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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # This code is modified from https://github.com/descriptinc/descript-audio-codec/blob/main/dac/nn/loss.py | |
| import typing | |
| from typing import List | |
| import torch | |
| import torch.nn.functional as F | |
| from audiotools import AudioSignal | |
| from audiotools import STFTParams | |
| from torch import nn | |
| class L1Loss(nn.L1Loss): | |
| """L1 Loss between AudioSignals. Defaults | |
| to comparing ``audio_data``, but any | |
| attribute of an AudioSignal can be used. | |
| Parameters | |
| ---------- | |
| attribute : str, optional | |
| Attribute of signal to compare, defaults to ``audio_data``. | |
| weight : float, optional | |
| Weight of this loss, defaults to 1.0. | |
| Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py | |
| """ | |
| def __init__(self, attribute: str = "audio_data", weight: float = 1.0, **kwargs): | |
| self.attribute = attribute | |
| self.weight = weight | |
| super().__init__(**kwargs) | |
| def forward(self, x: AudioSignal, y: AudioSignal): | |
| """ | |
| Parameters | |
| ---------- | |
| x : AudioSignal | |
| Estimate AudioSignal | |
| y : AudioSignal | |
| Reference AudioSignal | |
| Returns | |
| ------- | |
| torch.Tensor | |
| L1 loss between AudioSignal attributes. | |
| """ | |
| if isinstance(x, AudioSignal): | |
| x = getattr(x, self.attribute) | |
| y = getattr(y, self.attribute) | |
| return super().forward(x, y) | |
| class SISDRLoss(nn.Module): | |
| """ | |
| Computes the Scale-Invariant Source-to-Distortion Ratio between a batch | |
| of estimated and reference audio signals or aligned features. | |
| Parameters | |
| ---------- | |
| scaling : int, optional | |
| Whether to use scale-invariant (True) or | |
| signal-to-noise ratio (False), by default True | |
| reduction : str, optional | |
| How to reduce across the batch (either 'mean', | |
| 'sum', or none).], by default ' mean' | |
| zero_mean : int, optional | |
| Zero mean the references and estimates before | |
| computing the loss, by default True | |
| clip_min : int, optional | |
| The minimum possible loss value. Helps network | |
| to not focus on making already good examples better, by default None | |
| weight : float, optional | |
| Weight of this loss, defaults to 1.0. | |
| Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/distance.py | |
| """ | |
| def __init__( | |
| self, | |
| scaling: int = True, | |
| reduction: str = "mean", | |
| zero_mean: int = True, | |
| clip_min: int = None, | |
| weight: float = 1.0, | |
| ): | |
| self.scaling = scaling | |
| self.reduction = reduction | |
| self.zero_mean = zero_mean | |
| self.clip_min = clip_min | |
| self.weight = weight | |
| super().__init__() | |
| def forward(self, x: AudioSignal, y: AudioSignal): | |
| eps = 1e-8 | |
| # nb, nc, nt | |
| if isinstance(x, AudioSignal): | |
| references = x.audio_data | |
| estimates = y.audio_data | |
| else: | |
| references = x | |
| estimates = y | |
| nb = references.shape[0] | |
| references = references.reshape(nb, 1, -1).permute(0, 2, 1) | |
| estimates = estimates.reshape(nb, 1, -1).permute(0, 2, 1) | |
| # samples now on axis 1 | |
| if self.zero_mean: | |
| mean_reference = references.mean(dim=1, keepdim=True) | |
| mean_estimate = estimates.mean(dim=1, keepdim=True) | |
| else: | |
| mean_reference = 0 | |
| mean_estimate = 0 | |
| _references = references - mean_reference | |
| _estimates = estimates - mean_estimate | |
| references_projection = (_references**2).sum(dim=-2) + eps | |
| references_on_estimates = (_estimates * _references).sum(dim=-2) + eps | |
| scale = ( | |
| (references_on_estimates / references_projection).unsqueeze(1) | |
| if self.scaling | |
| else 1 | |
| ) | |
| e_true = scale * _references | |
| e_res = _estimates - e_true | |
| signal = (e_true**2).sum(dim=1) | |
