  The polyphonic nature of music makes the application of deep learning to
music modelling a challenging task. On the other hand, the Transformer
architecture seems to be a good fit for this kind of data. In this work, we
present Calliope, a novel autoencoder model based on Transformers for the
efficient modelling of multi-track sequences of polyphonic music. The
experiments show that our model is able to improve the state of the art on
musical sequence reconstruction and generation, with remarkably good results
especially on long sequences.
