369 lines
13 KiB
Python
369 lines
13 KiB
Python
|
from typing import Dict, Union
|
||
|
|
|
||
|
|
import torch
|
||
|
|
from torch import nn
|
||
|
|
from torch.nn import functional as F
|
||
|
|
|
||
|
|
from TTS.utils.audio.torch_transforms import TorchSTFT
|
||
|
|
from TTS.vocoder.utils.distribution import discretized_mix_logistic_loss, gaussian_loss
|
||
|
|
|
||
|
|
#################################
|
||
|
|
# GENERATOR LOSSES
|
||
|
|
#################################
|
||
|
|
|
||
|
|
|
||
|
|
class STFTLoss(nn.Module):
|
||
|
|
"""STFT loss. Input generate and real waveforms are converted
|
||
|
|
to spectrograms compared with L1 and Spectral convergence losses.
|
||
|
|
It is from ParallelWaveGAN paper https://arxiv.org/pdf/1910.11480.pdf"""
|
||
|
|
|
||
|
|
def __init__(self, n_fft, hop_length, win_length):
|
||
|
|
super().__init__()
|
||
|
|
self.n_fft = n_fft
|
||
|
|
self.hop_length = hop_length
|
||
|
|
self.win_length = win_length
|
||
|
|
self.stft = TorchSTFT(n_fft, hop_length, win_length)
|
||
|
|
|
||
|
|
def forward(self, y_hat, y):
|
||
|
|
y_hat_M = self.stft(y_hat)
|
||
|
|
y_M = self.stft(y)
|
||
|
|
# magnitude loss
|
||
|
|
loss_mag = F.l1_loss(torch.log(y_M), torch.log(y_hat_M))
|
||
|
|
# spectral convergence loss
|
||
|
|
loss_sc = torch.norm(y_M - y_hat_M, p="fro") / torch.norm(y_M, p="fro")
|
||
|
|
return loss_mag, loss_sc
|
||
|
|
|
||
|
|
|
||
|
|
class MultiScaleSTFTLoss(torch.nn.Module):
|
||
|
|
"""Multi-scale STFT loss. Input generate and real waveforms are converted
|
||
|
|
to spectrograms compared with L1 and Spectral convergence losses.
|
||
|
|
It is from ParallelWaveGAN paper https://arxiv.org/pdf/1910.11480.pdf"""
|
||
|
|
|
||
|
|
def __init__(self, n_ffts=(1024, 2048, 512), hop_lengths=(120, 240, 50), win_lengths=(600, 1200, 240)):
|
||
|
|
super().__init__()
|
||
|
|
self.loss_funcs = torch.nn.ModuleList()
|
||
|
|
for n_fft, hop_length, win_length in zip(n_ffts, hop_lengths, win_lengths):
|
||
|
|
self.loss_funcs.append(STFTLoss(n_fft, hop_length, win_length))
|
||
|
|
|
||
|
|
def forward(self, y_hat, y):
|
||
|
|
N = len(self.loss_funcs)
|
||
|
|
loss_sc = 0
|
||
|
|
loss_mag = 0
|
||
|
|
for f in self.loss_funcs:
|
||
|
|
lm, lsc = f(y_hat, y)
|
||
|
|
loss_mag += lm
|
||
|
|
loss_sc += lsc
|
||
|
|
loss_sc /= N
|
||
|
|
loss_mag /= N
|
||
|
|
return loss_mag, loss_sc
|
||
|
|
|
||
|
|
|
||
|
|
class L1SpecLoss(nn.Module):
|
||
|
|
"""L1 Loss over Spectrograms as described in HiFiGAN paper https://arxiv.org/pdf/2010.05646.pdf"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self, sample_rate, n_fft, hop_length, win_length, mel_fmin=None, mel_fmax=None, n_mels=None, use_mel=True
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.use_mel = use_mel
|
||
|
|
self.stft = TorchSTFT(
|
||
|
|
n_fft,
|
||
|
|
hop_length,
|
||
|
|
win_length,
|
||
|
|
sample_rate=sample_rate,
|
||
|
|
mel_fmin=mel_fmin,
|
||
|
|
mel_fmax=mel_fmax,
|
||
|
|
n_mels=n_mels,
|
||
|
|
use_mel=use_mel,
|
||
|
|
)
|
||
|
|
|
||
|
|
def forward(self, y_hat, y):
|
||
|
|
y_hat_M = self.stft(y_hat)
|
||
|
|
y_M = self.stft(y)
|
||
|
|
# magnitude loss
|
||
|
|
loss_mag = F.l1_loss(torch.log(y_M), torch.log(y_hat_M))
|
||
|
|
return loss_mag
|
||
|
|
|
||
|
|
|
||
|
|
class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
|
||
|
|
"""Multiscale STFT loss for multi band model outputs.
