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361 lines
13 KiB
361 lines
13 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""This module provides functions to calculate error rate in different level.
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e.g. wer for word-level, cer for char-level.
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"""
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import editdistance
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import numpy as np
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import logging
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import sys
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from itertools import groupby
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__all__ = ['word_errors', 'char_errors', 'wer', 'cer', "ErrorCalculator"]
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def _levenshtein_distance(ref, hyp):
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"""Levenshtein distance is a string metric for measuring the difference
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between two sequences. Informally, the levenshtein disctance is defined as
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the minimum number of single-character edits (substitutions, insertions or
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deletions) required to change one word into the other. We can naturally
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extend the edits to word level when calculate levenshtein disctance for
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two sentences.
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"""
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m = len(ref)
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n = len(hyp)
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# special case
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if ref == hyp:
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return 0
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if m == 0:
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return n
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if n == 0:
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return m
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if m < n:
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ref, hyp = hyp, ref
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m, n = n, m
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# use O(min(m, n)) space
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distance = np.zeros((2, n + 1), dtype=np.int32)
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# initialize distance matrix
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for j in range(n + 1):
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distance[0][j] = j
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# calculate levenshtein distance
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for i in range(1, m + 1):
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prev_row_idx = (i - 1) % 2
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cur_row_idx = i % 2
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distance[cur_row_idx][0] = i
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for j in range(1, n + 1):
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if ref[i - 1] == hyp[j - 1]:
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distance[cur_row_idx][j] = distance[prev_row_idx][j - 1]
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else:
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s_num = distance[prev_row_idx][j - 1] + 1
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i_num = distance[cur_row_idx][j - 1] + 1
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d_num = distance[prev_row_idx][j] + 1
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distance[cur_row_idx][j] = min(s_num, i_num, d_num)
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return distance[m % 2][n]
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def word_errors(reference, hypothesis, ignore_case=False, delimiter=' '):
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"""Compute the levenshtein distance between reference sequence and
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hypothesis sequence in word-level.
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:param reference: The reference sentence.
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:type reference: str
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:param hypothesis: The hypothesis sentence.
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:type hypothesis: str
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:param ignore_case: Whether case-sensitive or not.
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:type ignore_case: bool
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:param delimiter: Delimiter of input sentences.
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:type delimiter: char
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:return: Levenshtein distance and word number of reference sentence.
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:rtype: list
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"""
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if ignore_case:
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reference = reference.lower()
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hypothesis = hypothesis.lower()
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ref_words = list(filter(None, reference.split(delimiter)))
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hyp_words = list(filter(None, hypothesis.split(delimiter)))
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edit_distance = _levenshtein_distance(ref_words, hyp_words)
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# `editdistance.eavl precision` less than `_levenshtein_distance`
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# edit_distance = editdistance.eval(ref_words, hyp_words)
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return float(edit_distance), len(ref_words)
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def char_errors(reference, hypothesis, ignore_case=False, remove_space=False):
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"""Compute the levenshtein distance between reference sequence and
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hypothesis sequence in char-level.
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:param reference: The reference sentence.
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:type reference: str
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:param hypothesis: The hypothesis sentence.
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:type hypothesis: str
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:param ignore_case: Whether case-sensitive or not.
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:type ignore_case: bool
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:param remove_space: Whether remove internal space characters
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:type remove_space: bool
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:return: Levenshtein distance and length of reference sentence.
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:rtype: list
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"""
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if ignore_case:
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reference = reference.lower()
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hypothesis = hypothesis.lower()
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join_char = ' '
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if remove_space:
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join_char = ''
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reference = join_char.join(list(filter(None, reference.split(' '))))
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hypothesis = join_char.join(list(filter(None, hypothesis.split(' '))))
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edit_distance = _levenshtein_distance(reference, hypothesis)
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# `editdistance.eavl precision` less than `_levenshtein_distance`
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# edit_distance = editdistance.eval(reference, hypothesis)
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return float(edit_distance), len(reference)
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def wer(reference, hypothesis, ignore_case=False, delimiter=' '):
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"""Calculate word error rate (WER). WER compares reference text and
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hypothesis text in word-level. WER is defined as:
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.. math::
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WER = (Sw + Dw + Iw) / Nw
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where
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.. code-block:: text
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Sw is the number of words subsituted,
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Dw is the number of words deleted,
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Iw is the number of words inserted,
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Nw is the number of words in the reference
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We can use levenshtein distance to calculate WER. Please draw an attention
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that empty items will be removed when splitting sentences by delimiter.
