#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ @author: nl8590687 """ import platform as plat import os import time from general_function.file_wav import * from general_function.file_dict import * from general_function.gen_func import * # LSTM_CNN import keras as kr import numpy as np import random from keras.models import Sequential, Model from keras.layers import Dense, Dropout, Input, Reshape # , Flatten,LSTM,Convolution1D,MaxPooling1D,Merge from keras.layers import Conv1D,LSTM,MaxPooling1D, Lambda, TimeDistributed, Activation,Conv2D, MaxPooling2D #, Merge,Conv1D from keras import backend as K from keras.optimizers import SGD, Adadelta from readdata24 import DataSpeech class ModelSpeech(): # 语音模型类 def __init__(self, datapath): ''' 初始化 默认输出的拼音的表示大小是1422，即1421个拼音+1个空白块 ''' MS_OUTPUT_SIZE = 1422 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 = 200 self._model, self.base_model = self.CreateModel() self.datapath = datapath self.slash = '' 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'): 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 def CreateModel(self): ''' 定义CNN/LSTM/CTC模型，使用函数式模型 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） 隐藏层一：1024个神经元的卷积层 隐藏层二：池化层，池化窗口大小为2 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 隐藏层四：循环层、LSTM层 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 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， 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 ''' # 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=(self.AUDIO_LENGTH, self.AUDIO_FEATURE_LENGTH, 1)) layer_h1 = Conv2D(32, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(input_data) # 卷积层 layer_h1 = Dropout(0.1)(layer_h1) layer_h2 = Conv2D(32, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h1) # 卷积层 layer_h3 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h2) # 池化层 #layer_h3 = Dropout(0.2)(layer_h2) # 随机中断部分神经网络连接，防止过拟合 layer_h3 = Dropout(0.2)(layer_h3) layer_h4 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h3) # 卷积层 layer_h4 = Dropout(0.2)(layer_h4) layer_h5 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h4) # 卷积层 layer_h6 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h5) # 池化层 layer_h6 = Dropout(0.3)(layer_h6) layer_h7 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h6) # 卷积层 layer_h7 = Dropout(0.3)(layer_h7) layer_h8 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h7) # 卷积层 layer_h9 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h8) # 池化层 #test=Model(inputs = input_data, outputs = layer_h6) #test.summary() layer_h10 = Reshape((200, 3200))(layer_h9) #Reshape层 #layer_h5 = LSTM(256, activation='relu', use_bias=True, return_sequences=True)(layer_h4) # LSTM层 #layer_h6 = Dropout(0.2)(layer_h5) # 随机中断部分神经网络连接，防止过拟合 layer_h10 = Dropout(0.4)(layer_h10) layer_h11 = Dense(128, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h10) # 全连接层 layer_h11 = Dropout(0.4)(layer_h11) layer_h12 = Dense(self.MS_OUTPUT_SIZE, use_bias=True, kernel_initializer='he_normal')(layer_h11) # 全连接层 y_pred = Activation('softmax', name='Activation0')(layer_h12) model_data = Model(inputs = input_data, outputs = y_pred) #model_data.summary() labels = Input(name='the_labels', shape=[self.label_max_string_length], dtype='float32') input_length = Input(name='input_length', shape=[1], dtype='int64') label_length = Input(name='label_length', shape=[1], dtype='int64') # Keras doesn't currently support loss funcs with extra parameters # so CTC loss is implemented in a lambda layer #layer_out = Lambda(ctc_lambda_func,output_shape=(self.MS_OUTPUT_SIZE, ), name='ctc')([y_pred, labels, input_length, label_length])#(layer_h6) # CTC loss_out = Lambda(self.ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length]) model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out) #model.summary() # clipnorm seems to speeds up convergence #sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5) ada_d = Adadelta(lr = 0.01, rho = 0.95, epsilon = 1e-06) #model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd) model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer = ada_d) # captures output of softmax so we can decode the output during visualization test_func = K.function([input_data], [y_pred]) print('[*提示] 创建模型成功，模型编译成功') return model, model_data def ctc_lambda_func(self, args): y_pred, labels, input_length, label_length = args y_pred = y_pred[:, :, :] #y_pred = y_pred[:, 2:, :] return K.ctc_batch_cost(labels, y_pred, input_length, label_length) def TrainModel(self, datapath, epoch = 2, save_step = 1000, batch_size = 32, filename = 'model_speech/speech_model24'): ''' 训练模型 参数： datapath: 数据保存的路径 epoch: 迭代轮数 save_step: 每多少步保存一次模型 filename: 默认保存文件名，不含文件后缀名 ''' data=DataSpeech(datapath, 'train') num_data = data.GetDataNum() # 获取数据的数量 yielddatas = data.data_genetator(batch_size, self.AUDIO_LENGTH) for epoch in range(epoch): # 迭代轮数 print('[running] train epoch %d .' % epoch) n_step = 0 # 迭代数据数 while True: try: print('[message] epoch %d . Have train datas %d+'%(epoch, n_step*save_step)) # data_genetator是一个生成器函数 #self._model.fit_generator(yielddatas, save_step, nb_worker=2) self._model.fit_generator(yielddatas, save_step) n_step += 1 except StopIteration: print('[error] generator error. please check data format.') break self.SaveModel(comment='_e_'+str(epoch)+'_step_'+str(n_step * save_step)) self.TestModel(self.datapath, str_dataset='train', data_count = 4) self.TestModel(self.datapath, str_dataset='dev', data_count = 4) def LoadModel(self,filename='model_speech/speech_model24.model'): ''' 加载模型参数 ''' self._model.load_weights(filename) self.base_model.load_weights(filename + '.base') def SaveModel(self,filename='model_speech/speech_model24',comment=''): ''' 保存模型参数 ''' self._model.save_weights(filename+comment+'.model') self.base_model.save_weights(filename + comment + '.model.base') f = open('step24.txt','w') f.write(filename+comment) f.close() def TestModel(self, datapath='', str_dataset='dev', data_count = 32, out_report = False, show_ratio = True): ''' 测试检验模型效果 ''' data=DataSpeech(self.datapath, str_dataset) #data.LoadDataList(str_dataset) num_data = data.GetDataNum() # 获取数据的数量 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 data_count = num_data try: ran_num = random.randint(0,num_data - 1) # 获取一个随机数 words_num = 0 word_error_num = 0 nowtime = time.strftime('%Y%m%d_%H%M%S',time.localtime(time.time())) if(out_report == True): txt_obj = open('Test_Report_' + str_dataset + '_' + nowtime + '.txt', 'w', encoding='UTF-8') # 打开文件并读入 txt = '' for i in range(data_count): data_input, data_labels = data.GetData((ran_num + i) % num_data) # 从随机数开始连续向后取一定数量数据 # 数据格式出错处理 开始 # 当输入的wav文件长度过长时自动跳过该文件，转而使用下一个wav文件来运行 num_bias = 0 while(data_input.shape[0] > self.AUDIO_LENGTH): print('*[Error]','wave data lenghth of num',(ran_num + i) % num_data, 'is too long.','\n A Exception raise when test Speech Model.') num_bias += 1 data_input, data_labels = data.GetData((ran_num + i + num_bias) % num_data) # 从随机数开始连续向后取一定数量数据 # 数据格式出错处理 结束 pre = self.Predict(data_input, data_input.shape[0] // 8) words_n = data_labels.shape[0] # 获取每个句子的字数 words_num += words_n # 把句子的总字数加上 edit_distance = GetEditDistance(data_labels, pre) # 获取编辑距离 if(edit_distance <= words_n): # 当编辑距离小于等于句子字数时 word_error_num += edit_distance # 使用编辑距离作为错误字数 else: # 否则肯定是增加了一堆乱七八糟的奇奇怪怪的字 word_error_num += words_n # 就直接加句子本来的总字数就好了 if(i % 10 == 0 and show_ratio == True): print('测试进度：',i,'/',data_count) txt = '' if(out_report == True): txt += str(i) + '\n' txt += 