| program(1.3) |
| [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}, {"coremltools-component-torch", "2.7.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] |
| { |
| func main<ios18>(tensor<fp16, [1, 160, 1, 1503]> audio_rows, tensor<fp16, [1, 1, 1, 1500]> mel_mask) { |
| tensor<int32, [8]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])]; |
| string input_1_mode_0 = const()[name = string("input_1_mode_0"), val = string("constant")]; |
| fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 160, 1, 1504]> input_1_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = input_1_mode_0, pad = input_1_pad_0, x = audio_rows)[name = string("input_1_cast_fp16")]; |
| string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("valid")]; |
| tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; |
| tensor<fp16, [160, 160, 1, 2]> preemph_weight_to_fp16 = const()[name = string("preemph_weight_to_fp16"), val = tensor<fp16, [160, 160, 1, 2]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; |
| tensor<fp16, [1, 160, 1, 1503]> input_3_cast_fp16 = conv(dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = preemph_weight_to_fp16, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")]; |
| string spec_pad_type_0 = const()[name = string("spec_pad_type_0"), val = string("valid")]; |
| tensor<int32, [2]> spec_strides_0 = const()[name = string("spec_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> spec_pad_0 = const()[name = string("spec_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> spec_dilations_0 = const()[name = string("spec_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 spec_groups_0 = const()[name = string("spec_groups_0"), val = int32(1)]; |
| tensor<fp16, [514, 160, 1, 4]> stft_weight_to_fp16 = const()[name = string("stft_weight_to_fp16"), val = tensor<fp16, [514, 160, 1, 4]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102528)))]; |
| tensor<fp16, [1, 514, 1, 1500]> spec_cast_fp16 = conv(dilations = spec_dilations_0, groups = spec_groups_0, pad = spec_pad_0, pad_type = spec_pad_type_0, strides = spec_strides_0, weight = stft_weight_to_fp16, x = input_3_cast_fp16)[name = string("spec_cast_fp16")]; |
| tensor<int32, [4]> re_begin_0 = const()[name = string("re_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> re_end_0 = const()[name = string("re_end_0"), val = tensor<int32, [4]>([1, 257, 1, 1500])]; |
| tensor<bool, [4]> re_end_mask_0 = const()[name = string("re_end_mask_0"), val = tensor<bool, [4]>([true, false, true, true])]; |
| tensor<fp16, [1, 257, 1, 1500]> re_cast_fp16 = slice_by_index(begin = re_begin_0, end = re_end_0, end_mask = re_end_mask_0, x = spec_cast_fp16)[name = string("re_cast_fp16")]; |
| tensor<int32, [4]> im_begin_0 = const()[name = string("im_begin_0"), val = tensor<int32, [4]>([0, 257, 0, 0])]; |
| tensor<int32, [4]> im_end_0 = const()[name = string("im_end_0"), val = tensor<int32, [4]>([1, 514, 1, 1500])]; |
| tensor<bool, [4]> im_end_mask_0 = const()[name = string("im_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 257, 1, 1500]> im_cast_fp16 = slice_by_index(begin = im_begin_0, end = im_end_0, end_mask = im_end_mask_0, x = spec_cast_fp16)[name = string("im_cast_fp16")]; |
| tensor<fp16, [1, 257, 1, 1500]> var_56_cast_fp16 = mul(x = re_cast_fp16, y = re_cast_fp16)[name = string("op_56_cast_fp16")]; |
| tensor<fp16, [1, 257, 1, 1500]> var_57_cast_fp16 = mul(x = im_cast_fp16, y = im_cast_fp16)[name = string("op_57_cast_fp16")]; |
| tensor<fp16, [1, 257, 1, 1500]> input_cast_fp16 = add(x = var_56_cast_fp16, y = var_57_cast_fp16)[name = string("input_cast_fp16")]; |
| string melspec_pad_type_0 = const()[name = string("melspec_pad_type_0"), val = string("valid")]; |
| tensor<int32, [2]> melspec_strides_0 = const()[name = string("melspec_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> melspec_pad_0 = const()[name = string("melspec_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> melspec_dilations_0 = const()[name = string("melspec_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 melspec_groups_0 = const()[name = string("melspec_groups_0"), val = int32(1)]; |
