Keras Listener Network LSTM Autoencoder - https://towardsdatascience.com/human-like-machine-hearing-with-ai-2-3-f9fab903b20a

prepare_listener.py

from keras.models import Model
from keras.layers import Input, LSTM, RepeatVector
def prepare_listener(timesteps,
                     input_dim,
                     latent_dim,
                     optimizer_type,
                     loss_type):
    """Prepares Seq2Seq autoencoder model

        Args:
            :param timesteps: The number of timesteps in sequence
            :param input_dim: The dimensions of the input
            :param latent_dim: The latent dimensionality of LSTM
            :param optimizer_type: The type of optimizer to use
            :param loss_type: The type of loss to use

        Returns:
            Autoencoder model, Encoder model
    """

    inputs = Input(shape=(timesteps, input_dim))
    encoded = LSTM(int(input_dim / 2), 
                   activation="relu", 
                   return_sequences=True)(inputs)
    encoded = LSTM(latent_dim, 
                   activation="relu", 
                   return_sequences=False)(encoded)
    decoded = RepeatVector(timesteps)(encoded)
    decoded = LSTM(int(input_dim / 2), 
                   activation="relu", 
                   return_sequences=True)(decoded)
    decoded = LSTM(input_dim, 
                   return_sequences=True)(decoded)

    autoencoder = Model(inputs, decoded)
    encoder = Model(inputs, encoded)

    autoencoder.compile(optimizer=optimizer_type, 
                        loss=loss_type,  
                        metrics=['acc'])

    return autoencoder, encoder