|
||
|
|
From MultiBand-MelGAN paper https://arxiv.org/abs/2005.05106"""
|
||
|
|
|
||
|
|
# pylint: disable=no-self-use
|
||
|
|
def forward(self, y_hat, y):
|
||
|
|
y_hat = y_hat.view(-1, 1, y_hat.shape[2])
|
||
|
|
y = y.view(-1, 1, y.shape[2])
|
||
|
|
return super().forward(y_hat.squeeze(1), y.squeeze(1))
|
||
|
|
|
||
|
|
|
||
|
|
class MSEGLoss(nn.Module):
|
||
|
|
"""Mean Squared Generator Loss"""
|
||
|
|
|
||
|
|
# pylint: disable=no-self-use
|
||
|
|
def forward(self, score_real):
|
||
|
|
loss_fake = F.mse_loss(score_real, score_real.new_ones(score_real.shape))
|
||
|
|
return loss_fake
|
||
|
|
|
||
|
|
|
||
|
|
class HingeGLoss(nn.Module):
|
||
|
|
"""Hinge Discriminator Loss"""
|
||
|
|
|
||
|
|
# pylint: disable=no-self-use
|
||
|
|
def forward(self, score_real):
|
||
|
|
# TODO: this might be wrong
|
||
|
|
loss_fake = torch.mean(F.relu(1.0 - score_real))
|
||
|
|
return loss_fake
|
||
|
|
|
||
|
|
|
||
|
|
##################################
|
||
|
|
# DISCRIMINATOR LOSSES
|
||
|
|
##################################
|
||
|
|
|
||
|
|
|
||
|
|
class MSEDLoss(nn.Module):
|
||
|
|
"""Mean Squared Discriminator Loss"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.loss_func = nn.MSELoss()
|
||
|
|
|
||
|
|
# pylint: disable=no-self-use
|
||
|
|
def forward(self, score_fake, score_real):
|
||
|
|
loss_real = self.loss_func(score_real, score_real.new_ones(score_real.shape))
|
||
|
|
loss_fake = self.loss_func(score_fake, score_fake.new_zeros(score_fake.shape))
|
||
|
|
loss_d = loss_real + loss_fake
|
||
|
|
return loss_d, loss_real, loss_fake
|
||
|
|
|
||
|
|
|
||
|
|
class HingeDLoss(nn.Module):
|
||
|
|
"""Hinge Discriminator Loss"""
|
||
|
|
|
||
|
|
# pylint: disable=no-self-use
|
||
|
|
def forward(self, score_fake, score_real):
|
||
|
|
loss_real = torch.mean(F.relu(1.0 - score_real))
|
||
|
|
loss_fake = torch.mean(F.relu(1.0 + score_fake))
|
||
|
|
loss_d = loss_real + loss_fake
|
||
|
|
return loss_d, loss_real, loss_fake
|
||
|
|
|
||
|
|
|
||
|
|
class MelganFeatureLoss(nn.Module):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.loss_func = nn.L1Loss()
|
||
|
|
|
||
|
|
# pylint: disable=no-self-use
|
||
|
|
def forward(self, fake_feats, real_feats):
|
||
|
|
loss_feats = 0
|
||
|
|
num_feats = 0
|
||
|
|
for idx, _ in enumerate(fake_feats):
|
||
|
|
for fake_feat, real_feat in zip(fake_feats[idx], real_feats[idx]):
|
||
|
|
loss_feats += self.loss_func(fake_feat, real_feat)
|
||
|
|
num_feats += 1
|
||
|
|
loss_feats = loss_feats / num_feats
|
||
|
|
return loss_feats
|
||
|
|
|
||
|
|
|
||
|
|
#####################################
|
||
|
|
# LOSS WRAPPERS
|
||
|
|
#####################################
|
||
|
|
|
||
|
|
|
||
|
|
def _apply_G_adv_loss(scores_fake, loss_func):
|
||
|
|
"""Compute G adversarial loss function
|
||
|
|
and normalize values"""
|
||
|
|
adv_loss = 0
|
||
|
|
if isinstance(scores_fake, list):
|
||
|
|
for score_fake in scores_fake:
|
||
|
|
fake_loss = loss_func(score_fake)
|
||
|
|
adv_loss += fake_loss
|
||
|
|
adv_loss /= len(scores_fake)
|
||
|
|
else:
|
||
|
|
fake_loss = loss_func(scores_fake)
|
||
|
|
adv_loss = fake_loss
|
||
|
|
return adv_loss
|
||
|
|
|
||
|
|
|
||
|
|
def _apply_D_loss(scores_fake, scores_real, loss_func):
|
||
|
|
"""Compute D loss func and normalize loss values"""
|
||
|
|
loss = 0
|
||
|
|
real_loss = 0
|
||
|
|
fake_loss = 0
|
||
|
|
if isinstance(scores_fake, list):
|
||
|
|
# multi-scale loss
|
||
|
|
for score_fake, score_real in zip(scores_fake, scores_real):
|
||
|
|
total_loss, real_loss_, fake_loss_ = loss_func(score_fake=score_fake, score_real=score_real)
|
||
|
|
loss += total_loss
|
||
|
|
real_loss += real_loss_
|
||
|
|
fake_loss += fake_loss_
|
||
|
|
# normalize loss values with number of scales (discriminators)
|
||
|
|
loss /= len(scores_fake)
|
||
|
|
real_loss /= len(scores_real)
|
||
|
|
fake_loss /= len(scores_fake)
|
||
|
|
else:
|
||
|
|
# single scale loss
|
||
|
|
total_loss, real_loss, fake_loss = loss_func(scores_fake, scores_real)
|
||
|
|
loss = total_loss
|
||
|
|
return loss, real_loss, fake_loss
|
||
|
|
|
||
|
|
|
||
|
|
##################################
|
||
|
|
# MODEL LOSSES
|
||
|
|
##################################
|
||
|
|
|
||
|
|
|
||
|
|
class GeneratorLoss(nn.Module):
|
||
|
|
"""Generator Loss Wrapper. Based on model configuration it sets a right set of loss functions and computes
|
||
|
|
losses. It allows to experiment with different combinations of loss functions with different models by just
|
||
|
|
changing configurations.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
C (AttrDict): model configuration.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, C):
|
||
|
|
super().__init__()
|
||
|
|
assert not (
|
||
|
|
C.use_mse_gan_loss and C.use_hinge_gan_loss
|
||
|
|
), " [!] Cannot use HingeGANLoss and MSEGANLoss together."
