Source code for openspeech.models.listen_attend_spell_with_location_aware.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, OpenspeechEncoderDecoderModel
from openspeech.decoders import LSTMDecoder
from openspeech.encoders import LSTMEncoder
from openspeech.models.listen_attend_spell_with_location_aware.configurations import ListenAttendSpellWithLocationAwareConfigs
from openspeech.vocabs.vocab import Vocabulary


[docs]@register_model('listen_attend_spell_with_location_aware', dataclass=ListenAttendSpellWithLocationAwareConfigs) class ListenAttendSpellWithLocationAwareModel(OpenspeechEncoderDecoderModel): r""" Listen, Attend and Spell model with configurable encoder and decoder. Paper: https://arxiv.org/abs/1508.01211 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: * outputs (dict): Result of model predictions. """ def __init__(self, configs: DictConfig, vocab: Vocabulary, ) -> None: super(ListenAttendSpellWithLocationAwareModel, self).__init__(configs, vocab) def build_model(self): self.encoder = LSTMEncoder( input_dim=self.configs.audio.num_mels, num_layers=self.configs.model.num_encoder_layers, num_classes=self.num_classes, hidden_state_dim=self.configs.model.hidden_state_dim, dropout_p=self.configs.model.encoder_dropout_p, bidirectional=self.configs.model.encoder_bidirectional, rnn_type=self.configs.model.rnn_type, joint_ctc_attention=self.configs.model.joint_ctc_attention, ) decoder_hidden_state_dim = self.configs.model.hidden_state_dim << 1 \ if self.configs.model.encoder_bidirectional \ else self.configs.model.hidden_state_dim self.decoder = LSTMDecoder( num_classes=self.num_classes, max_length=self.configs.model.max_length, hidden_state_dim=decoder_hidden_state_dim, pad_id=self.vocab.pad_id, sos_id=self.vocab.sos_id, eos_id=self.vocab.eos_id, num_heads=self.configs.model.num_attention_heads, dropout_p=self.configs.model.decoder_dropout_p, num_layers=self.configs.model.num_decoder_layers, attn_mechanism=self.configs.model.decoder_attn_mechanism, rnn_type=self.configs.model.rnn_type, )
[docs] def set_beam_decoder(self, batch_size: int, beam_size: int = 3): """ Setting beam search decoder """ from openspeech.search import BeamSearchLSTM self.decoder = BeamSearchLSTM( decoder=self.decoder, beam_size=beam_size, batch_size=batch_size, )
[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(ListenAttendSpellWithLocationAwareModel, 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(ListenAttendSpellWithLocationAwareModel, 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(ListenAttendSpellWithLocationAwareModel, 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(ListenAttendSpellWithLocationAwareModel, self).test_step(batch, batch_idx)