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|
| """STFT-based Loss modules.""" |
| import librosa |
| import torch |
|
|
| from modules.parallel_wavegan.losses import LogSTFTMagnitudeLoss, SpectralConvergengeLoss, stft |
|
|
|
|
| class STFTLoss(torch.nn.Module): |
| """STFT loss module.""" |
|
|
| def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", |
| use_mel_loss=False): |
| """Initialize STFT loss module.""" |
| super(STFTLoss, self).__init__() |
| self.fft_size = fft_size |
| self.shift_size = shift_size |
| self.win_length = win_length |
| self.window = getattr(torch, window)(win_length) |
| self.spectral_convergenge_loss = SpectralConvergengeLoss() |
| self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss() |
| self.use_mel_loss = use_mel_loss |
| self.mel_basis = None |
|
|
| def forward(self, x, y): |
| """Calculate forward propagation. |
| |
| Args: |
| x (Tensor): Predicted signal (B, T). |
| y (Tensor): Groundtruth signal (B, T). |
| |
| Returns: |
| Tensor: Spectral convergence loss value. |
| Tensor: Log STFT magnitude loss value. |
| |
| """ |
| x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window) |
| y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window) |
| if self.use_mel_loss: |
| if self.mel_basis is None: |
| self.mel_basis = torch.from_numpy(librosa.filters.mel(22050, self.fft_size, 80)).cuda().T |
| x_mag = x_mag @ self.mel_basis |
| y_mag = y_mag @ self.mel_basis |
|
|
| sc_loss = self.spectral_convergenge_loss(x_mag, y_mag) |
| mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag) |
|
|
| return sc_loss, mag_loss |
|
|
|
|
| class MultiResolutionSTFTLoss(torch.nn.Module): |
| """Multi resolution STFT loss module.""" |
|
|
| def __init__(self, |
| fft_sizes=[1024, 2048, 512], |
| hop_sizes=[120, 240, 50], |
| win_lengths=[600, 1200, 240], |
| window="hann_window", |
| use_mel_loss=False): |
| """Initialize Multi resolution STFT loss module. |
| |
| Args: |
| fft_sizes (list): List of FFT sizes. |
| hop_sizes (list): List of hop sizes. |
| win_lengths (list): List of window lengths. |
| window (str): Window function type. |
| |
| """ |
| super(MultiResolutionSTFTLoss, self).__init__() |
| assert len(fft_sizes) == len(hop_sizes) == len(win_lengths) |
| self.stft_losses = torch.nn.ModuleList() |
| for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths): |
| self.stft_losses += [STFTLoss(fs, ss, wl, window, use_mel_loss)] |
|
|
| def forward(self, x, y): |
| """Calculate forward propagation. |
| |
| Args: |
| x (Tensor): Predicted signal (B, T). |
| y (Tensor): Groundtruth signal (B, T). |
| |
| Returns: |
| Tensor: Multi resolution spectral convergence loss value. |
| Tensor: Multi resolution log STFT magnitude loss value. |
| |
| """ |
| sc_loss = 0.0 |
| mag_loss = 0.0 |
| for f in self.stft_losses: |
| sc_l, mag_l = f(x, y) |
| sc_loss += sc_l |
| mag_loss += mag_l |
| sc_loss /= len(self.stft_losses) |
| mag_loss /= len(self.stft_losses) |
|
|
| return sc_loss, mag_loss |
|
|