| tensor<fp16, [128, 257, 1, 1]> mel_weight_to_fp16 = const()[name = string("mel_weight_to_fp16"), val = tensor<fp16, [128, 257, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(760512)))]; |
| tensor<fp16, [128]> const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(826368)))]; |
| tensor<fp16, [1, 128, 1, 1500]> var_72_cast_fp16 = conv(bias = const_2_to_fp16, dilations = melspec_dilations_0, groups = melspec_groups_0, pad = melspec_pad_0, pad_type = melspec_pad_type_0, strides = melspec_strides_0, weight = mel_weight_to_fp16, x = input_cast_fp16)[name = string("op_72_cast_fp16")]; |
| fp32 logmel_epsilon_0 = const()[name = string("logmel_epsilon_0"), val = fp32(0x1p-149)]; |
| tensor<fp16, [1, 128, 1, 1500]> logmel_cast_fp16 = log(epsilon = logmel_epsilon_0, x = var_72_cast_fp16)[name = string("logmel_cast_fp16")]; |
| tensor<int32, [1]> n_axes_0 = const()[name = string("n_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool n_keep_dims_0 = const()[name = string("n_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 1, 1, 1]> n_cast_fp16 = reduce_sum(axes = n_axes_0, keep_dims = n_keep_dims_0, x = mel_mask)[name = string("n_cast_fp16")]; |
| tensor<fp16, [1, 128, 1, 1500]> var_82_cast_fp16 = mul(x = logmel_cast_fp16, y = mel_mask)[name = string("op_82_cast_fp16")]; |
| tensor<int32, [1]> var_87_axes_0 = const()[name = string("op_87_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_87_keep_dims_0 = const()[name = string("op_87_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 128, 1, 1]> var_87_cast_fp16 = reduce_mean(axes = var_87_axes_0, keep_dims = var_87_keep_dims_0, x = var_82_cast_fp16)[name = string("op_87_cast_fp16")]; |
| tensor<fp16, [1]> cast_2_to_fp16 = const()[name = string("cast_2_to_fp16"), val = tensor<fp16, [1]>([0x1.77p+10])]; |
| tensor<fp16, [1, 1, 1, 1]> var_88_cast_fp16 = real_div(x = cast_2_to_fp16, y = n_cast_fp16)[name = string("op_88_cast_fp16")]; |
| tensor<fp16, [1, 128, 1, 1]> mean_cast_fp16 = mul(x = var_87_cast_fp16, y = var_88_cast_fp16)[name = string("mean_cast_fp16")]; |
| tensor<fp16, [1, 128, 1, 1500]> var_91_cast_fp16 = sub(x = logmel_cast_fp16, y = mean_cast_fp16)[name = string("op_91_cast_fp16")]; |
| tensor<fp16, [1, 128, 1, 1500]> var_92_cast_fp16 = mul(x = var_91_cast_fp16, y = mel_mask)[name = string("op_92_cast_fp16")]; |
| fp16 var_93_promoted_to_fp16 = const()[name = string("op_93_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 128, 1, 1500]> var_94_cast_fp16 = pow(x = var_92_cast_fp16, y = var_93_promoted_to_fp16)[name = string("op_94_cast_fp16")]; |
| tensor<int32, [1]> var_99_axes_0 = const()[name = string("op_99_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_99_keep_dims_0 = const()[name = string("op_99_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 128, 1, 1]> var_99_cast_fp16 = reduce_mean(axes = var_99_axes_0, keep_dims = var_99_keep_dims_0, x = var_94_cast_fp16)[name = string("op_99_cast_fp16")]; |
| fp16 var_101_promoted_to_fp16 = const()[name = string("op_101_promoted_to_fp16"), val = fp16(0x1p+0)]; |
| tensor<fp16, [1, 1, 1, 1]> var_102_cast_fp16 = sub(x = n_cast_fp16, y = var_101_promoted_to_fp16)[name = string("op_102_cast_fp16")]; |
| tensor<fp16, [1]> cast_4_to_fp16 = const()[name = string("cast_4_to_fp16"), val = tensor<fp16, [1]>([0x1.77p+10])]; |
| tensor<fp16, [1, 1, 1, 1]> var_103_cast_fp16 = real_div(x = cast_4_to_fp16, y = var_102_cast_fp16)[name = string("op_103_cast_fp16")]; |
| tensor<fp16, [1, 128, 1, 1]> var_cast_fp16 = mul(x = var_99_cast_fp16, y = var_103_cast_fp16)[name = string("var_cast_fp16")]; |
| tensor<fp16, [1, 128, 1, 1]> var_107_cast_fp16 = sqrt(x = var_cast_fp16)[name = string("op_107_cast_fp16")]; |
| fp16 std_eps_to_fp16 = const()[name = string("std_eps_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 128, 1, 1]> std_cast_fp16 = add(x = var_107_cast_fp16, y = std_eps_to_fp16)[name = string("std_cast_fp16")]; |
| tensor<fp16, [1, 128, 1, 1500]> mel = real_div(x = var_91_cast_fp16, y = std_cast_fp16)[name = string("op_110_cast_fp16")]; |
| } -> (mel); |
| } |
