Source code for espnet.nets.e2e_mt_common

#!/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."""

from itertools import groupby

import nltk
import numpy as np


[docs]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.pad in self.char_list: self.idx_blank = self.char_list.index(self.pad) else: # for OpenAI Whisper model, which doesn't use <blank> token self.idx_blank = None 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, is_ctc=False): """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 is_ctc: return self.calculate_bleu_ctc(ys_hat, ys_pad) elif not self.report_bleu: return bleu bleu = self.calculate_corpus_bleu(ys_hat, ys_pad) return bleu
[docs] 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
[docs] def calculate_bleu_ctc(self, ys_hat, ys_pad): """Calculate sentence-level BLEU score for CTC. :param torch.Tensor ys_hat: prediction (batch, seqlen) :param torch.Tensor ys_pad: reference (batch, seqlen) :return: corpus-level BLEU score :rtype float """ seqs_hat, seqs_true = [], [] for i, y in enumerate(ys_hat): y_hat = [x[0] for x in groupby(y)] y_true = ys_pad[i] seq_hat, seq_true = [], [] for idx in y_hat: idx = int(idx) if idx != -1 and idx != self.idx_blank and idx != self.idx_space: seq_hat.append(self.char_list[int(idx)]) for idx in y_true: idx = int(idx) if idx != -1 and idx != self.idx_blank and idx != self.idx_space: seq_true.append(self.char_list[int(idx)]) 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