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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# CopyRight WeNet Apache-2.0 License
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import re, sys, unicodedata
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import codecs
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remove_tag = True
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spacelist= [' ', '\t', '\r', '\n']
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puncts = ['!', ',', '?',
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'、', '。', '！', '，', '；', '？',
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'：', '「', '」', '︰', '『', '』', '《', '》']
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def characterize(string) :
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res = []
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i = 0
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while i < len(string):
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char = string[i]
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if char in puncts:
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i += 1
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continue
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cat1 = unicodedata.category(char)
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#https://unicodebook.readthedocs.io/unicode.html#unicode-categories
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if cat1 == 'Zs' or cat1 == 'Cn' or char in spacelist: # space or not assigned
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i += 1
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continue
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if cat1 == 'Lo': # letter-other
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res.append(char)
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i += 1
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else:
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# some input looks like: <unk><noise>, we want to separate it to two words.
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sep = ' '
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if char == '<': sep = '>'
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j = i+1
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while j < len(string):
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c = string[j]
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if ord(c) >= 128 or (c in spacelist) or (c==sep):
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break
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j += 1
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if j < len(string) and string[j] == '>':
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j += 1
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res.append(string[i:j])
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i = j
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return res
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def stripoff_tags(x):
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if not x: return ''
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chars = []
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i = 0; T=len(x)
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while i < T:
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if x[i] == '<':
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while i < T and x[i] != '>':
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i += 1
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i += 1
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else:
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chars.append(x[i])
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i += 1
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return ''.join(chars)
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def normalize(sentence, ignore_words, cs, split=None):
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""" sentence, ignore_words are both in unicode
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"""
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new_sentence = []
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for token in sentence:
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x = token
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if not cs:
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x = x.upper()
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if x in ignore_words:
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continue
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if remove_tag:
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x = stripoff_tags(x)
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if not x:
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continue
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if split and x in split:
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new_sentence += split[x]
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else:
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new_sentence.append(x)
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return new_sentence
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class Calculator :
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def __init__(self) :
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self.data = {}
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self.space = []
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self.cost = {}
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self.cost['cor'] = 0
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self.cost['sub'] = 1
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self.cost['del'] = 1
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self.cost['ins'] = 1
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def calculate(self, lab, rec) :
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# Initialization
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lab.insert(0, '')
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rec.insert(0, '')
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while len(self.space) < len(lab) :
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self.space.append([])
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for row in self.space :
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for element in row :
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element['dist'] = 0
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element['error'] = 'non'
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while len(row) < len(rec) :
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row.append({'dist' : 0, 'error' : 'non'})
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for i in range(len(lab)) :
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self.space[i][0]['dist'] = i
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self.space[i][0]['error'] = 'del'
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for j in range(len(rec)) :
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self.space[0][j]['dist'] = j
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self.space[0][j]['error'] = 'ins'
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self.space[0][0]['error'] = 'non'
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for token in lab :
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if token not in self.data and len(token) > 0 :
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self.data[token] = {'all' : 0, 'cor' : 0, 'sub' : 0, 'ins' : 0, 'del' : 0}
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for token in rec :
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if token not in self.data and len(token) > 0 :
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self.data[token] = {'all' : 0, 'cor' : 0, 'sub' : 0, 'ins' : 0, 'del' : 0}
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# Computing edit distance
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for i, lab_token in enumerate(lab) :
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for j, rec_token in enumerate(rec) :
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if i == 0 or j == 0 :
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continue
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min_dist = sys.maxsize
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min_error = 'none'
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dist = self.space[i-1][j]['dist'] + self.cost['del']
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error = 'del'
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if dist < min_dist :
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min_dist = dist
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min_error = error
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dist = self.space[i][j-1]['dist'] + self.cost['ins']
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error = 'ins'
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if dist < min_dist :
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min_dist = dist
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min_error = error
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if lab_token == rec_token :
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dist = self.space[i-1][j-1]['dist'] + self.cost['cor']
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error = 'cor'
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else :
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dist = self.space[i-1][j-1]['dist'] + self.cost['sub']
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error = 'sub'
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if dist < min_dist :
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min_dist = dist
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min_error = error
