140 lines
4.5 KiB
Python
140 lines
4.5 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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"""
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# LSTM_CNN
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import keras as kr
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import numpy as np
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, Input # , Flatten,LSTM,Convolution1D,MaxPooling1D,Merge
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from keras.layers import Conv1D,LSTM,MaxPooling1D, Lambda #, Merge, Conv2D, MaxPooling2D,Conv1D
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from keras import backend as K
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from readdata import DataSpeech
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class ModelSpeech(): # 语音模型类
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def __init__(self,MS_EMBED_SIZE = 64,BATCH_SIZE = 32):
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'''
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初始化
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'''
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self.MS_EMBED_SIZE = MS_EMBED_SIZE # LSTM 的大小
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self.BATCH_SIZE = BATCH_SIZE # 一次训练的batch
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self._model = self.CreateModel()
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def CreateModel(self):
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'''
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定义CNN/LSTM/CTC模型，使用函数式模型
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输入层：39维的特征值序列，一条语音数据的最大长度设为1500（大约15s）
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隐藏层一：1024个神经元的卷积层
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隐藏层二：池化层，池化窗口大小为2
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隐藏层三：Dropout层，需要断开的神经元的比例为0.3，防止过拟合
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隐藏层四：循环层、LSTM层
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隐藏层五：Dropout层，需要断开的神经元的比例为0.3，防止过拟合
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输出层：全连接层，神经元数量为1279，使用softmax作为激活函数，使用CTC的loss作为损失函数
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'''
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# 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
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layer_input = Input((1500,39))
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layer_h1 = Conv1D(256, 5, use_bias=True, padding="valid")(layer_input) # 卷积层
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layer_h2 = MaxPooling1D(pool_size=2, strides=None, padding="valid")(layer_h1) # 池化层
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layer_h3 = Dropout(0.2)(layer_h2) # 随机中断部分神经网络连接，防止过拟合
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layer_h4 = LSTM(256, activation='relu', use_bias=True)(layer_h3) # LSTM层
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layer_h5 = Dropout(0.2)(layer_h4) # 随机中断部分神经网络连接，防止过拟合
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layer_h6 = Dense(1279, activation="softmax")(layer_h5) # 全连接层
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#labels = Input(name='the_labels', shape=[60], dtype='float32')
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layer_out = Lambda(ctc_lambda_func,output_shape=(1279,), name='ctc')(layer_h6) # CTC
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_model = Model(inputs = layer_input, outputs = layer_out)
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#_model = Sequential()
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#_model.add(Conv1D(256, 5,input_shape=(1500,39), use_bias=True, padding="valid"))
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#_model.add(MaxPooling1D(pool_size=2, strides=None, padding="valid"))
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#_model.add(Dropout(0.3)) # 随机中断部分神经网络连接
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#_model.add(LSTM(256, activation='relu', use_bias=True))
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#_model.add(Dropout(0.3)) # 随机中断部分神经网络连接
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#_model.add(Dense(1279, activation="softmax"))
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##_model.add(Lambda(ctc_lambda_func,output_shape=(1,),name='ctc'))
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#_model.compile(optimizer="sgd", loss='categorical_crossentropy',metrics=["accuracy"])
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_model.compile(optimizer="sgd", loss='ctc',metrics=["accuracy"])
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return _model
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def ctc_lambda_func(args):
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#labels, y_pred, input_length, label_length = args
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y_pred = args
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#y_pred = y_pred[:, 2:, :]
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return K.ctc_decode(y_pred,1279)
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#return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
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def TrainModel(self,datapath,epoch = 2,save_step=1000,filename='model_speech/LSTM_CNN_model'):
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'''
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训练模型
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参数：
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datapath: 数据保存的路径
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epoch: 迭代轮数
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save_step: 每多少步保存一次模型
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filename: 默认保存文件名，不含文件后缀名
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'''
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data=DataSpeech(datapath)
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data.LoadDataList('train')
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num_data=DataSpeech.GetDataNum() # 获取数据的数量
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for epoch in range(epoch): # 迭代轮数
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n_step = 0 # 迭代数据数
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while True:
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try:
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data_input, data_label = data.GetData(n_step) # 读数据
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pass
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# 需要写一个生成器函数
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self._model.fit_generator(yielddatas, save_step)
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n_step += 1
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except StopIteration:
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print('[error] generator error. please check data format.')
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break
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self.SaveModel(comment='_e_'+str(epoch)+'_step_'+str(n_step))
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def LoadModel(self,filename='model_speech/LSTM_CNN_model.model'):
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'''
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加载模型参数
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'''
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self._model.load_weights(filename)
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def SaveModel(self,filename='model_speech/LSTM_CNN_model',comment=''):
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'''
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保存模型参数
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'''
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self._model.save_weights(filename+comment+'.model')
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def TestModel(self):
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'''
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测试检验模型效果
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'''
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pass
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def Predict(self,x):
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'''
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预测结果
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'''
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r = predict_on_batch(x)
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return r
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pass
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@property
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def model(self):
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'''
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返回keras model
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'''
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return self._model
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if(__name__=='__main__'):
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pass
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