Audio Classification Model

model.py

def conv_model():
    model = Sequential()
    model.add(Conv2D(16, (3, 3), activation='relu', strides=(1, 1),
                     padding='same', input_shape=input_shape))
    model.add(Conv2D(32, (3, 3), activation='relu', strides=(1,1),
                     padding='same'))
    model.add(Conv2D(64, (3, 3), activation='relu', strides=(1,1),
                     padding='same'))
    model.add(Conv2D(128, (3, 3), activation='relu', strides=(1,1),
                     padding='same'))
    model.add(MaxPool2D(2, 2))
    model.add(Dropout(0.5))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(64, activation='relu'))
    model.add(Dense(8, activation='softmax')) # Change
    model.summary()
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])
    return model

def recurrent_model():
    model = Sequential()
    model.add(LSTM(128, return_sequences=True, input_shape=input_shape))
    model.add(LSTM(128, return_sequences=True))
    model.add(Dropout(0.5))
    model.add(TimeDistributed(Dense(64, activation='relu')))
    model.add(TimeDistributed(Dense(32, activation='relu')))
    model.add(TimeDistributed(Dense(16, activation='relu')))
    model.add(TimeDistributed(Dense(8, activation='relu')))
    model.add(Flatten())
    model.add(Dense(8, activation='softmax')) # Change
    model.summary()
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['acc'])
    return model