espnet/nets/e2e_mt_common.py

#!/usr/bin/env python3
# encoding: utf-8

# Copyright 2019 Kyoto University (Hirofumi Inaguma)
#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

"""Common functions for ST and MT."""

import nltk
import numpy as np


class ErrorCalculator(object):
    """Calculate BLEU for ST and MT models during training.

    :param y_hats: numpy array with predicted text
    :param y_pads: numpy array with true (target) text
    :param char_list: vocabulary list
    :param sym_space: space symbol
    :param sym_pad: pad symbol
    :param report_bleu: report BLUE score if True
    """

    def __init__(self, char_list, sym_space, sym_pad, report_bleu=False):
        """Construct an ErrorCalculator object."""
        super(ErrorCalculator, self).__init__()
        self.char_list = char_list
        self.space = sym_space
        self.pad = sym_pad
        self.report_bleu = report_bleu
        if self.space in self.char_list:
            self.idx_space = self.char_list.index(self.space)
        else:
            self.idx_space = None

    def __call__(self, ys_hat, ys_pad):
        """Calculate corpus-level BLEU score.

        :param torch.Tensor ys_hat: prediction (batch, seqlen)
        :param torch.Tensor ys_pad: reference (batch, seqlen)
        :return: corpus-level BLEU score in a mini-batch
        :rtype float
        """
        bleu = None
        if not self.report_bleu:
            return bleu

        bleu = self.calculate_corpus_bleu(ys_hat, ys_pad)
        return bleu

    def calculate_corpus_bleu(self, ys_hat, ys_pad):
        """Calculate corpus-level BLEU score in a mini-batch.

        :param torch.Tensor seqs_hat: prediction (batch, seqlen)
        :param torch.Tensor seqs_true: reference (batch, seqlen)
        :return: corpus-level BLEU score
        :rtype float
        """
        seqs_hat, seqs_true = [], []
        for i, y_hat in enumerate(ys_hat):
            y_true = ys_pad[i]
            eos_true = np.where(y_true == -1)[0]
            ymax = eos_true[0] if len(eos_true) > 0 else len(y_true)
            # NOTE: padding index (-1) in y_true is used to pad y_hat
            # because y_hats is not padded with -1
            seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]]
            seq_true = [self.char_list[int(idx)] for idx in y_true if int(idx) != -1]
            seq_hat_text = "".join(seq_hat).replace(self.space, " ")
            seq_hat_text = seq_hat_text.replace(self.pad, "")
            seq_true_text = "".join(seq_true).replace(self.space, " ")
            seqs_hat.append(seq_hat_text)
            seqs_true.append(seq_true_text)
        bleu = nltk.bleu_score.corpus_bleu([[ref] for ref in seqs_true], seqs_hat)
        return bleu * 100