diff --git a/speech_model_zoo.py b/speech_model_zoo.py index c757a99..8e67a63 100644 --- a/speech_model_zoo.py +++ b/speech_model_zoo.py @@ -76,6 +76,132 @@ def ctc_lambda_func(args): y_pred = y_pred[:, :, :] return K.ctc_batch_cost(labels, y_pred, input_length, label_length) +class SpeechModel251BN(BaseModel): + ''' + 定义CNN+CTC模型，使用函数式模型 + + 输入层：200维的特征值序列，一条语音数据的最大长度设为1600（大约16s）\\ + 隐藏层：卷积池化层，卷积核大小为3x3，池化窗口大小为2 \\ + 隐藏层：全连接层 \\ + 输出层：全连接层，神经元数量为self.MS_OUTPUT_SIZE，使用softmax作为激活函数， \\ + CTC层：使用CTC的loss作为损失函数，实现连接性时序多输出 + + 参数： \\ + input_shape: tuple，默认值(1600, 200, 1) \\ + output_shape: tuple，默认值(200, 1428) + ''' + def __init__(self, input_shape :tuple=(1600, 200, 1), output_size :int=1428) -> None: + super().__init__() + self.input_shape = input_shape + self._pool_size = 8 + self.output_shape = (input_shape[0] // self._pool_size, output_size) + self._model_name = 'SpeechModel251bn' + self.model, self.model_base = self._define_model(self.input_shape, self.output_shape[1]) + + def _define_model(self, input_shape, output_size) -> tuple: + label_max_string_length = 64 + + input_data = Input(name='the_input', shape=input_shape) + + layer_h = Conv2D(32, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv0')(input_data) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN0')(layer_h) + layer_h = Activation('relu', name='Act0')(layer_h) + + layer_h = Conv2D(32, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv1')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN1')(layer_h) + layer_h = Activation('relu', name='Act1')(layer_h) + + layer_h = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h) # 池化层 + + layer_h = Conv2D(64, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv2')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN2')(layer_h) + layer_h = Activation('relu', name='Act2')(layer_h) + + layer_h = Conv2D(64, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv3')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN3')(layer_h) + layer_h = Activation('relu', name='Act3')(layer_h) + + layer_h = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h) # 池化层 + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv4')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN4')(layer_h) + layer_h = Activation('relu', name='Act4')(layer_h) + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv5')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN5')(layer_h) + layer_h = Activation('relu', name='Act5')(layer_h) + + layer_h = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h) # 池化层 + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv6')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN6')(layer_h) + layer_h = Activation('relu', name='Act6')(layer_h) + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv7')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN7')(layer_h) + layer_h = Activation('relu', name='Act7')(layer_h) + + layer_h = MaxPooling2D(pool_size=1, strides=None, padding="valid")(layer_h) # 池化层 + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv8')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN8')(layer_h) + layer_h = Activation('relu', name='Act8')(layer_h) + + layer_h = Conv2D(128, (3,3), use_bias=True, padding='same', kernel_initializer='he_normal', name='Conv9')(layer_h) # 卷积层 + layer_h = BatchNormalization(epsilon=0.0002, name='BN9')(layer_h) + layer_h = Activation('relu', name='Act9')(layer_h) + + layer_h = MaxPooling2D(pool_size=1, strides=None, padding="valid")(layer_h) # 池化层 + + #test=Model(inputs = input_data, outputs = layer_h12) + #test.summary() + + layer_h = Reshape((self.output_shape[0], input_shape[1] // self._pool_size * 128), name='Reshape0')(layer_h) #Reshape层 + + layer_h = Dense(128, activation="relu", use_bias=True, kernel_initializer='he_normal', name='Dense0')(layer_h) # 全连接层 + + layer_h = Dense(output_size, use_bias=True, kernel_initializer='he_normal', name='Dense1')(layer_h) # 全连接层 + y_pred = Activation('softmax', name='Activation0')(layer_h) + + model_base = Model(inputs = input_data, outputs = y_pred) + #model_data.summary() + + labels = Input(name='the_labels', shape=[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 + loss_out = Lambda(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) + + return model, model_base + + def get_loss_function(self) -> dict: + return {'ctc': lambda y_true, y_pred: y_pred} + + def forward(self, data_input): + batch_size = 1 + in_len = np.zeros((batch_size),dtype = np.int32) + + in_len[0] = self.output_shape[0] + + x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float) + + for i in range(batch_size): + x_in[i,0:len(data_input)] = data_input + + base_pred = self.model_base.predict(x = x_in) + r = K.ctc_decode(base_pred, in_len, greedy = True, beam_width=100, top_paths=1) + + if(tf.__version__[0:2] == '1.'): + r1 = r[0][0].eval(session=tf.compat.v1.Session()) + else: + r1 = r[0][0].numpy() + + speech_result = ctc_decode_delete_tail_blank(r1[0]) + return speech_result + class SpeechModel251(BaseModel): ''' 定义CNN+CTC模型，使用函数式模型