mechnader / rave

Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

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

You will need ffmpeg on your computer. You can install it locally inside your virtual environment using

conda install ffmpeg

Colab

A colab to train RAVEv2 is now available thanks to hexorcismos !

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

rave preprocess --input_path /audio/folder --output_path /dataset/path (--lazy)

Training

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_name

We 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)
	wassertein	Wassertein Auto Encoder objective (MMD)
	spherical	Spherical Auto Encoder objective
Discriminator	spectral_discriminator	Use the MultiScale discriminator from EnCodec.
Others	causal	Use causal convolutions

Export

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.

Pretrained models

Several pretrained streaming models are available here. We'll keep the list updated with new models.

Where is the prior ?

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).

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 !