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220 lines
8.2 KiB
220 lines
8.2 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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from pathlib import Path
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from paddle import distributed as dist
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from paddle.io import DataLoader
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from paddle.nn import Layer
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from paddle.optimizer import Optimizer
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from paddlespeech.t2s.modules.losses import GuidedAttentionLoss
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from paddlespeech.t2s.modules.losses import Tacotron2Loss
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from paddlespeech.t2s.training.extensions.evaluator import StandardEvaluator
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from paddlespeech.t2s.training.reporter import report
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from paddlespeech.t2s.training.updaters.standard_updater import StandardUpdater
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logging.basicConfig(
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format='%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s',
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datefmt='[%Y-%m-%d %H:%M:%S]')
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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class Tacotron2Updater(StandardUpdater):
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def __init__(self,
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model: Layer,
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optimizer: Optimizer,
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dataloader: DataLoader,
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init_state=None,
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use_masking: bool=True,
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use_weighted_masking: bool=False,
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bce_pos_weight: float=5.0,
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loss_type: str="L1+L2",
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use_guided_attn_loss: bool=True,
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guided_attn_loss_sigma: float=0.4,
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guided_attn_loss_lambda: float=1.0,
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output_dir: Path=None):
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super().__init__(model, optimizer, dataloader, init_state=None)
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self.loss_type = loss_type
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self.use_guided_attn_loss = use_guided_attn_loss
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self.taco2_loss = Tacotron2Loss(
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use_masking=use_masking,
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use_weighted_masking=use_weighted_masking,
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bce_pos_weight=bce_pos_weight, )
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if self.use_guided_attn_loss:
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self.attn_loss = GuidedAttentionLoss(
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sigma=guided_attn_loss_sigma,
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alpha=guided_attn_loss_lambda, )
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log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
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self.filehandler = logging.FileHandler(str(log_file))
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logger.addHandler(self.filehandler)
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self.logger = logger
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self.msg = ""
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def update_core(self, batch):
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self.msg = "Rank: {}, ".format(dist.get_rank())
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losses_dict = {}
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# spk_id!=None in multiple spk fastspeech2
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spk_id = batch["spk_id"] if "spk_id" in batch else None
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spk_emb = batch["spk_emb"] if "spk_emb" in batch else None
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if spk_emb is not None:
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spk_id = None
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after_outs, before_outs, logits, ys, stop_labels, olens, att_ws, olens_in = self.model(
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text=batch["text"],
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text_lengths=batch["text_lengths"],
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speech=batch["speech"],
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speech_lengths=batch["speech_lengths"],
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spk_id=spk_id,
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spk_emb=spk_emb)
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# calculate taco2 loss
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l1_loss, mse_loss, bce_loss = self.taco2_loss(
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after_outs=after_outs,
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before_outs=before_outs,
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logits=logits,
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ys=ys,
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stop_labels=stop_labels,
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olens=olens)
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if self.loss_type == "L1+L2":
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loss = l1_loss + mse_loss + bce_loss
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elif self.loss_type == "L1":
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loss = l1_loss + bce_loss
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elif self.loss_type == "L2":
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loss = mse_loss + bce_loss
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else:
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raise ValueError(f"unknown --loss-type {self.loss_type}")
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# calculate attention loss
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if self.use_guided_attn_loss:
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# NOTE: length of output for auto-regressive
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# input will be changed when r > 1
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attn_loss = self.attn_loss(
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att_ws=att_ws, ilens=batch["text_lengths"] + 1, olens=olens_in)
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loss = loss + attn_loss
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optimizer = self.optimizer
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optimizer.clear_grad()
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loss.backward()
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optimizer.step()
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report("train/l1_loss", float(l1_loss))
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report("train/mse_loss", float(mse_loss))
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report("train/bce_loss", float(bce_loss))
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report("train/attn_loss", float(attn_loss))
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report("train/loss", float(loss))
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losses_dict["l1_loss"] = float(l1_loss)
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losses_dict["mse_loss"] = float(mse_loss)
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losses_dict["bce_loss"] = float(bce_loss)
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losses_dict["attn_loss"] = float(attn_loss)
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losses_dict["loss"] = float(loss)
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self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
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for k, v in losses_dict.items())
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class Tacotron2Evaluator(StandardEvaluator):
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def __init__(self,
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model: Layer,
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dataloader: DataLoader,
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use_masking: bool=True,
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use_weighted_masking: bool=False,
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bce_pos_weight: float=5.0,
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loss_type: str="L1+L2",
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use_guided_attn_loss: bool=True,
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guided_attn_loss_sigma: float=0.4,
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guided_attn_loss_lambda: float=1.0,
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output_dir=None):
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super().__init__(model, dataloader)
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self.loss_type = loss_type
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self.use_guided_attn_loss = use_guided_attn_loss
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self.taco2_loss = Tacotron2Loss(
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use_masking=use_masking,
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use_weighted_masking=use_weighted_masking,
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bce_pos_weight=bce_pos_weight, )
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if self.use_guided_attn_loss:
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self.attn_loss = GuidedAttentionLoss(
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sigma=guided_attn_loss_sigma,
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alpha=guided_attn_loss_lambda, )
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log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
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self.filehandler = logging.FileHandler(str(log_file))
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logger.addHandler(self.filehandler)
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self.logger = logger
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self.msg = ""
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def evaluate_core(self, batch):
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self.msg = "Evaluate: "
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losses_dict = {}
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# spk_id!=None in multiple spk fastspeech2
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spk_id = batch["spk_id"] if "spk_id" in batch else None
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spk_emb = batch["spk_emb"] if "spk_emb" in batch else None
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if spk_emb is not None:
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spk_id = None
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after_outs, before_outs, logits, ys, stop_labels, olens, att_ws, olens_in = self.model(
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text=batch["text"],
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text_lengths=batch["text_lengths"],
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speech=batch["speech"],
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speech_lengths=batch["speech_lengths"],
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spk_id=spk_id,
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spk_emb=spk_emb)
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# calculate taco2 loss
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l1_loss, mse_loss, bce_loss = self.taco2_loss(
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after_outs=after_outs,
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before_outs=before_outs,
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logits=logits,
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ys=ys,
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stop_labels=stop_labels,
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olens=olens)
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if self.loss_type == "L1+L2":
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loss = l1_loss + mse_loss + bce_loss
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elif self.loss_type == "L1":
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loss = l1_loss + bce_loss
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elif self.loss_type == "L2":
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loss = mse_loss + bce_loss
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else:
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raise ValueError(f"unknown --loss-type {self.loss_type}")
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# calculate attention loss
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if self.use_guided_attn_loss:
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# NOTE: length of output for auto-regressive
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# input will be changed when r > 1
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attn_loss = self.attn_loss(
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att_ws=att_ws, ilens=batch["text_lengths"] + 1, olens=olens_in)
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loss = loss + attn_loss
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report("eval/l1_loss", float(l1_loss))
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report("eval/mse_loss", float(mse_loss))
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report("eval/bce_loss", float(bce_loss))
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report("eval/attn_loss", float(attn_loss))
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report("eval/loss", float(loss))
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losses_dict["l1_loss"] = float(l1_loss)
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losses_dict["mse_loss"] = float(mse_loss)
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losses_dict["bce_loss"] = float(bce_loss)
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losses_dict["attn_loss"] = float(attn_loss)
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losses_dict["loss"] = float(loss)
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self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
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for k, v in losses_dict.items())
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self.logger.info(self.msg)
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