python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
import subprocess
import matplotlib
import os
matplotlib.use('Agg')
import librosa
import librosa.filters
import numpy as np
from scipy import signal
from scipy.io import wavfile
def save_wav(wav, path, sr, norm=False):
if norm:
wav = wav / np.abs(wav).max()
wav *= 32767
# proposed by @dsmiller
... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/utils/audio.py |
from numpy import array, zeros, full, argmin, inf, ndim
from scipy.spatial.distance import cdist
from math import isinf
def dtw(x, y, dist, warp=1, w=inf, s=1.0):
"""
Computes Dynamic Time Warping (DTW) of two sequences.
:param array x: N1*M array
:param array y: N2*M array
:param func dist: dist... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/utils/dtw.py |
import os
import traceback
from multiprocessing import Queue, Process
def chunked_worker(worker_id, map_func, args, results_queue=None, init_ctx_func=None):
ctx = init_ctx_func(worker_id) if init_ctx_func is not None else None
for job_idx, arg in args:
try:
if ctx is not None:
... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/utils/multiprocess_utils.py |
import os
from data_gen.tts.base_preprocess import BasePreprocessor
import glob
class LibrittsPreAlign(BasePreprocessor):
def meta_data(self):
wav_fns = sorted(glob.glob(f'{self.raw_data_dir}/*/*/*.wav'))
for wav_fn in wav_fns:
item_name = os.path.basename(wav_fn)[:-4]
txt... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/configs/tts/libritts/pre_align.py |
import os
from data_gen.tts.base_preprocess import BasePreprocessor
import glob
import re
class EmoPreAlign(BasePreprocessor):
def meta_data(self):
spks = ['0012', '0011', '0013', '0014', '0015', '0016', '0017', '0018', '0019', '0020']
pattern = re.compile('[\t\n ]+')
for spk in spks:
... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/configs/tts/emotion/pre_align.py |
import torch
from inference.svs.base_svs_infer import BaseSVSInfer
from utils import load_ckpt
from utils.hparams import hparams
from modulesmodules.diff.shallow_diffusion_tts import GaussianDiffusion
from tasks.svs.diffsinger_task import DIFF_DECODERS
class DiffSingerCascadeInfer(BaseSVSInfer):
def build_model(se... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/inference/svs/ds_cascade.py |
import os
import torch
import numpy as np
from modules.hifigan.hifigan import HifiGanGenerator
from vocoders.hifigan import HifiGAN
from inference.svs.opencpop.map import cpop_pinyin2ph_func
from utils import load_ckpt
from utils.hparams import set_hparams, hparams
from utils.text_encoder import TokenTextEncoder
from... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/inference/svs/base_svs_infer.py |
import torch
# from inference.tts.fs import FastSpeechInfer
# from modules.tts.fs2_orig import FastSpeech2Orig
from inference.svs.base_svs_infer import BaseSVSInfer
from utils import load_ckpt
from utils.hparams import hparams
from modules.diff.shallow_diffusion_tts import GaussianDiffusion
from tasks.svs.diffsinger_ta... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/inference/svs/ds_e2e.py |
def cpop_pinyin2ph_func():
# In the README file of opencpop dataset, they defined a "pinyin to phoneme mapping table"
pinyin2phs = {'AP': 'AP', 'SP': 'SP'}
with open('NeuralSeq/inference/svs/opencpop/cpop_pinyin2ph.txt') as rf:
for line in rf.readlines():
elements = [x.strip() for x in l... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/inference/svs/opencpop/map.py |
import torch
from inference.tts.base_tts_infer import BaseTTSInfer
from utils.ckpt_utils import load_ckpt
from modules.portaspeech.portaspeech import PortaSpeech
class TTSInference(BaseTTSInfer):
def __init__(self, hparams, device=None):
super().__init__(hparams, device)
print("Initializing TTS mod... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/inference/tts/PortaSpeech.py |
import torch
import os
import importlib
from inference.tts.base_tts_infer import BaseTTSInfer
from utils.ckpt_utils import load_ckpt, get_last_checkpoint
from modules.GenerSpeech.model.generspeech import GenerSpeech
from data_gen.tts.emotion import inference as EmotionEncoder
from data_gen.tts.emotion.inference import ... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/inference/tts/GenerSpeech.py |
from tasks.tts.dataset_utils import FastSpeechWordDataset
from tasks.tts.tts_utils import load_data_preprocessor
from vocoders.hifigan import HifiGanGenerator
import os
import librosa
import soundfile as sf
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from string import punctuation
import torch
from utils... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/inference/tts/base_tts_infer.py |
EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/__init__.py | |
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""STFT-based Loss modules."""
