#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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from enum import Enum
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import re, sys, unicodedata
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import codecs
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import argparse
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from tqdm import tqdm
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import os
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import pdb
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remove_tag = False
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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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"?",
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"、",
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"。",
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"！",
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"，",
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"；",
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"？",
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"：",
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"「",
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"」",
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"︰",
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"『",
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"』",
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"《",
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"》",
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]
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class Code(Enum):
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match = 1
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substitution = 2
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insertion = 3
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deletion = 4
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class WordError(object):
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def __init__(self):
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self.errors = {
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Code.substitution: 0,
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Code.insertion: 0,
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Code.deletion: 0,
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}
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self.ref_words = 0
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def get_wer(self):
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assert self.ref_words != 0
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errors = (
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self.errors[Code.substitution]
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+ self.errors[Code.insertion]
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+ self.errors[Code.deletion]
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)
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return 100.0 * errors / self.ref_words
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def get_result_string(self):
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return (
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f"error_rate={self.get_wer():.4f}, "
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f"ref_words={self.ref_words}, "
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f"subs={self.errors[Code.substitution]}, "
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f"ins={self.errors[Code.insertion]}, "
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f"dels={self.errors[Code.deletion]}"
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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 == "<":
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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:
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return ""
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chars = []
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i = 0
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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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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.replace("<BIAS>", ""):
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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 = {
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"lab": [],
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"rec": [],
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"code": [],
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"all": 0,
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"cor": 0,
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"sub": 0,
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"ins": 0,
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"del": 0,
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}
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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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result["code"].insert(0, Code.match)
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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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result["code"].insert(0, Code.substitution)
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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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result["code"].insert(0, Code.deletion)
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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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result["code"].insert(0, Code.insertion)
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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(
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"this should not happen , i = {i} , j = {j} , error = {error}".format(
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i=i, j=j, error=self.space[i][j]["error"]
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)
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)
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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 unicode_names[
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i
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].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 unicode_names[
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i
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].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 (
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unicode_names[i].startswith("AMPERSAND")
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or unicode_names[i].startswith("APOSTROPHE")
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or unicode_names[i].startswith("COMMERCIAL AT")
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or unicode_names[i].startswith("DEGREE CELSIUS")
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or unicode_names[i].startswith("EQUALS SIGN")
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or unicode_names[i].startswith("FULL STOP")
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or unicode_names[i].startswith("HYPHEN-MINUS")
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or unicode_names[i].startswith("LOW LINE")
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or unicode_names[i].startswith("NUMBER SIGN")
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or unicode_names[i].startswith("PLUS SIGN")
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or unicode_names[i].startswith("SEMICOLON")
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):
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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 get_args():
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parser = argparse.ArgumentParser(description="wer cal")
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parser.add_argument("--ref", type=str, help="Text input path")
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parser.add_argument("--ref_ocr", type=str, help="Text input path")
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parser.add_argument("--rec_name", type=str, action="append", default=[])
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parser.add_argument("--rec_file", type=str, action="append", default=[])
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parser.add_argument("--verbose", type=int, default=1, help="show")
