a little change
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nl8590687
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@ -34,7 +34,7 @@ class ModelSpeech(): # 语音模型类
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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._model, self.base_model = self.CreateModel()
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@ -105,7 +105,7 @@ class ModelSpeech(): # 语音模型类
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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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return model, model_data
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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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@ -150,12 +150,14 @@ class ModelSpeech(): # 语音模型类
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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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self.base_model.load_weights(filename + '.base')
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def SaveModel(self,filename='model_speech/speech_model2',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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self.base_model.save_weights(filename + comment + '.model.base')
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def TestModel(self, datapath, str_dataset='dev'):
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'''
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140
SpeechModel5.py
140
SpeechModel5.py
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@ -134,6 +134,8 @@ class ModelSpeech(): # 语音模型类
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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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#top_k_decoded, _ = K.ctc_decode(y_pred, input_length, greedy = True, beam_width=100, top_paths=1)
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#self.decoder = K.function([input_data, input_length], [top_k_decoded[0]])
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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, model_data
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@ -256,15 +258,101 @@ class ModelSpeech(): # 语音模型类
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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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input_length = len(data_input)
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input_length = input_length // 4
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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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data_input = np.array(data_input, dtype = np.float)
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in_len = np.zeros((1),dtype = np.int32)
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print(in_len.shape)
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in_len[0] = input_length -2
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batch_size = 1
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x_in = np.zeros((batch_size, 1600, 200), dtype=np.float)
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for i in range(batch_size):
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x_in[i,0:len(data_input)] = data_input
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base_pred = self.base_model.predict(x = x_in)
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print('base_pred:\n', base_pred)
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y_p = base_pred
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print('base_pred0:\n',base_pred[0][0].shape)
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for j in range(200):
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mean = np.sum(y_p[0][j]) / y_p[0][j].shape[0]
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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])
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print('argmin:',np.argmin(y_p[0][j]),'argmax:',np.argmax(y_p[0][j]))
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count=0
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for i in range(y_p[0][j].shape[0]):
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if(y_p[0][j][i] < mean):
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count += 1
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print('count:',count)
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#for j in range(0,200):
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# mean = sum(y_p[0][0][j]) / len(y_p[0][0][j])
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# print('max y_p:',max(y_p[0][0][j]),'min y_p:',min(y_p[0][0][j]),'mean y_p:',mean,'mid y_p:',y_p[0][0][j][100])
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# print('argmin:',np.argmin(y_p[0][0][j]),'argmax:',np.argmax(y_p[0][0][j]))
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# count=0
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# for i in y_p[0][0][j]:
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# if(i < mean):
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# count += 1
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# print('count:',count)
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#decoded_sequences = self.decoder([base_pred, in_len])
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#print('decoded_sequences:\n', decoded_sequences)
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#input_length = tf.squeeze(input_length)
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#decode_pred = self.model_decode(x=[x_in, in_len])
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#print(decode_pred)
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base_pred =base_pred[:, 2:, :]
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r = K.ctc_decode(base_pred, in_len, greedy = True, beam_width=100, top_paths=1)
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print('r', r)
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#r = K.cast(r[0][0], dtype='float32')
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#print('r1', r)
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#print('解码完成')
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r1 = K.get_value(r[0][0])
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print('r1', r1)
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print('r0', r[1])
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r2 = K.get_value(r[1])
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print(r2)
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print('解码完成')
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list_symbol_dic = data.list_symbol # 获取拼音列表
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print('解码完成')
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return r1
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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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@ -275,42 +363,42 @@ class ModelSpeech(): # 语音模型类
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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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#list_symbol = data.list_symbol # 获取拼音列表
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labels = [ list_symbol[0] ]
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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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#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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#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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#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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#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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#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 = [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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#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(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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#return [labels,pred]
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pass
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@ -354,8 +442,8 @@ if(__name__=='__main__'):
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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 = 10)
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ms.LoadModel(modelpath + '5test\\speech_model_e_0_step_1400.model')
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#ms.TrainModel(datapath, epoch = 2, batch_size = 8, save_step = 10)
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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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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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@ -402,7 +402,7 @@ if(__name__=='__main__'):
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ms = ModelSpeech(datapath)
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ms.LoadModel(modelpath + 'speech_model5_e_0_step_1.model')
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ms.LoadModel(modelpath + '5test\\speech_model_e_0_step_100.model')
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#ms.TrainModel(datapath, epoch = 2, batch_size = 16, 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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@ -117,7 +117,7 @@ class DataSpeech():
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#print('wavsignal[0][j]:\n',wavsignal[0][j])
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#data_line = abs(fft(data_line)) / len(wavsignal[0])
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data_line = fft(data_line) / len(wavsignal[0])
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data_input.append(data_line[0:len(data_line)//2])
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data_input.append(data_line[0:len(data_line)//2]) # 除以2是取一半数据，因为是对称的
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#print('data_line:\n',data_line)
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return data_input
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