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360 lines
15 KiB
360 lines
15 KiB
# Copyright (c) 2022 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 typing import Dict
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import paddle
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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 paddle.optimizer.lr import LRScheduler
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from paddlespeech.t2s.modules.nets_utils import get_segments
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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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from paddlespeech.t2s.training.updaters.standard_updater import UpdaterState
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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 VITSUpdater(StandardUpdater):
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def __init__(self,
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model: Layer,
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optimizers: Dict[str, Optimizer],
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criterions: Dict[str, Layer],
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schedulers: Dict[str, LRScheduler],
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dataloader: DataLoader,
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generator_train_start_steps: int=0,
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discriminator_train_start_steps: int=100000,
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lambda_adv: float=1.0,
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lambda_mel: float=45.0,
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lambda_feat_match: float=2.0,
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lambda_dur: float=1.0,
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lambda_kl: float=1.0,
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generator_first: bool=False,
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output_dir=None):
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# it is designed to hold multiple models
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# 因为输入的是单模型，但是没有用到父类的 init(), 所以需要重新写这部分
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models = {"main": model}
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self.models: Dict[str, Layer] = models
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# self.model = model
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self.model = model._layers if isinstance(model,
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paddle.DataParallel) else model
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self.optimizers = optimizers
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self.optimizer_g: Optimizer = optimizers['generator']
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self.optimizer_d: Optimizer = optimizers['discriminator']
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self.criterions = criterions
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self.criterion_mel = criterions['mel']
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self.criterion_feat_match = criterions['feat_match']
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self.criterion_gen_adv = criterions["gen_adv"]
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self.criterion_dis_adv = criterions["dis_adv"]
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self.criterion_kl = criterions["kl"]
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self.schedulers = schedulers
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self.scheduler_g = schedulers['generator']
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self.scheduler_d = schedulers['discriminator']
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self.dataloader = dataloader
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self.generator_train_start_steps = generator_train_start_steps
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self.discriminator_train_start_steps = discriminator_train_start_steps
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self.lambda_adv = lambda_adv
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self.lambda_mel = lambda_mel
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self.lambda_feat_match = lambda_feat_match
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self.lambda_dur = lambda_dur
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self.lambda_kl = lambda_kl
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if generator_first:
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self.turns = ["generator", "discriminator"]
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else:
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self.turns = ["discriminator", "generator"]
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self.state = UpdaterState(iteration=0, epoch=0)
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self.train_iterator = iter(self.dataloader)
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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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for turn in self.turns:
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speech = batch["speech"]
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speech = speech.unsqueeze(1)
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outs = self.model(
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text=batch["text"],
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text_lengths=batch["text_lengths"],
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feats=batch["feats"],
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feats_lengths=batch["feats_lengths"],
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sids=batch.get("spk_id", None),
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spembs=batch.get("spk_emb", None),
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forward_generator=turn == "generator")
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# Generator
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if turn == "generator":
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# parse outputs
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speech_hat_, dur_nll, _, start_idxs, _, z_mask, outs_ = outs
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_, z_p, m_p, logs_p, _, logs_q = outs_
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speech_ = get_segments(
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x=speech,
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start_idxs=start_idxs *
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self.model.generator.upsample_factor,
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segment_size=self.model.generator.segment_size *
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self.model.generator.upsample_factor, )
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# calculate discriminator outputs
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p_hat = self.model.discriminator(speech_hat_)
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with paddle.no_grad():
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# do not store discriminator gradient in generator turn
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p = self.model.discriminator(speech_)
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# calculate losses
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mel_loss = self.criterion_mel(speech_hat_, speech_)
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kl_loss = self.criterion_kl(z_p, logs_q, m_p, logs_p, z_mask)
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dur_loss = paddle.sum(dur_nll)
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adv_loss = self.criterion_gen_adv(p_hat)