| noise = (e_res**2).sum(dim=1) | |
| sdr = -10 * torch.log10(signal / noise + eps) | |
| if self.clip_min is not None: | |
| sdr = torch.clamp(sdr, min=self.clip_min) | |
| if self.reduction == "mean": | |
| sdr = sdr.mean() | |
| elif self.reduction == "sum": | |
| sdr = sdr.sum() | |
| return sdr | |
| class MultiScaleSTFTLoss(nn.Module): | |
| """Computes the multi-scale STFT loss from [1]. | |
| Parameters | |
| ---------- | |
| window_lengths : List[int], optional | |
| Length of each window of each STFT, by default [2048, 512] | |
| loss_fn : typing.Callable, optional | |
| How to compare each loss, by default nn.L1Loss() | |
| clamp_eps : float, optional | |
| Clamp on the log magnitude, below, by default 1e-5 | |
| mag_weight : float, optional | |
| Weight of raw magnitude portion of loss, by default 1.0 | |
| log_weight : float, optional | |
| Weight of log magnitude portion of loss, by default 1.0 | |
| pow : float, optional | |
| Power to raise magnitude to before taking log, by default 2.0 | |
| weight : float, optional | |
| Weight of this loss, by default 1.0 | |
| match_stride : bool, optional | |
| Whether to match the stride of convolutional layers, by default False | |
| References | |
| ---------- | |
| 1. Engel, Jesse, Chenjie Gu, and Adam Roberts. | |
| "DDSP: Differentiable Digital Signal Processing." | |
| International Conference on Learning Representations. 2019. | |
| Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py | |
| """ | |
| def __init__( | |
| self, | |
| window_lengths: List[int] = [2048, 512], | |
| loss_fn: typing.Callable = nn.L1Loss(), | |
| clamp_eps: float = 1e-5, | |
| mag_weight: float = 1.0, | |
| log_weight: float = 1.0, | |
| pow: float = 2.0, | |
| weight: float = 1.0, | |
| match_stride: bool = False, | |
| window_type: str = None, | |
| ): | |
| super().__init__() | |
| self.stft_params = [ | |
| STFTParams( | |
| window_length=w, | |
| hop_length=w // 4, | |
| match_stride=match_stride, | |
| window_type=window_type, | |
| ) | |
| for w in window_lengths | |
| ] | |
| self.loss_fn = loss_fn | |
| self.log_weight = log_weight | |
| self.mag_weight = mag_weight | |
| self.clamp_eps = clamp_eps | |
| self.weight = weight | |
| self.pow = pow | |
| def forward(self, x: AudioSignal, y: AudioSignal): | |
| """Computes multi-scale STFT between an estimate and a reference | |
| signal. | |
| Parameters | |
| ---------- | |
| x : AudioSignal | |
| Estimate signal | |
| y : AudioSignal | |
| Reference signal | |
| Returns | |
| ------- | |
| torch.Tensor | |
| Multi-scale STFT loss. | |
| """ | |
| loss = 0.0 | |
| for s in self.stft_params: | |
| x.stft(s.window_length, s.hop_length, s.window_type) | |
| y.stft(s.window_length, s.hop_length, s.window_type) | |
| loss += self.log_weight * self.loss_fn( | |
| x.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(), | |
| y.magnitude.clamp(self.clamp_eps).pow(self.pow).log10(), | |
| ) | |
| loss += self.mag_weight * self.loss_fn(x.magnitude, y.magnitude) | |
| return loss | |
| class MelSpectrogramLoss(nn.Module): | |
| """Compute distance between mel spectrograms. Can be used | |
| in a multi-scale way. | |
| Parameters | |
| ---------- | |
| n_mels : List[int] | |
| Number of mels per STFT, by default [150, 80], | |
| window_lengths : List[int], optional | |
| Length of each window of each STFT, by default [2048, 512] | |
| loss_fn : typing.Callable, optional | |
| How to compare each loss, by default nn.L1Loss() | |
| clamp_eps : float, optional | |
| Clamp on the log magnitude, below, by default 1e-5 | |
| mag_weight : float, optional | |
| Weight of raw magnitude portion of loss, by default 1.0 | |
| log_weight : float, optional | |
| Weight of log magnitude portion of loss, by default 1.0 | |
| pow : float, optional | |
| Power to raise magnitude to before taking log, by default 2.0 | |
| weight : float, optional | |
| Weight of this loss, by default 1.0 | |
