You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
PaddleSpeech/paddlespeech/s2t/utils/bleu_score.py

118 lines
4.3 KiB

# 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.
"""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"]
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)
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)
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
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