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self.space[i][j]['dist'] = min_dist
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self.space[i][j]['error'] = min_error
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# Tracing back
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result = {'lab':[], 'rec':[], 'all':0, 'cor':0, 'sub':0, 'ins':0, 'del':0}
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i = len(lab) - 1
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j = len(rec) - 1
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while True :
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if self.space[i][j]['error'] == 'cor' : # correct
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if len(lab[i]) > 0 :
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self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
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self.data[lab[i]]['cor'] = self.data[lab[i]]['cor'] + 1
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result['all'] = result['all'] + 1
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result['cor'] = result['cor'] + 1
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result['lab'].insert(0, lab[i])
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result['rec'].insert(0, rec[j])
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i = i - 1
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j = j - 1
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elif self.space[i][j]['error'] == 'sub' : # substitution
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if len(lab[i]) > 0 :
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self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
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self.data[lab[i]]['sub'] = self.data[lab[i]]['sub'] + 1
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result['all'] = result['all'] + 1
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result['sub'] = result['sub'] + 1
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result['lab'].insert(0, lab[i])
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result['rec'].insert(0, rec[j])
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i = i - 1
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j = j - 1
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elif self.space[i][j]['error'] == 'del' : # deletion
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if len(lab[i]) > 0 :
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self.data[lab[i]]['all'] = self.data[lab[i]]['all'] + 1
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self.data[lab[i]]['del'] = self.data[lab[i]]['del'] + 1
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result['all'] = result['all'] + 1
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result['del'] = result['del'] + 1
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result['lab'].insert(0, lab[i])
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result['rec'].insert(0, "")
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i = i - 1
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elif self.space[i][j]['error'] == 'ins' : # insertion
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if len(rec[j]) > 0 :
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self.data[rec[j]]['ins'] = self.data[rec[j]]['ins'] + 1
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result['ins'] = result['ins'] + 1
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result['lab'].insert(0, "")
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result['rec'].insert(0, rec[j])
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j = j - 1
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elif self.space[i][j]['error'] == 'non' : # starting point
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break
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else : # shouldn't reach here
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print('this should not happen , i = {i} , j = {j} , error = {error}'.format(i = i, j = j, error = self.space[i][j]['error']))
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return result
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def overall(self) :
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result = {'all':0, 'cor':0, 'sub':0, 'ins':0, 'del':0}
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for token in self.data :
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result['all'] = result['all'] + self.data[token]['all']
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result['cor'] = result['cor'] + self.data[token]['cor']
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result['sub'] = result['sub'] + self.data[token]['sub']
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result['ins'] = result['ins'] + self.data[token]['ins']
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result['del'] = result['del'] + self.data[token]['del']
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return result
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def cluster(self, data) :
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result = {'all':0, 'cor':0, 'sub':0, 'ins':0, 'del':0}
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for token in data :
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if token in self.data :
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result['all'] = result['all'] + self.data[token]['all']
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result['cor'] = result['cor'] + self.data[token]['cor']
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result['sub'] = result['sub'] + self.data[token]['sub']
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result['ins'] = result['ins'] + self.data[token]['ins']
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result['del'] = result['del'] + self.data[token]['del']
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return result
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def keys(self) :
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return list(self.data.keys())
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def width(string):
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return sum(1 + (unicodedata.east_asian_width(c) in "AFW") for c in string)
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def default_cluster(word) :
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unicode_names = [ unicodedata.name(char) for char in word ]
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for i in reversed(range(len(unicode_names))) :
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if unicode_names[i].startswith('DIGIT') : # 1
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unicode_names[i] = 'Number' # 'DIGIT'
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elif (unicode_names[i].startswith('CJK UNIFIED IDEOGRAPH') or
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unicode_names[i].startswith('CJK COMPATIBILITY IDEOGRAPH')) :
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# 明 / 郎
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unicode_names[i] = 'Mandarin' # 'CJK IDEOGRAPH'
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elif (unicode_names[i].startswith('LATIN CAPITAL LETTER') or
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unicode_names[i].startswith('LATIN SMALL LETTER')) :
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# A / a
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unicode_names[i] = 'English' # 'LATIN LETTER'
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elif unicode_names[i].startswith('HIRAGANA LETTER') : # は こ め
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unicode_names[i] = 'Japanese' # 'GANA LETTER'
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elif (unicode_names[i].startswith('AMPERSAND') or
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unicode_names[i].startswith('APOSTROPHE') or
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unicode_names[i].startswith('COMMERCIAL AT') or
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unicode_names[i].startswith('DEGREE CELSIUS') or
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unicode_names[i].startswith('EQUALS SIGN') or
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unicode_names[i].startswith('FULL STOP') or
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unicode_names[i].startswith('HYPHEN-MINUS') or
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unicode_names[i].startswith('LOW LINE') or
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unicode_names[i].startswith('NUMBER SIGN') or
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unicode_names[i].startswith('PLUS SIGN') or
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unicode_names[i].startswith('SEMICOLON')) :
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# & / ' / @ / ℃ / = / . / - / _ / # / + / ;
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del unicode_names[i]
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else :
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return 'Other'
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if len(unicode_names) == 0 :
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return 'Other'
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if len(unicode_names) == 1 :
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return unicode_names[0]
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for i in range(len(unicode_names)-1) :
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if unicode_names[i] != unicode_names[i+1] :
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return 'Other'
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return unicode_names[0]
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def usage() :
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print("compute-wer.py : compute word error rate (WER) and align recognition results and references.")
