246 lines
7.4 KiB
Python
246 lines
7.4 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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@author: nl8590687
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语音识别的语言模型
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基于马尔可夫模型的语言模型
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"""
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import platform as plat
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class ModelLanguage(): # 语音模型类
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def __init__(self, modelpath):
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self.modelpath = modelpath
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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
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self.slash = ''
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if(system_type == 'Windows'):
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self.slash = '\\'
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elif(system_type == 'Linux'):
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self.slash = '/'
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else:
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print('*[Message] Unknown System\n')
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self.slash = '/'
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if(self.slash != self.modelpath[-1]): # 在目录路径末尾增加斜杠
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self.modelpath = self.modelpath + self.slash
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pass
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def LoadModel(self):
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self.dict_pinyin = self.GetSymbolDict('dict.txt')
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self.model1 = self.GetLanguageModel(self.modelpath + 'language_model1.txt')
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self.model2 = self.GetLanguageModel(self.modelpath + 'language_model2.txt')
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self.pinyin = self.GetPinyin(self.modelpath + 'dic_pinyin.txt')
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model = (self.dict_pinyin, self.model1, self.model2 )
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return model
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pass
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def SpeechToText(self, list_syllable):
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'''
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为语音识别专用的处理函数
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实现从语音拼音符号到最终文本的转换
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'''
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r=''
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length = len(list_syllable)
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if(length == 0): # 传入的参数没有包含任何拼音时
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return ''
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# 先取出一个字，即拼音列表中第一个字
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str_tmp = [list_syllable[0]]
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for i in range(0, length - 1):
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# 依次从第一个字开始每次连续取两个字拼音
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str_split = list_syllable[i] + ' ' + list_syllable[i+1]
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#print(str_split,str_tmp,r)
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# 如果这个拼音在汉语拼音状态转移字典里的话
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if(str_split in self.pinyin):
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# 将第二个字的拼音加入
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str_tmp.append(list_syllable[i+1])
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else:
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# 否则不加入，然后直接将现有的拼音序列进行解码
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str_decode = self.decode(str_tmp, 0.0000)
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#print('decode ',str_tmp,str_decode)
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if(str_decode != []):
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r += str_decode[0][0]
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# 再重新从i+1开始作为第一个拼音
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str_tmp = [list_syllable[i+1]]
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#print('最后：', str_tmp)
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str_decode = self.decode(str_tmp, 0.0000)
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#print('剩余解码：',str_decode)
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if(str_decode != []):
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r += str_decode[0][0]
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return r
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def decode(self,list_syllable, yuzhi = 0.0001):
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'''
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实现拼音向文本的转换
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基于马尔可夫链
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'''
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#assert self.dic_pinyin == null or self.model1 == null or self.model2 == null
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list_words = []
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num_pinyin = len(list_syllable)
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#print('======')
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#print('decode function: list_syllable\n',list_syllable)
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#print(num_pinyin)
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# 开始语音解码
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for i in range(num_pinyin):
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#print(i)
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ls = ''
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if(list_syllable[i] in self.dict_pinyin): # 如果这个拼音在汉语拼音字典里的话
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# 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
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ls = self.dict_pinyin[list_syllable[i]]
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else:
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break
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if(i == 0):
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# 第一个字做初始处理
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num_ls = len(ls)
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for j in range(num_ls):
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tuple_word = ['',0.0]
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# 设置马尔科夫模型初始状态值
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# 设置初始概率，置为1.0
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tuple_word = [ls[j], 1.0]
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#print(tuple_word)
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# 添加到可能的句子列表
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list_words.append(tuple_word)
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#print(list_words)
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continue
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else:
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# 开始处理紧跟在第一个字后面的字
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list_words_2 = []
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num_ls_word = len(list_words)
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#print('ls_wd: ',list_words)
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for j in range(0, num_ls_word):
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num_ls = len(ls)
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for k in range(0, num_ls):
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tuple_word = ['',0.0]
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tuple_word = list(list_words[j]) # 把现有的每一条短语取出来