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feat_match_loss = self.criterion_feat_match(p_hat, p)
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mel_loss = mel_loss * self.lambda_mel
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kl_loss = kl_loss * self.lambda_kl
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dur_loss = dur_loss * self.lambda_dur
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adv_loss = adv_loss * self.lambda_adv
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feat_match_loss = feat_match_loss * self.lambda_feat_match
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gen_loss = mel_loss + kl_loss + dur_loss + adv_loss + feat_match_loss
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report("train/generator_loss", float(gen_loss))
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report("train/generator_mel_loss", float(mel_loss))
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report("train/generator_kl_loss", float(kl_loss))
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report("train/generator_dur_loss", float(dur_loss))
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report("train/generator_adv_loss", float(adv_loss))
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report("train/generator_feat_match_loss",
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float(feat_match_loss))
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losses_dict["generator_loss"] = float(gen_loss)
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losses_dict["generator_mel_loss"] = float(mel_loss)
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losses_dict["generator_kl_loss"] = float(kl_loss)
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losses_dict["generator_dur_loss"] = float(dur_loss)
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losses_dict["generator_adv_loss"] = float(adv_loss)
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losses_dict["generator_feat_match_loss"] = float(
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feat_match_loss)
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self.optimizer_g.clear_grad()
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gen_loss.backward()
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self.optimizer_g.step()
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self.scheduler_g.step()
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# reset cache
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if self.model.reuse_cache_gen or not self.model.training:
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self.model._cache = None
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# Disctiminator
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elif turn == "discriminator":
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# parse outputs
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speech_hat_, _, _, start_idxs, *_ = outs
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speech_ = get_segments(
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x=speech,
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start_idxs=start_idxs *
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self.model.generator.upsample_factor,
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segment_size=self.model.generator.segment_size *
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self.model.generator.upsample_factor, )
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# calculate discriminator outputs
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p_hat = self.model.discriminator(speech_hat_.detach())
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p = self.model.discriminator(speech_)
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# calculate losses
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real_loss, fake_loss = self.criterion_dis_adv(p_hat, p)
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dis_loss = real_loss + fake_loss
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report("train/real_loss", float(real_loss))
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report("train/fake_loss", float(fake_loss))
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report("train/discriminator_loss", float(dis_loss))
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losses_dict["real_loss"] = float(real_loss)
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losses_dict["fake_loss"] = float(fake_loss)
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losses_dict["discriminator_loss"] = float(dis_loss)
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self.optimizer_d.clear_grad()
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dis_loss.backward()
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self.optimizer_d.step()
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self.scheduler_d.step()
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# reset cache
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if self.model.reuse_cache_dis or not self.model.training:
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self.model._cache = None
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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 VITSEvaluator(StandardEvaluator):
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def __init__(self,
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model,
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criterions: Dict[str, Layer],
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dataloader: DataLoader,
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lambda_adv: float=1.0,
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lambda_mel: float=45.0,
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lambda_feat_match: float=2.0,
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lambda_dur: float=1.0,
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lambda_kl: float=1.0,
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generator_first: bool=False,
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output_dir=None):
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# 因为输入的是单模型，但是没有用到父类的 init(), 所以需要重新写这部分
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models = {"main": model}
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self.models: Dict[str, Layer] = models
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# self.model = model
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self.model = model._layers if isinstance(model,
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paddle.DataParallel) else model
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self.criterions = criterions
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self.criterion_mel = criterions['mel']
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self.criterion_feat_match = criterions['feat_match']
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self.criterion_gen_adv = criterions["gen_adv"]
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self.criterion_dis_adv = criterions["dis_adv"]
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self.criterion_kl = criterions["kl"]
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self.dataloader = dataloader
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self.lambda_adv = lambda_adv
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self.lambda_mel = lambda_mel
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self.lambda_feat_match = lambda_feat_match
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self.lambda_dur = lambda_dur
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self.lambda_kl = lambda_kl
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if generator_first:
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self.turns = ["generator", "discriminator"]
