3.12"
+faiss-cpu>=1.7.3; python_version >= "3.12"
+
+# Machine learning, NLP and deep learning
+transformers>=4.49.0
+scikit-learn
+einops>=0.8.0
+
+# Pitch and sound processing
+librosa>=0.10.2
+pydub>=0.25.1
+praat-parselmouth
+soundfile>=0.13.0
+pedalboard
+
+# Data processing and calculation
+numpy>=1.25.2
+numba>=0.57.0
+scipy>=1.15.0
+matplotlib>=3.7.2
+
+# Implementation and web framework
+gradio==5.23.3
+requests>=2.32.3
+aiohttp
+pysrt
+
+# Utility section
+yt-dlp>=2026.03.17
+edge-tts>=7.2.7
+beautifulsoup4
+cffi>=2.0.0
+
+# Tensorboard and ONNX
+tensorboard
+onnx>=1.14
+onnxslim
+onnxscript
+onnx2torch>=1.5.15
+
+# Cryptography section
+pycryptodome>=3.9.6,<4.0.0
\ No newline at end of file
diff --git a/run_app.bat b/run_app.bat
new file mode 100644
index 0000000000000000000000000000000000000000..f4034ff51824977c05ea57bb73974cf6db12bcef
--- /dev/null
+++ b/run_app.bat
@@ -0,0 +1,14 @@
+@echo off
+chcp 65001 > nul
+
+setlocal
+title Vietnamese RVC (Windows)
+
+if exist "runtime\python.exe" (
+ runtime\python.exe main\app\app.py --open --allow_all_disk
+) else if exist "env\\Scripts\\python.exe" (
+ env\Scripts\python.exe main\app\app.py --open --allow_all_disk
+)
+
+echo.
+pause
\ No newline at end of file
diff --git a/run_app.sh b/run_app.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e9d9be4e34abb57046af35e6dd59dff5c1ac127c
--- /dev/null
+++ b/run_app.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+clear
+echo "Vietnamese RVC (Linux)"
+
+source "./venv/bin/activate"
+python "./main/app/app.py" --open
+deactivate
diff --git a/run_install.bat b/run_install.bat
new file mode 100644
index 0000000000000000000000000000000000000000..4d30fa7837eab95cfffa32b7ced151a89d8ab54b
--- /dev/null
+++ b/run_install.bat
@@ -0,0 +1,38 @@
+@echo off
+chcp 65001 > nul
+
+setlocal
+title Vietnamese RVC Install (Windows)
+
+set ZIP_PATH=%~dp0assets\runtime.zip
+set DEST_PATH=%~dp0
+
+
+if exist "%ZIP_PATH%" (
+ echo Giải nén runtime...
+ powershell -command "Expand-Archive -Path '%ZIP_PATH%' -DestinationPath '%DEST_PATH%' -Force"
+ echo Giải nén runtime hoàn tất!
+) else (
+ echo Không tìm thấy runtime!
+ pause
+)
+
+if exist "runtime\python.exe" (
+ echo Bắt đầu cài đặt gói pip...
+ runtime\python.exe runtime\get-pip.py
+
+ cls
+ echo Hoàn tất cài gói pip, bắt đầu cài thư viện...
+
+ runtime\python.exe main\app\install.py
+
+ cls
+ echo Hoàn tất!
+) else (
+ echo Không tìm thấy phần runtime đã giải nén!
+ pause
+)
+
+echo Bắt đầu khởi động giao diện!
+call run_app.bat
+pause
\ No newline at end of file
diff --git a/run_install.sh b/run_install.sh
new file mode 100644
index 0000000000000000000000000000000000000000..63d93e74893bd68085a81fc01d30eb81dd5f0498
--- /dev/null
+++ b/run_install.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+clear
+echo "Vietnamese RVC Install (Linux)"
+
+echo "Đang cài đặt Python 3.12 và thư viện hệ thống..."
+sudo apt update
+sudo apt install -y python3.12 python3.12-venv
+
+echo "Khởi tạo môi trường ảo..."
+python3.12 -m venv venv
+source "./venv/bin/activate"
+
+echo "Bắt đầu cài đặt thư viện..."
+python "./main/app/install.py"
+
+echo "Hoàn tất! Bắt đầu khởi động giao diện..."
+bash "./run_app.sh"
diff --git a/run_tensorboard.bat b/run_tensorboard.bat
new file mode 100644
index 0000000000000000000000000000000000000000..a6bae0951489e95e5e412655e72397bb9b9e23ae
--- /dev/null
+++ b/run_tensorboard.bat
@@ -0,0 +1,14 @@
+@echo off
+chcp 65001 > nul
+
+setlocal
+title Vietnamese RVC Tensorboard
+
+if exist "runtime\\python.exe" (
+ runtime\python.exe main\app\run_tensorboard.py --open
+) else if exist "env\\Scripts\\python.exe" (
+ env\Scripts\python.exe main\app\run_tensorboard.py --open
+)
+
+echo.
+pause
\ No newline at end of file
diff --git a/run_tensorboard.sh b/run_tensorboard.sh
new file mode 100644
index 0000000000000000000000000000000000000000..eb7bafefe21fd88e254d07d44cc9900493c3d9b6
--- /dev/null
+++ b/run_tensorboard.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+clear
+echo "Vietnamese RVC Tensorboard"
+
+source venv/bin/activate
+python main/app/run_tensorboard.py --open
+deactivate
\ No newline at end of file