Source code for espnet.nets.chainer_backend.transformer.ctc

# encoding: utf-8
"""Class Declaration of Transformer's CTC."""
import logging

import chainer
import chainer.functions as F
import chainer.links as L
import numpy as np

# TODO(nelson): Merge chainer_backend/transformer/ in chainer_backend/
[docs]class CTC(chainer.Chain): """Chainer implementation of ctc layer. Args: odim (int): The output dimension. eprojs (int | None): Dimension of input vectors from encoder. dropout_rate (float): Dropout rate. """ def __init__(self, odim, eprojs, dropout_rate): """Initialize CTC.""" super(CTC, self).__init__() self.dropout_rate = dropout_rate self.loss = None with self.init_scope(): self.ctc_lo = L.Linear(eprojs, odim) def __call__(self, hs, ys): """CTC forward. Args: hs (list of chainer.Variable | N-dimension array): Input variable from encoder. ys (list of chainer.Variable | N-dimension array): Input variable of decoder. Returns: chainer.Variable: A variable holding a scalar value of the CTC loss. """ self.loss = None ilens = [x.shape[0] for x in hs] olens = [x.shape[0] for x in ys] # zero padding for hs y_hat = self.ctc_lo( F.dropout(F.pad_sequence(hs), ratio=self.dropout_rate), n_batch_axes=2 ) y_hat = F.separate(y_hat, axis=1) # ilen list of batch x hdim # zero padding for ys y_true = F.pad_sequence(ys, padding=-1) # batch x olen # get length info input_length = chainer.Variable(self.xp.array(ilens, dtype=np.int32)) label_length = chainer.Variable(self.xp.array(olens, dtype=np.int32)) self.__class__.__name__ + " input lengths: " + str( ) self.__class__.__name__ + " output lengths: " + str( ) # get ctc loss self.loss = F.connectionist_temporal_classification( y_hat, y_true, 0, input_length, label_length )"ctc loss:" + str( return self.loss
[docs] def log_softmax(self, hs): """Log_softmax of frame activations. Args: hs (list of chainer.Variable | N-dimension array): Input variable from encoder. Returns: chainer.Variable: A n-dimension float array. """ y_hat = self.ctc_lo(F.pad_sequence(hs), n_batch_axes=2) return F.log_softmax(y_hat.reshape(-1, y_hat.shape[-1])).reshape(y_hat.shape)