diff --git a/SpeechModel.py b/SpeechModel.py index 45f7253..c5cb4c9 100644 --- a/SpeechModel.py +++ b/SpeechModel.py @@ -118,11 +118,11 @@ class ModelSpeech(): # 语音模型类 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, metrics=['accuracy']) - model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer = ada_d, metrics=['accuracy']) + model.compile(loss={'ctc': lambda labels, y_pred: y_pred}, optimizer = ada_d, metrics=['accuracy']) # captures output of softmax so we can decode the output during visualization - test_func = K.function([input_data], [y_pred]) + 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 @@ -210,6 +210,16 @@ class ModelSpeech(): # 语音模型类 return r pass + def decode_batch(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 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 @@ -220,24 +230,25 @@ class ModelSpeech(): # 语音模型类 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.GetMfccFeature(wavsignal, fs) + data_input = data.GetFrequencyFeature(wavsignal, fs) - arr_zero = np.zeros((1, 39), dtype=np.int16) #一个全是0的行向量 + 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.T, cmap=plt.get_cmap('gray')) - plt.show() + #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)) + #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('') + #while(len(labels) < 64): + # labels.append('') labels_num = [] for i in labels: @@ -245,18 +256,28 @@ class ModelSpeech(): # 语音模型类 - #data_input = np.array([data_input], dtype=np.int16) - #labels_num = np.array([labels_num], dtype=np.int16) + 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) - label_length = np.array([64], dtype=np.int16) - x = data_input, labels_num, input_length, label_length + 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[0], batch_size = None) + pred = self._model.predict(x=x) #pred = self._model.predict_on_batch([data_input, labels_num, input_length, label_length]) return [labels,pred] @@ -286,7 +307,7 @@ if(__name__=='__main__'): if(not os.path.exists(modelpath)): # 判断保存模型的目录是否存在 - os.makedirs(path) # 如果不存在，就新建一个，避免之后保存模型的时候炸掉 + 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'): diff --git a/SpeechModel2.py b/SpeechModel2.py index f614efe..70b7c29 100644 --- a/SpeechModel2.py +++ b/SpeechModel2.py @@ -14,7 +14,7 @@ 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 +from keras.optimizers import SGD, Adadelta from readdata2 import DataSpeech from neural_network.ctc_layer import ctc_layer @@ -77,9 +77,6 @@ class ModelSpeech(): # 语音模型类 #model_data.summary() - #layer_out = ctc_layer(64, self.BATCH_SIZE)(layer_h6) # CTC层 可能有bug - #layer_out = ctc_layer(1283, 32)(layer_h6) # CTC层 可能有bug - #labels = Input(name='the_labels', shape=[60], dtype='float32') labels = Input(name='the_labels', shape=[self.label_max_string_length], dtype='float32') @@ -91,15 +88,18 @@ class ModelSpeech(): # 语音模型类 #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]) - # clipnorm seems to speeds up convergence - sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5) + model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out) model.summary() - model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd, metrics=["accuracy"]) + # 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 = 0.0001, 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']) # captures output of softmax so we can decode the output during visualization @@ -198,7 +198,7 @@ if(__name__=='__main__'): if(not os.path.exists(modelpath)): # 判断保存模型的目录是否存在 - os.makedirs(path) # 如果不存在，就新建一个，避免之后保存模型的时候炸掉 + 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'):