bug修复以及一些尝试
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nl8590687
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@ -118,11 +118,11 @@ class ModelSpeech(): # 语音模型类
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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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model.compile(loss={'ctc': lambda labels, 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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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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@ -210,6 +210,16 @@ class ModelSpeech(): # 语音模型类
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return r
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pass
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def decode_batch(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 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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@ -220,24 +230,25 @@ class ModelSpeech(): # 语音模型类
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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.GetMfccFeature(wavsignal, fs)
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data_input = data.GetFrequencyFeature(wavsignal, fs)
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arr_zero = np.zeros((1, 39), dtype=np.int16) #一个全是0的行向量
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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.T, cmap=plt.get_cmap('gray'))
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plt.show()
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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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#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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#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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@ -245,18 +256,28 @@ class ModelSpeech(): # 语音模型类
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#data_input = np.array([data_input], dtype=np.int16)
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#labels_num = np.array([labels_num], dtype=np.int16)
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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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label_length = np.array([64], dtype=np.int16)
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x = data_input, labels_num, input_length, label_length
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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[0], batch_size = None)
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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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@ -286,7 +307,7 @@ if(__name__=='__main__'):
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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(path) # 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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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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@ -14,7 +14,7 @@ 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
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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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@ -77,9 +77,6 @@ class ModelSpeech(): # 语音模型类
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#model_data.summary()
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#layer_out = ctc_layer(64, self.BATCH_SIZE)(layer_h6) # CTC层 可能有bug
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#layer_out = ctc_layer(1283, 32)(layer_h6) # CTC层 可能有bug
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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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@ -91,15 +88,18 @@ class ModelSpeech(): # 语音模型类
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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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# clipnorm seems to speeds up convergence
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sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
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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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model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd, metrics=["accuracy"])
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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 = 0.0001, 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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@ -198,7 +198,7 @@ if(__name__=='__main__'):
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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(path) # 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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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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