import librosa
import torch
from modules.parallel_wavegan.losses import LogSTFTMagnitudeLoss, SpectralConvergengeLoss, stft
class STFTLoss(torch.nn.Module):
"""STFT loss m... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/stft_loss.py |
EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/__init__.py | |
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""STFT-based Loss modules."""
import torch
import torch.nn.functional as F
def stft(x, fft_size, hop_size, win_length, window):
"""Perform STFT and convert to magnitude spectrogram.
Args:
... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/losses/stft_loss.py |
from .stft_loss import * # NOQA
| EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/losses/__init__.py |
# -*- coding: utf-8 -*-
# Copyright 2020 MINH ANH (@dathudeptrai)
# MIT License (https://opensource.org/licenses/MIT)
"""Tensorflow Layer modules complatible with pytorch."""
import tensorflow as tf
class TFReflectionPad1d(tf.keras.layers.Layer):
"""Tensorflow ReflectionPad1d module."""
def __init__(self... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/tf_layers.py |
# -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Causal convolusion layer modules."""
import torch
class CausalConv1d(torch.nn.Module):
"""CausalConv1d module with customized initialization."""
def __init__(self, in_channels, out_channels, ke... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/causal_conv.py |
from .causal_conv import * # NOQA
from .pqmf import * # NOQA
from .residual_block import * # NOQA
from modules.parallel_wavegan.layers.residual_stack import * # NOQA
from .upsample import * # NOQA
| EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/__init__.py |
# -*- coding: utf-8 -*-
"""Upsampling module.
This code is modified from https://github.com/r9y9/wavenet_vocoder.
"""
import numpy as np
import torch
import torch.nn.functional as F
from . import Conv1d
class Stretch2d(torch.nn.Module):
"""Stretch2d module."""
def __init__(self, x_scale, y_scale, mode="... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/upsample.py |
# -*- coding: utf-8 -*-
"""Residual block module in WaveNet.
This code is modified from https://github.com/r9y9/wavenet_vocoder.
"""
import math
import torch
import torch.nn.functional as F
class Conv1d(torch.nn.Conv1d):
"""Conv1d module with customized initialization."""
def __init__(self, *args, **kwa... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/residual_block.py |
# -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Pseudo QMF modules."""
import numpy as np
import torch
import torch.nn.functional as F
from scipy.signal import kaiser
def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):
"""Design pr... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/pqmf.py |
# -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Residual stack module in MelGAN."""
import torch
from . import CausalConv1d
class ResidualStack(torch.nn.Module):
"""Residual stack module introduced in MelGAN."""
def __init__(self,
... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/layers/residual_stack.py |
from .utils import * # NOQA
| EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/utils/__init__.py |
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Utility functions."""
import fnmatch
import logging
import os
import sys
import h5py
import numpy as np
def find_files(root_dir, query="*.wav", include_root_dir=True):
"""Find files recursively.
... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/utils/utils.py |
from .melgan import * # NOQA
from .parallel_wavegan import * # NOQA
| EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/models/__init__.py |
# -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""MelGAN Modules."""
import logging
import numpy as np
import torch
from modules.parallel_wavegan.layers import CausalConv1d
from modules.parallel_wavegan.layers import CausalConvTranspose1d
from modules.p... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/models/melgan.py |
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Parallel WaveGAN Modules."""
import logging
import math
import torch
from torch import nn
from modules.parallel_wavegan.layers import Conv1d
from modules.parallel_wavegan.layers import Conv1d1x1
from mod... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/models/parallel_wavegan.py |
import torch
import numpy as np
import sys
import torch.nn.functional as torch_nn_func
class SineGen(torch.nn.Module):
""" Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
sine_amp = 0.1, noise_std = 0.003,
voiced_threshold = 0,
flag_for_pulse=False)
s... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/models/source.py |
from torch.optim import * # NOQA
from .radam import * # NOQA
| EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/optimizers/__init__.py |
# -*- coding: utf-8 -*-
"""RAdam optimizer.
This code is drived from https://github.com/LiyuanLucasLiu/RAdam.
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class RAdam(Optimizer):
"""Rectified Adam optimizer."""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, ... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/parallel_wavegan/optimizers/radam.py |
from modules.commons.common_layers import *
from modules.commons.common_layers import Embedding
from modules.fastspeech.tts_modules import FastspeechDecoder, DurationPredictor, LengthRegulator, PitchPredictor, \
EnergyPredictor, FastspeechEncoder
from utils.cwt import cwt2f0
from utils.hparams import hparams
from u... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/diffsinger_midi/fs2.py |
import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.onnx.operators
import torch.nn.functional as F
import utils
class Reshape(nn.Module):
def __init__(self, *args):
super(Reshape, self).__init__()
self.shape = args
def forward(self, x):
return x.v... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/common_layers.py |
# '''
# https://github.com/One-sixth/ms_ssim_pytorch/blob/master/ssim.py
# '''
#
# import torch
# import torch.jit
# import torch.nn.functional as F
#
#
# @torch.jit.script
# def create_window(window_size: int, sigma: float, channel: int):
# '''
# Create 1-D gauss kernel
# :param window_size: the size of ga... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/ssim.py |
import torch
from torch import nn
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts = t_act * s_act
return acts... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/wavenet.py |
import math
import torch
from torch import nn
from torch.nn import functional as F
from utils.hparams import hparams
from modules.commons.common_layers import Embedding
from utils.tts_utils import group_hidden_by_segs, expand_word2ph
import transformers
def convert_pad_shape(pad_shape):
l = pad_shape[::-1]
pa... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/rel_transformer.py |
import math
import torch
from torch import nn
from torch.nn import Parameter, Linear
from modules.commons.common_layers import LayerNorm, Embedding
from utils.tts_utils import get_incremental_state, set_incremental_state, softmax, make_positions
import torch.nn.functional as F
DEFAULT_MAX_SOURCE_POSITIONS = 2000
DEFAU... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/transformer.py |
import torch
import torch.nn.functional as F
def build_word_mask(x2word, y2word):
return (x2word[:, :, None] == y2word[:, None, :]).long()
def mel2ph_to_mel2word(mel2ph, ph2word):
mel2word = (ph2word - 1).gather(1, (mel2ph - 1).clamp(min=0)) + 1
mel2word = mel2word * (mel2ph > 0).long()
return mel2w... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/align_ops.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from modules.commons.common_layers import Embedding
from modules.fastspeech.tts_modules import LayerNorm
class LambdaLayer(nn.Module):
def __init__(self, lambd):
super(LambdaLayer, self).__init__()
self.lambd = lambd
... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/conv.py |
import math
import torch
class PositionalEncoding(torch.nn.Module):
"""Positional encoding.
Args:
d_model (int): Embedding dimension.
dropout_rate (float): Dropout rate.
max_len (int): Maximum input length.
reverse (bool): Whether to reverse the input position.
