220 lines
8.1 KiB
Python
220 lines
8.1 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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import platform as plat
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import os
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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, Model
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from keras.layers import Dense, Dropout, Input, Reshape # , Flatten,LSTM,Convolution1D,MaxPooling1D,Merge
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from keras.layers import Conv1D,LSTM,MaxPooling1D, Lambda, TimeDistributed, Activation,Conv2D, MaxPooling2D #, Merge,Conv1D
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from keras import backend as K
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from keras.optimizers import SGD, Adadelta
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from readdata2 import DataSpeech
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from neural_network.ctc_layer import ctc_layer
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from neural_network.ctc_loss import ctc_batch_loss
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#from keras.backend.tensorflow_backend import ctc_batch_cost
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class ModelSpeech(): # 语音模型类
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def __init__(self, datapath):
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'''
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初始化
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默认输出的拼音的表示大小是1283，即1282个拼音+1个空白块
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'''
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MS_OUTPUT_SIZE = 1417
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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
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#self.BATCH_SIZE = BATCH_SIZE # 一次训练的batch
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self.label_max_string_length = 64
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self.AUDIO_LENGTH = 1600
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self.AUDIO_FEATURE_LENGTH = 200
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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0.2，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0.2，防止过拟合
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隐藏层六：全连接层，神经元数量为self.MS_OUTPUT_SIZE，使用softmax作为激活函数，
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输出层：自定义层，即CTC层，使用CTC的loss作为损失函数，实现连接性时序多输出
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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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input_data = Input(name='the_input', shape=(self.AUDIO_LENGTH, self.AUDIO_FEATURE_LENGTH, 1))
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layer_h1 = Conv2D(32, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(input_data) # 卷积层
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layer_h2 = Conv2D(32, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h1) # 卷积层
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layer_h3 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h2) # 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 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h3) # 卷积层
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layer_h5 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h4) # 卷积层
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layer_h6 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h5) # 池化层
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#test=Model(inputs = input_data, outputs = layer_h6)
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#test.summary()
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layer_h7 = Reshape((400, 3200))(layer_h6) #Reshape层
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#layer_h5 = LSTM(256, activation='relu', use_bias=True, return_sequences=True)(layer_h4) # LSTM层
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#layer_h6 = Dropout(0.2)(layer_h5) # 随机中断部分神经网络连接，防止过拟合
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layer_h8 = Dense(256, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h7) # 全连接层
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layer_h9 = Dense(1417, use_bias=True, kernel_initializer='he_normal')(layer_h8) # 全连接层
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y_pred = Activation('softmax', name='Activation0')(layer_h9)
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model_data = Model(inputs = input_data, outputs = y_pred)
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#model_data.summary()
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#labels = Input(name='the_labels', shape=[60], dtype='float32')
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labels = Input(name='the_labels', shape=[self.label_max_string_length], dtype='float32')
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input_length = Input(name='input_length', shape=[1], dtype='int64')
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label_length = Input(name='label_length', shape=[1], dtype='int64')
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# Keras doesn't currently support loss funcs with extra parameters
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# so CTC loss is implemented in a lambda layer
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#layer_out = Lambda(ctc_lambda_func,output_shape=(self.MS_OUTPUT_SIZE, ), name='ctc')([y_pred, labels, input_length, label_length])#(layer_h6) # CTC
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loss_out = Lambda(self.ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])
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model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)
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model.summary()
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# clipnorm seems to speeds up convergence
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#sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
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ada_d = Adadelta(lr = 0.01, rho = 0.95, epsilon = 1e-06)
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#model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd, metrics=["accuracy"])
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model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer = ada_d, metrics=['accuracy'])
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# captures output of softmax so we can decode the output during visualization
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test_func = K.function([input_data], [y_pred])
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print('[*提示] 创建模型成功，模型编译成功')
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return model
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def ctc_lambda_func(self, args):
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y_pred, labels, input_length, label_length = args
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y_pred = y_pred[:, 2:, :]
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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, batch_size = 32, filename = 'model_speech/speech_model2'):
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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 = data.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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print('[running] train epoch %d .' % epoch)
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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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print('[message] epoch %d . Have train datas %d+'%(epoch, n_step*save_step))
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# data_genetator是一个生成器函数
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yielddatas = data.data_genetator(batch_size, self.AUDIO_LENGTH)
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#self._model.fit_generator(yielddatas, save_step, nb_worker=2)
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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 * save_step))
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def LoadModel(self,filename='model_speech/speech_model2.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/speech_model2',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, datapath, str_dataset='dev'):
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'''
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测试检验模型效果
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'''
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data=DataSpeech(datapath)
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data.LoadDataList(str_dataset)
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num_data = DataSpeech.GetDataNum() # 获取数据的数量
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try:
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gen = data.data_genetator(num_data)
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for i in range(1):
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X, y = gen
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r = self._model.test_on_batch(X, y)
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print(r)
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except StopIteration:
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print('[Error] Model Test Error. please check data format.')
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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 = self._model.predict_on_batch(x)
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print(r)
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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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datapath = ''
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modelpath = 'model_speech'
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if(not os.path.exists(modelpath)): # 判断保存模型的目录是否存在
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os.makedirs(modelpath) # 如果不存在，就新建一个，避免之后保存模型的时候炸掉
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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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if(system_type == 'Windows'):
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datapath = 'E:\\语音数据集'
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modelpath = modelpath + '\\'
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elif(system_type == 'Linux'):
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datapath = 'dataset'
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modelpath = modelpath + '/'
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else:
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print('*[Message] Unknown System\n')
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datapath = 'dataset'
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modelpath = modelpath + '/'
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ms = ModelSpeech(datapath)
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#ms.LoadModel(modelpath + 'speech_model_e_0_step_1.model')
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ms.TrainModel(datapath, epoch = 2, batch_size = 4, save_step = 1)
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#ms.TestModel(datapath, str_dataset='dev', data_count = 32)
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#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\test\\D4\\D4_750.wav')
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#print('*[提示] 语音识别结果：\n',r) |