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d98780c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 23 16:54:56 2024
@author: louis
"""
import sys
import os
import torch
from torch import nn
from model.utils.abs_models import AbsSpeechModel
# Add FullSubNet's audio_zen to path
sys.path.append(os.path.join(os.path.dirname(__file__), "FullSubNet"))
from model.speech_models.FullSubNet.audio_zen.acoustics.mask import decompress_cIRM, build_complex_ideal_ratio_mask
from model.speech_models.FullSubNet.recipes.dns_interspeech_2020.fullsubnet.model import Model as OriginalFullSubNet
from torchmetrics.audio import (
ShortTimeObjectiveIntelligibility,
ScaleInvariantSignalDistortionRatio,
)
class FullSubNet(AbsSpeechModel):
def __init__(
self,
metrics: list[nn.Module] = [
ShortTimeObjectiveIntelligibility(fs=16000),
ScaleInvariantSignalDistortionRatio(),
],
num_freqs=257,
# look_ahead=2,
look_ahead=5,
sequence_model="LSTM",
fb_num_neighbors=0,
sb_num_neighbors=15,
fb_output_activate_function="ReLU",
sb_output_activate_function=False,
fb_model_hidden_size=512,
sb_model_hidden_size=384,
norm_type="offline_laplace_norm",
num_groups_in_drop_band=1,
# num_groups_in_drop_band=2,
weight_init=False,
):
super().__init__(metrics=metrics, crop_input_to_target=True)
self.original_fullsubnet = OriginalFullSubNet(
num_freqs=num_freqs,
look_ahead=look_ahead,
sequence_model=sequence_model,
fb_num_neighbors=fb_num_neighbors,
sb_num_neighbors=sb_num_neighbors,
fb_output_activate_function=fb_output_activate_function,
sb_output_activate_function=sb_output_activate_function,
fb_model_hidden_size=fb_model_hidden_size,
sb_model_hidden_size=sb_model_hidden_size,
norm_type=norm_type,
num_groups_in_drop_band=num_groups_in_drop_band,
# num_groups_in_drop_band=2,
weight_init=weight_init,
)
self.loss_function = nn.MSELoss()
def forward(self, input):
Y = self.stft_module(input)
noisy_mag, noisy_real, noisy_imag = Y.abs(), Y.real, Y.imag
cRM_compressed = self.original_fullsubnet(noisy_mag)
return cRM_compressed.permute(0, 2, 3, 1), (noisy_real, noisy_imag)
def get_stft(self, pred, **kwargs):
cRM, (noisy_real, noisy_imag) = pred
cRM_decompressed = decompress_cIRM(cRM)
noisy_real = noisy_real[:, 0, ...]
noisy_imag = noisy_imag[:, 0, ...]
# enhanced_real = cRM[:, 0, None, ...] * noisy_real - cRM[:, 1, None, ...] * noisy_imag
# enhanced_imag = cRM[:, 1, None, ...] * noisy_real + cRM[:, 0, None, ...] * noisy_imag
# enhanced_stft = (enhanced_real + 1j * enhanced_imag)[..., 0, :, :]
# cRM_complex = torch.view_as_complex(cRM.permute(0, 2, 3, 1).contiguous())
# enhanced_stft = cRM_complex * torch.complex(noisy_real, noisy_imag)[:, 0, ...]
enhanced_real = cRM_decompressed[..., 0] * noisy_real - cRM_decompressed[..., 1] * noisy_imag
enhanced_imag = cRM_decompressed[..., 1] * noisy_real + cRM_decompressed[..., 0] * noisy_imag
enhanced_stft = torch.complex(enhanced_real, enhanced_imag)
return enhanced_stft.unsqueeze(-3) # unsqueeze to match B, C, F, T shape
def get_time(self, pred, length=None):
enhanced_stft = self.get_stft(pred)
return self.istft_module(enhanced_stft, length=length)
def internal_loss(self, pred, target):
cRM, (noisy_real, noisy_imag) = pred
S = self.stft_module(target)
clean_real, clean_imag = S.real, S.imag
cIRM = build_complex_ideal_ratio_mask(
noisy_real=noisy_real[:, 0, ...],
noisy_imag=noisy_imag[:, 0, ...],
clean_real=clean_real[:, 0, ...],
clean_imag=clean_imag[:, 0, ...],
) # [B, F, T, 2]
# cRM = cRM.permute(0, 2, 3, 1)
loss = self.loss_function(cRM, cIRM)
return loss
class PhaseInvariantFullSubNet(FullSubNet):
"""
Phase-invariant version of FullSubNet.
Computes a real mask instead of a complex one and does not compute any phase correction.
"""
def get_stft(self, pred, **kwargs):
cRM, (noisy_real, noisy_imag) = pred
cRM_decompressed = decompress_cIRM(cRM)
noisy_real = noisy_real[:, 0, ...]
noisy_imag = noisy_imag[:, 0, ...]
mask = torch.sigmoid(cRM_decompressed[..., 0])
enhanced_real = mask * noisy_real
enhanced_imag = mask * noisy_imag
enhanced_stft = torch.complex(enhanced_real, enhanced_imag)
return enhanced_stft.unsqueeze(-3) # unsqueeze to match B, C, F, T shape
def internal_loss(self, pred, target):
enhanced_stft = self.get_stft(pred)
original_stft = self.stft_module(target)
loss = self.loss_function(enhanced_stft.abs(), original_stft.abs())
return loss
if __name__ == "__main__":
model = FullSubNet(
num_freqs=257,
# look_ahead=2,
look_ahead=5,
sequence_model="LSTM",
fb_num_neighbors=0,
sb_num_neighbors=15,
fb_output_activate_function="ReLU",
sb_output_activate_function=False,
fb_model_hidden_size=512,
sb_model_hidden_size=384,
norm_type="offline_laplace_norm",
num_groups_in_drop_band=1,
# num_groups_in_drop_band=2,
weight_init=False,
)
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