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184 lines
6.9 KiB
Python
184 lines
6.9 KiB
Python
"""STFT-based Loss modules."""
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import torch
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import torch.nn.functional as F
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import numpy as np
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import torchaudio
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def stft(x, fft_size, hop_size, win_length, window):
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"""Perform STFT and convert to magnitude spectrogram.
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Args:
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x (Tensor): Input signal tensor (B, T).
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fft_size (int): FFT size.
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hop_size (int): Hop size.
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win_length (int): Window length.
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window (str): Window function type.
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Returns:
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Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
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"""
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#x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=False)
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#real = x_stft[..., 0]
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#imag = x_stft[..., 1]
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# (kan-bayashi): clamp is needed to avoid nan or inf
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#return torchaudio.functional.amplitude_to_DB(torch.abs(x_stft),db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80)
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#return torch.clamp(torch.abs(x_stft), min=1e-7)
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x_stft = torch.stft(x, fft_size, hop_size, win_length, window, return_complex=True)
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return torch.clamp(torch.abs(x_stft), min=1e-7)
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class SpectralConvergenceLoss(torch.nn.Module):
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"""Spectral convergence loss module."""
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def __init__(self):
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"""Initilize spectral convergence loss module."""
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super(SpectralConvergenceLoss, self).__init__()
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def forward(self, x_mag, y_mag):
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"""Calculate forward propagation.
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Args:
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x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
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y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
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Returns:
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Tensor: Spectral convergence loss value.
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"""
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return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro")
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class LogSTFTMagnitudeLoss(torch.nn.Module):
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"""Log STFT magnitude loss module."""
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def __init__(self):
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"""Initilize los STFT magnitude loss module."""
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super(LogSTFTMagnitudeLoss, self).__init__()
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def forward(self, x, y):
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"""Calculate forward propagation.
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Args:
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x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
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y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
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Returns:
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Tensor: Log STFT magnitude loss value.
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"""
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#F.l1_loss(torch.sqrt(y_mag), torch.sqrt(x_mag)) +
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#F.l1_loss(torchaudio.functional.amplitude_to_DB(y_mag,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80),\
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#torchaudio.functional.amplitude_to_DB(x_mag,db_multiplier=0.0, multiplier=20,amin=1e-05,top_db=80))
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#y_mag[:,:y_mag.size(1)//2,:] = y_mag[:,:y_mag.size(1)//2,:] *0.0
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#return F.l1_loss(torch.log(y_mag) + torch.sqrt(y_mag), torch.log(x_mag) + torch.sqrt(x_mag))
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#return F.l1_loss(y_mag, x_mag)
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error_loss = F.l1_loss(y, x) #+ F.l1_loss(torch.sqrt(y), torch.sqrt(x))#F.l1_loss(torch.log(y), torch.log(x))#
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#x = torch.log(x)
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#y = torch.log(y)
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#x = x.permute(0,2,1).contiguous()
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#y = y.permute(0,2,1).contiguous()
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'''mean_x = torch.mean(x, dim=1, keepdim=True)
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mean_y = torch.mean(y, dim=1, keepdim=True)
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var_x = torch.var(x, dim=1, keepdim=True)
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var_y = torch.var(y, dim=1, keepdim=True)
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std_x = torch.std(x, dim=1, keepdim=True)
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std_y = torch.std(y, dim=1, keepdim=True)
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x_minus_mean = x - mean_x
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y_minus_mean = y - mean_y
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pearson_corr = torch.sum(x_minus_mean * y_minus_mean, dim=1, keepdim=True) / \
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(torch.sqrt(torch.sum(x_minus_mean ** 2, dim=1, keepdim=True) + 1e-7) * \
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torch.sqrt(torch.sum(y_minus_mean ** 2, dim=1, keepdim=True) + 1e-7))
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numerator = 2.0 * pearson_corr * std_x * std_y
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denominator = var_x + var_y + (mean_y - mean_x)**2
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ccc = numerator/denominator
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ccc_loss = F.l1_loss(1.0 - ccc, torch.zeros_like(ccc))'''
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return error_loss #+ ccc_loss#+ ccc_loss
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class STFTLoss(torch.nn.Module):
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"""STFT loss module."""
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def __init__(self, device, fft_size=1024, shift_size=120, win_length=600, window="hann_window"):
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"""Initialize STFT loss module."""
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super(STFTLoss, self).__init__()
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self.fft_size = fft_size
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self.shift_size = shift_size
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self.win_length = win_length
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self.window = getattr(torch, window)(win_length).to(device)
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self.spectral_convergenge_loss = SpectralConvergenceLoss()
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self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
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def forward(self, x, y):
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"""Calculate forward propagation.
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Args:
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x (Tensor): Predicted signal (B, T).
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y (Tensor): Groundtruth signal (B, T).
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Returns:
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Tensor: Spectral convergence loss value.
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Tensor: Log STFT magnitude loss value.
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"""
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x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
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y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
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sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
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mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
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return sc_loss, mag_loss
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class MultiResolutionSTFTLoss(torch.nn.Module):
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def __init__(self,
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device,
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fft_sizes=[2048, 1024, 512, 256, 128, 64],
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hop_sizes=[512, 256, 128, 64, 32, 16],
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win_lengths=[2048, 1024, 512, 256, 128, 64],
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window="hann_window"):
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'''def __init__(self,
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device,
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fft_sizes=[2048, 1024, 512, 256, 128, 64],
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hop_sizes=[256, 128, 64, 32, 16, 8],
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win_lengths=[1024, 512, 256, 128, 64, 32],
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window="hann_window"):'''
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'''def __init__(self,
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device,
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fft_sizes=[2560, 1280, 640, 320, 160, 80],
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hop_sizes=[640, 320, 160, 80, 40, 20],
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win_lengths=[2560, 1280, 640, 320, 160, 80],
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window="hann_window"):'''
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super(MultiResolutionSTFTLoss, self).__init__()
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assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
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self.stft_losses = torch.nn.ModuleList()
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for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
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self.stft_losses += [STFTLoss(device, fs, ss, wl, window)]
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def forward(self, x, y):
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"""Calculate forward propagation.
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Args:
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x (Tensor): Predicted signal (B, T).
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y (Tensor): Groundtruth signal (B, T).
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Returns:
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Tensor: Multi resolution spectral convergence loss value.
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Tensor: Multi resolution log STFT magnitude loss value.
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"""
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sc_loss = 0.0
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mag_loss = 0.0
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for f in self.stft_losses:
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sc_l, mag_l = f(x, y)
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sc_loss += sc_l
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#mag_loss += mag_l
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sc_loss /= len(self.stft_losses)
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mag_loss /= len(self.stft_losses)
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return sc_loss #mag_loss #+
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