evaluation/compute-wer.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import re, sys, unicodedata
import codecs

remove_tag = True
spacelist = [' ', '\t', '\r', '\n']
puncts = [
    '!', ',', '?', '、', '。', '！', '，', '；', '？', '：', '「', '」', '︰', '『', '』',
    '《', '》'
]


def characterize(string):
    res = []
    i = 0
    while i < len(string):
        char = string[i]
        if char in puncts:
            i += 1
            continue
        cat1 = unicodedata.category(char)
        #https://unicodebook.readthedocs.io/unicode.html#unicode-categories
        if cat1 == 'Zs' or cat1 == 'Cn' or char in spacelist:  # space or not assigned
            i += 1
            continue
        if cat1 == 'Lo':  # letter-other
            res.append(char)
            i += 1
        else:
            # some input looks like: <unk><noise>, we want to separate it to two words.
            sep = ' '
            if char == '<': sep = '>'
            j = i + 1
            while j < len(string):
                c = string[j]
                if ord(c) >= 128 or (c in spacelist) or (c == sep):
                    break
                j += 1
            if j < len(string) and string[j] == '>':
                j += 1
            res.append(string[i:j])
            i = j
    return res


def stripoff_tags(x):
    if not x: return ''
    chars = []
    i = 0
    T = len(x)
    while i < T:
        if x[i] == '<':
            while i < T and x[i] != '>':
                i += 1
            i += 1
        else:
            chars.append(x[i])
            i += 1
    return ''.join(chars)


def normalize(sentence, ignore_words, cs, split=None):
    """ sentence, ignore_words are both in unicode
    """
    new_sentence = []
    for token in sentence:
        x = token
        if not cs:
            x = x.upper()
        if x in ignore_words:
            continue
        if remove_tag:
            x = stripoff_tags(x)
        if not x:
            continue
        if split and x in split:
            new_sentence += split[x]
        else:
            new_sentence.append(x)
    return new_sentence


class Calculator:

    def __init__(self):
        self.data = {}
        self.space = []
        self.cost = {}
        self.cost['cor'] = 0
        self.cost['sub'] = 1
        self.cost['del'] = 1
        self.cost['ins'] = 1

    def calculate(self, lab, rec):
        # Initialization
        lab.insert(0, '')
        rec.insert(0, '')
        while len(self.space) < len(lab):
            self.space.append([])
        for row in self.space:
            for element in row:
                element['dist'] = 0
                element['error'] = 'non'
            while len(row) < len(rec):
                row.append({'dist': 0, 'error': 'non'})
        for i in range(len(lab)):
            self.space[i][0]['dist'] = i
            self.space[i][0]['error'] = 'del'
        for j in range(len(rec)):
            self.space[0][j]['dist'] = j
            self.space[0][j]['error'] = 'ins'
        self.space[0][0]['error'] = 'non'
        for token in lab:
            if token not in self.data and len(token) > 0:
                self.data[token] = {
                    'all': 0,
                    'cor': 0,
                    'sub': 0,
                    'ins': 0,
                    'del': 0
                }
        for token in rec:
            if token not in self.data and len(token) > 0:
                self.data[token] = {
                    'all': 0,
                    'cor': 0,
                    'sub': 0,
                    'ins': 0,
                    'del': 0
                }
        # Computing edit distance
        for i, lab_token in enumerate(lab):
            for j, rec_token in enumerate(rec):
                if i == 0 or j == 0:
                    continue
                min_dist = sys.maxsize
                min_error = 'none'
                dist = self.space[i - 1][j]['dist'] + self.cost['del']
                error = 'del'
                if dist < min_dist:
                    min_dist = dist
                    min_error = error
                dist = self.space[i][j - 1]['dist'] + self.cost['ins']
                error = 'ins'
                if dist < min_dist:
                    min_dist = dist
                    min_error = error
                if lab_token == rec_token:
                    dist = self.space[i - 1][j - 1]['dist'] + self.cost['cor']
                    error = 'cor'
                else:
                    dist = self.space[i - 1][j - 1]['dist'] + self.cost['sub']
                    error = 'sub'
                if dist < min_dist:
                    min_dist = dist
                    min_error = error
                self.space[i][j]['dist'] = min_dist
                self.space[i][j]['error'] = min_error
        # Tracing back
        result = {
            'lab': [],
            'rec': [],
            'all': 0,
            'cor': 0,
            'sub': 0,
            'ins': 0,
            'del': 0
        }
        i = len(lab) - 1
        j = len(rec) - 1
        while True:
            if self.space[i][j]['error'] == 'cor':  # correct
                if len(lab[i]) > 0:
                    self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
                    self.data[lab[i]]['cor'] = self.data[lab[i]]['cor'] + 1
                    result['all'] = result['all'] + 1
                    result['cor'] = result['cor'] + 1
                result['lab'].insert(0, lab[i])
                result['rec'].insert(0, rec[j])
                i = i - 1
                j = j - 1
            elif self.space[i][j]['error'] == 'sub':  # substitution
                if len(lab[i]) > 0:
                    self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
                    self.data[lab[i]]['sub'] = self.data[lab[i]]['sub'] + 1
                    result['all'] = result['all'] + 1
                    result['sub'] = result['sub'] + 1
                result['lab'].insert(0, lab[i])
                result['rec'].insert(0, rec[j])
                i = i - 1
                j = j - 1
            elif self.space[i][j]['error'] == 'del':  # deletion
                if len(lab[i]) > 0:
                    self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
                    self.data[lab[i]]['del'] = self.data[lab[i]]['del'] + 1
                    result['all'] = result['all'] + 1
                    result['del'] = result['del'] + 1
                result['lab'].insert(0, lab[i])
                result['rec'].insert(0, "")
                i = i - 1
            elif self.space[i][j]['error'] == 'ins':  # insertion
                if len(rec[j]) > 0:
                    self.data[rec[j]]['ins'] = self.data[rec[j]]['ins'] + 1
                    result['ins'] = result['ins'] + 1
                result['lab'].insert(0, "")
                result['rec'].insert(0, rec[j])
                j = j - 1
            elif self.space[i][j]['error'] == 'non':  # starting point
                break
            else:  # shouldn't reach here
                print(
                    'this should not happen , i = {i} , j = {j} , error = {error}'
                    .format(i=i, j=j, error=self.space[i][j]['error']))
        return result

