Source code for paddlespeech.s2t.utils.bleu_score

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from espnet(https://github.com/espnet/espnet)
"""This module provides functions to calculate bleu score in different level.
e.g. wer for word-level, cer for char-level.
"""
import numpy as np
import sacrebleu

__all__ = ['bleu', 'char_bleu', "ErrorCalculator"]


[docs]def bleu(hypothesis, reference): """Calculate BLEU. BLEU compares reference text and hypothesis text in word-level using scarebleu. :param reference: The reference sentences. :type reference: list[list[str]] :param hypothesis: The hypothesis sentence. :type hypothesis: list[str] :raises ValueError: If the reference length is zero. """ return sacrebleu.corpus_bleu(hypothesis, reference)
[docs]def char_bleu(hypothesis, reference): """Calculate BLEU. BLEU compares reference text and hypothesis text in char-level using scarebleu. :param reference: The reference sentences. :type reference: list[list[str]] :param hypothesis: The hypothesis sentence. :type hypothesis: list[str] :raises ValueError: If the reference number is zero. """ hypothesis = [' '.join(list(hyp.replace(' ', ''))) for hyp in hypothesis] reference = [[' '.join(list(ref_i.replace(' ', ''))) for ref_i in ref] for ref in reference] return sacrebleu.corpus_bleu(hypothesis, reference)
[docs]class ErrorCalculator(): """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().__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
[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 = sacrebleu.corpus_bleu(seqs_hat, [[ref] for ref in seqs_true]) return bleu.score * 100