# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from pathlib import Path from typing import Dict import paddle from paddle import distributed as dist from paddle.io import DataLoader from paddle.nn import Layer from paddle.optimizer import Optimizer from paddle.optimizer.lr import LRScheduler from paddlespeech.t2s.training.extensions.evaluator import StandardEvaluator from paddlespeech.t2s.training.reporter import report from paddlespeech.t2s.training.updaters.standard_updater import StandardUpdater from paddlespeech.t2s.training.updaters.standard_updater import UpdaterState logging.basicConfig( format='%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s', datefmt='[%Y-%m-%d %H:%M:%S]') logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class MBMelGANUpdater(StandardUpdater): def __init__(self, models: Dict[str, Layer], optimizers: Dict[str, Optimizer], criterions: Dict[str, Layer], schedulers: Dict[str, LRScheduler], dataloader: DataLoader, discriminator_train_start_steps: int, lambda_adv: float, output_dir: Path=None): self.models = models self.generator: Layer = models['generator'] self.discriminator: Layer = models['discriminator'] self.optimizers = optimizers self.optimizer_g: Optimizer = optimizers['generator'] self.optimizer_d: Optimizer = optimizers['discriminator'] self.criterions = criterions self.criterion_stft = criterions['stft'] self.criterion_sub_stft = criterions['sub_stft'] self.criterion_pqmf = criterions['pqmf'] self.criterion_gen_adv = criterions["gen_adv"] self.criterion_dis_adv = criterions["dis_adv"] self.schedulers = schedulers self.scheduler_g = schedulers['generator'] self.scheduler_d = schedulers['discriminator'] self.dataloader = dataloader self.discriminator_train_start_steps = discriminator_train_start_steps self.lambda_adv = lambda_adv self.state = UpdaterState(iteration=0, epoch=0) self.train_iterator = iter(self.dataloader) log_file = output_dir / 'worker_{}.log'.format(dist.get_rank()) self.filehandler = logging.FileHandler(str(log_file)) logger.addHandler(self.filehandler) self.logger = logger self.msg = "" def update_core(self, batch): self.msg = "Rank: {}, ".format(dist.get_rank()) losses_dict = {} # parse batch wav, mel = batch # Generator # (B, out_channels, T ** prod(upsample_scales) wav_ = self.generator(mel) wav_mb_ = wav_ # (B, 1, out_channels*T ** prod(upsample_scales) wav_ = self.criterion_pqmf.synthesis(wav_mb_) # initialize gen_loss = 0.0 # full band Multi-resolution stft loss sc_loss, mag_loss = self.criterion_stft(wav_, wav) # for balancing with subband stft loss # Eq.(9) in paper gen_loss += 0.5 * (sc_loss + mag_loss) report("train/spectral_convergence_loss", float(sc_loss)) report("train/log_stft_magnitude_loss", float(mag_loss)) losses_dict["spectral_convergence_loss"] = float(sc_loss) losses_dict["log_stft_magnitude_loss"] = float(mag_loss) # sub band Multi-resolution stft loss # (B, subbands, T // subbands) wav_mb = self.criterion_pqmf.analysis(wav) sub_sc_loss, sub_mag_loss = self.criterion_sub_stft(wav_mb_, wav_mb) # Eq.(9) in paper gen_loss += 0.5 * (sub_sc_loss + sub_mag_loss) report("train/sub_spectral_convergence_loss", float(sub_sc_loss)) report("train/sub_log_stft_magnitude_loss", float(sub_mag_loss)) losses_dict["sub_spectral_convergence_loss"] = float(sub_sc_loss) losses_dict["sub_log_stft_magnitude_loss"] = float(sub_mag_loss) ## Adversarial loss if self.state.iteration > self.discriminator_train_start_steps: p_ = self.discriminator(wav_) adv_loss = self.criterion_gen_adv(p_) report("train/adversarial_loss", float(adv_loss)) losses_dict["adversarial_loss"] = float(adv_loss) gen_loss += self.lambda_adv * adv_loss