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Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder
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| 2026-07-24 | 1773 |
| 2026-07-25 | 1774 |
| 2026-07-28 | 1774 |
| 2026-07-30 | 1774 |
| 2026-08-06 | 1774 |
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 # RAVE: Realtime Audio Variational autoEncoder Official implementation of _RAVE: A variational autoencoder for fast and high-quality neural audio synthesis_ ([article link](https://arxiv.org/abs/2111.05011)) 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 and other research from ACIDS, you can do so in our [discord server](https://discord.gg/r9umPrGEWv) ! Please check the FAQ before posting an issue! **RAVE VST** RAVE VST for Windows, Mac and Linux is available as beta on the [corresponding Forum IRCAM webpage](https://forum.ircam.fr/projects/detail/rave-vst/). For problems, please write an issue here or [on the Forum IRCAM discussion page](https://discussion.forum.ircam.fr/c/rave-vst/651). **Tutorials** : new tutorials are available on the Forum IRCAM webpage, and video versions are coming soon! - [Tutorial: Neural Synthesis in a DAW with RAVE](https://forum.ircam.fr/article/detail/neural-synthesis-in-a-daw-with-rave/) - [Tutorial: Neural Synthesis in Max 8 with RAVE](https://forum.ircam.fr/article/detail/tutorial-neural-synthesis-in-max-8-with-rave/) - [Tutorial: Training RAVE models on custom data](https://forum.ircam.fr/article/detail/training-rave-models-on-custom-data/) ## Previous versions The original implementation of the RAVE model can be restored using ```bash git checkout v1 ``` ## Installation Install RAVE using ```bash pip install acids-rave ``` **Warning** It is strongly advised to install `torch` and `torchaudio` before `acids-rave`, so you can choose the appropriate version of torch on the [library website](http://www.pytorch.org). For future compatibility with new devices (and modern Python environments), `rave-acids` does not enforce torch==1.13 anymore. You will need **ffmpeg** on your computer. You can install it locally inside your virtual environment using ```bash conda install ffmpeg ``` <!-- Detailed instructions to setup a training station for this project are available [here](docs/training_setup.md). --> ## Colab A colab to train RAVEv2 is now available thanks to [hexorcismos](https://github.com/moiseshorta) ! [](https://colab.research.google.com/drive/1ih-gv1iHEZNuGhHPvCHrleLNXvooQMvI?usp=sharing) ## Usage Training a RAVE model usually involves 3 separate steps, namely _dataset preparation_, _training_ and _export_. ### Dataset preparation 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 ```bash rave preprocess --input_path /audio/folder --output_path /dataset/path --channels X (--lazy) ``` ### Training RAVEv2 has many different configurations. The improved version of the v1 is called `v2`, and can therefore be trained with ```bash rave train --config v2 --db_path /dataset/path --out_path /model/out --name give_a_name --channels X ``` We also provide a discrete configuration, similar to SoundStream or EnCodec ```bash 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 ```bash rave train --config discrete --config causal ... ``` New in 2.3, data augmentations are also available to improve the model's generalization in low data regimes. You can add data augmentation by adding augmentation configuration files with the `--augment` keyword ```bas
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