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211 lines
7.1 KiB
211 lines
7.1 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 numpy as np
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__all__ = ['word_errors', 'char_errors', 'wer', 'cer']
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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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Args:
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reference (str):
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The reference sentence.
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hypothesis (str):
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The hypothesis sentence.
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ignore_case (bool):
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Whether case-sensitive or not.
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delimiter (char(str)):
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Delimiter of input sentences.
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Returns:
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list: Levenshtein distance and word number of reference sentence.
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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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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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Args:
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reference (str): The reference sentence.
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hypothesis (str): The hypothesis sentence.
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ignore_case (bool): Whether case-sensitive or not.
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remove_space (bool): Whether remove internal space characters
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Returns:
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list: Levenshtein distance and length of reference sentence.
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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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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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Args:
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reference (str): The reference sentence.
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hypothesis (str): The hypothesis sentence.
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ignore_case (bool): Whether case-sensitive or not.
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delimiter (char): Delimiter of input sentences.
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Returns:
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float: Word error rate.
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Raises:
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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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Args:
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reference (str): The reference sentence.
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hypothesis (str): The hypothesis sentence.
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ignore_case (bool): Whether case-sensitive or not.
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remove_space (bool): Whether remove internal space characters
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Returns:
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float: Character error rate.
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Raises:
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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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if __name__ == "__main__":
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reference = [
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'j', 'iou4', 'zh', 'e4', 'iang5', 'x', 'v2', 'b', 'o1', 'k', 'ai1',
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'sh', 'iii3', 'l', 'e5', 'b', 'ei3', 'p', 'iao1', 'sh', 'eng1', 'ia2'
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]
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hypothesis = [
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'j', 'iou4', 'zh', 'e4', 'iang4', 'x', 'v2', 'b', 'o1', 'k', 'ai1',
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'sh', 'iii3', 'l', 'e5', 'b', 'ei3', 'p', 'iao1', 'sh', 'eng1', 'ia2'
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]
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reference = " ".join(reference)
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hypothesis = " ".join(hypothesis)
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print(wer(reference, hypothesis))
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