|
||
|
|
|
||
|
|
self.use_stft_loss = C.use_stft_loss if "use_stft_loss" in C else False
|
||
|
|
self.use_subband_stft_loss = C.use_subband_stft_loss if "use_subband_stft_loss" in C else False
|
||
|
|
self.use_mse_gan_loss = C.use_mse_gan_loss if "use_mse_gan_loss" in C else False
|
||
|
|
self.use_hinge_gan_loss = C.use_hinge_gan_loss if "use_hinge_gan_loss" in C else False
|
||
|
|
self.use_feat_match_loss = C.use_feat_match_loss if "use_feat_match_loss" in C else False
|
||
|
|
self.use_l1_spec_loss = C.use_l1_spec_loss if "use_l1_spec_loss" in C else False
|
||
|
|
|
||
|
|
self.stft_loss_weight = C.stft_loss_weight if "stft_loss_weight" in C else 0.0
|
||
|
|
self.subband_stft_loss_weight = C.subband_stft_loss_weight if "subband_stft_loss_weight" in C else 0.0
|
||
|
|
self.mse_gan_loss_weight = C.mse_G_loss_weight if "mse_G_loss_weight" in C else 0.0
|
||
|
|
self.hinge_gan_loss_weight = C.hinge_G_loss_weight if "hinde_G_loss_weight" in C else 0.0
|
||
|
|
self.feat_match_loss_weight = C.feat_match_loss_weight if "feat_match_loss_weight" in C else 0.0
|
||
|
|
self.l1_spec_loss_weight = C.l1_spec_loss_weight if "l1_spec_loss_weight" in C else 0.0
|
||
|
|
|
||
|
|
if C.use_stft_loss:
|
||
|
|
self.stft_loss = MultiScaleSTFTLoss(**C.stft_loss_params)
|
||
|
|
if C.use_subband_stft_loss:
|
||
|
|
self.subband_stft_loss = MultiScaleSubbandSTFTLoss(**C.subband_stft_loss_params)
|
||
|
|
if C.use_mse_gan_loss:
|
||
|
|
self.mse_loss = MSEGLoss()
|
||
|
|
if C.use_hinge_gan_loss:
|
||
|
|
self.hinge_loss = HingeGLoss()
|
||
|
|
if C.use_feat_match_loss:
|
||
|
|
self.feat_match_loss = MelganFeatureLoss()
|
||
|
|
if C.use_l1_spec_loss:
|
||
|
|
assert C.audio["sample_rate"] == C.l1_spec_loss_params["sample_rate"]
|
||
|
|
self.l1_spec_loss = L1SpecLoss(**C.l1_spec_loss_params)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self, y_hat=None, y=None, scores_fake=None, feats_fake=None, feats_real=None, y_hat_sub=None, y_sub=None
|
||
|
|
):
|
||
|
|
gen_loss = 0
|
||
|
|
adv_loss = 0
|
||
|
|
return_dict = {}
|
||
|
|
|
||
|
|
# STFT Loss
|
||
|
|
if self.use_stft_loss:
|
||
|
|
stft_loss_mg, stft_loss_sc = self.stft_loss(y_hat[:, :, : y.size(2)].squeeze(1), y.squeeze(1))
|
||
|
|
return_dict["G_stft_loss_mg"] = stft_loss_mg