    def overall(self):
        result = {'all': 0, 'cor': 0, 'sub': 0, 'ins': 0, 'del': 0}
        for token in self.data:
            result['all'] = result['all'] + self.data[token]['all']
            result['cor'] = result['cor'] + self.data[token]['cor']
            result['sub'] = result['sub'] + self.data[token]['sub']
            result['ins'] = result['ins'] + self.data[token]['ins']
            result['del'] = result['del'] + self.data[token]['del']
        return result

    def cluster(self, data):
        result = {'all': 0, 'cor': 0, 'sub': 0, 'ins': 0, 'del': 0}
        for token in data:
            if token in self.data:
                result['all'] = result['all'] + self.data[token]['all']
                result['cor'] = result['cor'] + self.data[token]['cor']
                result['sub'] = result['sub'] + self.data[token]['sub']
                result['ins'] = result['ins'] + self.data[token]['ins']
                result['del'] = result['del'] + self.data[token]['del']
        return result

    def keys(self):
        return list(self.data.keys())


def width(string):
    return sum(1 + (unicodedata.east_asian_width(c) in "AFW") for c in string)


def default_cluster(word):
    unicode_names = [unicodedata.name(char) for char in word]
    for i in reversed(range(len(unicode_names))):
        if unicode_names[i].startswith('DIGIT'):  # 1
            unicode_names[i] = 'Number'  # 'DIGIT'
        elif (unicode_names[i].startswith('CJK UNIFIED IDEOGRAPH')
              or unicode_names[i].startswith('CJK COMPATIBILITY IDEOGRAPH')):
            # 明 / 郎
            unicode_names[i] = 'Mandarin'  # 'CJK IDEOGRAPH'
        elif (unicode_names[i].startswith('LATIN CAPITAL LETTER')
              or unicode_names[i].startswith('LATIN SMALL LETTER')):
            # A / a
            unicode_names[i] = 'English'  # 'LATIN LETTER'
        elif unicode_names[i].startswith('HIRAGANA LETTER'):  # は こ め
            unicode_names[i] = 'Japanese'  # 'GANA LETTER'
        elif (unicode_names[i].startswith('AMPERSAND')
              or unicode_names[i].startswith('APOSTROPHE')
              or unicode_names[i].startswith('COMMERCIAL AT')
              or unicode_names[i].startswith('DEGREE CELSIUS')
              or unicode_names[i].startswith('EQUALS SIGN')
              or unicode_names[i].startswith('FULL STOP')
              or unicode_names[i].startswith('HYPHEN-MINUS')
              or unicode_names[i].startswith('LOW LINE')
              or unicode_names[i].startswith('NUMBER SIGN')
              or unicode_names[i].startswith('PLUS SIGN')
              or unicode_names[i].startswith('SEMICOLON')):
            # & / ' / @ / ℃ / = / . / - / _ / # / + / ;
            del unicode_names[i]
        else:
            return 'Other'
    if len(unicode_names) == 0:
        return 'Other'
    if len(unicode_names) == 1:
        return unicode_names[0]
    for i in range(len(unicode_names) - 1):
        if unicode_names[i] != unicode_names[i + 1]:
            return 'Other'
    return unicode_names[0]


