# 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 paddle import distributed as dist from paddle.io import DataLoader from paddle.nn import Layer from paddle.optimizer import Optimizer from paddlespeech.t2s.modules.losses import GuidedAttentionLoss from paddlespeech.t2s.modules.losses import Tacotron2Loss from paddlespeech.t2s.training.extensions.evaluator import StandardEvaluator from paddlespeech.t2s.training.reporter import report from paddlespeech.t2s.training.updaters.standard_updater import StandardUpdater 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 Tacotron2Updater(StandardUpdater): def __init__(self, model: Layer, optimizer: Optimizer, dataloader: DataLoader, init_state=None, use_masking: bool=True, use_weighted_masking: bool=False, bce_pos_weight: float=5.0, loss_type: str="L1+L2", use_guided_attn_loss: bool=True, guided_attn_loss_sigma: float=0.4, guided_attn_loss_lambda: float=1.0, output_dir: Path=None): super().__init__(model, optimizer, dataloader, init_state=None) self.loss_type = loss_type self.use_guided_attn_loss = use_guided_attn_loss self.taco2_loss = Tacotron2Loss( use_masking=use_masking, use_weighted_masking=use_weighted_masking, bce_pos_weight=bce_pos_weight, ) if self.use_guided_attn_loss: self.attn_loss = GuidedAttentionLoss( sigma=guided_attn_loss_sigma, alpha=guided_attn_loss_lambda, ) 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 = {} # spk_id!=None in multiple spk fastspeech2 spk_id = batch["spk_id"] if "spk_id" in batch else None spk_emb = batch["spk_emb"] if "spk_emb" in batch else None if spk_emb is not None: spk_id = None after_outs, before_outs, logits, ys, stop_labels, olens, att_ws, olens_in = self.model( text=batch["text"], text_lengths=batch["text_lengths"], speech=batch["speech"], speech_lengths=batch["speech_lengths"], spk_id=spk_id, spk_emb=spk_emb) # calculate taco2 loss l1_loss, mse_loss, bce_loss = self.taco2_loss( after_outs=after_outs, before_outs=before_outs, logits=logits, ys=ys, stop_labels=stop_labels, olens=olens) if self.loss_type == "L1+L2": loss = l1_loss + mse_loss + bce_loss elif self.loss_type == "L1": loss = l1_loss + bce_loss elif self.loss_type == "L2": loss = mse_loss + bce_loss else: raise ValueError(f"unknown --loss-type {self.loss_type}") # calculate attention loss if self.use_guided_attn_loss: # NOTE: length of output for auto-regressive # input will be changed when r > 1 attn_loss = self.attn_loss( att_ws=att_ws, ilens=batch["text_lengths"] + 1, olens=olens_in) loss = loss + attn_loss optimizer = self.optimizer optimizer.clear_grad() loss.backward() optimizer.step() report("train/l1_loss", float(l1_loss)) report("train/mse_loss", float(mse_loss)) report("train/bce_loss", float(bce_loss)) report("train/attn_loss", float(attn_loss)) report("train/loss", float(loss)) losses_dict["l1_loss"] = float(l1_loss) losses_dict["mse_loss"] = float(mse_loss) losses_dict["bce_loss"] = float(bce_loss) losses_dict["attn_loss"] = float(attn_loss) losses_dict["loss"] = float(loss) self.msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in losses_dict.items()) class Tacotron2Evaluator(StandardEvaluator): def __init__(self, model: Layer, dataloader: DataLoader, use_masking: bool=True, use_weighted_masking: bool=False, bce_pos_weight: float=5.0, loss_type: str="L1+L2", use_guided_attn_loss: bool=True, guided_attn_loss_sigma: float=0.4, guided_attn_loss_lambda: float=1.0, output_dir=None): super().__init__(model, dataloader) self.loss_type = loss_type self.use_guided_attn_loss = use_guided_attn_loss self.taco2_loss = Tacotron2Loss( use_masking=use_masking, use_weighted_masking=use_weighted_masking, bce_pos_weight=bce_pos_weight, ) if self.use_guided_attn_loss: self.attn_loss = GuidedAttentionLoss( sigma=guided_attn_loss_sigma, alpha=guided_attn_loss_lambda, ) 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): self.msg = "Evaluate: " losses_dict = {} # spk_id!=None in multiple spk fastspeech2 spk_id = batch["spk_id"] if "spk_id" in batch else None spk_emb = batch["spk_emb"] if "spk_emb" in batch else None if spk_emb is not None: spk_id = None after_outs, before_outs, logits, ys, stop_labels, olens, att_ws, olens_in = self.model( text=batch["text"], text_lengths=batch["text_lengths"], speech=batch["speech"], speech_lengths=batch["speech_lengths"], spk_id=spk_id, spk_emb=spk_emb) # calculate taco2 loss l1_loss, mse_loss, bce_loss = self.taco2_loss( after_outs=after_outs, before_outs=before_outs, logits=logits, ys=ys, stop_labels=stop_labels, olens=olens) if self.loss_type == "L1+L2": loss = l1_loss + mse_loss + bce_loss elif self.loss_type == "L1": loss = l1_loss + bce_loss elif self.loss_type == "L2": loss = mse_loss + bce_loss else: raise ValueError(f"unknown --loss-type {self.loss_type}") # calculate attention loss if self.use_guided_attn_loss: # NOTE: length of output for auto-regressive # input will be changed when r > 1 attn_loss = self.attn_loss( att_ws=att_ws, ilens=batch["text_lengths"] + 1, olens=olens_in) loss = loss + attn_loss report("eval/l1_loss", float(l1_loss)) report("eval/mse_loss", float(mse_loss)) report("eval/bce_loss", float(bce_loss)) report("eval/attn_loss", float(attn_loss)) report("eval/loss", float(loss)) losses_dict["l1_loss"] = float(l1_loss) losses_dict["mse_loss"] = float(mse_loss) losses_dict["bce_loss"] = float(bce_loss) losses_dict["attn_loss"] = float(attn_loss) losses_dict["loss"] = float(loss) self.msg += ', '.join('{}: {:>.6f}'.format(k, v) for k, v in losses_dict.items()) self.logger.info(self.msg)