|
||
|
|
return_dict["G_stft_loss_sc"] = stft_loss_sc
|
||
|
|
gen_loss = gen_loss + self.stft_loss_weight * (stft_loss_mg + stft_loss_sc)
|
||
|
|
|
||
|
|
# L1 Spec loss
|
||
|
|
if self.use_l1_spec_loss:
|
||
|
|
l1_spec_loss = self.l1_spec_loss(y_hat, y)
|
||
|
|
return_dict["G_l1_spec_loss"] = l1_spec_loss
|
||
|
|
gen_loss = gen_loss + self.l1_spec_loss_weight * l1_spec_loss
|
||
|
|
|
||
|
|
# subband STFT Loss
|
||
|
|
if self.use_subband_stft_loss:
|
||
|
|
subband_stft_loss_mg, subband_stft_loss_sc = self.subband_stft_loss(y_hat_sub, y_sub)
|
||
|
|
return_dict["G_subband_stft_loss_mg"] = subband_stft_loss_mg
|
||
|
|
return_dict["G_subband_stft_loss_sc"] = subband_stft_loss_sc
|
||
|
|
gen_loss = gen_loss + self.subband_stft_loss_weight * (subband_stft_loss_mg + subband_stft_loss_sc)
|
||
|
|
|
||
|
|
# multiscale MSE adversarial loss
|
||
|
|
if self.use_mse_gan_loss and scores_fake is not None:
|
||
|
|
mse_fake_loss = _apply_G_adv_loss(scores_fake, self.mse_loss)
|
||
|
|
return_dict["G_mse_fake_loss"] = mse_fake_loss
|
||
|
|
adv_loss = adv_loss + self.mse_gan_loss_weight * mse_fake_loss
|
||
|
|
|
||
|
|
# multiscale Hinge adversarial loss
|
||
|
|
if self.use_hinge_gan_loss and not scores_fake is not None:
|
||
|
|
hinge_fake_loss = _apply_G_adv_loss(scores_fake, self.hinge_loss)
|
||
|
|
return_dict["G_hinge_fake_loss"] = hinge_fake_loss
|
||
|
|
adv_loss = adv_loss + self.hinge_gan_loss_weight * hinge_fake_loss
|
||
|
|
|
||
|
|
# Feature Matching Loss
|
||
|
|
if self.use_feat_match_loss and not feats_fake is None:
|
||
|
|
feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
|
||
|
|
return_dict["G_feat_match_loss"] = feat_match_loss
|
||
|
|
adv_loss = adv_loss + self.feat_match_loss_weight * feat_match_loss
|
||
|
|
return_dict["loss"] = gen_loss + adv_loss
|
||
|
|
return_dict["G_gen_loss"] = gen_loss
|
||
|
|
return_dict["G_adv_loss"] = adv_loss
|
||
|
|
return return_dict
|
||
|
|
|
||
|
|
|
||
|
|
class DiscriminatorLoss(nn.Module):
|
||
|
|
"""Like ```GeneratorLoss```"""
|
||
|
|
|
||
|
|
def __init__(self, C):
|
||
|
|
super().__init__()
|
||
|
|
assert not (
|
||
|
|
C.use_mse_gan_loss and C.use_hinge_gan_loss
|
||
|
|
), " [!] Cannot use HingeGANLoss and MSEGANLoss together."