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:param reference: The reference sentence.
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:type reference: str
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:param hypothesis: The hypothesis sentence.
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:type hypothesis: str
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:param ignore_case: Whether case-sensitive or not.
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:type ignore_case: bool
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:param delimiter: Delimiter of input sentences.
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:type delimiter: char
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:return: Word error rate.
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:rtype: float
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:raises ValueError: If word number of reference is zero.
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"""
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edit_distance, ref_len = word_errors(reference, hypothesis, ignore_case,
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delimiter)
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if ref_len == 0:
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raise ValueError("Reference's word number should be greater than 0.")
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wer = float(edit_distance) / ref_len
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return wer
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def cer(reference, hypothesis, ignore_case=False, remove_space=False):
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"""Calculate charactor error rate (CER). CER compares reference text and
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hypothesis text in char-level. CER is defined as:
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.. math::
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CER = (Sc + Dc + Ic) / Nc
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where
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.. code-block:: text
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Sc is the number of characters substituted,
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Dc is the number of characters deleted,
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Ic is the number of characters inserted
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Nc is the number of characters in the reference
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We can use levenshtein distance to calculate CER. Chinese input should be
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encoded to unicode. Please draw an attention that the leading and tailing
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space characters will be truncated and multiple consecutive space
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characters in a sentence will be replaced by one space character.
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:param reference: The reference sentence.
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:type reference: str
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:param hypothesis: The hypothesis sentence.
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:type hypothesis: str
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:param ignore_case: Whether case-sensitive or not.
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:type ignore_case: bool
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:param remove_space: Whether remove internal space characters
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:type remove_space: bool
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:return: Character error rate.
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:rtype: float
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:raises ValueError: If the reference length is zero.
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"""
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edit_distance, ref_len = char_errors(reference, hypothesis, ignore_case,
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remove_space)
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if ref_len == 0:
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raise ValueError("Length of reference should be greater than 0.")
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cer = float(edit_distance) / ref_len
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return cer
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class ErrorCalculator():
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"""Calculate CER and WER for E2E_ASR and CTC models during training.
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:param y_hats: numpy array with predicted text
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:param y_pads: numpy array with true (target) text
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:param char_list: List[str]
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:param sym_space: <space>
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:param sym_blank: <blank>
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:return:
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"""
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def __init__(
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self, char_list, sym_space, sym_blank, report_cer=False, report_wer=False
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):
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"""Construct an ErrorCalculator object."""
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super().__init__()
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self.report_cer = report_cer
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self.report_wer = report_wer
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self.char_list = char_list
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self.space = sym_space
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self.blank = sym_blank
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self.idx_blank = self.char_list.index(self.blank)
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if self.space in self.char_list:
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self.idx_space = self.char_list.index(self.space)
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else:
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self.idx_space = None
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def __call__(self, ys_hat, ys_pad, is_ctc=False):
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"""Calculate sentence-level WER/CER score.
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:param paddle.Tensor ys_hat: prediction (batch, seqlen)
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:param paddle.Tensor ys_pad: reference (batch, seqlen)
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:param bool is_ctc: calculate CER score for CTC
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:return: sentence-level WER score
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:rtype float
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:return: sentence-level CER score
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:rtype float
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"""
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cer, wer = None, None
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if is_ctc:
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return self.calculate_cer_ctc(ys_hat, ys_pad)
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elif not self.report_cer and not self.report_wer:
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return cer, wer
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seqs_hat, seqs_true = self.convert_to_char(ys_hat, ys_pad)
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if self.report_cer:
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cer = self.calculate_cer(seqs_hat, seqs_true)
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if self.report_wer:
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wer = self.calculate_wer(seqs_hat, seqs_true)
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return cer, wer
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def calculate_cer_ctc(self, ys_hat, ys_pad):
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"""Calculate sentence-level CER score for CTC.