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parser.add_argument("--char", type=bool, default=True, help="show")
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args = parser.parse_args()
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return args
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def main(args):
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cluster_file = ""
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ignore_words = set()
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tochar = args.char
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verbose = args.verbose
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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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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 = args.ref
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ref_ocr = args.ref_ocr
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rec_files = args.rec_file
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rec_names = args.rec_name
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assert len(rec_files) == len(rec_names)
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# load ocr
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ref_ocr_dict = {}
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with codecs.open(ref_ocr, "r", "utf-8") as fh:
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for line in fh:
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if "$" in line:
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line = line.replace("$", " ")
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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:
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continue
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fid = array[0]
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ref_ocr_dict[fid] = normalize(array[1:], ignore_words, case_sensitive, split)
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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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rec_sets = {}
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calculators_dict = dict()
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ub_wer_dict = dict()
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hotwords_related_dict = dict() # 记录recall相关的内容
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for i, hyp_file in enumerate(rec_files):
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rec_sets[rec_names[i]] = dict()
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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:
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continue
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fid = array[0]
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rec_sets[rec_names[i]][fid] = normalize(array[1:], ignore_words, case_sensitive, split)
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calculators_dict[rec_names[i]] = Calculator()
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ub_wer_dict[rec_names[i]] = {"u_wer": WordError(), "b_wer": WordError(), "wer": WordError()}
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hotwords_related_dict[rec_names[i]] = {'tp': 0, 'tn': 0, 'fp': 0, 'fn': 0}
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# tp: 热词在label里，同时在rec里
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# tn: 热词不在label里，同时不在rec里
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# fp: 热词不在label里，但是在rec里
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# fn: 热词在label里，但是不在rec里
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# record wrong label but in ocr
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wrong_rec_but_in_ocr_dict = {}
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for rec_name in rec_names:
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wrong_rec_but_in_ocr_dict[rec_name] = 0
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_file_total_len = 0
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with os.popen("cat {} | wc -l".format(ref_file)) as pipe:
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_file_total_len = int(pipe.read().strip())
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# compute error rate on the interaction of reference file and hyp file
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for line in tqdm(open(ref_file, 'r', encoding='utf-8'), total=_file_total_len):
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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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lab = normalize(array[1:], ignore_words, case_sensitive, split)
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if verbose:
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print('\nutt: %s' % fid)
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ocr_text = ref_ocr_dict[fid]
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ocr_set = set(ocr_text)
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print('ocr: {}'.format(" ".join(ocr_text)))
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list_match = [] # 指label里面在ocr里面的内容
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list_not_mathch = []
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tmp_error = 0
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tmp_match = 0
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for index in range(len(lab)):
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# text_list.append(uttlist[index+1])
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if lab[index] not in ocr_set:
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tmp_error += 1
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list_not_mathch.append(lab[index])
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else:
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tmp_match += 1
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list_match.append(lab[index])
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print('label in ocr: {}'.format(" ".join(list_match)))
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# for each reco file
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base_wrong_ocr_wer = None
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ocr_wrong_ocr_wer = None
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for rec_name in rec_names:
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rec_set = rec_sets[rec_name]
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if fid not in rec_set:
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continue
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rec = rec_set[fid]
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# print(rec)
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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 = calculators_dict[rec_name].calculate(lab.copy(), rec.copy())
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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(%s): %4.2f %%' % (rec_name, wer), end=' ')
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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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# print(result['rec'])
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wrong_rec_but_in_ocr = []
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for idx in range(len(result['lab'])):
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if result['lab'][idx] != "":
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if result['lab'][idx] != result['rec'][idx].replace("<BIAS>", ""):
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if result['lab'][idx] in list_match:
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wrong_rec_but_in_ocr.append(result['lab'][idx])
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wrong_rec_but_in_ocr_dict[rec_name] += 1
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print('wrong_rec_but_in_ocr: {}'.format(" ".join(wrong_rec_but_in_ocr)))
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if rec_name == "base":
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base_wrong_ocr_wer = len(wrong_rec_but_in_ocr)
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if "ocr" in rec_name or "hot" in rec_name:
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ocr_wrong_ocr_wer = len(wrong_rec_but_in_ocr)
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if ocr_wrong_ocr_wer < base_wrong_ocr_wer:
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print("{} {} helps, {} -> {}".format(fid, rec_name, base_wrong_ocr_wer, ocr_wrong_ocr_wer))
