RAVE: a Realtime Audio Variatione autoEncoder
Project description
RAVE: Realtime Audio Variational autoEncoder
Official implementation of RAVE: A variational autoencoder for fast and high-quality neural audio synthesis (article link) by Antoine Caillon and Philippe Esling.
If you use RAVE as a part of a music performance or installation, be sure to cite either this repository or the article !
Previous versions
The original implementation of the RAVE model can be restored using
git checkout v1
Installation
Install RAVE using
pip install acids-rave
Usage
Training a RAVE model usually involves 3 separate steps, namely dataset preparation, training and export.
Dataset preparation
Training
Export
Discussion
If you have questions, want to share your experience with RAVE or share musical pieces done with the model, you can use the Discussion tab !
Demonstration
RAVE x nn~
Demonstration of what you can do with RAVE and the nn~ external for maxmsp !
embedded RAVE
Using nn~ for puredata, RAVE can be used in realtime on embedded platforms !
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Source Distributions
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