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 !
If you want to share / discuss / ask things about RAVE you can do so in our discord server !
The original implementation of the RAVE model can be restored using
git checkout v1Install RAVE using
pip install acids-raveYou will need ffmpeg on your computer. You can install it locally inside your virtual environment using
conda install ffmpegA colab to train RAVEv2 is now available thanks to hexorcismos !
Training a RAVE model usually involves 3 separate steps, namely dataset preparation, training and export.
You can know prepare a dataset using two methods: regular and lazy. Lazy preprocessing allows RAVE to be trained directly on the raw files (i.e. mp3, ogg), without converting them first. Warning: lazy dataset loading will increase your CPU load by a large margin during training, especially on Windows. This can however be useful when training on large audio corpus which would not fit on a hard drive when uncompressed. In any case, prepare your dataset using
rave preprocess --input_path /audio/folder --output_path /dataset/path (--lazy)RAVEv2 has many different configurations. The improved version of the v1 is called v2, and can therefore be trained with
rave train --config v2 --db_path /dataset/path --name give_a_nameWe also provide a discrete configuration, similar to SoundStream or EnCodec
rave train --config discrete ...By default, RAVE is built with non-causal convolutions. If you want to make the model causal (hence lowering the overall latency of the model), you can use the causal mode
rave train --config discrete --config causal ...Many other configuration files are available in rave/configs and can be combined. Here is a list of all the available configurations
| Type | Name | Description |
|---|---|---|
| Architecture | v1 | Original continuous model |
| v2 | Improved continuous model (faster, higher quality) | |
| discrete | Discrete model (similar to SoundStream or EnCodec) | |
| onnx | Noiseless v1 configuration for onnx usage | |
| raspberry | Lightweight configuration compatible with realtime RaspberryPi 4 inference | |
| Regularization (v2 only) | default | Variational Auto Encoder objective (ELBO) |
| wasserstein | Wasserstein Auto Encoder objective (MMD) | |
| spherical | Spherical Auto Encoder objective | |
| Discriminator | spectral_discriminator | Use the MultiScale discriminator from EnCodec. |
| Others | causal | Use causal convolutions |
Once trained, export your model to a torchscript file using
rave export --run /path/to/your/run (--streaming)Setting the --streaming flag will enable cached convolutions, making the model compatible with realtime processing. If you forget to use the streaming mode and try to load the model in Max, you will hear clicking artifacts.
Several pretrained streaming models are available here. We'll keep the list updated with new models.
The prior model was an experimental feature from RAVEv1 and has been removed from this repository. However, we will release a new improved version of the prior soon (very soon in fact).
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 of what you can do with RAVE and the nn~ external for maxmsp !
Using nn~ for puredata, RAVE can be used in realtime on embedded platforms !
