RAVE (Realtime Audio Variational autoEncoder) is an algorithm designed for real-time, high-quality audio waveform synthesis using neural networks. It leverages a variational autoencoder (VAE) architecture, which compresses audio data into a compact latent representation, allowing efficient reconstruction of audio signals.

Key features of RAVE include:

Fast, high-quality audio generation: It excels at producing accurate audio in real-time, making it ideal for interactive applications (20x real-time at 48 kHz sampling rate on standard CPU)
Real-time use: Integrated with tools like Max and Pure Data (Pd), RAVE can be used with the nn~ decoder for real-time sound generation and transformation. A VST plugin makes it easy to use in any DAW.
Applications: Common uses include audio synthesis, timbre transformation, and style transfer.
In short, RAVE is a powerful tool for real-time audio generation, offering both speed and quality.

In just a few months, RAVE popularized the creation of models based on audio recordings, thanks in particular to the publication of a series of tutorials and open-source code. A growing and ebullient community of users took hold of the algorithm, and numerous models emerged. Although these models can be quite costly to produce (around twenty GPU hours), very few have so far been published, often due to copyright issues. This challenge concerns models trained on personal recordings for which the authors own all rights.

The aim of this challenge is to support the authors of the best models and to collectively establish a repertoire of RAVE models, enabling everyone to benefit from the richness and variety of approaches in the field of timbre/music transfer.

The challenge is hosted by the DAFNE+ platform, which promotes content using NFTs.

A public vote awards three prizes to participants.