RAVE is an audio processing/generativity based on deep learning. RAVE (Realtime Audio Variational autoEncoder) is a learning framework for generating a neural network model from audio data. RAVE allowing both fast and high-quality audio waveform synthesis (20x real-time at 48 kHz sampling rate on standard CPU). In Max and Pd, it is accompanied by its nn~ decoder, which enables these models to be used in real time for various applications, audio generativity/timbre transformation/transfer.
Year: 2022
Website: https://forum.ircam.fr/collections/detail/rave/
Input types: Audio
Output types: Audio
Output length: Variable / Audio buffer size
AI Technique: VAE
Dataset: N/A
License type: MIT
Real time:
Free:
Open source:
Checkpoints:
Fine-tune:
Train from scratch:
IRCAM provides very detailed guides to using the RAVE model with their tools as well as training the model on custom data.
https://forum.ircam.fr/projects/detail/rave-vst/
https://forum.ircam.fr/article/detail/tutorial-neural-synthesis-in-max-8-with-rave/
https://forum.ircam.fr/article/detail/training-rave-models-on-custom-data/