update daily 20170831
This commit is contained in:
nl8590687
parent
179225cd81
commit
740b65f884
|
|
@ -52,11 +52,14 @@ def get_wav_list(filename):
|
|||
txt_text=txt_obj.read()
|
||||
txt_lines=txt_text.split('\n') # 文本分割
|
||||
dic_filelist={} # 初始化字典
|
||||
list_wavmark=[] # 初始化wav列表
|
||||
for i in txt_lines:
|
||||
if(i!=''):
|
||||
txt_l=i.split(' ')
|
||||
dic_filelist[txt_l[0]]=txt_l[1]
|
||||
return dic_filelist
|
||||
dic_filelist[txt_l[0]]='wav/'+txt_l[1]
|
||||
list_wavmark.append(txt_l[0])
|
||||
txt_obj.close()
|
||||
return dic_filelist,list_wavmark
|
||||
|
||||
def get_wav_symbol(filename):
|
||||
'''
|
||||
|
|
@ -67,11 +70,14 @@ def get_wav_symbol(filename):
|
|||
txt_text=txt_obj.read()
|
||||
txt_lines=txt_text.split('\n') # 文本分割
|
||||
dic_symbol_list={} # 初始化字典
|
||||
list_symbolmark=[] # 初始化symbol列表
|
||||
for i in txt_lines:
|
||||
if(i!=''):
|
||||
txt_l=i.split(' ')
|
||||
dic_symbol_list[txt_l[0]]=txt_l[1:]
|
||||
return dic_symbol_list
|
||||
list_symbolmark.append(txt_l[0])
|
||||
txt_obj.close()
|
||||
return dic_symbol_list,list_symbolmark
|
||||
|
||||
if(__name__=='__main__'):
|
||||
#dic=get_wav_symbol('E:\\语音数据集\\doc\\doc\\trans\\train.syllable.txt')
|
||||
|
|
|
|||
19
log.md
19
log.md
|
|
@ -1 +1,18 @@
|
|||
# ASRT_SpeechRecognition
基于深度学习的语音识别系统
## Introduction
这里是更新记录日志文件
如果有什么问题，团队内部需要在这里直接写出来
## Log
### 2017-08-29
准备使用现有的包[python_speech_features](https://github.com/jameslyons/python_speech_features)来实现特征的提取，以及求一阶二阶差分。
### 2017-08-28
开始准备制作语音信号处理方面的功能
### 2017-08-22
准备使用Keras基于LSTM/CNN尝试实现
|
||||
# ASRT_SpeechRecognition
|
||||
基于深度学习的语音识别系统
|
||||
|
||||
## Introduction
|
||||
|
||||
这里是更新记录日志文件
|
||||
|
||||
如果有什么问题，团队内部需要在这里直接写出来
|
||||
|
||||
## Log
|
||||
### 2017-08-31
|
||||
数据处理部分的代码基本完成，现在准备撸模型
|
||||
### 2017-08-29
|
||||
准备使用现有的包[python_speech_features](https://github.com/jameslyons/python_speech_features)来实现特征的提取，以及求一阶二阶差分。
|
||||
### 2017-08-28
|
||||
开始准备制作语音信号处理方面的功能
|
||||
### 2017-08-22
|
||||
准备使用Keras基于LSTM/CNN尝试实现
|
||||
|
|
|
|||
15
main.py
15
main.py
|
|
@ -40,23 +40,30 @@ class ModelSpeech(): # 语音模型类
|
|||
_model.compile(optimizer="adam", loss='categorical_crossentropy',metrics=["accuracy"])
|
||||
return _model
|
||||
|
||||
def TrainModel(self,datas,epoch = 2,save_step=5000,filename='model_speech/LSTM_CNN_model'):
|
||||
def TrainModel(self,datapath,epoch = 2,save_step=1000,filename='model_speech/LSTM_CNN_model'):
|
||||
'''
|
||||
训练模型
|
||||
参数：
|
||||
datapath: 数据保存的路径
|
||||
epoch: 迭代轮数
|
||||
save_step: 每多少步保存一次模型
|
||||
filename: 默认保存文件名，不含文件后缀名
|
||||
'''
|
||||
for epoch in range(epoch):
