Source code for openspeech.encoders.lstm_encoder

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# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
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import torch
import torch.nn as nn
from typing import Tuple, Optional

from openspeech.encoders import OpenspeechEncoder
from openspeech.modules import Transpose, Linear


[docs]class LSTMEncoder(OpenspeechEncoder): r""" Converts low level speech signals into higher level features Args: input_dim (int): dimension of input vector num_classes (int): number of classification hidden_state_dim (int): the number of features in the encoders hidden state `h` num_layers (int, optional): number of recurrent layers (default: 3) bidirectional (bool, optional): if True, becomes a bidirectional encoders (default: False) rnn_type (str, optional): type of RNN cell (default: lstm) dropout_p (float, optional): dropout probability of encoders (default: 0.2) joint_ctc_attention (bool, optional): flag indication joint ctc attention or not Inputs: inputs, input_lengths - **inputs**: list of sequences, whose length is the batch size and within which each sequence is list of tokens - **input_lengths**: list of sequence lengths Returns: (Tensor, Tensor, Tensor): * outputs: A output sequence of encoders. `FloatTensor` of size ``(batch, seq_length, dimension)`` * encoder_logits: Log probability of encoders outputs will be passed to CTC Loss. If joint_ctc_attention is False, return None. * encoder_output_lengths: The length of encoders outputs. ``(batch)`` """ supported_rnns = { 'lstm': nn.LSTM, 'gru': nn.GRU, 'rnn': nn.RNN, } def __init__( self, input_dim: int, num_classes: int = None, hidden_state_dim: int = 512, dropout_p: float = 0.3, num_layers: int = 3, bidirectional: bool = True, rnn_type: str = 'lstm', joint_ctc_attention: bool = False, ) -> None: super(LSTMEncoder, self).__init__() self.num_classes = num_classes self.joint_ctc_attention = joint_ctc_attention self.hidden_state_dim = hidden_state_dim self.rnn = self.supported_rnns[rnn_type.lower()]( input_size=input_dim, hidden_size=hidden_state_dim, num_layers=num_layers, bias=True, batch_first=True, dropout=dropout_p, bidirectional=bidirectional, ) if self.joint_ctc_attention: self.fc = nn.Sequential( Transpose(shape=(1, 2)), nn.Dropout(dropout_p), Linear(hidden_state_dim << 1, num_classes, bias=False), )
[docs] def forward( self, inputs: torch.Tensor, input_lengths: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: r""" Forward propagate a `inputs` for encoders training. Args: 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: (Tensor, Tensor, Tensor): * outputs: A output sequence of encoders. `FloatTensor` of size ``(batch, seq_length, dimension)`` * encoder_logits: Log probability of encoders outputs will be passed to CTC Loss. If joint_ctc_attention is False, return None. * encoder_output_lengths: The length of encoders outputs. ``(batch)`` """ encoder_logits = None conv_outputs = nn.utils.rnn.pack_padded_sequence(inputs.transpose(0, 1), input_lengths.cpu()) outputs, hidden_states = self.rnn(conv_outputs) outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs) outputs = outputs.transpose(0, 1) if self.joint_ctc_attention: encoder_logits = self.fc(outputs.transpose(1, 2)).log_softmax(dim=2) return outputs, encoder_logits, input_lengths