397 lines
14 KiB
Python
397 lines
14 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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from general_function.file_wav import *
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import numpy as np
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# LSTM_CNN
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import keras as kr
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import tensorflow as tf
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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 # , Flatten,LSTM,Convolution1D,MaxPooling1D,Merge
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from keras.layers import Conv1D,LSTM,MaxPooling1D, Lambda, TimeDistributed, Activation #, Merge, Conv2D, MaxPooling2D,Conv1D
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from keras.layers.normalization import BatchNormalization
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from keras.layers.advanced_activations import LeakyReLU
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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 readdata3 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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self.data = DataSpeech(datapath)
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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
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当前未完成，网络模型可能还需要修改
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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))
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layer_h1_c = Conv1D(filters=256, kernel_size=5, strides=1, use_bias=True, padding="valid")(input_data) # 卷积层
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#layer_h1_a = Activation('relu', name='relu0')(layer_h1_c)
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layer_h1_a = LeakyReLU(alpha=0.3)(layer_h1_c) # 高级激活层
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layer_h1 = MaxPooling1D(pool_size=2, strides=None, padding="valid")(layer_h1_a) # 池化层
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layer_h2 = BatchNormalization()(layer_h1)
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layer_h3_c = Conv1D(filters=256, kernel_size=5, strides=1, use_bias=True, padding="valid")(layer_h2) # 卷积层
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layer_h3_a = LeakyReLU(alpha=0.3)(layer_h3_c) # 高级激活层
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#layer_h3_a = Activation('relu', name='relu1')(layer_h3_c)
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layer_h3 = MaxPooling1D(pool_size=2, strides=None, padding="valid")(layer_h3_a) # 池化层
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layer_h4 = Dropout(0.1)(layer_h3) # 随机中断部分神经网络连接，防止过拟合
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layer_h5 = Dense(256, use_bias=True, activation="softmax")(layer_h4) # 全连接层
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layer_h6 = Dense(256, use_bias=True, activation="softmax")(layer_h5) # 全连接层
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#layer_h4 = Activation('softmax', name='softmax0')(layer_h4_d1)
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layer_h7 = LSTM(256, activation='softmax', use_bias=True, return_sequences=True)(layer_h6) # LSTM层
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layer_h8 = LSTM(256, activation='softmax', use_bias=True, return_sequences=True)(layer_h7) # LSTM层
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layer_h9 = LSTM(256, activation='softmax', use_bias=True, return_sequences=True)(layer_h8) # LSTM层
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layer_h10 = LSTM(256, activation='softmax', use_bias=True, return_sequences=True)(layer_h9) # LSTM层
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#layer_h10 = Activation('softmax', name='softmax1')(layer_h9)
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layer_h10_dropout = Dropout(0.1)(layer_h10) # 随机中断部分神经网络连接，防止过拟合
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layer_h11 = Dense(512, use_bias=True, activation="softmax")(layer_h10_dropout) # 全连接层
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layer_h12 = Dense(self.MS_OUTPUT_SIZE, use_bias=True, activation="softmax")(layer_h11) # 全连接层
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#layer_h6 = Dense(1283, activation="softmax")(layer_h5) # 全连接层
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y_pred = Activation('softmax', name='softmax2')(layer_h12)
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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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ctc_out = Lambda(self.ctc_lambda_func2, output_shape = (397, self.MS_OUTPUT_SIZE), name='ctc')([y_pred, input_length])
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#y_out = Activation('softmax', name='softmax3')(loss_out)
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model = Model(inputs=[input_data, input_length], outputs = ctc_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-8, momentum=0.9, nesterov=True, clipnorm=5)
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ada_d = Adadelta(lr = 0.001, 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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model.compile(loss={'ctc': lambda y_true, y_pred: self.ctc_lambda_func([y_true, 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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self.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_true, y_pred = args
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#print(y_pred)
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y_pred = y_pred[:, 2:, :]
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#return K.ctc_decode(y_pred,self.MS_OUTPUT_SIZE)
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return K.ctc_batch_cost(y_true, y_pred, y_true.shape[1], y_pred.shape[1])
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def ctc_lambda_func2(self, args):
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y_pred, input_length = args
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print('**',y_pred,input_length,'**')
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#y_pred = y_pred[:, :, 0:-1]
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#y_pred = K.reverse(y_pred, axes=-1)
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input_length = K.reshape(input_length, (-1,))
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print(input_length)
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#input_length = input_length.reshape(input_length.shape[0],input_length.shape[1])
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#return K.ctc_decode(y_pred,self.MS_OUTPUT_SIZE)
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#dict = []
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#for i in range(0,self.MS_OUTPUT_SIZE + 1):
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# dict.append(i)
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#, input_length
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top_k_decoded, _ = K.ctc_decode(y_pred, input_length , greedy=True, beam_width=64)
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print(top_k_decoded, top_k_decoded[0].shape)
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top_k_decoded = top_k_decoded[0]
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#num=top_k_decoded.shape[0]
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#num=len(top_k_decoded0.shape[0])
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#with tf.Session():
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#top_k_decoded = top_k_decoded.eval()
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print(top_k_decoded)
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print('num: ',num)
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for j in range(num):
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print(top_k_decoded)
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tmp_decoded = top_k_decoded[j,0:-1]
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max_arg = np.argmax(tmp_decoded, 1)
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print(max_arg)
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out_best = list(max_arg)
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out_best = [k for k, g in itertools.groupby(out_best)]
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#outstr = labels_to_text(out_best)
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#ret.append(outstr)
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ret.append(outbest)
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ret = np.array(ret)
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return ret
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#def GetEditDistance(self, x0, x1):
