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LM1.py
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LM1.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: nl8590687
语音识别的语言模型
添加恐慌模式算法，通过解码失败时回退若干字，实现鲁棒性更高的连续文本拼音转汉字HMM统计语言模型
"""
import platform as plat
class ModelLanguage(): # 语音模型类
def __init__(self, modelpath):
self.modelpath = modelpath
system_type = plat.system() # 由于不同的系统的文件路径表示不一样，需要进行判断
self.slash = ''
if(system_type == 'Windows'):
self.slash = '\\'
elif(system_type == 'Linux'):
self.slash = '/'
else:
print('*[Message] Unknown System\n')
self.slash = '/'
if(self.slash != self.modelpath[-1]): # 在目录路径末尾增加斜杠
self.modelpath = self.modelpath + self.slash
pass
def LoadModel(self):
self.dict_pinyin = self.GetSymbolDict('model_language/dict.txt')
self.model1 = self.GetLanguageModel(self.modelpath + 'language_model1.txt')
self.model2 = self.GetLanguageModel(self.modelpath + 'language_model2.txt')
self.pinyin = self.GetPinyin(self.modelpath + 'dic_pinyin.txt')
model = (self.dict_pinyin, self.model1, self.model2 )
return model
pass
def SpeechToText(self, list_syllable):
'''
语音识别专用的处理函数
实现从语音拼音符号到最终文本的转换
使用恐慌模式处理一次解码失败的情况
'''
length = len(list_syllable)
if(length == 0): # 传入的参数没有包含任何拼音时
return ''
lst_syllable_remain = [] # 存储剩余的拼音序列
str_result = ''
# 存储临时输入拼音序列
tmp_list_syllable = list_syllable
while(len(tmp_list_syllable) > 0):
# 进行拼音转汉字解码，存储临时结果
tmp_lst_result = self.decode(tmp_list_syllable, 0.0)
if(len(tmp_lst_result) > 0): # 有结果，不用恐慌
str_result = str_result + tmp_lst_result[0][0]
while(len(tmp_lst_result) == 0): # 没结果，开始恐慌
# 插入最后一个拼音
lst_syllable_remain.insert(0, tmp_list_syllable[-1])
# 删除最后一个拼音
tmp_list_syllable = tmp_list_syllable[:-1]
# 再次进行拼音转汉字解码
tmp_lst_result = self.decode(tmp_list_syllable, 0.0)
if(len(tmp_lst_result) > 0):
# 将得到的结果加入进来
str_result = str_result + tmp_lst_result[0][0]
# 将剩余的结果补回来
tmp_list_syllable = lst_syllable_remain
lst_syllable_remain = [] # 清空
return str_result
def decode(self,list_syllable, yuzhi = 0.0001):
'''
实现拼音向文本的转换
基于马尔可夫链
'''
#assert self.dic_pinyin == null or self.model1 == null or self.model2 == null
list_words = []
num_pinyin = len(list_syllable)
#print('======')
#print('decode function: list_syllable\n',list_syllable)
#print(num_pinyin)
# 开始语音解码
for i in range(num_pinyin):
#print(i)
ls = ''
if(list_syllable[i] in self.dict_pinyin): # 如果这个拼音在汉语拼音字典里的话
# 获取拼音下属的字的列表，ls包含了该拼音对应的所有的字
ls = self.dict_pinyin[list_syllable[i]]
else:
break
if(i == 0):
# 第一个字做初始处理
num_ls = len(ls)
for j in range(num_ls):
tuple_word = ['',0.0]
# 设置马尔科夫模型初始状态值
# 设置初始概率，置为1.0
tuple_word = [ls[j], 1.0]
#print(tuple_word)
# 添加到可能的句子列表
list_words.append(tuple_word)
#print(list_words)
continue
else:
# 开始处理紧跟在第一个字后面的字
list_words_2 = []
num_ls_word = len(list_words)
#print('ls_wd: ',list_words)
for j in range(0, num_ls_word):
num_ls = len(ls)
for k in range(0, num_ls):
tuple_word = ['',0.0]
tuple_word = list(list_words[j]) # 把现有的每一条短语取出来
#print('tw1: ',tuple_word)
tuple_word[0] = tuple_word[0] + ls[k] # 尝试按照下一个音可能对应的全部的字进行组合
#print('ls[k] ',ls[k])
