  The quality of outputs produced by deep generative models for music have seen
a dramatic improvement in the last few years. However, most deep learning
models perform in "offline" mode, with few restrictions on the processing time.
Integrating these types of models into a live structured performance poses a
challenge because of the necessity to respect the beat and harmony. Further,
these deep models tend to be agnostic to the style of a performer, which often
renders them impractical for live performance. In this paper we propose a
system which enables the integration of out-of-the-box generative models by
leveraging the musician's creativity and expertise.