def usage():
    print(
        "compute-wer.py : compute word error rate (WER) and align recognition results and references."
    )
    print(
        "         usage : python compute-wer.py [--cs={0,1}] [--cluster=foo] [--ig=ignore_file] [--char={0,1}] [--v={0,1}] [--padding-symbol={space,underline}] test.ref test.hyp > test.wer"
    )


if __name__ == '__main__':
    if len(sys.argv) == 1:
        usage()
        sys.exit(0)
    calculator = Calculator()
    cluster_file = ''
    ignore_words = set()
    tochar = False
    verbose = 1
    padding_symbol = ' '
    case_sensitive = False
    max_words_per_line = sys.maxsize
    split = None
    while len(sys.argv) > 3:
        a = '--maxw='
        if sys.argv[1].startswith(a):
            b = sys.argv[1][len(a):]
            del sys.argv[1]
            max_words_per_line = int(b)
            continue
        a = '--rt='
        if sys.argv[1].startswith(a):
            b = sys.argv[1][len(a):].lower()
            del sys.argv[1]
            remove_tag = (b == 'true') or (b != '0')
            continue
        a = '--cs='
        if sys.argv[1].startswith(a):
            b = sys.argv[1][len(a):].lower()
            del sys.argv[1]
            case_sensitive = (b == 'true') or (b != '0')
            continue
        a = '--cluster='
        if sys.argv[1].startswith(a):
            cluster_file = sys.argv[1][len(a):]
            del sys.argv[1]
            continue
        a = '--splitfile='
        if sys.argv[1].startswith(a):
            split_file = sys.argv[1][len(a):]
            del sys.argv[1]
            split = dict()
            with codecs.open(split_file, 'r', 'utf-8') as fh:
                for line in fh:  # line in unicode
                    words = line.strip().split()
                    if len(words) >= 2:
                        split[words[0]] = words[1:]
            continue
        a = '--ig='
        if sys.argv[1].startswith(a):
            ignore_file = sys.argv[1][len(a):]
            del sys.argv[1]
            with codecs.open(ignore_file, 'r', 'utf-8') as fh:
                for line in fh:  # line in unicode
                    line = line.strip()
                    if len(line) > 0:
                        ignore_words.add(line)
            continue
        a = '--char='
        if sys.argv[1].startswith(a):
            b = sys.argv[1][len(a):].lower()
            del sys.argv[1]
            tochar = (b == 'true') or (b != '0')
            continue
        a = '--v='
        if sys.argv[1].startswith(a):
            b = sys.argv[1][len(a):].lower()
            del sys.argv[1]
            verbose = 0
            try:
                verbose = int(b)
            except:
                if b == 'true' or b != '0':
                    verbose = 1
            continue
        a = '--padding-symbol='
        if sys.argv[1].startswith(a):
            b = sys.argv[1][len(a):].lower()
            del sys.argv[1]
            if b == 'space':
                padding_symbol = ' '
            elif b == 'underline':
                padding_symbol = '_'
            continue
        if True or sys.argv[1].startswith('-'):
            #ignore invalid switch
            del sys.argv[1]
            continue

    if not case_sensitive:
        ig = set([w.upper() for w in ignore_words])
        ignore_words = ig

    default_clusters = {}
    default_words = {}

    ref_file = sys.argv[1]
    hyp_file = sys.argv[2]
    rec_set = {}
    if split and not case_sensitive:
        newsplit = dict()
        for w in split:
            words = split[w]
            for i in range(len(words)):
                words[i] = words[i].upper()
            newsplit[w.upper()] = words
        split = newsplit

    with codecs.open(hyp_file, 'r', 'utf-8') as fh:
        for line in fh:
            if tochar:
                array = characterize(line)
            else:
                array = line.strip().split()
            if len(array) == 0: continue
            fid = array[0]
            rec_set[fid] = normalize(array[1:], ignore_words, case_sensitive,
                                     split)

