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import torch.nn.functional as F
from torch import nn
from text_to_speech.modules.vocoder.hifigan.hifigan import HifiGanGenerator, MultiPeriodDiscriminator, MultiScaleDiscriminator, \
generator_loss, feature_loss, discriminator_loss
from text_to_speech.modules.vocoder.hifigan.mel_utils import mel_spectrogram
from text_to_speech.modules.vocoder.hifigan.stft_loss import MultiResolutionSTFTLoss
from tasks.vocoder.vocoder_base import VocoderBaseTask
from text_to_speech.utils.commons.hparams import hparams
from text_to_speech.utils.nn.model_utils import print_arch
class HifiGanTask(VocoderBaseTask):
def build_model(self):
self.model_gen = HifiGanGenerator(hparams)
self.model_disc = nn.ModuleDict()
self.model_disc['mpd'] = MultiPeriodDiscriminator()
self.model_disc['msd'] = MultiScaleDiscriminator()
self.stft_loss = MultiResolutionSTFTLoss()
print_arch(self.model_gen)
if hparams['load_ckpt'] != '':
self.load_ckpt(hparams['load_ckpt'], 'model_gen', 'model_gen', force=True, strict=True)
self.load_ckpt(hparams['load_ckpt'], 'model_disc', 'model_disc', force=True, strict=True)
return self.model_gen
def _training_step(self, sample, batch_idx, optimizer_idx):
mel = sample['mels']
y = sample['wavs']
f0 = sample['f0']
loss_output = {}
if optimizer_idx == 0:
#######################
# Generator #
#######################
y_ = self.model_gen(mel, f0)
y_mel = mel_spectrogram(y.squeeze(1), hparams).transpose(1, 2)
y_hat_mel = mel_spectrogram(y_.squeeze(1), hparams).transpose(1, 2)
loss_output['mel'] = F.l1_loss(y_hat_mel, y_mel) * hparams['lambda_mel']
_, y_p_hat_g, fmap_f_r, fmap_f_g = self.model_disc['mpd'](y, y_, mel)
_, y_s_hat_g, fmap_s_r, fmap_s_g = self.model_disc['msd'](y, y_, mel)
loss_output['a_p'] = generator_loss(y_p_hat_g) * hparams['lambda_adv']
loss_output['a_s'] = generator_loss(y_s_hat_g) * hparams['lambda_adv']
if hparams['use_fm_loss']:
loss_output['fm_f'] = feature_loss(fmap_f_r, fmap_f_g)
loss_output['fm_s'] = feature_loss(fmap_s_r, fmap_s_g)
if hparams['use_ms_stft']:
loss_output['sc'], loss_output['mag'] = self.stft_loss(y.squeeze(1), y_.squeeze(1))
self.y_ = y_.detach()
self.y_mel = y_mel.detach()
self.y_hat_mel = y_hat_mel.detach()
else:
#######################
# Discriminator #
#######################
y_ = self.y_
# MPD
y_p_hat_r, y_p_hat_g, _, _ = self.model_disc['mpd'](y, y_.detach(), mel)
loss_output['r_p'], loss_output['f_p'] = discriminator_loss(y_p_hat_r, y_p_hat_g)
# MSD
y_s_hat_r, y_s_hat_g, _, _ = self.model_disc['msd'](y, y_.detach(), mel)
loss_output['r_s'], loss_output['f_s'] = discriminator_loss(y_s_hat_r, y_s_hat_g)
total_loss = sum(loss_output.values())
return total_loss, loss_output
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