PaddleSpeech/deepspeech/utils/error_rate.py

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# Licensed under the Apache License, Version 2.0 (the "License");
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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.
"""
from itertools import groupby

import editdistance
import numpy as np

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


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.

    :param reference: The reference sentence.
    :type reference: str
    :param hypothesis: The hypothesis sentence.
    :type hypothesis: str
    :param ignore_case: Whether case-sensitive or not.
    :type ignore_case: bool
    :param delimiter: Delimiter of input sentences.
    :type delimiter: char
    :return: Levenshtein distance and word number of reference sentence.
    :rtype: list
    """
    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)
    # `editdistance.eavl precision` less than `_levenshtein_distance`
    # edit_distance = editdistance.eval(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.

    :param reference: The reference sentence.
    :type reference: str
    :param hypothesis: The hypothesis sentence.
    :type hypothesis: str
    :param ignore_case: Whether case-sensitive or not.
    :type ignore_case: bool
    :param remove_space: Whether remove internal space characters
    :type remove_space: bool
    :return: Levenshtein distance and length of reference sentence.
    :rtype: list
    """
    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)
    # `editdistance.eavl precision` less than `_levenshtein_distance`
    # edit_distance = editdistance.eval(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.

    :param reference: The reference sentence.
    :type reference: str
    :param hypothesis: The hypothesis sentence.
    :type hypothesis: str
    :param ignore_case: Whether case-sensitive or not.
    :type ignore_case: bool
    :param delimiter: Delimiter of input sentences.
    :type delimiter: char
    :return: Word error rate.
    :rtype: float
    :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.

    :param reference: The reference sentence.
    :type reference: str
    :param hypothesis: The hypothesis sentence.
    :type hypothesis: str
    :param ignore_case: Whether case-sensitive or not.
    :type ignore_case: bool
    :param remove_space: Whether remove internal space characters
    :type remove_space: bool
    :return: Character error rate.
    :rtype: float
    :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


class ErrorCalculator():
    """Calculate CER and WER for E2E_ASR and CTC models during training.

    :param y_hats: numpy array with predicted text
    :param y_pads: numpy array with true (target) text
    :param char_list: List[str]
    :param sym_space: <space>
    :param sym_blank: <blank>
    :return:
    """

    def __init__(self,
                 char_list,
                 sym_space,
                 sym_blank,
                 report_cer=False,
                 report_wer=False):
        """Construct an ErrorCalculator object."""
        super().__init__()

        self.report_cer = report_cer
        self.report_wer = report_wer

        self.char_list = char_list
        self.space = sym_space
        self.blank = sym_blank
        self.idx_blank = self.char_list.index(self.blank)
        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, is_ctc=False):
        """Calculate sentence-level WER/CER score.

        :param paddle.Tensor ys_hat: prediction (batch, seqlen)
        :param paddle.Tensor ys_pad: reference (batch, seqlen)
        :param bool is_ctc: calculate CER score for CTC
        :return: sentence-level WER score
        :rtype float
        :return: sentence-level CER score
        :rtype float
        """
        cer, wer = None, None
        if is_ctc:
            return self.calculate_cer_ctc(ys_hat, ys_pad)
        elif not self.report_cer and not self.report_wer:
            return cer, wer

        seqs_hat, seqs_true = self.convert_to_char(ys_hat, ys_pad)
        if self.report_cer:
            cer = self.calculate_cer(seqs_hat, seqs_true)

        if self.report_wer:
            wer = self.calculate_wer(seqs_hat, seqs_true)
        return cer, wer

    def calculate_cer_ctc(self, ys_hat, ys_pad):
        """Calculate sentence-level CER score for CTC.

        :param paddle.Tensor ys_hat: prediction (batch, seqlen)
        :param paddle.Tensor ys_pad: reference (batch, seqlen)
        :return: average sentence-level CER score
        :rtype float
        """
        cers, char_ref_lens = [], []
        for i, y in enumerate(ys_hat):
            y_hat = [x[0] for x in groupby(y)]
            y_true = ys_pad[i]
            seq_hat, seq_true = [], []
            for idx in y_hat:
                idx = int(idx)
                if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
                    seq_hat.append(self.char_list[int(idx)])

            for idx in y_true:
                idx = int(idx)
                if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
                    seq_true.append(self.char_list[int(idx)])

            hyp_chars = "".join(seq_hat)
            ref_chars = "".join(seq_true)
            if len(ref_chars) > 0:
                cers.append(editdistance.eval(hyp_chars, ref_chars))
                char_ref_lens.append(len(ref_chars))

        cer_ctc = float(sum(cers)) / sum(char_ref_lens) if cers else None
        return cer_ctc

    def convert_to_char(self, ys_hat, ys_pad):
        """Convert index to character.

        :param paddle.Tensor seqs_hat: prediction (batch, seqlen)
        :param paddle.Tensor seqs_true: reference (batch, seqlen)
        :return: token list of prediction
        :rtype list
        :return: token list of reference
        :rtype list
        """
        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
            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.blank, "")
            seq_true_text = "".join(seq_true).replace(self.space, " ")
            seqs_hat.append(seq_hat_text)
            seqs_true.append(seq_true_text)
        return seqs_hat, seqs_true

    def calculate_cer(self, seqs_hat, seqs_true):
        """Calculate sentence-level CER score.

        :param list seqs_hat: prediction
        :param list seqs_true: reference
        :return: average sentence-level CER score
        :rtype float
        """
        char_eds, char_ref_lens = [], []
        for i, seq_hat_text in enumerate(seqs_hat):
            seq_true_text = seqs_true[i]
            hyp_chars = seq_hat_text.replace(" ", "")
            ref_chars = seq_true_text.replace(" ", "")
            char_eds.append(editdistance.eval(hyp_chars, ref_chars))
            char_ref_lens.append(len(ref_chars))
        return float(sum(char_eds)) / sum(char_ref_lens)

    def calculate_wer(self, seqs_hat, seqs_true):
        """Calculate sentence-level WER score.

        :param list seqs_hat: prediction
        :param list seqs_true: reference
        :return: average sentence-level WER score
        :rtype float
        """
        word_eds, word_ref_lens = [], []
        for i, seq_hat_text in enumerate(seqs_hat):
            seq_true_text = seqs_true[i]
            hyp_words = seq_hat_text.split()
            ref_words = seq_true_text.split()
            word_eds.append(editdistance.eval(hyp_words, ref_words))
            word_ref_lens.append(len(ref_words))
        return float(sum(word_eds)) / sum(word_ref_lens)