    # compute error rate on the interaction of reference file and hyp file
    for line in open(ref_file, 'r', encoding='utf-8'):
        if tochar:
            array = characterize(line)
        else:
            array = line.rstrip('\n').split()
        if len(array) == 0: continue
        fid = array[0]
        if fid not in rec_set:
            continue
        lab = normalize(array[1:], ignore_words, case_sensitive, split)
        rec = rec_set[fid]
        if verbose:
            print('\nutt: %s' % fid)

        for word in rec + lab:
            if word not in default_words:
                default_cluster_name = default_cluster(word)
                if default_cluster_name not in default_clusters:
                    default_clusters[default_cluster_name] = {}
                if word not in default_clusters[default_cluster_name]:
                    default_clusters[default_cluster_name][word] = 1
                default_words[word] = default_cluster_name

        result = calculator.calculate(lab, rec)
        if verbose:
            if result['all'] != 0:
                wer = float(result['ins'] + result['sub'] +
                            result['del']) * 100.0 / result['all']
            else:
                wer = 0.0
            print('WER: %4.2f %%' % wer, end=' ')
            print('N=%d C=%d S=%d D=%d I=%d' %
                  (result['all'], result['cor'], result['sub'], result['del'],
                   result['ins']))
            space = {}
            space['lab'] = []
            space['rec'] = []
            for idx in range(len(result['lab'])):
                len_lab = width(result['lab'][idx])
                len_rec = width(result['rec'][idx])
                length = max(len_lab, len_rec)
                space['lab'].append(length - len_lab)
                space['rec'].append(length - len_rec)
            upper_lab = len(result['lab'])
            upper_rec = len(result['rec'])
            lab1, rec1 = 0, 0
            while lab1 < upper_lab or rec1 < upper_rec:
                if verbose > 1:
                    print('lab(%s):' % fid.encode('utf-8'), end=' ')
                else:
                    print('lab:', end=' ')
                lab2 = min(upper_lab, lab1 + max_words_per_line)
                for idx in range(lab1, lab2):
                    token = result['lab'][idx]
                    print('{token}'.format(token=token), end='')
                    for n in range(space['lab'][idx]):
                        print(padding_symbol, end='')
                    print(' ', end='')
                print()
                if verbose > 1:
                    print('rec(%s):' % fid.encode('utf-8'), end=' ')
                else:
                    print('rec:', end=' ')
                rec2 = min(upper_rec, rec1 + max_words_per_line)
                for idx in range(rec1, rec2):
                    token = result['rec'][idx]
                    print('{token}'.format(token=token), end='')
                    for n in range(space['rec'][idx]):
                        print(padding_symbol, end='')
                    print(' ', end='')
                print('\n', end='\n')
                lab1 = lab2
                rec1 = rec2

    if verbose:
        print(
            '==========================================================================='
        )
        print()

    result = calculator.overall()
    if result['all'] != 0:
        wer = float(result['ins'] + result['sub'] +
                    result['del']) * 100.0 / result['all']
    else:
        wer = 0.0
    print('Overall -> %4.2f %%' % wer, end=' ')
    print('N=%d C=%d S=%d D=%d I=%d' %
          (result['all'], result['cor'], result['sub'], result['del'],
           result['ins']))
    if not verbose:
        print()

    if verbose:
        for cluster_id in default_clusters:
            result = calculator.cluster(
                [k for k in default_clusters[cluster_id]])
            if result['all'] != 0:
                wer = float(result['ins'] + result['sub'] +
                            result['del']) * 100.0 / result['all']
            else:
                wer = 0.0
            print('%s -> %4.2f %%' % (cluster_id, wer), end=' ')
            print('N=%d C=%d S=%d D=%d I=%d' %
                  (result['all'], result['cor'], result['sub'], result['del'],
                   result['ins']))
        if len(cluster_file) > 0:  # compute separated WERs for word clusters
            cluster_id = ''
            cluster = []
            for line in open(cluster_file, 'r', encoding='utf-8'):
                for token in line.decode('utf-8').rstrip('\n').split():
                    # end of cluster reached, like </Keyword>
                    if token[0:2] == '</' and token[len(token)-1] == '>' and \
                       token.lstrip('</').rstrip('>') == cluster_id :
                        result = calculator.cluster(cluster)
                        if result['all'] != 0:
                            wer = float(result['ins'] + result['sub'] +
                                        result['del']) * 100.0 / result['all']
                        else:
                            wer = 0.0
                        print('%s -> %4.2f %%' % (cluster_id, wer), end=' ')
                        print('N=%d C=%d S=%d D=%d I=%d' %
                              (result['all'], result['cor'], result['sub'],
                               result['del'], result['ins']))
                        cluster_id = ''
                        cluster = []
                    # begin of cluster reached, like <Keyword>
                    elif token[0] == '<' and token[len(token)-1] == '>' and \
                         cluster_id == '' :
                        cluster_id = token.lstrip('<').rstrip('>')
                        cluster = []
                    # general terms, like WEATHER / CAR / ...
                    else:
                        cluster.append(token)
        print()
        print(
            '==========================================================================='
        )