增加了跨平台支持

SpeechModel.py

@@ -3,6 +3,8 @@
 """
 @author: nl8590687
 """
+import platform as plat
+
 # LSTM_CNN
 import keras as kr
 import numpy as np
@@ -56,7 +58,7 @@ class ModelSpeech(): # 语音模型类
 		layer_h3 = Conv1D(256, 5, use_bias=True, padding="valid")(layer_h2) # 卷积层
 		layer_h4 = MaxPooling1D(pool_size=2, strides=None, padding="valid")(layer_h3) # 池化层
 		layer_h5 = Dropout(0.2)(layer_h4) # 随机中断部分神经网络连接，防止过拟合
-		layer_h6 = Dense(self.MS_OUTPUT_SIZE, use_bias=True, activation="softmax")(layer_h5) # 全连接层
+		layer_h6 = Dense(256, use_bias=True, activation="softmax")(layer_h5) # 全连接层
 		layer_h7 = LSTM(256, activation='relu', use_bias=True, return_sequences=True)(layer_h6) # LSTM层
 		layer_h8 = Dropout(0.2)(layer_h7) # 随机中断部分神经网络连接，防止过拟合
 		layer_h9 = Dense(self.MS_OUTPUT_SIZE, use_bias=True, activation="softmax")(layer_h8) # 全连接层
@@ -150,16 +152,16 @@ class ModelSpeech(): # 语音模型类
 					print('[error] generator error. please check data format.')
 					break
 				
-				self.SaveModel(comment='_e_'+str(epoch)+'_step_'+str(n_step))
+				self.SaveModel(comment='_e_'+str(epoch)+'_step_'+str(n_step * save_step))
 				
 				
-	def LoadModel(self,filename='model_speech/LSTM_CNN_model.model'):
+	def LoadModel(self,filename='model_speech/speech_model_e_0_step_1.model'):
 		'''
 		加载模型参数
 		'''
 		self._model.load_weights(filename)
 
-	def SaveModel(self,filename='model_speech/LSTM_CNN_model',comment=''):
+	def SaveModel(self,filename='model_speech/speech_model',comment=''):
 		'''
 		保存模型参数
 		'''
@@ -199,8 +201,23 @@ class ModelSpeech(): # 语音模型类
 
 
 if(__name__=='__main__'):
-	datapath = 'E:\\语音数据集'
+	datapath = ''
+	modelpath = ''
 	ms = ModelSpeech()
+	
+	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
+	if(system_type == 'Windows'):
+		datapath = 'E:\\语音数据集'
+		modelpath = 'model_speech\\'
+	elif(system_type == 'Linux'):
+		datapath = 'dataset'
+		modelpath = 'model_speech/'
+	else:
+		print('*[Message] Unknown System\n')
+		datapath = 'dataset'
+		modelpath = 'model_speech/'
+	
+	#ms.LoadModel(modelpath + 'speech_model_e_0_step_1.model')
 	ms.TrainModel(datapath)
 	#ms.TestModel(datapath)
 	

general_function/file_wav.py

@@ -56,7 +56,7 @@ def get_wav_list(filename):
 	for i in txt_lines:
 		if(i!=''):
 			txt_l=i.split(' ')
-			dic_filelist[txt_l[0]]='wav/'+txt_l[1]
+			dic_filelist[txt_l[0]] = txt_l[1]
 			list_wavmark.append(txt_l[0])
 	txt_obj.close()
 	return dic_filelist,list_wavmark

readdata.py

@@ -1,6 +1,8 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
+import platform as plat
+
 import numpy as np
 from general_function.file_wav import *
 
@@ -20,15 +22,29 @@ class DataSpeech():
 		参数：
 			path：数据存放位置根目录
 		'''
+		
+		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.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.slash = ''
+		if(system_type == 'Windows'):
+			self.slash='\\' # 反斜杠
+		elif(system_type == 'Linux'):
+			self.slash='/' # 正斜杠
+		else:
+			print('*[Message] Unknown System\n')
+			self.slash='/' # 正斜杠
+		
+		if(self.slash != self.datapath[-1]): # 在目录路径末尾增加斜杠
+				self.datapath = self.datapath + self.slash
+		
 		self.dic_wavlist = {}
 		self.dic_symbollist = {}
 		self.SymbolNum = 0 # 记录拼音符号数量
 		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
+		self.list_wavnum = [] # wav文件标记列表
+		self.list_symbolnum = [] # symbol标记列表
 		
 		self.DataNum = 0 # 记录数据量
 		
@@ -45,20 +61,20 @@ class DataSpeech():
 		'''
 		# 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'
+			filename_wavlist = 'doc' + self.slash + 'list' + self.slash + 'train.wav.lst'
+			filename_symbollist = 'doc' + self.slash + 'trans' + self.slash + 'train.syllable.txt'
 		elif(type=='dev'):
-			filename_wavlist='doc\\doc\\list\\cv.wav.lst'
-			filename_symbollist='doc\\doc\\trans\\cv.syllable.txt'
+			filename_wavlist = 'doc' + self.slash + 'list' + self.slash + 'cv.wav.lst'
+			filename_symbollist = 'doc' + self.slash + 'trans' + self.slash + 'cv.syllable.txt'
 		elif(type=='test'):
-			filename_wavlist='doc\\doc\\list\\test.wav.lst'
-			filename_symbollist='doc\\doc\\trans\\test.syllable.txt'
+			filename_wavlist = 'doc' + self.slash + 'list' + self.slash + 'test.wav.lst'
+			filename_symbollist = 'doc' + self.slash + 'trans' + self.slash + 'test.syllable.txt'
 		else:
-			filename_wavlist='' # 默认留空
-			filename_symbollist=''
+			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)
+		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)
 		self.DataNum = self.GetDataNum()
 	
 	def GetDataNum(self):
@@ -85,7 +101,8 @@ class DataSpeech():
 		# 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
+		if('Windows' == plat.system()):
+			filename=filename.replace('/','\\') # windows系统下需要执行这一行，对文件路径做特别处理
 		
 		wavsignal,fs=read_wav_data(self.datapath+filename)
 		# 获取输入特征