|
||
|
|
|
||
|
|
self.use_mse_gan_loss = C.use_mse_gan_loss
|
||
|
|
self.use_hinge_gan_loss = C.use_hinge_gan_loss
|
||
|
|
|
||
|
|
if C.use_mse_gan_loss:
|
||
|
|
self.mse_loss = MSEDLoss()
|
||
|
|
if C.use_hinge_gan_loss:
|
||
|
|
self.hinge_loss = HingeDLoss()
|
||
|
|
|
||
|
|
def forward(self, scores_fake, scores_real):
|
||
|
|
loss = 0
|
||
|
|
return_dict = {}
|
||
|
|
|
||
|
|
if self.use_mse_gan_loss:
|
||
|
|
mse_D_loss, mse_D_real_loss, mse_D_fake_loss = _apply_D_loss(
|
||
|
|
scores_fake=scores_fake, scores_real=scores_real, loss_func=self.mse_loss
|
||
|
|
)
|
||
|
|
return_dict["D_mse_gan_loss"] = mse_D_loss
|
||
|
|
return_dict["D_mse_gan_real_loss"] = mse_D_real_loss
|
||
|
|
return_dict["D_mse_gan_fake_loss"] = mse_D_fake_loss
|
||
|
|
loss += mse_D_loss
|
||
|
|
|
||
|
|
if self.use_hinge_gan_loss:
|
||
|
|
hinge_D_loss, hinge_D_real_loss, hinge_D_fake_loss = _apply_D_loss(
|
||
|
|
scores_fake=scores_fake, scores_real=scores_real, loss_func=self.hinge_loss
|
||
|
|
)
|
||
|
|
return_dict["D_hinge_gan_loss"] = hinge_D_loss
|
||
|
|
return_dict["D_hinge_gan_real_loss"] = hinge_D_real_loss
|
||
|
|
return_dict["D_hinge_gan_fake_loss"] = hinge_D_fake_loss
|
||
|
|
loss += hinge_D_loss
|
||
|
|
|
||
|
|
return_dict["loss"] = loss
|
||
|
|
return return_dict
|
||
|
|
|
||
|
|
|
||
|
|
class WaveRNNLoss(nn.Module):
|
||
|
|
def __init__(self, wave_rnn_mode: Union[str, int]):
|
||
|
|
super().__init__()
|
||
|
|
if wave_rnn_mode == "mold":
|
||
|
|
self.loss_func = discretized_mix_logistic_loss
|
||
|
|
elif wave_rnn_mode == "gauss":
|
||
|
|
self.loss_func = gaussian_loss
|
||
|
|
elif isinstance(wave_rnn_mode, int):
|
||
|
|
self.loss_func = torch.nn.CrossEntropyLoss()
|
||
|
|
else:
|
||
|
|
raise ValueError(" [!] Unknown mode for Wavernn.")
|
||
|
|
|
||
|
|
def forward(self, y_hat, y) -> Dict:
|
||
|
|
loss = self.loss_func(y_hat, y)
|
||
|
|
return {"loss": loss}
|