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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # Adapted from MIT code under the original license | |
| # Copyright 2019 Tomoki Hayashi | |
| # MIT License (https://opensource.org/licenses/MIT) | |
| import typing as tp | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| # TODO: Replace with torchaudio.STFT? | |
| def _stft(x: torch.Tensor, fft_size: int, hop_length: int, win_length: int, | |
| window: tp.Optional[torch.Tensor], normalized: bool) -> torch.Tensor: | |
| """Perform STFT and convert to magnitude spectrogram. | |
| Args: | |
| x: Input signal tensor (B, C, T). | |
| fft_size (int): FFT size. | |
| hop_length (int): Hop size. | |
| win_length (int): Window length. | |
| window (torch.Tensor or None): Window function type. | |
| normalized (bool): Whether to normalize the STFT or not. | |
| Returns: | |
| torch.Tensor: Magnitude spectrogram (B, C, #frames, fft_size // 2 + 1). | |
| """ | |
| B, C, T = x.shape | |
| x_stft = torch.stft( | |
| x.view(-1, T), fft_size, hop_length, win_length, window, | |
| normalized=normalized, return_complex=True, | |
| ) | |
| x_stft = x_stft.view(B, C, *x_stft.shape[1:]) | |
| real = x_stft.real | |
| imag = x_stft.imag | |
| # 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(nn.Module): | |
| """Spectral convergence loss. | |
| """ | |
| def __init__(self, epsilon: float = torch.finfo(torch.float32).eps): | |
| super().__init__() | |
| self.epsilon = epsilon | |
| def forward(self, x_mag: torch.Tensor, y_mag: torch.Tensor): | |
| """Calculate forward propagation. | |
| Args: | |
| x_mag: Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). | |
| y_mag: Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). | |
| Returns: | |
| torch.Tensor: Spectral convergence loss value. | |
| """ | |
| return torch.norm(y_mag - x_mag, p="fro") / (torch.norm(y_mag, p="fro") + self.epsilon) | |
| class LogSTFTMagnitudeLoss(nn.Module): | |
| """Log STFT magnitude loss. | |
| Args: | |
| epsilon (float): Epsilon value for numerical stability. | |
| """ | |
| def __init__(self, epsilon: float = torch.finfo(torch.float32).eps): | |
| super().__init__() | |
| self.epsilon = epsilon | |
| def forward(self, x_mag: torch.Tensor, y_mag: torch.Tensor): | |
| """Calculate forward propagation. | |
| Args: | |
| x_mag (torch.Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins). | |
| y_mag (torch.Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins). | |
| Returns: | |
| torch.Tensor: Log STFT magnitude loss value. | |
| """ | |
| return F.l1_loss(torch.log(self.epsilon + y_mag), torch.log(self.epsilon + x_mag)) | |
| class STFTLosses(nn.Module): | |
| """STFT losses. | |
| Args: | |
| n_fft (int): Size of FFT. | |
| hop_length (int): Hop length. | |
| win_length (int): Window length. | |
| window (str): Window function type. | |
| normalized (bool): Whether to use normalized STFT or not. | |
| epsilon (float): Epsilon for numerical stability. | |
| """ | |
| def __init__(self, n_fft: int = 1024, hop_length: int = 120, win_length: int = 600, | |
| window: str = "hann_window", normalized: bool = False, | |
| epsilon: float = torch.finfo(torch.float32).eps): | |
| super().__init__() | |
| self.n_fft = n_fft | |
| self.hop_length = hop_length | |
| self.win_length = win_length | |
| self.normalized = normalized | |
| self.register_buffer("window", getattr(torch, window)(win_length)) | |
| self.spectral_convergenge_loss = SpectralConvergenceLoss(epsilon) | |
| self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss(epsilon) | |
| def forward(self, x: torch.Tensor, y: torch.Tensor) -> tp.Tuple[torch.Tensor, torch.Tensor]: | |