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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")
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if __name__ == '__main__':
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if len(sys.argv) == 1 :
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usage()
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sys.exit(0)
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calculator = Calculator()
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cluster_file = ''
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ignore_words = set()
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tochar = False
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verbose= 1
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padding_symbol= ' '
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case_sensitive = False
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max_words_per_line = sys.maxsize
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split = None
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while len(sys.argv) > 3:
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a = '--maxw='
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if sys.argv[1].startswith(a):
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b = sys.argv[1][len(a):]
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del sys.argv[1]
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max_words_per_line = int(b)
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continue
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a = '--rt='
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if sys.argv[1].startswith(a):
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b = sys.argv[1][len(a):].lower()
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del sys.argv[1]
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remove_tag = (b == 'true') or (b != '0')
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continue
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a = '--cs='
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if sys.argv[1].startswith(a):
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b = sys.argv[1][len(a):].lower()
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del sys.argv[1]
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case_sensitive = (b == 'true') or (b != '0')
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continue
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a = '--cluster='
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if sys.argv[1].startswith(a):
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cluster_file = sys.argv[1][len(a):]
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del sys.argv[1]
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continue
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a = '--splitfile='
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if sys.argv[1].startswith(a):
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split_file = sys.argv[1][len(a):]
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del sys.argv[1]
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split = dict()
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with codecs.open(split_file, 'r', 'utf-8') as fh:
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for line in fh: # line in unicode
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words = line.strip().split()
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if len(words) >= 2:
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split[words[0]] = words[1:]
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continue
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a = '--ig='
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if sys.argv[1].startswith(a):
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ignore_file = sys.argv[1][len(a):]
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del sys.argv[1]
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with codecs.open(ignore_file, 'r', 'utf-8') as fh:
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for line in fh: # line in unicode
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line = line.strip()
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if len(line) > 0:
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ignore_words.add(line)
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continue
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a = '--char='
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if sys.argv[1].startswith(a):
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b = sys.argv[1][len(a):].lower()
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del sys.argv[1]
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tochar = (b == 'true') or (b != '0')
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continue
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a = '--v='
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if sys.argv[1].startswith(a):
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b = sys.argv[1][len(a):].lower()
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del sys.argv[1]
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verbose=0
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try:
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verbose=int(b)
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except:
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if b == 'true' or b != '0':
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verbose = 1
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continue
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a = '--padding-symbol='
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if sys.argv[1].startswith(a):
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b = sys.argv[1][len(a):].lower()
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del sys.argv[1]
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if b == 'space':
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padding_symbol= ' '
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elif b == 'underline':
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padding_symbol= '_'
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continue
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if True or sys.argv[1].startswith('-'):
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#ignore invalid switch
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del sys.argv[1]
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continue
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if not case_sensitive:
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ig=set([w.upper() for w in ignore_words])
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ignore_words = ig
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default_clusters = {}
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default_words = {}
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ref_file = sys.argv[1]
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hyp_file = sys.argv[2]
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rec_set = {}
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if split and not case_sensitive:
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newsplit = dict()
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for w in split:
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words = split[w]
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for i in range(len(words)):
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words[i] = words[i].upper()
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newsplit[w.upper()] = words
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split = newsplit
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with codecs.open(hyp_file, 'r', 'utf-8') as fh:
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for line in fh:
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if tochar:
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array = characterize(line)
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else:
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array = line.strip().split()
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if len(array)==0: continue
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fid = array[0]
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rec_set[fid] = normalize(array[1:], ignore_words, case_sensitive, split)
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# compute error rate on the interaction of reference file and hyp file
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for line in open(ref_file, 'r', encoding='utf-8') :
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if tochar:
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array = characterize(line)
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else:
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array = line.rstrip('\n').split()
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if len(array)==0: continue
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fid = array[0]
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if fid not in rec_set:
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continue
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lab = normalize(array[1:], ignore_words, case_sensitive, split)
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rec = rec_set[fid]
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if verbose:
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print('\nutt: %s' % fid)
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for word in rec + lab :
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if word not in default_words :
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default_cluster_name = default_cluster(word)
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if default_cluster_name not in default_clusters :
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default_clusters[default_cluster_name] = {}
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if word not in default_clusters[default_cluster_name] :
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default_clusters[default_cluster_name][word] = 1
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default_words[word] = default_cluster_name
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result = calculator.calculate(lab, rec)
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if verbose:
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if result['all'] != 0 :
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wer = float(result['ins'] + result['sub'] + result['del']) * 100.0 / result['all']
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else :
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wer = 0.0
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print('WER: %4.2f %%' % wer, end = ' ')
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|
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print('N=%d C=%d S=%d D=%d I=%d' %
|
|
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(result['all'], result['cor'], result['sub'], result['del'], result['ins']))
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|
|
space = {}
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|
space['lab'] = []
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space['rec'] = []
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for idx in range(len(result['lab'])) :
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|
len_lab = width(result['lab'][idx])
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|
|
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 = ' ')
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|
|
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('===========================================================================')
|
