Spaces:
Running
Running
File size: 5,421 Bytes
cba47e4 94ba8b5 cba47e4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
from typing import List
import torch
import torch.nn as nn
from torch.nn import functional as F
class CIRMLoss(nn.Module):
def __init__(self,
n_fft: int = 512,
win_size: int = 512,
hop_size: int = 256,
center: bool = True,
eps: float = 1e-8,
reduction: str = "mean",
):
super(CIRMLoss, self).__init__()
self.n_fft = n_fft
self.win_size = win_size
self.hop_size = hop_size
self.center = center
self.eps = eps
self.reduction = reduction
self.window = nn.Parameter(torch.hann_window(win_size), requires_grad=False)
if reduction not in ("sum", "mean"):
raise AssertionError(f"param reduction must be sum or mean.")
def forward(self, clean: torch.Tensor, noisy: torch.Tensor, mask_real: torch.Tensor, mask_imag: torch.Tensor):
"""
:param clean: waveform
:param noisy: waveform
:param mask_real: shape: [b, f, t]
:param mask_imag: shape: [b, f, t]
:return:
"""
if noisy.shape != clean.shape:
raise AssertionError("Input signals must have the same shape")
# clean_stft, noisy_stft shape: [b, f, t]
clean_stft = torch.stft(
clean,
n_fft=self.n_fft,
win_length=self.win_size,
hop_length=self.hop_size,
window=self.window,
center=self.center,
pad_mode="reflect",
normalized=False,
return_complex=True
)
noisy_stft = torch.stft(
noisy,
n_fft=self.n_fft,
win_length=self.win_size,
hop_length=self.hop_size,
window=self.window,
center=self.center,
pad_mode="reflect",
normalized=False,
return_complex=True
)
# [b, f, t]
clean_stft_spec_real = torch.real(clean_stft)
clean_stft_spec_imag = torch.imag(clean_stft)
noisy_stft_spec_real = torch.real(noisy_stft)
noisy_stft_spec_imag = torch.imag(noisy_stft)
noisy_power = noisy_stft_spec_real ** 2 + noisy_stft_spec_imag ** 2
sr = clean_stft_spec_real
yr = noisy_stft_spec_real
si = clean_stft_spec_imag
yi = noisy_stft_spec_imag
y_pow = noisy_power
# (Sr * Yr + Si * Yi) / (Y_pow + 1e-8)
gth_mask_real = (sr * yr + si * yi) / (y_pow + self.eps)
# (Si * Yr - Sr * Yi) / (Y_pow + 1e-8)
gth_mask_imag = (sr * yr - si * yi) / (y_pow + self.eps)
gth_mask_real[gth_mask_real > 2] = 1
gth_mask_real[gth_mask_real < -2] = -1
gth_mask_imag[gth_mask_imag > 2] = 1
gth_mask_imag[gth_mask_imag < -2] = -1
amp_loss = F.mse_loss(gth_mask_real, mask_real)
phase_loss = F.mse_loss(gth_mask_imag, mask_imag)
loss = amp_loss + phase_loss
return loss
class IRMLoss(nn.Module):
"""
https://github.com/Rikorose/DeepFilterNet/blob/main/DeepFilterNet/df/loss.py#L25
"""
def __init__(self,
n_fft: int = 512,
win_size: int = 512,
hop_size: int = 256,
center: bool = True,
eps: float = 1e-8,
reduction: str = "mean",
):
super(IRMLoss, self).__init__()
self.n_fft = n_fft
self.win_size = win_size
self.hop_size = hop_size
self.center = center
self.eps = eps
self.reduction = reduction
self.window = nn.Parameter(torch.hann_window(win_size), requires_grad=False)
if reduction not in ("sum", "mean"):
raise AssertionError(f"param reduction must be sum or mean.")
def forward(self, mask: torch.Tensor, clean: torch.Tensor, noisy: torch.Tensor):
if noisy.shape != clean.shape:
raise AssertionError("Input signals must have the same shape")
noise = noisy - clean
# clean_stft, noisy_stft shape: [b, f, t]
stft_clean = torch.stft(
clean,
n_fft=self.n_fft,
win_length=self.win_size,
hop_length=self.hop_size,
window=self.window,
center=self.center,
pad_mode="reflect",
normalized=False,
return_complex=True
)
stft_noise = torch.stft(
noise,
n_fft=self.n_fft,
win_length=self.win_size,
hop_length=self.hop_size,
window=self.window,
center=self.center,
pad_mode="reflect",
normalized=False,
return_complex=True
)
mag_clean = torch.abs(stft_clean)
mag_noise = torch.abs(stft_noise)
gth_irm_mask = (mag_clean / (mag_clean + mag_noise + self.eps)).clamp(0, 1)
loss = F.l1_loss(gth_irm_mask, mask, reduction=self.reduction)
return loss
def main():
batch_size = 2
signal_length = 16000
estimated_signal = torch.randn(batch_size, signal_length)
target_signal = torch.randn(batch_size, signal_length)
loss_fn = CIRMLoss()
loss = loss_fn.forward(estimated_signal, target_signal)
print(f"loss: {loss.item()}")
return
if __name__ == "__main__":
main()
|