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#print('tw1: ',tuple_word)
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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
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#print('ls[k] ',ls[k])
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tmp_words = tuple_word[0][-2:] # 取出用于计算的最后两个字
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#print('tmp_words: ',tmp_words,tmp_words in self.model2)
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if(tmp_words in self.model2): # 判断它们是不是再状态转移表里
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#print(tmp_words,tmp_words in self.model2)
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tuple_word[1] = tuple_word[1] * float(self.model2[tmp_words]) / float(self.model1[tmp_words[-2]])
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# 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
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#print(self.model2[tmp_words],self.model1[tmp_words[-2]])
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else:
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tuple_word[1] = 0.0
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continue
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#print('tw2: ',tuple_word)
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#print(tuple_word[1] >= pow(yuzhi, i))
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if(tuple_word[1] >= pow(yuzhi, i)):
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# 大于阈值之后保留，否则丢弃
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list_words_2.append(tuple_word)
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list_words = list_words_2
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#print(list_words,'\n')
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#print(list_words)
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for i in range(0, len(list_words)):
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for j in range(i + 1, len(list_words)):
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if(list_words[i][1] < list_words[j][1]):
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tmp = list_words[i]
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list_words[i] = list_words[j]
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list_words[j] = tmp
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return list_words
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pass
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def GetSymbolDict(self, dictfilename):
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'''
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读取拼音汉字的字典文件
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返回读取后的字典
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'''
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txt_obj = open(dictfilename, 'r', encoding='UTF-8') # 打开文件并读入
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txt_text = txt_obj.read()
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txt_obj.close()
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txt_lines = txt_text.split('\n') # 文本分割
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dic_symbol = {} # 初始化符号字典
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for i in txt_lines:
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list_symbol=[] # 初始化符号列表
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if(i!=''):
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txt_l=i.split('\t')
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pinyin = txt_l[0]
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for word in txt_l[1]:
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list_symbol.append(word)
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dic_symbol[pinyin] = list_symbol
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return dic_symbol
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def GetLanguageModel(self, modelLanFilename):
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'''
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读取语言模型的文件
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返回读取后的模型
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'''
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txt_obj = open(modelLanFilename, 'r', encoding='UTF-8') # 打开文件并读入
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txt_text = txt_obj.read()
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txt_obj.close()
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txt_lines = txt_text.split('\n') # 文本分割
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dic_model = {} # 初始化符号字典
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for i in txt_lines:
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if(i!=''):
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txt_l=i.split('\t')
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if(len(txt_l) == 1):
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continue
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#print(txt_l)
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dic_model[txt_l[0]] = txt_l[1]
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return dic_model
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def GetPinyin(self, filename):
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file_obj = open(filename,'r',encoding='UTF-8')
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txt_all = file_obj.read()
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file_obj.close()
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txt_lines = txt_all.split('\n')
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dic={}
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for line in txt_lines:
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if(line == ''):
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continue
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pinyin_split = line.split('\t')
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list_pinyin=pinyin_split[0]
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if(list_pinyin not in dic and int(pinyin_split[1]) > 1):
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dic[list_pinyin] = pinyin_split[1]
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return dic
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if(__name__=='__main__'):
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ml = ModelLanguage('model_language')
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ml.LoadModel()
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#str_pinyin = ['zhe4','zhen1','shi4','ji2', 'hao3','de5']
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#str_pinyin = ['jin1', 'tian1', 'shi4', 'xing1', 'qi1', 'san1']
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#str_pinyin = ['ni3', 'hao3','a1']
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#str_pinyin = ['wo3','dui4','shi4','mei2','cuo4','ni3','hao3']
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#str_pinyin = ['wo3','dui4','shi4','tian1','mei2','na5','li3','hai4']
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#str_pinyin = ['ba3','zhe4','xie1','zuo4','wan2','wo3','jiu4','qu4','shui4','jiao4']
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#str_pinyin = ['wo3','qu4','a4','mei2','shi4','er2','la1']
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#str_pinyin = ['wo3', 'men5', 'qun2', 'li3', 'xiong1', 'di4', 'jian4', 'mei4', 'dou1', 'zai4', 'shuo1']
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#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']
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#str_pinyin = ['da4', 'jia1', 'hao3']
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str_pinyin = ['kao3', 'yan2', 'yan1', 'yu3', 'ci2', 'hui4']
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#r = ml.decode(str_pinyin)
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r=ml.SpeechToText(str_pinyin)
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print('语音转文字结果：\n',r)
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