"""
def... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/espnet_positional_embedding.py |
import scipy
from torch.nn import functional as F
import torch
from torch import nn
import numpy as np
from modules.commons.wavenet import WN
from modules.glow import utils
class ActNorm(nn.Module):
def __init__(self, channels, ddi=False, **kwargs):
super().__init__()
self.channels = channels
... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/normalizing_flow/glow_modules.py |
import torch
from torch import nn
from modules.commons.conv import ConditionalConvBlocks
from modules.commons.wavenet import WN
class FlipLayer(nn.Module):
def forward(self, x, nonpadding, cond=None, reverse=False):
x = torch.flip(x, [1])
return x
class CouplingLayer(nn.Module):
def __init__... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/normalizing_flow/res_flow.py |
import torch
def squeeze(x, x_mask=None, n_sqz=2):
b, c, t = x.size()
t = (t // n_sqz) * n_sqz
x = x[:, :, :t]
x_sqz = x.view(b, c, t // n_sqz, n_sqz)
x_sqz = x_sqz.permute(0, 3, 1, 2).contiguous().view(b, c * n_sqz, t // n_sqz)
if x_mask is not None:
x_mask = x_mask[:, :, n_sqz - 1:... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/commons/normalizing_flow/utils.py |
import numpy as np
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
from scipy.io.wavfile import read
MAX_WAV_VALUE = 32768.0
def load_wav(full_path):
sampling_rate, data = read(full_path)
return data, sampling_rate
def dynamic_range_compression(x, C=1, clip_val=1e-5):... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/hifigan/mel_utils.py |
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from modules.parallel_wavegan.layers import UpsampleNetwork, ConvInUpsampleNetwork
from modules.parallel_wavegan.m... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/hifigan/hifigan.py |
import math
import random
from functools import partial
from inspect import isfunction
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm
from einops import rearrange
from modules.fastspeech.fs2 import FastSpeech2
from modules.diffsinger_... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/diff/diffusion.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt
from .diffusion import Mish
from utils.hparams import hparams
Linear = nn.Linear
ConvTranspose2d = nn.ConvTranspose2d
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/diff/net.py |
import math
import random
from collections import deque
from functools import partial
from inspect import isfunction
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from tqdm import tqdm
from einops import rearrange
from modules.fastspeech.fs2 import FastSp... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/diff/shallow_diffusion_tts.py |
from modules.fastspeech.tts_modules import FastspeechDecoder
# from modules.fastspeech.fast_tacotron import DecoderRNN
# from modules.fastspeech.speedy_speech.speedy_speech import ConvBlocks
# from modules.fastspeech.conformer.conformer import ConformerDecoder
import torch
from torch.nn import functional as F
import to... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/diff/candidate_decoder.py |
import matplotlib
matplotlib.use('Agg')
from data_gen.tts.data_gen_utils import get_pitch
from modules.fastspeech.tts_modules import mel2ph_to_dur
import matplotlib.pyplot as plt
from utils import audio
from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse
from vocoders.base_vocoder import get_vocoder_c... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/GenerSpeech/task/generspeech.py |
import matplotlib
matplotlib.use('Agg')
from tasks.base_task import data_loader
from tasks.tts.fs2 import FastSpeech2Task
from tasks.tts.dataset_utils import FastSpeechDataset, BaseTTSDataset
import glob
import importlib
from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse
from inference.base_tts_infer... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/GenerSpeech/task/dataset.py |
import scipy
from torch.nn import functional as F
import torch
from torch import nn
import numpy as np
from modules.commons.common_layers import Permute
from modules.fastspeech.tts_modules import FFTBlocks
from modules.GenerSpeech.model.wavenet import fused_add_tanh_sigmoid_multiply, WN
class LayerNorm(nn.Module):
... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/GenerSpeech/model/glow_modules.py |
from modules.commons.common_layers import *
# @torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b
t_act = torch.tanh(in_act[:, :n_channels_int, :])
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
acts =... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/GenerSpeech/model/wavenet.py |
from torch import nn
import copy
import torch
from utils.hparams import hparams
from modules.GenerSpeech.model.wavenet import WN
import math
from modules.fastspeech.tts_modules import LayerNorm
import torch.nn.functional as F
from utils.tts_utils import group_hidden_by_segs, sequence_mask
from scipy.cluster.vq import... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/GenerSpeech/model/prosody_util.py |
import torch
from modules.GenerSpeech.model.glow_modules import Glow
from modules.fastspeech.tts_modules import PitchPredictor
import random
from modules.GenerSpeech.model.prosody_util import ProsodyAligner, LocalStyleAdaptor
from utils.pitch_utils import f0_to_coarse, denorm_f0
from modules.commons.common_layers impor... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/GenerSpeech/model/generspeech.py |
from modules.commons.common_layers import *
import random
class MixStyle(nn.Module):
"""MixStyle.