| match_stride : bool, optional | |
| Whether to match the stride of convolutional layers, by default False | |
| Implementation copied from: https://github.com/descriptinc/lyrebird-audiotools/blob/961786aa1a9d628cca0c0486e5885a457fe70c1a/audiotools/metrics/spectral.py | |
| """ | |
| def __init__( | |
| self, | |
| n_mels: List[int] = [150, 80], | |
| window_lengths: List[int] = [2048, 512], | |
| loss_fn: typing.Callable = nn.L1Loss(), | |
| clamp_eps: float = 1e-5, | |
| mag_weight: float = 1.0, | |
| log_weight: float = 1.0, | |
| pow: float = 2.0, | |
| weight: float = 1.0, | |
| match_stride: bool = False, | |
| mel_fmin: List[float] = [0.0, 0.0], | |
| mel_fmax: List[float] = [None, None], | |
| window_type: str = None, | |
| ): | |
| super().__init__() | |
| self.stft_params = [ | |
| STFTParams( | |
| window_length=w, | |
| hop_length=w // 4, | |
| match_stride=match_stride, | |
| window_type=window_type, | |
| ) | |
| for w in window_lengths | |
| ] | |
| self.n_mels = n_mels | |
| self.loss_fn = loss_fn | |
| self.clamp_eps = clamp_eps | |
| self.log_weight = log_weight | |
| self.mag_weight = mag_weight | |
| self.weight = weight | |
| self.mel_fmin = mel_fmin | |
| self.mel_fmax = mel_fmax | |
| self.pow = pow | |
| def forward(self, x: AudioSignal, y: AudioSignal): | |
| """Computes mel loss between an estimate and a reference | |
| signal. | |
| Parameters | |
| ---------- | |
| x : AudioSignal | |
| Estimate signal | |
| y : AudioSignal | |
| Reference signal | |
| Returns | |
| ------- | |
| torch.Tensor | |
| Mel loss. | |
| """ | |
| loss = 0.0 | |
| for n_mels, fmin, fmax, s in zip( | |
| self.n_mels, self.mel_fmin, self.mel_fmax, self.stft_params | |
| ): | |
| kwargs = { | |
| "window_length": s.window_length, | |
| "hop_length": s.hop_length, | |
| "window_type": s.window_type, | |
| } | |
| x_mels = x.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs) | |
| y_mels = y.mel_spectrogram(n_mels, mel_fmin=fmin, mel_fmax=fmax, **kwargs) | |
| loss += self.log_weight * self.loss_fn( | |
| x_mels.clamp(self.clamp_eps).pow(self.pow).log10(), | |
| y_mels.clamp(self.clamp_eps).pow(self.pow).log10(), | |
| ) | |
| loss += self.mag_weight * self.loss_fn(x_mels, y_mels) | |
| return loss | |
| class FocalLoss(torch.nn.Module): | |
| def __init__(self, gamma=0, eps=1e-7): | |
| super(FocalLoss, self).__init__() | |
| self.gamma = gamma | |
| self.eps = eps | |
| self.ce = torch.nn.CrossEntropyLoss() | |
| def forward(self, input, target): | |
| logp = self.ce(input, target) | |
| p = torch.exp(-logp) | |
| loss = (1 - p) ** self.gamma * logp | |
| return loss.mean() | |
| class GANLoss(nn.Module): | |
| """ | |
| Computes a discriminator loss, given a discriminator on | |
| generated waveforms/spectrograms compared to ground truth | |
| waveforms/spectrograms. Computes the loss for both the | |
| discriminator and the generator in separate functions. | |
| """ | |
| def __init__(self, discriminator): | |
| super().__init__() | |
| self.discriminator = discriminator | |
| def forward(self, fake, real): | |
| d_fake = self.discriminator(fake.audio_data) | |
| d_real = self.discriminator(real.audio_data) | |
| return d_fake, d_real | |
| def discriminator_loss(self, fake, real): | |
| d_fake, d_real = self.forward(fake.clone().detach(), real) | |
| loss_d = 0 | |
| for x_fake, x_real in zip(d_fake, d_real): | |
| loss_d += torch.mean(x_fake[-1] ** 2) | |
| loss_d += torch.mean((1 - x_real[-1]) ** 2) | |
| return loss_d | |
| def generator_loss(self, fake, real): | |
| d_fake, d_real = self.forward(fake, real) | |
| loss_g = 0 | |
| for x_fake in d_fake: | |
| loss_g += torch.mean((1 - x_fake[-1]) ** 2) | |
| loss_feature = 0 | |
| for i in range(len(d_fake)): | |
| for j in range(len(d_fake[i]) - 1): | |
| loss_feature += F.l1_loss(d_fake[i][j], d_real[i][j].detach()) | |
| return loss_g, loss_feature | |