Source code for openspeech.models.conformer.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
from openspeech.models import OpenspeechCTCModel
from openspeech.models.conformer.configurations import ConformerConfigs
from openspeech.encoders import ConformerEncoder
from openspeech.modules.wrapper import Linear
from openspeech.vocabs.vocab import Vocabulary


[docs]@register_model('conformer', dataclass=ConformerConfigs) class ConformerModel(OpenspeechCTCModel): r""" Conformer Encoder Only Model. Args: configs (DictConfig): configuration set. vocab (Vocabulary): the class of vocabulary 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: * dict (dict): Result of model predictions that contains `y_hats`, `logits`, `output_lengths` """ def __init__(self, configs: DictConfig, vocab: Vocabulary,) -> None: super(ConformerModel, self).__init__(configs, vocab) self.fc = Linear(self.configs.model.encoder_dim, self.num_classes, bias=False) 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, )
[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: * dict (dict): Result of model predictions that contains `y_hats`, `logits`, `output_lengths` """ return super(ConformerModel, 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 """ inputs, targets, input_lengths, target_lengths = batch encoder_outputs, encoder_logits, output_lengths = self.encoder(inputs, input_lengths) logits = self.fc(encoder_outputs).log_softmax(dim=-1) return self.collect_outputs( stage='train', logits=logits, output_lengths=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: 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, output_lengths = self.encoder(inputs, input_lengths) logits = self.fc(encoder_outputs).log_softmax(dim=-1) return self.collect_outputs( stage='valid', logits=logits, output_lengths=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: 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, output_lengths = self.encoder(inputs, input_lengths) logits = self.fc(encoder_outputs).log_softmax(dim=-1) return self.collect_outputs( stage='test', logits=logits, output_lengths=output_lengths, targets=targets, target_lengths=target_lengths, )