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| # Adapted from https://github.com/kan-bayashi/ParallelWaveGAN | |
| # Original Copyright 2019 Tomoki Hayashi | |
| # MIT License (https://opensource.org/licenses/MIT) | |
| """STFT-based Loss modules.""" | |
| import torch | |
| import torch.nn.functional as F | |
| from distutils.version import LooseVersion | |
| is_pytorch_17plus = LooseVersion(torch.__version__) >= LooseVersion("1.7") | |
| def stft(x, fft_size, hop_size, win_length, window): | |
| """Perform STFT and convert to magnitude spectrogram. | |
| Args: | |
| x (Tensor): Input signal tensor (B, T). | |
| fft_size (int): FFT size. | |
| hop_size (int): Hop size. | |
| win_length (int): Window length. | |
| window (str): Window function type. | |
| Returns: | |
| Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1). | |
| """ | |
| if is_pytorch_17plus: | |
| x_stft = torch.stft( | |
| x, fft_size, hop_size, win_length, window, return_complex=False | |
| ) | |
| else: | |
| x_stft = torch.stft(x, fft_size, hop_size, win_length, window) | |
| real = x_stft[..., 0] | |
| imag = x_stft[..., 1] | |
| # NOTE(kan-bayashi): clamp is needed to avoid nan or inf | |
| return torch.sqrt(torch.clamp(real**2 + imag**2, min=1e-7)).transpose(2, 1) | |
| class SpectralConvergenceLoss(torch.nn.Module): | |
| """Spectral convergence loss module.""" | |
| def __init__(self): | |
| """Initilize spectral convergence loss module.""" | |
| super(SpectralConvergenceLoss, self).__init__() | |
| def forward(self, x_mag, y_mag): | |
| """Calculate forward propagation. | |
| Args: | |
| x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). | |
| y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). | |
| Returns: | |
| Tensor: Spectral convergence loss value. | |
| """ | |
| return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro") | |
| class LogSTFTMagnitudeLoss(torch.nn.Module): | |
| """Log STFT magnitude loss module.""" | |
| def __init__(self): | |
| """Initilize los STFT magnitude loss module.""" | |
| super(LogSTFTMagnitudeLoss, self).__init__() | |
| def forward(self, x_mag, y_mag): | |
| """Calculate forward propagation. | |
| Args: | |
| x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). | |
| y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). | |
| Returns: | |
| Tensor: Log STFT magnitude loss value. | |
| """ | |
| return F.l1_loss(torch.log(y_mag), torch.log(x_mag)) | |
| class STFTLoss(torch.nn.Module): | |
| """STFT loss module.""" | |
| def __init__( | |
| self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", | |
| band="full" | |
| ): | |
| """Initialize STFT loss module.""" | |
| super(STFTLoss, self).__init__() | |
| self.fft_size = fft_size | |
| self.shift_size = shift_size | |
| self.win_length = win_length | |
| self.band = band | |
| self.spectral_convergence_loss = SpectralConvergenceLoss() | |
| self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss() | |
| # NOTE(kan-bayashi): Use register_buffer to fix #223 | |
| self.register_buffer("window", getattr(torch, window)(win_length)) | |
| 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.band == "high": | |
| freq_mask_ind = x_mag.shape[1] // 2 # only select high frequency bands | |
| sc_loss = self.spectral_convergence_loss(x_mag[:,freq_mask_ind:,:], y_mag[:,freq_mask_ind:,:]) | |
| mag_loss = self.log_stft_magnitude_loss(x_mag[:,freq_mask_ind:,:], y_mag[:,freq_mask_ind:,:]) | |
| elif self.band == "full": | |
| sc_loss = self.spectral_convergence_loss(x_mag, y_mag) | |
| mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag) | |
| else: | |
| raise NotImplementedError | |
| 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", sc_lambda=0.1, mag_lambda=0.1, band="full" | |
| ): | |
| """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. | |
| *_lambda (float): a balancing factor across different losses. | |
| band (str): high-band or full-band loss | |
| """ | |
| super(MultiResolutionSTFTLoss, self).__init__() | |
| self.sc_lambda = sc_lambda | |
| self.mag_lambda = mag_lambda | |
| 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, band)] | |
| def forward(self, x, y): | |
| """Calculate forward propagation. | |
| Args: | |
| x (Tensor): Predicted signal (B, T) or (B, #subband, T). | |
| y (Tensor): Groundtruth signal (B, T) or (B, #subband, T). | |
| Returns: | |
| Tensor: Multi resolution spectral convergence loss value. | |
| Tensor: Multi resolution log STFT magnitude loss value. | |
| """ | |
| if len(x.shape) == 3: | |
| x = x.view(-1, x.size(2)) # (B, C, T) -> (B x C, T) | |
| y = y.view(-1, y.size(2)) # (B, C, T) -> (B x C, T) | |
| 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 *= self.sc_lambda | |
| sc_loss /= len(self.stft_losses) | |
| mag_loss *= self.mag_lambda | |
| mag_loss /= len(self.stft_losses) | |
| return sc_loss, mag_loss |