Reference:
Zhou et al. Domain Generalization with MixStyle. ICLR 2021.
"""
def __init__(self, p=0.5, alpha=0.1, eps=1e-6, hidden_size=256):
"""
Args:
p (float): probabilit... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/GenerSpeech/model/mixstyle.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from dgl.nn.pytorch import GatedGraphConv
def sequence_mask(lengths, maxlen, dtype=torch.bool):
if maxlen is None:
maxlen = lengths.max()
mask = ~(torch.ones((len(lengths), maxlen)).to(lengths.device).cumsum(dim=1).t() > len... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/syntaspeech/syntactic_graph_encoder.py |
from copy import deepcopy
import torch
import dgl
import stanza
import networkx as nx
class Sentence2GraphParser:
def __init__(self, language='zh', use_gpu=False, download=False):
self.language = language
if download:
self.stanza_parser = stanza.Pipeline(lang=language, use_gpu=use_gpu)... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/syntaspeech/syntactic_graph_buider.py |
import numpy as np
import torch
import torch.nn as nn
class SingleWindowDisc(nn.Module):
def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128):
super().__init__()
padding = (kernel[0] // 2, kernel[1] // 2)
self.model = nn.ModuleList([
nn.Sequen... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/syntaspeech/multi_window_disc.py |
import math
import torch
from torch import nn
from torch.nn import Linear
from utils.hparams import hparams
from modules.commons.conv import ConvBlocks, ConditionalConvBlocks
from modules.commons.common_layers import Embedding
from modules.commons.rel_transformer import RelTransformerEncoder
from modules.commons.transf... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/syntaspeech/syntaspeech.py |
from utils.hparams import hparams
from modules.commons.common_layers import *
from modules.commons.common_layers import Embedding
from modules.fastspeech.tts_modules import FastspeechDecoder, DurationPredictor, LengthRegulator, PitchPredictor, \
EnergyPredictor, FastspeechEncoder
from utils.cwt import cwt2f0
from u... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/fastspeech/fs2.py |
import logging
import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from modules.commons.espnet_positional_embedding import RelPositionalEncoding
from modules.commons.common_layers import SinusoidalPositionalEmbedding, Linear, EncSALayer, DecSALayer, BatchNorm1dTBC
from utils.hparams im... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/fastspeech/tts_modules.py |
from modules.commons.common_layers import *
from utils.hparams import hparams
from modules.fastspeech.tts_modules import PitchPredictor
from utils.pitch_utils import denorm_f0
class Prenet(nn.Module):
def __init__(self, in_dim=80, out_dim=256, kernel=5, n_layers=3, strides=None):
super(Prenet, self).__ini... | EXA-1-master | exa/models/AudioGPT/NeuralSeq/modules/fastspeech/pe.py |
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from transformers import AutoModel
from .audio import get_audio_encoder
class Projection(nn.Module):
def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None:
super().__init__()
self.linear1 = nn.Linear(d_in... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/wav_evaluation/models/clap.py |
from . import clap
from . import audio
from . import utils | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/wav_evaluation/models/__init__.py |
import argparse
import yaml
import sys
def read_config_as_args(config_path,args=None,is_config_str=False):
return_dict = {}
if config_path is not None:
if is_config_str:
yml_config = yaml.load(config_path, Loader=yaml.FullLoader)
else:
with open(config_path, "r") as f:
... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/wav_evaluation/models/utils.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
def get_audio_encoder(name: str):
if name == "Cnn14":
return Cnn14
else:
raise Exception('The audio encoder name {} is incorrect or not supported'.format(name))
class... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/wav_evaluation/models/audio.py |
import random
import torchaudio
from torch._six import string_classes
import collections
import re
import torch.nn.functional as F
import numpy as np
from transformers import AutoTokenizer
from wav_evaluation.models.utils import read_config_as_args
from wav_evaluation.models.clap import CLAP
import math
import torchau... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/wav_evaluation/models/CLAPWrapper.py |
