# 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 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 HiFiGANUpdater(StandardUpdater): def __init__(self, models: Dict[str, Layer], optimizers: Dict[str, Optimizer], criterions: Dict[str, Layer], schedulers: Dict[str, LRScheduler], dataloader: DataLoader, generator_train_start_steps: int=0, discriminator_train_start_steps: int=100000, lambda_adv: float=1.0, lambda_aux: float=1.0, lambda_feat_match: float=1.0, output_dir=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_feat_match = criterions['feat_match'] self.criterion_mel = criterions['mel'] 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.generator_train_start_steps = generator_train_start_steps self.discriminator_train_start_steps = discriminator_train_start_steps self.lambda_adv = lambda_adv self.lambda_aux = lambda_aux self.lambda_feat_match = lambda_feat_match 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 if self.state.iteration > self.generator_train_start_steps: # (B, out_channels, T ** prod(upsample_scales) wav_ = self.generator(mel) # initialize gen_loss = 0.0 aux_loss = 0.0 # mel spectrogram loss mel_loss = self.criterion_mel(wav_, wav) aux_loss += mel_loss report("train/mel_loss", float(mel_loss)) losses_dict["mel_loss"] = float(mel_loss) gen_loss += aux_loss * self.lambda_aux # 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) # feature matching loss # no need to track gradients with paddle.no_grad(): p = self.discriminator(wav) fm_loss = self.criterion_feat_match(p_, p) report("train/feature_matching_loss", float(fm_loss)) losses_dict["feature_matching_loss"] = float(fm_loss) adv_loss += self.lambda_feat_match * fm_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) 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 HiFiGANEvaluator(StandardEvaluator): def __init__(self, models: Dict[str, Layer], criterions: Dict[str, Layer], dataloader: DataLoader, lambda_adv: float=1.0, lambda_aux: float=1.0, lambda_feat_match: float=1.0, output_dir=None): self.models = models self.generator = models['generator'] self.discriminator = models['discriminator'] self.criterions = criterions self.criterion_feat_match = criterions['feat_match'] self.criterion_mel = criterions['mel'] self.criterion_gen_adv = criterions["gen_adv"] self.criterion_dis_adv = criterions["dis_adv"] self.dataloader = dataloader self.lambda_adv = lambda_adv self.lambda_aux = lambda_aux self.lambda_feat_match = lambda_feat_match 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) # initialize gen_loss = 0.0 aux_loss = 0.0 ## 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) # feature matching loss p = self.discriminator(wav) fm_loss = self.criterion_feat_match(p_, p) report("eval/feature_matching_loss", float(fm_loss)) losses_dict["feature_matching_loss"] = float(fm_loss) adv_loss += self.lambda_feat_match * fm_loss gen_loss += self.lambda_adv * adv_loss # mel spectrogram loss mel_loss = self.criterion_mel(wav_, wav) aux_loss += mel_loss report("eval/mel_loss", float(mel_loss)) losses_dict["mel_loss"] = float(mel_loss) gen_loss += aux_loss * self.lambda_aux 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)