rnnoise | Recurrent neural network for audio noise reduction | Machine Learning library

I am building a model based on this code for noise suppression. My problem with the vanilla implementation is that it loads all data at once, which is not the best idea when the training data gets really large; my input file, denoted in the linked code as training.h5, is over 30 GB.

I decided to instead go with tf.data interface that should allow me to work with large data sets; my problem here is that I don't know how to properly shape TFRecordDataset so that it meets what's required by the Model API.

If you check model.fit(x_train, [y_train, vad_train], it essentially requires the following:

• x_train, shape [nb_sequences, window, 42]
• y_train, shape [nb_sequences, window, 22]
• vad_train, shape [nb_sequences, window, 1]

window one typically fixes (in the code: 2000), so the only variable nb_sequences that stems from how large is your data set. However, with tf.data, we don't supply x and y, but only x (see Model API docs).

In an effort to make the code reproducible, I created the input file with the following code: