#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ @author: nl8590687 """ import platform as plat import os from general_function.file_wav import * import numpy as np # LSTM_CNN import keras as kr import tensorflow as tf import numpy as np from keras.models import Sequential, Model from keras.layers import Dense, Dropout, Input # , Flatten,LSTM,Convolution1D,MaxPooling1D,Merge from keras.layers import Conv1D,LSTM,MaxPooling1D, Lambda, TimeDistributed, Activation #, Merge, Conv2D, MaxPooling2D,Conv1D from keras.layers.normalization import BatchNormalization from keras.layers.advanced_activations import LeakyReLU from keras import backend as K from keras.optimizers import SGD, Adadelta from readdata3 import DataSpeech from neural_network.ctc_layer import ctc_layer from neural_network.ctc_loss import ctc_batch_loss #from keras.backend.tensorflow_backend import ctc_batch_cost 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1283，即1282个拼音+1个空白块 ''' MS_OUTPUT_SIZE = 1417 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.CreateModel() self.data = DataSpeech(datapath) 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 当前未完成，网络模型可能还需要修改 ''' # 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)) layer_h1_c = Conv1D(filters=256, kernel_size=5, strides=1, use_bias=True, padding="valid")(input_data) # 卷积层 #layer_h1_a = Activation('relu', name='relu0')(layer_h1_c) layer_h1_a = LeakyReLU(alpha=0.3)(layer_h1_c) # 高级激活层 layer_h1 = MaxPooling1D(pool_size=2, strides=None, padding="valid")(layer_h1_a) # 池化层 layer_h2 = BatchNormalization()(layer_h1) layer_h3_c = Conv1D(filters=256, kernel_size=5, strides=1, use_bias=True, padding="valid")(layer_h2) # 卷积层 layer_h3_a = LeakyReLU(alpha=0.3)(layer_h3_c) # 高级激活层 #layer_h3_a = Activation('relu', name='relu1')(layer_h3_c) layer_h3 = MaxPooling1D(pool_size=2, strides=None, padding="valid")(layer_h3_a) # 池化层 layer_h4 = Dropout(0.1)(layer_h3) # 随机中断部分神经网络连接，防止过拟合 layer_h5 = Dense(256, use_bias=True, activation="softmax")(layer_h4) # 全连接层 layer_h6 = Dense(256, use_bias=True, activation="softmax")(layer_h5) # 全连接层 #layer_h4 = Activation('softmax', name='softmax0')(layer_h4_d1) layer_h7 = LSTM(256, activation='softmax', use_bias=True, return_sequences=True)(layer_h6) # LSTM层 layer_h8 = LSTM(256, activation='softmax', use_bias=True, return_sequences=True)(layer_h7) # LSTM层 layer_h9 = LSTM(256, activation='softmax', use_bias=True, return_sequences=True)(layer_h8) # LSTM层 layer_h10 = LSTM(256, activation='softmax', use_bias=True, return_sequences=True)(layer_h9) # LSTM层 #layer_h10 = Activation('softmax', name='softmax1')(layer_h9) 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 layer_h11 = Dense(512, use_bias=True, activation="softmax")(layer_h10_dropout) # 全连接层 layer_h12 = Dense(self.MS_OUTPUT_SIZE, use_bias=True, activation="softmax")(layer_h11) # 全连接层 #layer_h6 = Dense(1283, activation="softmax")(layer_h5) # 全连接层 y_pred = Activation('softmax', name='softmax2')(layer_h12) model_data = Model(inputs = input_data, outputs = y_pred) #model_data.summary() #labels = Input(name='the_labels', shape=[60], dtype='float32') #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 ctc_out = Lambda(self.ctc_lambda_func2, output_shape = (397, self.MS_OUTPUT_SIZE), name='ctc')([y_pred, input_length]) #y_out = Activation('softmax', name='softmax3')(loss_out) model = Model(inputs=[input_data, input_length], outputs = ctc_out) model.summary() # clipnorm seems to speeds up convergence #sgd = SGD(lr=0.0001, decay=1e-8, momentum=0.9, nesterov=True, clipnorm=5) ada_d = Adadelta(lr = 0.001, rho = 0.95, epsilon = 1e-06) #model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer = sgd, metrics=['accuracy']) #model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer = ada_d, metrics=['accuracy']) model.compile(loss={'ctc': lambda y_true, y_pred: self.ctc_lambda_func([y_true, y_pred])}, optimizer = ada_d, metrics=['accuracy']) # captures output of softmax so we can decode the output during visualization self.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 def ctc_lambda_func(self, args): #y_pred, labels, input_length, label_length = args y_true, y_pred = args #print(y_pred) y_pred = y_pred[:, 2:, :] #return K.ctc_decode(y_pred,self.MS_OUTPUT_SIZE) return K.ctc_batch_cost(y_true, y_pred, y_true.shape[1], y_pred.shape[1]) def ctc_lambda_func2(self, args): y_pred, input_length = args print('**',y_pred,input_length,'**') #y_pred = y_pred[:, :, 0:-1] #y_pred = K.reverse(y_pred, axes=-1) input_length = K.reshape(input_length, (-1,)) print(input_length) #input_length = input_length.reshape(input_length.shape[0],input_length.shape[1]) #return K.ctc_decode(y_pred,self.MS_OUTPUT_SIZE) #dict = [] #for i in range(0,self.MS_OUTPUT_SIZE + 1): # dict.append(i) #, input_length top_k_decoded, _ = K.ctc_decode(y_pred, input_length , greedy=True, beam_width=64) print(top_k_decoded, top_k_decoded[0].shape) top_k_decoded = top_k_decoded[0] #num=top_k_decoded.shape[0] #num=len(top_k_decoded0.shape[0]) #with tf.Session(): #top_k_decoded = top_k_decoded.eval() print(top_k_decoded) print('num: ',num) for j in range(num): print(top_k_decoded) tmp_decoded = top_k_decoded[j,0:-1] max_arg = np.argmax(tmp_decoded, 1) print(max_arg) out_best = list(max_arg) out_best = [k for k, g in itertools.groupby(out_best)] #outstr = labels_to_text(out_best) #ret.append(outstr) ret.append(outbest) ret = np.array(ret) return ret #def GetEditDistance(self, x0, x1): def TrainModel(self, datapath, epoch = 2, batch_size = 32, save_step = 1000, filename = 'model_speech/speech_model'): ''' 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) data = self.data data.LoadDataList('train') num_data = data.GetDataNum() # 获取数据的数量 for epoch in range(epoch): # 迭代轮数 print('[running] train epoch %d .' % epoch) n_step = 0 # 迭代数据数 while (n_step * save_step < num_data): 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(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 except StopIteration: print('[error] generator error. please check data format.') break self.SaveModel(comment='_e_'+str(epoch)+'_step_'+str(n_step * save_step)) def LoadModel(self, filename = 'model_speech/speech_model_e_0_step_1.model'): ''' 加载模型参数 ''' self._model.load_weights(filename) print('*[提示] 已加载模型') def SaveModel(self, filename = 'model_speech/speech_model', comment = ''): ''' 保存模型参数 ''' self._model.save_weights(filename + comment + '.model') def TestModel(self, datapath, str_dataset='dev', data_count = 32): ''' 测试检验模型效果 ''' #data=DataSpeech(datapath) data = self.data 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: gen = data.data_genetator(data_count) #for i in range(1): # [X, y, input_length, label_length ], labels = gen #r = self._model.test_on_batch([X, y, input_length, label_length ], labels) r = self._model.evaluate_generator(generator = gen, steps = 1, max_queue_size = data_count, workers = 1, use_multiprocessing = False) print(r) except StopIteration: print('[Error] Model Test Error. please check data format.') def Predict(self,x): ''' 预测结果 ''' r = self._model.predict_on_batch(x) print(r) return r pass def decode_batch(self, test_func, word_batch): out = test_func([word_batch])[0] ret = [] for j in range(out.shape[0]): out_best = list(np.argmax(out[j, 2:], 1)) out_best = [k for k, g in itertools.groupby(out_best)] outstr = labels_to_text(out_best) ret.append(outstr) return ret def show_edit_distance(self, num): num_left = num mean_norm_ed = 0.0 mean_ed = 0.0 while num_left > 0: word_batch = next(self.text_img_gen)[0] num_proc = min(word_batch['the_input'].shape[0], num_left) decoded_res = decode_batch(self.test_func, word_batch['the_input'][0:num_proc]) for j in range(num_proc): edit_dist = editdistance.eval(decoded_res[j], word_batch['source_str'][j]) mean_ed += float(edit_dist) mean_norm_ed += float(edit_dist) / len(word_batch['source_str'][j]) num_left -= num_proc mean_norm_ed = mean_norm_ed / num mean_ed = mean_ed / num print('\nOut of %d samples: Mean edit distance: %.3f Mean normalized edit distance: %0.3f' % (num, mean_ed, mean_norm_ed)) 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 = data.GetMfccFeature(wavsignal, fs) data_input = data.GetFrequencyFeature(wavsignal, fs) arr_zero = np.zeros((1, 200), dtype=np.int16) #一个全是0的行向量 #import matplotlib.pyplot as plt #plt.subplot(111) #plt.imshow(data_input, cmap=plt.get_cmap('gray')) #plt.show() #while(len(data_input)<1600): #长度不够时补全到1600 # data_input = np.row_stack((data_input,arr_zero)) #print(len(data_input)) list_symbol = data.list_symbol # 获取拼音列表 labels = [ list_symbol[0] ] #while(len(labels) < 64): # labels.append('') labels_num = [] for i in labels: labels_num.append(data.SymbolToNum(i)) data_input = np.array(data_input, dtype=np.int16) data_input = data_input.reshape(data_input.shape[0],data_input.shape[1]) labels_num = np.array(labels_num, dtype=np.int16) labels_num = labels_num.reshape(labels_num.shape[0]) input_length = np.array([data_input.shape[0] // 4 - 3], dtype=np.int16) input_length = np.array(input_length) input_length = input_length.reshape(input_length.shape[0]) label_length = np.array([labels_num.shape[0]], dtype=np.int16) label_length = np.array(label_length) label_length = label_length.reshape(label_length.shape[0]) x = [data_input, labels_num, input_length, label_length] #x = next(data.data_genetator(1, self.AUDIO_LENGTH)) #x = kr.utils.np_utils.to_categorical(x) print(x) x=np.array(x) pred = self._model.predict(x=x) #pred = self._model.predict_on_batch([data_input, labels_num, input_length, label_length]) return [labels,pred] 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) return self.RecognizeSpeech(wavsignal, fs) pass @property def model(self): ''' 返回keras model ''' return self._model if(__name__=='__main__'): 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 + 'speech_model_e_0_step_1.model') ms.TrainModel(datapath, epoch = 2, batch_size = 8, save_step = 1) #ms.TestModel(datapath, str_dataset='dev', data_count = 32) #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)