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| """STFT-based Loss modules."""
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| import torch
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| import torch.nn.functional as F
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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)
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| real = x_stft[..., 0]
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| imag = x_stft[..., 1]
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| return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1)
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| class SpectralConvergengeLoss(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(SpectralConvergengeLoss, 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_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: Log STFT magnitude loss value.
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| """
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| return F.l1_loss(torch.log(y_mag), torch.log(x_mag))
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| class STFTLoss(torch.nn.Module):
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| """STFT loss module."""
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| def __init__(self, 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)
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| self.spectral_convergenge_loss = SpectralConvergengeLoss()
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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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| """Multi resolution STFT loss module."""
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| def __init__(self,
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| fft_sizes=[1024, 2048, 512],
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| hop_sizes=[120, 240, 50],
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| win_lengths=[600, 1200, 240],
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| window="hann_window"):
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| """Initialize Multi resolution STFT loss module.
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| Args:
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| fft_sizes (list): List of FFT sizes.
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| hop_sizes (list): List of hop sizes.
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| win_lengths (list): List of window lengths.
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| window (str): Window function type.
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| """
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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(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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|
