一个暂时能训练的版本，可能会炸

SpeechModel.py

@@ -28,6 +28,8 @@ class ModelSpeech(): # 语音模型类
 		self.MS_OUTPUT_SIZE = MS_OUTPUT_SIZE # 神经网络最终输出的每一个字符向量维度的大小
 		self.BATCH_SIZE = BATCH_SIZE # 一次训练的batch
 		self.label_max_string_length = 64
+		self.AUDIO_LENGTH = 1600
+		self.AUDIO_FEATURE_LENGTH = 39
 		self._model = self.CreateModel() 
 
 	
@@ -47,7 +49,7 @@ class ModelSpeech(): # 语音模型类
 		当前未完成，针对多输出的CTC层尚未实现
 		'''
 		# 每一帧使用13维mfcc特征及其13维一阶差分和13维二阶差分表示，最大信号序列长度为1500
-		input_data = Input(name='the_input', shape=(1500,39))
+		input_data = Input(name='the_input', shape=(self.AUDIO_LENGTH,self.AUDIO_FEATURE_LENGTH))
 		
 		layer_h1 = Conv1D(256, 5, use_bias=True, padding="valid")(input_data) # 卷积层
 		layer_h2 = MaxPooling1D(pool_size=2, strides=None, padding="valid")(layer_h1) # 池化层
@@ -77,7 +79,7 @@ class ModelSpeech(): # 语音模型类
 		loss_out = Lambda(self.ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])
 		
 		# clipnorm seems to speeds up convergence
-		sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
+		sgd = SGD(lr=0.002, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
 		
 		model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)
 		
@@ -137,7 +139,7 @@ class ModelSpeech(): # 语音模型类
 				try:
 					print('[message] epoch %d . Have train datas %d+'%(epoch, n_step*save_step))
 					# data_genetator是一个生成器函数
-					yielddatas = data.data_genetator(self.BATCH_SIZE)
+					yielddatas = data.data_genetator(self.BATCH_SIZE, self.AUDIO_LENGTH)
 					#self._model.fit_generator(yielddatas, save_step, nb_worker=2)
 					self._model.fit_generator(yielddatas, save_step)
 					n_step += 1

readdata.py

@@ -108,13 +108,13 @@ class DataSpeech():
 		data_label = np.array(feat_out)
 		return data_input, data_label
 	
-	def data_genetator(self, batch_size=32):
+	def data_genetator(self, batch_size=32, audio_length = 1600):
 		'''
 		数据生成器函数，用于Keras的generator_fit训练
 		batch_size: 一次产生的数据量
 		需要再修改。。。
 		'''
-		X = np.zeros((batch_size, 1500,39), dtype=np.int16)
+		X = np.zeros((batch_size, audio_length,39), dtype=np.int16)
 		#y = np.zeros((batch_size, 64, self.SymbolNum), dtype=np.int16)
 		y = np.zeros((batch_size, 64), dtype=np.int16)
 		
@@ -123,7 +123,7 @@ class DataSpeech():
 		labels = []
 		for i in range(0,batch_size):
 			#input_length.append([1500])
-			label_length.append([39])
+			label_length.append([30])
 			labels.append([1])