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def TrainModel(self, datapath, epoch = 2, batch_size = 32, save_step = 1000, filename = 'model_speech/speech_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 = self.data
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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 (n_step * save_step < num_data):
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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_model_e_0_step_1.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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print('*[提示] 已加载模型')
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def SaveModel(self, filename = 'model_speech/speech_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, datapath, str_dataset='dev', data_count = 32):
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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 = self.data
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data.LoadDataList(str_dataset)
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num_data = data.GetDataNum() # 获取数据的数量
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if(data_count <= 0 or data_count > num_data): # 当data_count为小于等于0或者大于测试数据量的值时，则使用全部数据来测试
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data_count = num_data
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try:
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gen = data.data_genetator(data_count)
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#for i in range(1):
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# [X, y, input_length, label_length ], labels = gen
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#r = self._model.test_on_batch([X, y, input_length, label_length ], labels)
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r = self._model.evaluate_generator(generator = gen, steps = 1, max_queue_size = data_count, workers = 1, use_multiprocessing = False)
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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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def decode_batch(self, test_func, word_batch):
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out = test_func([word_batch])[0]
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ret = []
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for j in range(out.shape[0]):
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out_best = list(np.argmax(out[j, 2:], 1))
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out_best = [k for k, g in itertools.groupby(out_best)]
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outstr = labels_to_text(out_best)
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ret.append(outstr)
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return ret
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def show_edit_distance(self, num):
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num_left = num
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mean_norm_ed = 0.0
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mean_ed = 0.0
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while num_left > 0:
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word_batch = next(self.text_img_gen)[0]
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num_proc = min(word_batch['the_input'].shape[0], num_left)
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decoded_res = decode_batch(self.test_func, word_batch['the_input'][0:num_proc])
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for j in range(num_proc):
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edit_dist = editdistance.eval(decoded_res[j], word_batch['source_str'][j])
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mean_ed += float(edit_dist)
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mean_norm_ed += float(edit_dist) / len(word_batch['source_str'][j])
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num_left -= num_proc
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mean_norm_ed = mean_norm_ed / num
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mean_ed = mean_ed / num
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print('\nOut of %d samples: Mean edit distance: %.3f Mean normalized edit distance: %0.3f'
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% (num, mean_ed, mean_norm_ed))
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def RecognizeSpeech(self, wavsignal, fs):
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'''
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最终做语音识别用的函数，识别一个wav序列的语音
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不过这里现在还有bug
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'''
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#data = self.data
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data = DataSpeech('E:\\语音数据集')
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data.LoadDataList('dev')
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# 获取输入特征
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#data_input = data.GetMfccFeature(wavsignal, fs)
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data_input = data.GetFrequencyFeature(wavsignal, fs)
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arr_zero = np.zeros((1, 200), dtype=np.int16) #一个全是0的行向量
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#import matplotlib.pyplot as plt
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#plt.subplot(111)
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#plt.imshow(data_input, cmap=plt.get_cmap('gray'))
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#plt.show()
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#while(len(data_input)<1600): #长度不够时补全到1600
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# data_input = np.row_stack((data_input,arr_zero))
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#print(len(data_input))
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list_symbol = data.list_symbol # 获取拼音列表
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labels = [ list_symbol[0] ]
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#while(len(labels) < 64):
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# labels.append('')
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labels_num = []
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for i in labels:
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labels_num.append(data.SymbolToNum(i))
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data_input = np.array(data_input, dtype=np.int16)
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data_input = data_input.reshape(data_input.shape[0],data_input.shape[1])
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labels_num = np.array(labels_num, dtype=np.int16)
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labels_num = labels_num.reshape(labels_num.shape[0])
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input_length = np.array([data_input.shape[0] // 4 - 3], dtype=np.int16)
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input_length = np.array(input_length)
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input_length = input_length.reshape(input_length.shape[0])
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label_length = np.array([labels_num.shape[0]], dtype=np.int16)
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label_length = np.array(label_length)
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label_length = label_length.reshape(label_length.shape[0])
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x = [data_input, labels_num, input_length, label_length]
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#x = next(data.data_genetator(1, self.AUDIO_LENGTH))
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#x = kr.utils.np_utils.to_categorical(x)
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print(x)
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x=np.array(x)
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pred = self._model.predict(x=x)
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#pred = self._model.predict_on_batch([data_input, labels_num, input_length, label_length])
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return [labels,pred]
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pass
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def RecognizeSpeech_FromFile(self, filename):
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'''
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最终做语音识别用的函数，识别指定文件名的语音
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'''
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wavsignal,fs = read_wav_data(filename)
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return self.RecognizeSpeech(wavsignal, fs)
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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 = 8, 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)
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