tmp_words = tuple_word[0][-2:] # 取出用于计算的最后两个字
#print('tmp_words: ',tmp_words,tmp_words in self.model2)
if(tmp_words in self.model2): # 判断它们是不是再状态转移表里
#print(tmp_words,tmp_words in self.model2)
tuple_word[1] = tuple_word[1] * float(self.model2[tmp_words]) / float(self.model1[tmp_words[-2]])
# 核心！在当前概率上乘转移概率，公式化简后为第n-1和n个字出现的次数除以第n-1个字出现的次数
#print(self.model2[tmp_words],self.model1[tmp_words[-2]])
else:
tuple_word[1] = 0.0
continue
#print('tw2: ',tuple_word)
#print(tuple_word[1] >= pow(yuzhi, i))
if(tuple_word[1] >= pow(yuzhi, i)):
# 大于阈值之后保留，否则丢弃
list_words_2.append(tuple_word)
list_words = list_words_2
#print(list_words,'\n')
#print(list_words)
for i in range(0, len(list_words)):
for j in range(i + 1, len(list_words)):
if(list_words[i][1] < list_words[j][1]):
tmp = list_words[i]
list_words[i] = list_words[j]
list_words[j] = tmp
return list_words
pass
def GetSymbolDict(self, dictfilename):
'''
读取拼音汉字的字典文件
返回读取后的字典
'''
txt_obj = open(dictfilename, 'r', encoding='UTF-8') # 打开文件并读入
txt_text = txt_obj.read()
txt_obj.close()
txt_lines = txt_text.split('\n') # 文本分割
dic_symbol = {} # 初始化符号字典
for i in txt_lines:
list_symbol=[] # 初始化符号列表
if(i!=''):
txt_l=i.split('\t')
pinyin = txt_l[0]
for word in txt_l[1]:
list_symbol.append(word)
dic_symbol[pinyin] = list_symbol
return dic_symbol
def GetLanguageModel(self, modelLanFilename):
'''
读取语言模型的文件
返回读取后的模型
'''
txt_obj = open(modelLanFilename, 'r', encoding='UTF-8') # 打开文件并读入
txt_text = txt_obj.read()
txt_obj.close()
txt_lines = txt_text.split('\n') # 文本分割
dic_model = {} # 初始化符号字典
for i in txt_lines:
if(i!=''):
txt_l=i.split('\t')
if(len(txt_l) == 1):
continue
#print(txt_l)
dic_model[txt_l[0]] = txt_l[1]
return dic_model
def GetPinyin(self, filename):
file_obj = open(filename,'r',encoding='UTF-8')
txt_all = file_obj.read()
file_obj.close()
txt_lines = txt_all.split('\n')
dic={}
for line in txt_lines:
if(line == ''):
continue
pinyin_split = line.split('\t')
list_pinyin=pinyin_split[0]
if(list_pinyin not in dic and int(pinyin_split[1]) > 1):
dic[list_pinyin] = pinyin_split[1]
return dic
if(__name__=='__main__'):
ml = ModelLanguage('model_language')
ml.LoadModel()
#str_pinyin = ['zhe4','zhen1','shi4','ji2', 'hao3','de5']
#str_pinyin = ['jin1', 'tian1', 'shi4', 'xing1', 'qi1', 'san1']
#str_pinyin = ['ni3', 'hao3','a1']
#str_pinyin = ['wo3','dui4','shi4','mei2','cuo4','ni3','hao3']
#str_pinyin = ['wo3','dui4','shi4','tian1','mei2','na5','li3','hai4']
#str_pinyin = ['ba3','zhe4','xie1','zuo4','wan2','wo3','jiu4','qu4','shui4','jiao4']
#str_pinyin = ['wo3','qu4','a4','mei2','shi4','er2','la1']
#str_pinyin = ['wo3', 'men5', 'qun2', 'li3', 'xiong1', 'di4', 'jian4', 'mei4', 'dou1', 'zai4', 'shuo1']
#str_pinyin = ['su1', 'an1', 'ni3', 'sui4', 'li4', 'yun4', 'sui2', 'cong2', 'jiao4', 'ming2', 'tao2', 'qi3', 'yu2', 'peng2', 'ya4', 'yang4', 'chao1', 'dao3', 'jiang1', 'li3', 'yuan2', 'kang1', 'zhua1', 'zou3']
#str_pinyin = ['da4', 'jia1', 'hao3']
str_pinyin = ['kao3', 'yan2', 'yan1', 'yu3', 'ci2', 'hui4']
#r = ml.decode(str_pinyin)
r=ml.SpeechToText(str_pinyin)
print('语音转文字结果：\n',r)