Source code for openspeech.models.conformer_transducer.model
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# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
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from omegaconf import DictConfig
from torch import Tensor
from typing import Dict
from collections import OrderedDict
from openspeech.models import register_model, OpenspeechTransducerModel
from openspeech.models.conformer.model import ConformerEncoder
from openspeech.models.conformer_transducer.configurations import ConformerTransducerConfigs
from openspeech.models.rnn_transducer.model import RNNTransducerDecoder
from openspeech.vocabs.vocab import Vocabulary
[docs]@register_model('conformer_transducer', dataclass=ConformerTransducerConfigs)
class ConformerTransducerModel(OpenspeechTransducerModel):
r"""
Conformer: Convolution-augmented Transformer for Speech Recognition
Paper: https://arxiv.org/abs/2005.08100
Args:
configs (DictConfig): configuraion set
vocab (Vocabulary): vocab of training data
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.
"""
def __init__(self, configs: DictConfig, vocab: Vocabulary, ) -> None:
super(ConformerTransducerModel, self).__init__(configs, vocab)
def build_model(self):
self.encoder = ConformerEncoder(
num_classes=self.num_classes,
input_dim=self.configs.audio.num_mels,
encoder_dim=self.configs.model.encoder_dim,
num_layers=self.configs.model.num_encoder_layers,
num_attention_heads=self.configs.model.num_attention_heads,
feed_forward_expansion_factor=self.configs.model.feed_forward_expansion_factor,
conv_expansion_factor=self.configs.model.conv_expansion_factor,
input_dropout_p=self.configs.model.input_dropout_p,
feed_forward_dropout_p=self.configs.model.feed_forward_dropout_p,
attention_dropout_p=self.configs.model.attention_dropout_p,
conv_dropout_p=self.configs.model.conv_dropout_p,
conv_kernel_size=self.configs.model.conv_kernel_size,
half_step_residual=self.configs.model.half_step_residual,
joint_ctc_attention=False,
)
self.decoder = RNNTransducerDecoder(
num_classes=self.num_classes,
hidden_state_dim=self.configs.model.decoder_hidden_state_dim,
output_dim=self.configs.model.decoder_output_dim,
num_layers=self.configs.model.num_decoder_layers,
rnn_type=self.configs.model.rnn_type,
sos_id=self.vocab.sos_id,
eos_id=self.vocab.eos_id,
dropout_p=self.configs.model.decoder_dropout_p,
)
[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.
"""
return super(ConformerTransducerModel, self).forward(inputs, input_lengths)
[docs] def training_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
r"""
Forward propagate a `inputs` and `targets` pair for training.
Inputs:
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
"""
return super(ConformerTransducerModel, self).training_step(batch, batch_idx)
[docs] def validation_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
r"""
Forward propagate a `inputs` and `targets` pair for validation.
Inputs:
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
"""
return super(ConformerTransducerModel, self).validation_step(batch, batch_idx)
[docs] def test_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
r"""
Forward propagate a `inputs` and `targets` pair for test.
Inputs:
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
"""
return super(ConformerTransducerModel, self).test_step(batch, batch_idx)