report("train/generator_loss", float(gen_loss)) losses_dict["generator_loss"] = float(gen_loss) self.optimizer_g.clear_grad() gen_loss.backward() self.optimizer_g.step() self.scheduler_g.step() # Disctiminator if self.state.iteration > self.discriminator_train_start_steps: # re-compute wav_ which leads better quality with paddle.no_grad(): wav_ = self.generator(mel) wav_ = self.criterion_pqmf.synthesis(wav_) p = self.discriminator(wav) p_ = self.discriminator(wav_.detach()) real_loss, fake_loss = self.criterion_dis_adv(p_, p) dis_loss = real_loss + fake_loss report("train/real_loss", float(real_loss)) report("train/fake_loss", float(fake_loss)) report("train/discriminator_loss", float(dis_loss)) losses_dict["real_loss"] = float(real_loss) losses_dict["fake_loss"] = float(fake_loss) losses_dict["discriminator_loss"] = float(dis_loss) self.optimizer_d.clear_grad() dis_loss.backward() self.optimizer_d.step() self.scheduler_d.step() self.msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in losses_dict.items()) class MBMelGANEvaluator(StandardEvaluator): def __init__(self, models: Dict[str, Layer], criterions: Dict[str, Layer], dataloader: DataLoader, lambda_adv: float, output_dir: Path=None): self.models = models self.generator = models['generator'] self.discriminator = models['discriminator'] self.criterions = criterions self.criterion_stft = criterions['stft'] self.criterion_sub_stft = criterions['sub_stft'] self.criterion_pqmf = criterions['pqmf'] self.criterion_gen_adv = criterions["gen_adv"] self.criterion_dis_adv = criterions["dis_adv"] self.dataloader = dataloader self.lambda_adv = lambda_adv log_file = output_dir / 'worker_{}.log'.format(dist.get_rank()) self.filehandler = logging.FileHandler(str(log_file)) logger.addHandler(self.filehandler) self.logger = logger self.msg = "" def evaluate_core(self, batch): # logging.debug("Evaluate: ") self.msg = "Evaluate: " losses_dict = {} wav, mel = batch # Generator # (B, out_channels, T ** prod(upsample_scales) wav_ = self.generator(mel) wav_mb_ = wav_ # (B, 1, out_channels*T ** prod(upsample_scales) wav_ = self.criterion_pqmf.synthesis(wav_mb_) ## Adversarial loss p_ = self.discriminator(wav_) adv_loss = self.criterion_gen_adv(p_) report("eval/adversarial_loss", float(adv_loss)) losses_dict["adversarial_loss"] = float(adv_loss) gen_loss = self.lambda_adv * adv_loss # Multi-resolution stft loss sc_loss, mag_loss = self.criterion_stft(wav_, wav) # Eq.(9) in paper gen_loss += 0.5 * (sc_loss + mag_loss) report("eval/spectral_convergence_loss", float(sc_loss)) report("eval/log_stft_magnitude_loss", float(mag_loss)) losses_dict["spectral_convergence_loss"] = float(sc_loss) losses_dict["log_stft_magnitude_loss"] = float(mag_loss) # sub band Multi-resolution stft loss # (B, subbands, T // subbands) wav_mb = self.criterion_pqmf.analysis(wav) sub_sc_loss, sub_mag_loss = self.criterion_sub_stft(wav_mb_, wav_mb) # Eq.(9) in paper gen_loss += 0.5 * (sub_sc_loss + sub_mag_loss) report("eval/sub_spectral_convergence_loss", float(sub_sc_loss)) report("eval/sub_log_stft_magnitude_loss", float(sub_mag_loss)) losses_dict["sub_spectral_convergence_loss"] = float(sub_sc_loss) losses_dict["sub_log_stft_magnitude_loss"] = float(sub_mag_loss) report("eval/generator_loss", float(gen_loss)) losses_dict["generator_loss"] = float(gen_loss) # Disctiminator p = self.discriminator(wav) real_loss, fake_loss = self.criterion_dis_adv(p_, p) dis_loss = real_loss + fake_loss report("eval/real_loss", float(real_loss)) report("eval/fake_loss", float(fake_loss)) report("eval/discriminator_loss", float(dis_loss)) losses_dict["real_loss"] = float(real_loss) losses_dict["fake_loss"] = float(fake_loss) losses_dict["discriminator_loss"] = float(dis_loss) self.msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in losses_dict.items()) self.logger.info(self.msg)