|
||||
pass
|
||||
pass
|
||||
|
||||
def LoadModel(self,filename='model_speech/LSTM_CNN_model'):
|
||||
def LoadModel(self,filename='model_speech/LSTM_CNN_model.model'):
|
||||
'''
|
||||
加载模型参数
|
||||
'''
|
||||
self._model.load_weights(filename)
|
||||
|
||||
def SaveModel(self,filename='model_speech/LSTM_CNN_model'):
|
||||
def SaveModel(self,filename='model_speech/LSTM_CNN_model',comment=''):
|
||||
'''
|
||||
保存模型参数
|
||||
'''
|
||||
self._model.save_weights(filename+'.model')
|
||||
self._model.save_weights(filename+comment+'.model')
|
||||
|
||||
def TestModel(self):
|
||||
'''
|
||||
|
|
|
|||
122
readdata.py
122
readdata.py
|
|
@ -11,39 +11,141 @@ from python_speech_features import logfbank
|
|||
#import scipy.io.wavfile as wav
|
||||
|
||||
class DataSpeech():
|
||||
|
||||
|
||||
def __init__(self,path):
|
||||
'''
|
||||
初始化
|
||||
参数：
|
||||
path：数据存放位置根目录
|
||||
'''
|
||||
self.datapath = path; # 数据存放位置根目录
|
||||
if('\\'!=self.datapath[-1]): # 在目录路径末尾增加斜杠
|
||||
self.datapath=self.datapath+'\\'
|
||||
self.dic_wavlist = {}
|
||||
self.dic_symbollist = {}
|
||||
self.list_symbol = self.GetSymbolList() # 全部汉语拼音符号列表
|
||||
self.list_wavnum=[] # wav文件标记列表
|
||||
self.list_symbolnum=[] # symbol标记列表
|
||||
pass
|
||||
|
||||
def GetData(self,n):
|
||||
|
||||
def LoadDataList(self,type):
|
||||
'''
|
||||
读取数据，返回神经网络输入值和输出值矩阵
|
||||
加载用于计算的数据列表
|
||||
参数：
|
||||
n：第几个数据
|
||||
type：选取的数据集类型
|
||||
train 训练集
|
||||
dev 开发集
|
||||
test 测试集
|
||||
'''
|
||||
pass
|
||||
# 设定选取哪一项作为要使用的数据集
|
||||
if(type=='train'):
|
||||
filename_wavlist='doc\\doc\\list\\train.wav.lst'
|
||||
filename_symbollist='doc\\doc\\trans\\train.syllable.txt'
|
||||
elif(type=='dev'):
|
||||
filename_wavlist='doc\\doc\\list\\cv.wav.lst'
|
||||
filename_symbollist='doc\\doc\\trans\\cv.syllable.txt'
|
||||
elif(type=='test'):
|
||||
filename_wavlist='doc\\doc\\list\\test.wav.lst'
|
||||
filename_symbollist='doc\\doc\\trans\\test.syllable.txt'
|
||||
else:
|
||||
filename_wavlist='' # 默认留空
|
||||
filename_symbollist=''
|
||||
# 读取数据列表，wav文件列表和其对应的符号列表
|
||||
self.dic_wavlist,self.list_wavnum = get_wav_list(self.datapath+filename_wavlist)
|
||||
self.dic_symbollist,self.list_symbolnum = get_wav_symbol(self.datapath+filename_symbollist)
|
||||
|
||||
def GetDataNum(self):
|
||||
'''
|
||||
获取数据的数量
|
||||
当wav数量和symbol数量一致的时候返回正确的值，否则返回-1，代表出错。
|
||||
'''
|
||||
pass
|
||||
if(len(self.dic_wavlist) == len(self.dic_symbollist)):
|
||||
return len(self.dic_wavlist)
|
||||
else:
|
||||
return -1
|
||||
|
||||
def GetData(self,n_start,n_amount=1):
|
||||
'''
|
||||
读取数据，返回神经网络输入值和输出值矩阵(可直接用于神经网络训练的那种)
