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| # -*- coding: utf-8 -*- | |
| # Copyright 2019 Tomoki Hayashi | |
| # MIT License (https://opensource.org/licenses/MIT) | |
| """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 | |