import os
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from pathlib import Path
import yaml
import numpy as np
from argparse import Namespace
LRELU_SLOPE = 0.1
... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/vocoder/hifigan/modules.py |
import numpy as np
class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min
... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/lr_scheduler.py |
import importlib
import torch
import numpy as np
from tqdm import tqdm
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
import hashlib
import requests
import os
URL_MAP = {
'vggishish_lpaps': 'https://a3s.fi/swift/v1/AUTH_a235c0f452d648828f745589cde1219a/specvqgan_public/vggishish16.pt',... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/util.py |
"""
与autoencoder.py的区别在于，autoencoder.py计算loss时只有一个discriminator，而此处又多了个multiwindowDiscriminator，所以优化器
优化的参数改为：
opt_disc = torch.optim.Adam(list(self.loss.discriminator.parameters()) + list(self.loss.discriminator_multi.parameters()),
lr=lr, betas=(0.5, 0.9))
"""
import os
import torch
impor... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/models/autoencoder_multi.py |
import os
import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager
from packaging import version
import numpy as np
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
fr... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/models/autoencoder.py |
"""SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
extract_into_tensor
class DDIMSampler(object):
def __init__(self, model, schedule="linear",... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/models/diffusion/ddim.py |
import os
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from copy import deepcopy
from einops import rearrange
from glob import glob
from natsort import natsorted
from ldm.modu... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/models/diffusion/classifier.py |
"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/models/diffusion/ddpm_audio.py |
EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/models/diffusion/__init__.py | |
"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/models/diffusion/ddpm_audio_inpaint.py |
"""SAMPLING ONLY."""
import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
class PLMSSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
super()... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/models/diffusion/plms.py |
"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/models/diffusion/ddpm.py |
from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in arr}.keys()
d... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/attention.py |
"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
# constants
DE... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/x_transformer.py |
import torch
from torch import nn
class LitEma(nn.Module):
def __init__(self, model, decay=0.9999, use_num_upates=True):
super().__init__()
if decay < 0.0 or decay > 1.0:
raise ValueError('Decay must be between 0 and 1')
self.m_name2s_name = {}
self.register_buffer('de... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/ema.py |
import functools
import torch.nn as nn
class ActNorm(nn.Module):
def __init__(self, num_features, logdet=False, affine=True,
allow_reverse_init=False):
assert affine
super().__init__()
self.logdet = logdet
self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/discriminator/model.py |
import numpy as np
import torch
import torch.nn as nn
class Discriminator2DFactory(nn.Module):
def __init__(self, time_length, freq_length=80, kernel=(3, 3), c_in=1, hidden_size=128,
norm_type='bn', reduction='sum'):
super(Discriminator2DFactory, self).__init__()
padding = (kernel... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/discriminator/multi_window_disc.py |
"""
Based on https://github.com/CompVis/taming-transformers/blob/52720829/taming/modules/losses/lpips.py
Adapted for spectrograms by Vladimir Iashin (v-iashin)
"""
from collections import namedtuple
import numpy as np
import torch
import torch.nn as nn
import sys
sys.path.insert(0, '.') # nopep8
from ldm.mod... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/lpaps.py |
from ldm.modules.losses_audio.vqperceptual import DummyLoss
# relative imports pain
import os
import sys
path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'vggishish')
sys.path.append(path)
| EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/__init__.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
