115 lines
4.2 KiB
Python
115 lines
4.2 KiB
Python
# 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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from parakeet.models.fastspeech2 import FastSpeech2Loss
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from parakeet.training.extensions.evaluator import StandardEvaluator
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from parakeet.training.reporter import report
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from parakeet.training.updaters.standard_updater import StandardUpdater
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class FastSpeech2Updater(StandardUpdater):
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def __init__(self,
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model,
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optimizer,
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dataloader,
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init_state=None,
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use_masking=False,
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use_weighted_masking=False):
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super().__init__(model, optimizer, dataloader, init_state=None)
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self.use_masking = use_masking
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self.use_weighted_masking = use_weighted_masking
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def update_core(self, batch):
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before_outs, after_outs, d_outs, p_outs, e_outs, ys, olens = 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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durations=batch["durations"],
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pitch=batch["pitch"],
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energy=batch["energy"], )
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criterion = FastSpeech2Loss(
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use_masking=self.use_masking,
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use_weighted_masking=self.use_weighted_masking)
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l1_loss, duration_loss, pitch_loss, energy_loss = criterion(
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after_outs=after_outs,
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before_outs=before_outs,
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d_outs=d_outs,
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p_outs=p_outs,
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e_outs=e_outs,
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ys=ys,
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ds=batch["durations"],
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ps=batch["pitch"],
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es=batch["energy"],
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ilens=batch["text_lengths"],
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olens=olens)
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loss = l1_loss + duration_loss + pitch_loss + energy_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/loss", float(loss))
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report("train/l1_loss", float(l1_loss))
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report("train/duration_loss", float(duration_loss))
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report("train/pitch_loss", float(pitch_loss))
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report("train/energy_loss", float(energy_loss))
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class FastSpeech2Evaluator(StandardEvaluator):
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def __init__(self,
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model,
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dataloader,
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use_masking=False,
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use_weighted_masking=False):
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super().__init__(model, dataloader)
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self.use_masking = use_masking
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self.use_weighted_masking = use_weighted_masking
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def evaluate_core(self, batch):
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before_outs, after_outs, d_outs, p_outs, e_outs, ys, olens = 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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durations=batch["durations"],
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pitch=batch["pitch"],
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energy=batch["energy"])
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criterion = FastSpeech2Loss(
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use_masking=self.use_masking,
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use_weighted_masking=self.use_weighted_masking)
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l1_loss, duration_loss, pitch_loss, energy_loss = criterion(
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after_outs=after_outs,
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before_outs=before_outs,
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d_outs=d_outs,
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p_outs=p_outs,
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e_outs=e_outs,
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ys=ys,
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ds=batch["durations"],
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ps=batch["pitch"],
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es=batch["energy"],
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ilens=batch["text_lengths"],
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olens=olens, )
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loss = l1_loss + duration_loss + pitch_loss + energy_loss
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report("eval/loss", float(loss))
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report("eval/l1_loss", float(l1_loss))
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report("eval/duration_loss", float(duration_loss))
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report("eval/pitch_loss", float(pitch_loss))
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report("eval/energy_loss", float(energy_loss))
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