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:param paddle.Tensor ys_hat: prediction (batch, seqlen)
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:param paddle.Tensor ys_pad: reference (batch, seqlen)
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:return: average sentence-level CER score
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:rtype float
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"""
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cers, char_ref_lens = [], []
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for i, y in enumerate(ys_hat):
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y_hat = [x[0] for x in groupby(y)]
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y_true = ys_pad[i]
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seq_hat, seq_true = [], []
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for idx in y_hat:
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idx = int(idx)
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if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
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seq_hat.append(self.char_list[int(idx)])
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for idx in y_true:
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idx = int(idx)
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if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
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seq_true.append(self.char_list[int(idx)])
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hyp_chars = "".join(seq_hat)
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ref_chars = "".join(seq_true)
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if len(ref_chars) > 0:
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cers.append(editdistance.eval(hyp_chars, ref_chars))
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char_ref_lens.append(len(ref_chars))
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cer_ctc = float(sum(cers)) / sum(char_ref_lens) if cers else None
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return cer_ctc
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def convert_to_char(self, ys_hat, ys_pad):
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"""Convert index to character.
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:param paddle.Tensor seqs_hat: prediction (batch, seqlen)
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:param paddle.Tensor seqs_true: reference (batch, seqlen)
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:return: token list of prediction
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:rtype list
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:return: token list of reference
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:rtype list
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"""
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seqs_hat, seqs_true = [], []
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for i, y_hat in enumerate(ys_hat):
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y_true = ys_pad[i]
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eos_true = np.where(y_true == -1)[0]
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ymax = eos_true[0] if len(eos_true) > 0 else len(y_true)
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# NOTE: padding index (-1) in y_true is used to pad y_hat
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seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]]
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seq_true = [self.char_list[int(idx)] for idx in y_true if int(idx) != -1]
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seq_hat_text = "".join(seq_hat).replace(self.space, " ")
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seq_hat_text = seq_hat_text.replace(self.blank, "")
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seq_true_text = "".join(seq_true).replace(self.space, " ")
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seqs_hat.append(seq_hat_text)
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seqs_true.append(seq_true_text)
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return seqs_hat, seqs_true
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def calculate_cer(self, seqs_hat, seqs_true):
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"""Calculate sentence-level CER score.
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:param list seqs_hat: prediction
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:param list seqs_true: reference
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:return: average sentence-level CER score
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:rtype float
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"""
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char_eds, char_ref_lens = [], []
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for i, seq_hat_text in enumerate(seqs_hat):
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seq_true_text = seqs_true[i]
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hyp_chars = seq_hat_text.replace(" ", "")
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ref_chars = seq_true_text.replace(" ", "")
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char_eds.append(editdistance.eval(hyp_chars, ref_chars))
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char_ref_lens.append(len(ref_chars))
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return float(sum(char_eds)) / sum(char_ref_lens)
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def calculate_wer(self, seqs_hat, seqs_true):
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"""Calculate sentence-level WER score.
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:param list seqs_hat: prediction
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:param list seqs_true: reference
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:return: average sentence-level WER score
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:rtype float
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"""
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word_eds, word_ref_lens = [], []
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for i, seq_hat_text in enumerate(seqs_hat):
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seq_true_text = seqs_true[i]
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hyp_words = seq_hat_text.split()
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ref_words = seq_true_text.split()
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word_eds.append(editdistance.eval(hyp_words, ref_words))
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word_ref_lens.append(len(ref_words))
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return float(sum(word_eds)) / sum(word_ref_lens)
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