'True:\t' + str(data_labels) + '\n' txt += 'Pred:\t' + str(pre) + '\n' txt += '\n' txt_obj.write(txt) print('*[测试结果] 语音识别 ' + str_dataset + ' 集语音单字错误率：', word_error_num / words_num * 100, '%') if(out_report == True): txt = '*[测试结果] 语音识别 ' + str_dataset + ' 集语音单字错误率： ' + str(word_error_num / words_num * 100) + ' %' txt_obj.write(txt) txt_obj.close() except StopIteration: print('[Error] Model Test Error. please check data format.') def Predict(self, data_input, input_len): ''' 预测结果 返回语音识别后的拼音符号列表 ''' batch_size = 1 in_len = np.zeros((batch_size),dtype = np.int32) in_len[0] = input_len x_in = np.zeros((batch_size, 1600, self.AUDIO_FEATURE_LENGTH, 1), dtype=np.float) for i in range(batch_size): x_in[i,0:len(data_input)] = data_input base_pred = self.base_model.predict(x = x_in) #print('base_pred:\n', base_pred) #y_p = base_pred #for j in range(200): # mean = np.sum(y_p[0][j]) / y_p[0][j].shape[0] # print('max y_p:',np.max(y_p[0][j]),'min y_p:',np.min(y_p[0][j]),'mean y_p:',mean,'mid y_p:',y_p[0][j][100]) # print('argmin:',np.argmin(y_p[0][j]),'argmax:',np.argmax(y_p[0][j])) # count=0 # for i in range(y_p[0][j].shape[0]): # if(y_p[0][j][i] < mean): # count += 1 # print('count:',count) base_pred =base_pred[:, :, :] #base_pred =base_pred[:, 2:, :] r = K.ctc_decode(base_pred, in_len, greedy = True, beam_width=100, top_paths=1) #print('r', r) r1 = K.get_value(r[0][0]) #print('r1', r1) #r2 = K.get_value(r[1]) #print(r2) r1=r1[0] return r1 pass def RecognizeSpeech(self, wavsignal, fs): ''' 最终做语音识别用的函数，识别一个wav序列的语音 不过这里现在还有bug ''' #data = self.data #data = DataSpeech('E:\\语音数据集') #data.LoadDataList('dev') # 获取输入特征 #data_input = GetMfccFeature(wavsignal, fs) #t0=time.time() data_input = GetFrequencyFeature2(wavsignal, fs) #t1=time.time() #print('time cost:',t1-t0) input_length = len(data_input) input_length = input_length // 8 data_input = np.array(data_input, dtype = np.float) #print(data_input,data_input.shape) data_input = data_input.reshape(data_input.shape[0],data_input.shape[1],1) #t2=time.time() r1 = self.Predict(data_input, input_length) #t3=time.time() #print('time cost:',t3-t2) list_symbol_dic = GetSymbolList(self.datapath) # 获取拼音列表 r_str=[] for i in r1: r_str.append(list_symbol_dic[i]) return r_str pass def RecognizeSpeech_FromFile(self, filename): ''' 最终做语音识别用的函数，识别指定文件名的语音 ''' wavsignal,fs = read_wav_data(filename) r = self.RecognizeSpeech(wavsignal, fs) return r pass @property def model(self): ''' 返回keras model ''' return self._model if(__name__=='__main__'): import tensorflow as tf from keras.backend.tensorflow_backend import set_session os.environ["CUDA_VISIBLE_DEVICES"] = "0" #进行配置，使用70%的GPU config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.93 #config.gpu_options.allow_growth=True #不全部占满显存, 按需分配 set_session(tf.Session(config=config)) datapath = '' modelpath = 'model_speech' if(not os.path.exists(modelpath)): # 判断保存模型的目录是否存在 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 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 = modelpath + '\\' elif(system_type == 'Linux'): datapath = 'dataset' modelpath = modelpath + '/' else: print('*[Message] Unknown System\n') datapath = 'dataset' modelpath = modelpath + '/' ms = ModelSpeech(datapath) #ms.LoadModel(modelpath + 'm24/speech_model24_e_0_step_411000.model') ms.TrainModel(datapath, epoch = 50, batch_size = 16, save_step = 500) #ms.TestModel(datapath, str_dataset='test', data_count = 128, out_report = True) #r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00241I0053.wav') #r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00020I0087.wav') #r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\train\\A11\\A11_167.WAV') #r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\test\\D4\\D4_750.wav') #print('*[提示] 语音识别结果：\n',r)