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| """STFT-based Loss modules."""
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| import librosa
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| import torch
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| from modules.parallel_wavegan.losses import LogSTFTMagnitudeLoss, SpectralConvergengeLoss, stft
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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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| use_mel_loss=False):
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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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| self.use_mel_loss = use_mel_loss
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| self.mel_basis = None
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|
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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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| """
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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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| if self.use_mel_loss:
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| if self.mel_basis is None:
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| self.mel_basis = torch.from_numpy(librosa.filters.mel(22050, self.fft_size, 80)).cuda().T
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| x_mag = x_mag @ self.mel_basis
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| y_mag = y_mag @ self.mel_basis
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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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| use_mel_loss=False):
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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, use_mel_loss)]
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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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|
