语音识别HResults统计工具以及字根据关键词进行合并

#主要想说一下关键词合并

1.HResults统计工具可以参考其他博主的：
https://blog.csdn.net/weixin_30348519/article/details/98426654?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522172088587416800215066191%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=172088587416800215066191&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduend~default-1-98426654-null-null.142^v100^pc_search_result_base3&utm_term=hresults&spm=1018.2226.3001.4187

2.HResults统计工具传入的文本格式：

#!MLF!#
"*No1.lab"
安
徽
省
pro
合
肥
市
ai
bot
a
i
和
en
open
a
i
chat
g
p
t
的
大
家
g
p
t
.

 

关键词内容：
it
ai
aibot
chatgpt
openai
合肥市
合肥
gpt
省市
安徽

这里说明一下，中文按照字一一换行，英文按照单词换行，但是语音识别发音可能就拆开了，但是想根据关键词，将相邻的字组成关键词如下：

#!MLF!#
"*No1.lab"
安徽
省
pro
合肥市
aibot
ai
和
en
openai
chatgpt
的
大
家
gpt
.

 代码实现：（根据DFA算法改的）

# -*- coding:utf-8 -*-
import copy
import time,retime1 = time.time()
"""
DFA算法：
它的基本思想是基于状态转移来检索敏感词，只需要扫描一次待检测文本，就能对所有敏感词进行检测
"""# DFA算法
class DFAFilter(object):def __init__(self):self.keyword_chains = {}  # 关键词链表self.delimit = '\x00'  # 限定# 将敏感词内容添加关键词链表def add(self, keyword):keyword = keyword.lower()  # 关键词英文变为小写chars = keyword.strip()  # 关键字去除首尾空格和换行if not chars:  # 如果关键词为空直接返回returnlevel = self.keyword_chains# 遍历关键字的每个字for i in range(len(chars)):# 如果这个字已经存在字符链的key中就进入其子字典if chars[i] in level:level = level[chars[i]]print(level)else:# isinstance(level, dict)判断是否是一个类型，是返回Trueif not isinstance(level, dict):print("print(level)", level)breakfor j in range(i, len(chars)):level[chars[j]] = {}last_level, last_char = level, chars[j]level = level[chars[j]]last_level[last_char] = {self.delimit: 0}breakif i == len(chars) - 1:level[self.delimit] = 0# 读取关键词def parse(self, path):with open(path, encoding='utf-8') as f:for keyword in f:self.add(str(keyword).strip())# print(self.keyword_chains)def two_eng(self,char):if len(char)>=2 and re.match(r"[a-zA-Z\']",char):for letter in char:# print("letter",letter, level)if letter in level:level = level[letter]if self.delimit in level:matched = Trueelse:print("break", char, level)break# 合并关键词def filter(self, message, repl="*"):ret = []start = 0print(self.keyword_chains)while start < len(message):level = self.keyword_chainsmatched = Falseend = Falsekey_word = ""letter_word=""for i in range(start, len(message)):char = message[i]# print(char)if len(char)>=2:stop=Falsecomin=copy.copy(matched)for letter in char:if letter in level:level = level[letter]letter_word += letterelse:if comin:matched = Trueelse:matched = Falsestop=Truebreak# 判断这个char在level还是不在if stop:letter_word = ""breakelse:# print('ok',char)key_word+=letter_wordletter_word = ""if self.delimit in level:matched = Trueif i+1 == len(message):end=Trueelse:#一直遍历，直到不存在的时候退出,看char是否okif char in level:level = level[char]key_word+=charif self.delimit in level:matched = Trueif i+1 == len(message):end=Trueelse:breakif matched:ret.append(key_word)start = iif end:breakelse:# ifret.append(message[start])start += 1return retif __name__ == "__main__":gfw = DFAFilter()path = 'key_word.txt'gfw.parse(path)text  = ['安', '徽', '省','pro', '合', '肥','市','ai', 'bot','a', 'i', '和','en', 'open', 'a', 'i', 'chat','g','p','t','的', '大', '家','g','p','t']result = gfw.filter(text)print(text)print(result)time2 = time.time()print('总共耗时：' + str(time2 - time1) + 's')for I in result:print(I)