from ldm.util import exists
sys.path.insert(0, '.') # nopep8
from ldm.modules.discriminator.model import (NLayerDiscriminator, NLayerDiscriminator1dFeats,
NLayerDiscriminator1dSpecs,
... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/vqperceptual.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
sys.path.insert(0, '.') # nopep8
from ldm.modules.losses_audio.vqperceptual import *
class LPAPSWithDiscriminator(nn.Module):
def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
disc_n... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/contperceptual.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
sys.path.insert(0, '.') # nopep8
from ldm.modules.losses_audio.vqperceptual import *
from ldm.modules.discriminator.multi_window_disc import Discriminator
class LPAPSWithDiscriminator(nn.Module):# 相比于contperceptual.py添加了MultiWindowDiscrimi... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/contperceptual_dis.py |
import logging
import numpy as np
import scipy
import torch
from sklearn.metrics import average_precision_score, roc_auc_score
logger = logging.getLogger(f'main.{__name__}')
def metrics(targets, outputs, topk=(1, 5)):
"""
Adapted from https://github.com/hche11/VGGSound/blob/master/utils.py
Calculate sta... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/vggishish/metrics.py |
import logging
import os
from pathlib import Path
import albumentations
import numpy as np
import torch
from tqdm import tqdm
logger = logging.getLogger(f'main.{__name__}')
class StandardNormalizeAudio(object):
'''
Frequency-wise normalization
'''
def __init__(self, specs_dir, train_ids_path='./... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/vggishish/transforms.py |
import os
from torch.utils.data import DataLoader
import torchvision
from tqdm import tqdm
from dataset import VGGSound
import torch
import torch.nn as nn
from metrics import metrics
from omegaconf import OmegaConf
from model import VGGishish
from transforms import Crop, StandardNormalizeAudio, ToTensor
if __name__ =... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/vggishish/predict.py |
import logging
import os
import time
from shutil import copytree, ignore_patterns
import torch
from omegaconf import OmegaConf
from torch.utils.tensorboard import SummaryWriter, summary
class LoggerWithTBoard(SummaryWriter):
def __init__(self, cfg):
# current time stamp and experiment log directory
... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/vggishish/logger.py |
import torch
import torch.nn as nn
class VGGishish(nn.Module):
def __init__(self, conv_layers, use_bn, num_classes):
'''
Mostly from
https://pytorch.org/vision/0.8/_modules/torchvision/models/vgg.html
'''
super().__init__()
layers = []
in_channels = 1
... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/vggishish/model.py |
import collections
import csv
import logging
import os
import random
from glob import glob
from pathlib import Path
import numpy as np
import torch
import torchvision
logger = logging.getLogger(f'main.{__name__}')
class VGGSound(torch.utils.data.Dataset):
def __init__(self, split, specs_dir, transforms=None, s... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/vggishish/dataset.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class WeightedCrossEntropy(nn.CrossEntropyLoss):
def __init__(self, weights, **pytorch_ce_loss_args) -> None:
super().__init__(reduction='none', **pytorch_ce_loss_args)
self.weights = weights
def __... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/vggishish/loss.py |
from loss import WeightedCrossEntropy
import random
import numpy as np
import torch
import torchvision
from omegaconf import OmegaConf
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
from dataset import VGGSound
from transforms import Crop, StandardNormalizeAudio, ToTensor
from logger import ... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/vggishish/train_vggishish.py |
import random
import numpy as np
import torch
import torchvision
from omegaconf import OmegaConf
from torch.utils.data.dataloader import DataLoader
from torchvision.models.inception import BasicConv2d, Inception3
from tqdm import tqdm
from dataset import VGGSound
from logger import LoggerWithTBoard
from loss import W... | EXA-1-master | exa/models/AudioGPT/text_to_audio/MakeAnAudio/ldm/modules/losses_audio/vggishish/train_melception.py |
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