| """Calculate forward propagation. | |
| Args: | |
| x (torch.Tensor): Predicted signal (B, T). | |
| y (torch.Tensor): Groundtruth signal (B, T). | |
| Returns: | |
| torch.Tensor: Spectral convergence loss value. | |
| torch.Tensor: Log STFT magnitude loss value. | |
| """ | |
| x_mag = _stft(x, self.n_fft, self.hop_length, | |
| self.win_length, self.window, self.normalized) # type: ignore | |
| y_mag = _stft(y, self.n_fft, self.hop_length, | |
| self.win_length, self.window, self.normalized) # type: ignore | |
| 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 STFTLoss(nn.Module): | |
| """Single Resolution STFT loss. | |
| Args: | |
| n_fft (int): Nb of FFT. | |
| hop_length (int): Hop length. | |
| win_length (int): Window length. | |
| window (str): Window function type. | |
| normalized (bool): Whether to use normalized STFT or not. | |
| epsilon (float): Epsilon for numerical stability. | |
| factor_sc (float): Coefficient for the spectral loss. | |
| factor_mag (float): Coefficient for the magnitude loss. | |
| """ | |
| def __init__(self, n_fft: int = 1024, hop_length: int = 120, win_length: int = 600, | |
| window: str = "hann_window", normalized: bool = False, | |
| factor_sc: float = 0.1, factor_mag: float = 0.1, | |
| epsilon: float = torch.finfo(torch.float32).eps): | |
| super().__init__() | |
| self.loss = STFTLosses(n_fft, hop_length, win_length, window, normalized, epsilon) | |
| self.factor_sc = factor_sc | |
| self.factor_mag = factor_mag | |
| def forward(self, x: torch.Tensor, y: torch.Tensor) -> tp.Tuple[torch.Tensor, torch.Tensor]: | |
| """Calculate forward propagation. | |
| Args: | |
| x (torch.Tensor): Predicted signal (B, T). | |
| y (torch.Tensor): Groundtruth signal (B, T). | |
| Returns: | |
| torch.Tensor: Single resolution STFT loss. | |
| """ | |
| sc_loss, mag_loss = self.loss(x, y) | |
| return self.factor_sc * sc_loss + self.factor_mag * mag_loss | |
| class MRSTFTLoss(nn.Module): | |
| """Multi resolution STFT loss. | |
| Args: | |
| n_ffts (Sequence[int]): Sequence of FFT sizes. | |
| hop_lengths (Sequence[int]): Sequence of hop sizes. | |
| win_lengths (Sequence[int]): Sequence of window lengths. | |
| window (str): Window function type. | |
| factor_sc (float): Coefficient for the spectral loss. | |
| factor_mag (float): Coefficient for the magnitude loss. | |
| normalized (bool): Whether to use normalized STFT or not. | |
| epsilon (float): Epsilon for numerical stability. | |
| """ | |
| def __init__(self, n_ffts: tp.Sequence[int] = [1024, 2048, 512], hop_lengths: tp.Sequence[int] = [120, 240, 50], | |
| win_lengths: tp.Sequence[int] = [600, 1200, 240], window: str = "hann_window", | |
| factor_sc: float = 0.1, factor_mag: float = 0.1, | |
| normalized: bool = False, epsilon: float = torch.finfo(torch.float32).eps): | |
| super().__init__() | |
| assert len(n_ffts) == len(hop_lengths) == len(win_lengths) | |
| self.stft_losses = torch.nn.ModuleList() | |
| for fs, ss, wl in zip(n_ffts, hop_lengths, win_lengths): | |
| self.stft_losses += [STFTLosses(fs, ss, wl, window, normalized, epsilon)] | |
| self.factor_sc = factor_sc | |
| self.factor_mag = factor_mag | |
| def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: | |
| """Calculate forward propagation. | |
| Args: | |
| x (torch.Tensor): Predicted signal (B, T). | |
| y (torch.Tensor): Groundtruth signal (B, T). | |
| Returns: | |
| torch.Tensor: Multi resolution STFT loss. | |
| """ | |
| sc_loss = torch.Tensor([0.0]) | |
| mag_loss = torch.Tensor([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 self.factor_sc * sc_loss + self.factor_mag * mag_loss | |