PaddleSpeech/paddlespeech/t2s/utils/error_rate.py

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#
# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""This module provides functions to calculate error rate in different level.
e.g. wer for word-level, cer for char-level.
"""
import numpy as np

__all__ = ['word_errors', 'char_errors', 'wer', 'cer']


def _levenshtein_distance(ref, hyp):
    """Levenshtein distance is a string metric for measuring the difference
    between two sequences. Informally, the levenshtein disctance is defined as
    the minimum number of single-character edits (substitutions, insertions or
    deletions) required to change one word into the other. We can naturally
    extend the edits to word level when calculate levenshtein disctance for
    two sentences.
    """
    m = len(ref)
    n = len(hyp)

    # special case
    if ref == hyp:
        return 0
    if m == 0:
        return n
    if n == 0:
        return m

    if m < n:
        ref, hyp = hyp, ref
        m, n = n, m

    # use O(min(m, n)) space
    distance = np.zeros((2, n + 1), dtype=np.int32)

    # initialize distance matrix
    for j in range(n + 1):
        distance[0][j] = j

    # calculate levenshtein distance
    for i in range(1, m + 1):
        prev_row_idx = (i - 1) % 2
        cur_row_idx = i % 2
        distance[cur_row_idx][0] = i
        for j in range(1, n + 1):
            if ref[i - 1] == hyp[j - 1]:
                distance[cur_row_idx][j] = distance[prev_row_idx][j - 1]
            else:
                s_num = distance[prev_row_idx][j - 1] + 1
                i_num = distance[cur_row_idx][j - 1] + 1
                d_num = distance[prev_row_idx][j] + 1
                distance[cur_row_idx][j] = min(s_num, i_num, d_num)

    return distance[m % 2][n]


def word_errors(reference, hypothesis, ignore_case=False, delimiter=' '):
    """Compute the levenshtein distance between reference sequence and
    hypothesis sequence in word-level.

    Parameters
    ----------
    reference : str
        The reference sentence.
    hypothesis : str
        The hypothesis sentence.
    ignore_case : bool
        Whether case-sensitive or not.
    delimiter : char(str)
        Delimiter of input sentences.

    Returns
    ----------
    list
        Levenshtein distance and word number of reference sentence.
    """
    if ignore_case:
        reference = reference.lower()
        hypothesis = hypothesis.lower()

    ref_words = list(filter(None, reference.split(delimiter)))
    hyp_words = list(filter(None, hypothesis.split(delimiter)))

    edit_distance = _levenshtein_distance(ref_words, hyp_words)
    return float(edit_distance), len(ref_words)


def char_errors(reference, hypothesis, ignore_case=False, remove_space=False):
    """Compute the levenshtein distance between reference sequence and
    hypothesis sequence in char-level.

    Parameters
    ----------
    reference: str
        The reference sentence.
    hypothesis: str
        The hypothesis sentence.
    ignore_case: bool
        Whether case-sensitive or not.
    remove_space: bool
        Whether remove internal space characters

    Returns
    ----------
    list
        Levenshtein distance and length of reference sentence.
    """
    if ignore_case:
        reference = reference.lower()
        hypothesis = hypothesis.lower()

    join_char = ' '
    if remove_space:
        join_char = ''

    reference = join_char.join(list(filter(None, reference.split(' '))))
    hypothesis = join_char.join(list(filter(None, hypothesis.split(' '))))

    edit_distance = _levenshtein_distance(reference, hypothesis)
    return float(edit_distance), len(reference)


def wer(reference, hypothesis, ignore_case=False, delimiter=' '):
    """Calculate word error rate (WER). WER compares reference text and
    hypothesis text in word-level. WER is defined as:
    .. math::
        WER = (Sw + Dw + Iw) / Nw
    where
    .. code-block:: text
        Sw is the number of words subsituted,
        Dw is the number of words deleted,
        Iw is the number of words inserted,
        Nw is the number of words in the reference
    We can use levenshtein distance to calculate WER. Please draw an attention
    that empty items will be removed when splitting sentences by delimiter.

    Parameters
    ----------
    reference: str
        The reference sentence.

    hypothesis: str
        The hypothesis sentence.
    ignore_case: bool
        Whether case-sensitive or not.
    delimiter: char
        Delimiter of input sentences.

    Returns
    ----------
    float
         Word error rate.

    Raises
    ----------
    ValueError
        If word number of reference is zero.
    """
    edit_distance, ref_len = word_errors(reference, hypothesis, ignore_case,
                                         delimiter)

    if ref_len == 0:
        raise ValueError("Reference's word number should be greater than 0.")

    wer = float(edit_distance) / ref_len
    return wer


def cer(reference, hypothesis, ignore_case=False, remove_space=False):
    """Calculate charactor error rate (CER). CER compares reference text and
    hypothesis text in char-level. CER is defined as:
    .. math::
        CER = (Sc + Dc + Ic) / Nc
    where
    .. code-block:: text
        Sc is the number of characters substituted,
        Dc is the number of characters deleted,
        Ic is the number of characters inserted
        Nc is the number of characters in the reference
    We can use levenshtein distance to calculate CER. Chinese input should be
    encoded to unicode. Please draw an attention that the leading and tailing
    space characters will be truncated and multiple consecutive space
    characters in a sentence will be replaced by one space character.

    Parameters
    ----------
    reference: str
        The reference sentence.
    hypothesis: str
        The hypothesis sentence.
    ignore_case: bool
        Whether case-sensitive or not.
    remove_space: bool
        Whether remove internal space characters

    Returns
    ----------
    float
        Character error rate.

    Raises
    ----------
    ValueError
        If the reference length is zero.
    """
    edit_distance, ref_len = char_errors(reference, hypothesis, ignore_case,
                                         remove_space)

    if ref_len == 0:
        raise ValueError("Length of reference should be greater than 0.")

    cer = float(edit_distance) / ref_len
    return cer


if __name__ == "__main__":
    reference = [
        'j', 'iou4', 'zh', 'e4', 'iang5', 'x', 'v2', 'b', 'o1', 'k', 'ai1',
        'sh', 'iii3', 'l', 'e5', 'b', 'ei3', 'p', 'iao1', 'sh', 'eng1', 'ia2'
    ]
    hypothesis = [
        'j', 'iou4', 'zh', 'e4', 'iang4', 'x', 'v2', 'b', 'o1', 'k', 'ai1',
        'sh', 'iii3', 'l', 'e5', 'b', 'ei3', 'p', 'iao1', 'sh', 'eng1', 'ia2'
    ]
    reference = " ".join(reference)
    hypothesis = " ".join(hypothesis)
    print(wer(reference, hypothesis))