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else:
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self.turns = ["discriminator", "generator"]
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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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# logging.debug("Evaluate: ")
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self.msg = "Evaluate: "
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losses_dict = {}
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for turn in self.turns:
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speech = batch["speech"]
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speech = speech.unsqueeze(1)
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outs = self.model(
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text=batch["text"],
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text_lengths=batch["text_lengths"],
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feats=batch["feats"],
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feats_lengths=batch["feats_lengths"],
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sids=batch.get("spk_id", None),
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spembs=batch.get("spk_emb", None),
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forward_generator=turn == "generator")
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# Generator
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if turn == "generator":
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# parse outputs
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speech_hat_, dur_nll, _, start_idxs, _, z_mask, outs_ = outs
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_, z_p, m_p, logs_p, _, logs_q = outs_
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speech_ = get_segments(
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x=speech,
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start_idxs=start_idxs *
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self.model.generator.upsample_factor,
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segment_size=self.model.generator.segment_size *
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self.model.generator.upsample_factor, )
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# calculate discriminator outputs
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p_hat = self.model.discriminator(speech_hat_)
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with paddle.no_grad():
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# do not store discriminator gradient in generator turn
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p = self.model.discriminator(speech_)
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# calculate losses
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mel_loss = self.criterion_mel(speech_hat_, speech_)
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kl_loss = self.criterion_kl(z_p, logs_q, m_p, logs_p, z_mask)
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dur_loss = paddle.sum(dur_nll)
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adv_loss = self.criterion_gen_adv(p_hat)
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feat_match_loss = self.criterion_feat_match(p_hat, p)
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mel_loss = mel_loss * self.lambda_mel
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kl_loss = kl_loss * self.lambda_kl
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dur_loss = dur_loss * self.lambda_dur
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adv_loss = adv_loss * self.lambda_adv
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feat_match_loss = feat_match_loss * self.lambda_feat_match
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gen_loss = mel_loss + kl_loss + dur_loss + adv_loss + feat_match_loss
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report("eval/generator_loss", float(gen_loss))
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report("eval/generator_mel_loss", float(mel_loss))
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report("eval/generator_kl_loss", float(kl_loss))
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report("eval/generator_dur_loss", float(dur_loss))
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report("eval/generator_adv_loss", float(adv_loss))
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report("eval/generator_feat_match_loss", float(feat_match_loss))
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losses_dict["generator_loss"] = float(gen_loss)
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losses_dict["generator_mel_loss"] = float(mel_loss)
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losses_dict["generator_kl_loss"] = float(kl_loss)
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losses_dict["generator_dur_loss"] = float(dur_loss)
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losses_dict["generator_adv_loss"] = float(adv_loss)
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losses_dict["generator_feat_match_loss"] = float(
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feat_match_loss)
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# reset cache
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if self.model.reuse_cache_gen or not self.model.training:
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self.model._cache = None
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# Disctiminator
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elif turn == "discriminator":
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# parse outputs
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speech_hat_, _, _, start_idxs, *_ = outs
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speech_ = get_segments(
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x=speech,
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start_idxs=start_idxs *
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self.model.generator.upsample_factor,
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segment_size=self.model.generator.segment_size *
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self.model.generator.upsample_factor, )
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# calculate discriminator outputs
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p_hat = self.model.discriminator(speech_hat_.detach())
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p = self.model.discriminator(speech_)
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# calculate losses
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real_loss, fake_loss = self.criterion_dis_adv(p_hat, p)
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dis_loss = real_loss + fake_loss
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report("eval/real_loss", float(real_loss))
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report("eval/fake_loss", float(fake_loss))
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report("eval/discriminator_loss", float(dis_loss))
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losses_dict["real_loss"] = float(real_loss)
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losses_dict["fake_loss"] = float(fake_loss)
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losses_dict["discriminator_loss"] = float(dis_loss)
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# reset cache
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if self.model.reuse_cache_dis or not self.model.training:
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self.model._cache = None
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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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