Shadhil's picture
voice-clone with single audio sample input
9b2107c
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}