# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
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# of this software and associated documentation files (the "Software"), to deal
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# SOFTWARE.
from torch import Tensor
from collections import OrderedDict
from typing import Dict
from omegaconf import DictConfig
from openspeech.models import OpenspeechModel
from openspeech.utils import get_class_name
from openspeech.tokenizers.tokenizer import Tokenizer
[docs]class OpenspeechEncoderDecoderModel(OpenspeechModel):
r"""
Base class for OpenSpeech's encoder-decoder models.
Args:
configs (DictConfig): configuration set.
tokenizer (Tokenizer): tokenizer is in charge of preparing the inputs for a model.
Inputs:
- **inputs** (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
- **input_lengths** (torch.LongTensor): The length of input tensor. ``(batch)``
Returns:
outputs (dict): Result of model predictions that contains `predictions`, `logits`, `encoder_outputs`,
`encoder_logits`, `encoder_output_lengths`.
"""
def __init__(self, configs: DictConfig, tokenizer: Tokenizer, ) -> None:
super(OpenspeechEncoderDecoderModel, self).__init__(configs, tokenizer)
self.teacher_forcing_ratio = configs.model.teacher_forcing_ratio
self.encoder = None
self.decoder = None
self.criterion = self.configure_criterion(self.configs.criterion.criterion_name)
def set_beam_decoder(self, beam_size: int = 3):
raise NotImplementedError
def collect_outputs(
self,
stage: str,
logits: Tensor,
encoder_logits: Tensor,
encoder_output_lengths: Tensor,
targets: Tensor,
target_lengths: Tensor,
) -> OrderedDict:
cross_entropy_loss, ctc_loss = None, None
if get_class_name(self.criterion) == "JointCTCCrossEntropyLoss":
loss, ctc_loss, cross_entropy_loss = self.criterion(
encoder_logits=encoder_logits.transpose(0, 1),
logits=logits,
output_lengths=encoder_output_lengths,
targets=targets[:, 1:],
target_lengths=target_lengths,
)
elif get_class_name(self.criterion) == "LabelSmoothedCrossEntropyLoss" \
or get_class_name(self.criterion) == "CrossEntropyLoss":
loss = self.criterion(logits, targets[:, 1:])
else:
raise ValueError(f"Unsupported criterion: {self.criterion}")
predictions = logits.max(-1)[1]
wer = self.wer_metric(targets[:, 1:], predictions)
cer = self.cer_metric(targets[:, 1:], predictions)
self.info({
f"{stage}_loss": loss,
f"{stage}_cross_entropy_loss": cross_entropy_loss,
f"{stage}_ctc_loss": ctc_loss,
f"{stage}_wer": wer,
f"{stage}_cer": cer,
})
return OrderedDict({
"loss": loss,
"cross_entropy_loss": cross_entropy_loss,
"ctc_loss": ctc_loss,
"predictions": predictions,
"targets": targets,
"logits": logits,
"learning_rate": self.get_lr(),
})
[docs] def forward(self, inputs: Tensor, input_lengths: Tensor) -> Dict[str, Tensor]:
r"""
Forward propagate a `inputs` and `targets` pair for inference.
Inputs:
inputs (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
input_lengths (torch.LongTensor): The length of input tensor. ``(batch)``
Returns:
outputs (dict): Result of model predictions that contains `predictions`, `logits`, `encoder_outputs`,
`encoder_logits`, `encoder_output_lengths`.
"""
logits = None
encoder_outputs, encoder_logits, encoder_output_lengths = self.encoder(inputs, input_lengths)
if get_class_name(self.decoder) in ("BeamSearchLSTM", "BeamSearchTransformer"):
predictions = self.decoder(encoder_outputs, encoder_output_lengths)
else:
logits = self.decoder(
encoder_outputs=encoder_outputs,
encoder_output_lengths=encoder_output_lengths,
teacher_forcing_ratio=0.0,
)
predictions = logits.max(-1)[1]
return {
"predictions": predictions,
"logits": logits,
"encoder_outputs": encoder_outputs,
"encoder_logits": encoder_logits,
"encoder_output_lengths": encoder_output_lengths,
}
[docs] def training_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
r"""
Forward propagate a `inputs` and `targets` pair for training.
Inputs:
train_batch (tuple): A train batch contains `inputs`, `targets`, `input_lengths`, `target_lengths`
batch_idx (int): The index of batch
Returns:
loss (torch.Tensor): loss for training
"""
inputs, targets, input_lengths, target_lengths = batch
encoder_outputs, encoder_logits, encoder_output_lengths = self.encoder(inputs, input_lengths)
if get_class_name(self.decoder) == "TransformerDecoder":
logits = self.decoder(
encoder_outputs=encoder_outputs,
targets=targets,
encoder_output_lengths=encoder_output_lengths,
target_lengths=target_lengths,
teacher_forcing_ratio=self.teacher_forcing_ratio,
)
else:
logits = self.decoder(
encoder_outputs=encoder_outputs,
targets=targets,
encoder_output_lengths=encoder_output_lengths,
teacher_forcing_ratio=self.teacher_forcing_ratio,
)
return self.collect_outputs(
stage='train',
logits=logits,
encoder_logits=encoder_logits,
encoder_output_lengths=encoder_output_lengths,
targets=targets,
target_lengths=target_lengths,
)
[docs] def validation_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
r"""
Forward propagate a `inputs` and `targets` pair for validation.
Inputs:
train_batch (tuple): A train batch contains `inputs`, `targets`, `input_lengths`, `target_lengths`
batch_idx (int): The index of batch
Returns:
loss (torch.Tensor): loss for training
"""
inputs, targets, input_lengths, target_lengths = batch
encoder_outputs, encoder_logits, encoder_output_lengths = self.encoder(inputs, input_lengths)
logits = self.decoder(
encoder_outputs,
encoder_output_lengths=encoder_output_lengths,
teacher_forcing_ratio=0.0,
)
return self.collect_outputs(
stage='val',
logits=logits,
encoder_logits=encoder_logits,
encoder_output_lengths=encoder_output_lengths,
targets=targets,
target_lengths=target_lengths,
)
[docs] def test_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
r"""
Forward propagate a `inputs` and `targets` pair for test.
Inputs:
train_batch (tuple): A train batch contains `inputs`, `targets`, `input_lengths`, `target_lengths`
batch_idx (int): The index of batch
Returns:
loss (torch.Tensor): loss for training
"""
inputs, targets, input_lengths, target_lengths = batch
encoder_outputs, encoder_logits, encoder_output_lengths = self.encoder(inputs, input_lengths)
logits = self.decoder(
encoder_outputs,
encoder_output_lengths=encoder_output_lengths,
teacher_forcing_ratio=0.0,
)
return self.collect_outputs(
stage='test',
logits=logits,
encoder_logits=encoder_logits,
encoder_output_lengths=encoder_output_lengths,
targets=targets,
target_lengths=target_lengths,
)