|
||||
参数：
|
||||
n_start：从编号为n_start数据开始选取数据
|
||||
n_amount：选取的数据数量，默认为1，即一次一个wav文件
|
||||
返回：
|
||||
三个包含wav特征矩阵的神经网络输入值，和一个标定的类别矩阵神经网络输出值
|
||||
'''
|
||||
# 读取一个文件
|
||||
filename = self.dic_wavlist[self.list_wavnum[n_start]]
|
||||
|
||||
filename=filename.replace('/','\\') # windows系统下需要添加这一行
|
||||
|
||||
wavsignal,fs=read_wav_data(self.datapath+filename)
|
||||
# 获取输入特征
|
||||
feat_mfcc=mfcc(wavsignal[0],fs)
|
||||
feat_mfcc_d=delta(feat_mfcc,2)
|
||||
feat_mfcc_dd=delta(feat_mfcc_d,2)
|
||||
# 获取输出特征
|
||||
list_symbol=self.dic_symbollist[self.list_symbolnum[n_start]]
|
||||
feat_out=[]
|
||||
for i in list_symbol:
|
||||
if(''!=i):
|
||||
n=self.SymbolToNum(i)
|
||||
v=self.NumToVector(n)
|
||||
feat_out.append(v)
|
||||
# 返回值分别是mfcc特征向量的矩阵及其一阶差分和二阶差分矩阵，以及对应的拼音符号矩阵
|
||||
return feat_mfcc,feat_mfcc_d,feat_mfcc_dd,np.array(feat_out)
|
||||
|
||||
def GetSymbolList(self):
|
||||
'''
|
||||
加载拼音符号列表，用于标记符号
|
||||
返回一个列表list类型变量
|
||||
'''
|
||||
txt_obj=open(self.datapath+'dict.txt','r',encoding='UTF-8') # 打开文件并读入
|
||||
txt_text=txt_obj.read()
|
||||
txt_lines=txt_text.split('\n') # 文本分割
|
||||
list_symbol=[] # 初始化符号列表
|
||||
for i in txt_lines:
|
||||
if(i!=''):
|
||||
txt_l=i.split('\t')
|
||||
list_symbol.append(txt_l[0])
|
||||
txt_obj.close()
|
||||
list_symbol.append(' ')
|
||||
return list_symbol
|
||||
|
||||
def SymbolToNum(self,symbol):
|
||||
'''
|
||||
符号转为数字
|
||||
'''
|
||||
return self.list_symbol.index(symbol)
|
||||
|
||||
def NumToVector(self,num):
|
||||
'''
|
||||
数字转为对应的向量
|
||||
'''
|
||||
v_tmp=[]
|
||||
for i in range(0,len(self.list_symbol)):
|
||||
if(i==num):
|
||||
v_tmp.append(1)
|
||||
else:
|
||||
v_tmp.append(0)
|
||||
v=np.array([v_tmp])
|
||||
return v
|
||||
|
||||
if(__name__=='__main__'):
|
||||
wave_data, fs = read_wav_data("general_function\\A2_0.wav")
|
||||
print(wave_data)
|
||||
#wave_data, fs = read_wav_data("general_function\\A2_0.wav")
|
||||
#print(wave_data)
|
||||
#(fs,wave_data)=wav.read('E:\\国创项目工程\代码\\ASRT_SpeechRecognition\\general_function\\A2_0.wav')
|
||||
wav_show(wave_data[0],fs)
|
||||
#wav_show(wave_data[0],fs)
|
||||
#mfcc_feat = mfcc(wave_data[0],fs) # 计算MFCC特征
|
||||
#print(mfcc_feat[100:110,:])
|
||||
#print(mfcc_feat[0:3,:])
|
||||
#d_mfcc_feat_1 = delta(mfcc_feat, 2)
|
||||
#print(d_mfcc_feat_1[0,:])
|
||||
#d_mfcc_feat_2 = delta(d_mfcc_feat_1, 2)
|
||||
#print(d_mfcc_feat_2[0,:])
|
||||
#path='E:\\语音数据集'
|
||||
#l=DataSpeech(path)
|
||||
#l.LoadDataList('train')
|
||||
#print(l.GetDataNum())
|
||||
#print(l.GetData(0))
|
||||
pass
|
||||
|
||||
Loading…
Reference in New Issue