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elif ocr_wrong_ocr_wer > base_wrong_ocr_wer:
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print("{} {} hurts, {} -> {}".format(fid, rec_name, base_wrong_ocr_wer, ocr_wrong_ocr_wer))
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# recall = 0
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# false_alarm = 0
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# for idx in range(len(result['lab'])):
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# if "<BIAS>" in result['rec'][idx]:
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# if result['rec'][idx].replace("<BIAS>", "") in list_match:
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# recall += 1
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# else:
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# false_alarm += 1
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# print("bias hotwords recall: {}, fa: {}, list_match {}, recall: {:.2f}, fa: {:.2f}".format(
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# recall, false_alarm, len(list_match), recall / len(list_match) if len(list_match) != 0 else 0, false_alarm / len(list_match) if len(list_match) != 0 else 0
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# ))
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# tp: 热词在label里，同时在rec里
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# tn: 热词不在label里，同时不在rec里
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# fp: 热词不在label里，但是在rec里
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# fn: 热词在label里，但是不在rec里
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_rec_list = [word.replace("<BIAS>", "") for word in rec]
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_label_list = [word for word in lab]
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_tp = _tn = _fp = _fn = 0
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hot_true_list = [hotword for hotword in ocr_text if hotword in _label_list]
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hot_bad_list = [hotword for hotword in ocr_text if hotword not in _label_list]
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for badhotword in hot_bad_list:
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count = len([word for word in _rec_list if word == badhotword])
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# print(f"bad {badhotword} count: {count}")
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# for word in _rec_list:
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# if badhotword == word:
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# count += 1
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if count == 0:
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hotwords_related_dict[rec_name]['tn'] += 1
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_tn += 1
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# fp: 0
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else:
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hotwords_related_dict[rec_name]['fp'] += count
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_fp += count
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# tn: 0
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# if badhotword in _rec_list:
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# hotwords_related_dict[rec_name]['fp'] += 1
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# else:
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# hotwords_related_dict[rec_name]['tn'] += 1
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for hotword in hot_true_list:
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true_count = len([word for word in _label_list if hotword == word])
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rec_count = len([word for word in _rec_list if hotword == word])
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# print(f"good {hotword} true_count: {true_count}, rec_count: {rec_count}")
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if rec_count == true_count:
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hotwords_related_dict[rec_name]['tp'] += true_count
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_tp += true_count
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elif rec_count > true_count:
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hotwords_related_dict[rec_name]['tp'] += true_count
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# fp: 不在label里，但是在rec里
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hotwords_related_dict[rec_name]['fp'] += rec_count - true_count
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_tp += true_count
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_fp += rec_count - true_count
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else:
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hotwords_related_dict[rec_name]['tp'] += rec_count
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# fn: 热词在label里，但是不在rec里
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hotwords_related_dict[rec_name]['fn'] += true_count - rec_count
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_tp += rec_count
|
_fn += true_count - rec_count
|
print("hotword: tp: {}, tn: {}, fp: {}, fn: {}, all: {}, recall: {:.2f}%".format(
|
_tp, _tn, _fp, _fn, sum([_tp, _tn, _fp, _fn]), _tp / (_tp + _fn) * 100 if (_tp + _fn) != 0 else 0
|
))
|
|
# if hotword in _rec_list:
|
# hotwords_related_dict[rec_name]['tp'] += 1
|
# else:
|
# hotwords_related_dict[rec_name]['fn'] += 1
|
# 计算uwer, bwer, wer
|
for code, rec_word, lab_word in zip(result["code"], result["rec"], result["lab"]):
|
if code == Code.match:
|
ub_wer_dict[rec_name]["wer"].ref_words += 1
|
if lab_word in hot_true_list:
|
# tmp_ref.append(ref_tokens[ref_idx])
|
ub_wer_dict[rec_name]["b_wer"].ref_words += 1
|
else:
|
ub_wer_dict[rec_name]["u_wer"].ref_words += 1
|
elif code == Code.substitution:
|
ub_wer_dict[rec_name]["wer"].ref_words += 1
|
ub_wer_dict[rec_name]["wer"].errors[Code.substitution] += 1
|
if lab_word in hot_true_list:
|
# tmp_ref.append(ref_tokens[ref_idx])
|
ub_wer_dict[rec_name]["b_wer"].ref_words += 1
|
ub_wer_dict[rec_name]["b_wer"].errors[Code.substitution] += 1
|
else:
|
ub_wer_dict[rec_name]["u_wer"].ref_words += 1
|
ub_wer_dict[rec_name]["u_wer"].errors[Code.substitution] += 1
|
elif code == Code.deletion:
|
ub_wer_dict[rec_name]["wer"].ref_words += 1
|
ub_wer_dict[rec_name]["wer"].errors[Code.deletion] += 1
|
if lab_word in hot_true_list:
|
# tmp_ref.append(ref_tokens[ref_idx])
|
ub_wer_dict[rec_name]["b_wer"].ref_words += 1
|
ub_wer_dict[rec_name]["b_wer"].errors[Code.deletion] += 1
|
else:
|
ub_wer_dict[rec_name]["u_wer"].ref_words += 1
|
ub_wer_dict[rec_name]["u_wer"].errors[Code.deletion] += 1
|
elif code == Code.insertion:
|
ub_wer_dict[rec_name]["wer"].errors[Code.insertion] += 1
|
if rec_word in hot_true_list:
|
ub_wer_dict[rec_name]["b_wer"].errors[Code.insertion] += 1
|
else:
|
ub_wer_dict[rec_name]["u_wer"].errors[Code.insertion] += 1
|
|
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()
|
# print('\n', end='\n')
|
lab1 = lab2
|
rec1 = rec2
|
print('\n', end='\n')
|
# break
|
if verbose:
|
print('===========================================================================')
|
print()
|
|
print(wrong_rec_but_in_ocr_dict)
|
for rec_name in rec_names:
|
result = calculators_dict[rec_name].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} %'.format(rec_name, wer), end=' ')
|
print('N=%d C=%d S=%d D=%d I=%d' %
|
(result['all'], result['cor'], result['sub'], result['del'], result['ins']))
|
print(f"WER: {ub_wer_dict[rec_name]['wer'].get_result_string()}")
|
print(f"U-WER: {ub_wer_dict[rec_name]['u_wer'].get_result_string()}")
|
print(f"B-WER: {ub_wer_dict[rec_name]['b_wer'].get_result_string()}")
|
|
print('hotword: tp: {}, tn: {}, fp: {}, fn: {}, all: {}, recall: {:.2f}%'.format(
|
hotwords_related_dict[rec_name]['tp'],
|
hotwords_related_dict[rec_name]['tn'],
|
hotwords_related_dict[rec_name]['fp'],
|
hotwords_related_dict[rec_name]['fn'],
|
sum([v for k, v in hotwords_related_dict[rec_name].items()]),
|
hotwords_related_dict[rec_name]['tp'] / (
|
hotwords_related_dict[rec_name]['tp'] + hotwords_related_dict[rec_name]['fn']
|
) * 100 if hotwords_related_dict[rec_name]['tp'] + hotwords_related_dict[rec_name]['fn'] != 0 else 0
|
))
|
|
# tp: 热词在label里，同时在rec里
|
# tn: 热词不在label里，同时不在rec里
|
# fp: 热词不在label里，但是在rec里
|
# fn: 热词在label里，但是不在rec里
|
if not verbose:
|
print()
|
print()
|
|
|
if __name__ == "__main__":
|
args = get_args()
|
|
# print("")
|
print(args)
|
main(args)
|
