{ "info": { "author": "Antoine CAILLON", "author_email": "caillon@ircam.fr", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "![rave_logo](docs/rave.png)\n\n# RAVE: Realtime Audio Variational autoEncoder\n\nOfficial 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.\n\nIf you use RAVE as a part of a music performance or installation, be sure to cite either this repository or the article !\n\nIf you want to share / discuss / ask things about RAVE you can do so in our [discord server](https://discord.gg/dhX73sPTBb) !\n\n## Previous versions\n\nThe original implementation of the RAVE model can be restored using\n\n```bash\ngit checkout v1\n```\n\n## Installation\n\nInstall RAVE using\n\n```bash\npip install acids-rave\n```\n\nYou will need **ffmpeg** on your computer. You can install it locally inside your virtual environment using\n\n```bash\nconda install ffmpeg\n```\n\n\n\n## Colab\n\nA colab to train RAVEv2 is now available thanks to [hexorcismos](https://github.com/moiseshorta) !\n[![colab_badge](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ih-gv1iHEZNuGhHPvCHrleLNXvooQMvI?usp=sharing)\n\n## Usage\n\nTraining a RAVE model usually involves 3 separate steps, namely _dataset preparation_, _training_ and _export_.\n\n### Dataset preparation\n\nYou 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\n\n```bash\nrave preprocess --input_path /audio/folder --output_path /dataset/path (--lazy)\n```\n\n### Training\n\nRAVEv2 has many different configurations. The improved version of the v1 is called `v2`, and can therefore be trained with\n\n```bash\nrave train --config v2 --db_path /dataset/path --out_path /model/out --name give_a_name\n```\n\nWe also provide a discrete configuration, similar to SoundStream or EnCodec\n\n```bash\nrave train --config discrete ...\n```\n\nBy 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\n\n```bash\nrave train --config discrete --config causal ...\n```\n\nNew 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\n\n```bash\nrave train --config v2 --augment mute --augment compress\n```\n\nMany other configuration files are available in `rave/configs` and can be combined. Here is a list of all the available configurations & augmentations :\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
TypeNameDescription
Architecturev1Original continuous model
v2Improved continuous model (faster, higher quality)
v2_smallv2 with a smaller receptive field, adpated adversarial training, and noise generator, adapted for timbre transfer for stationary signals
v2_nopqmf(experimental) v2 without pqmf in generator (more efficient for bending purposes)
v3v2 with Snake activation, descript discriminator and Adaptive Instance Normalization for real style transfer
discreteDiscrete model (similar to SoundStream or EnCodec)
onnxNoiseless v1 configuration for onnx usage
raspberryLightweight configuration compatible with realtime RaspberryPi 4 inference
Regularization (v2 only)defaultVariational Auto Encoder objective (ELBO)
wassersteinWasserstein Auto Encoder objective (MMD)
sphericalSpherical Auto Encoder objective
Discriminatorspectral_discriminatorUse the MultiScale discriminator from EnCodec.
OtherscausalUse causal convolutions
noiseEnables noise synthesizer V2
hybridEnable mel-spectrogram input
AugmentationsmuteRandomly mutes data batches (default prob : 0.1). Enforces the model to learn silence
compressRandomly compresses the waveform (equivalent to light non-linear amplification of batches)
gainApplies a random gain to waveform (default range : [-6, 3])
\n\n### Export\n\nOnce trained, export your model to a torchscript file using\n\n```bash\nrave export --run /path/to/your/run (--streaming)\n```\n\nSetting 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.**\n\n## Prior\n\nFor discrete models, we redirect the user to the `msprior` library [here](https://github.com/caillonantoine/msprior). However, as this library is still experimental, the prior from version 1.x has been re-integrated in v2.3.\n\n### Training\n\nTo train a prior for a pretrained RAVE model :\n\n```bash\nrave train_prior --model /path/to/your/run --db_path /path/to/your_preprocessed_data --out_path /path/to/output\n```\n\nthis will train a prior over the latent of the pretrained model `path/to/your/run`, and save the model and tensorboard logs to folder `/path/to/output`.\n\n### Scripting\n\nTo script a prior along with a RAVE model, export your model by providing the `--prior` keyword to your pretrained prior :\n\n```bash\nrave export --run /path/to/your/run --prior /path/to/your/prior (--streaming)\n```\n\n## Pretrained models\n\nSeveral pretrained streaming models [are available here](https://acids-ircam.github.io/rave_models_download). We'll keep the list updated with new models.\n\n## Realtime usage\n\nThis section presents how RAVE can be loaded inside [`nn~`](https://acids-ircam.github.io/nn_tilde/) in order to be used live with Max/MSP or PureData.\n\n### Reconstruction\n\nA pretrained RAVE model named `darbouka.gin` available on your computer can be loaded inside `nn~` using the following syntax, where the default method is set to forward (i.e. encode then decode)\n\n\n\nThis does the same thing as the following patch, but slightly faster.\n\n\n\n### High-level manipulation\n\nHaving an explicit access to the latent representation yielded by RAVE allows us to interact with the representation using Max/MSP or PureData signal processing tools:\n\n\n\n### Style transfer\n\nBy default, RAVE can be used as a style transfer tool, based on the large compression ratio of the model. We recently added a technique inspired from StyleGAN to include Adaptive Instance Normalization to the reconstruction process, effectively allowing to define _source_ and _target_ styles directly inside Max/MSP or PureData, using the attribute system of `nn~`.\n\n\n\nOther attributes, such as `enable` or `gpu` can enable/disable computation, or use the gpu to speed up things (still experimental).\n\n## Offline usage\n\nA batch generation script has been released in v2.3 to allow transformation of large amount of files\n\n```bash\nrave generate model_path path_1 path_2 --out out_path\n```\n\nwhere `model_path` is the path to your trained model (original or scripted), `path_X` a list of audio files or directories, and `out_path` the out directory of the generations.\n\n## Discussion\n\nIf you have questions, want to share your experience with RAVE or share musical pieces done with the model, you can use the [Discussion tab](https://github.com/acids-ircam/RAVE/discussions) !\n\n## Demonstration\n\n### RAVE x nn~\n\nDemonstration of what you can do with RAVE and the nn~ external for maxmsp !\n\n[![RAVE x nn~](http://img.youtube.com/vi/dMZs04TzxUI/mqdefault.jpg)](https://www.youtube.com/watch?v=dMZs04TzxUI)\n\n### embedded RAVE\n\nUsing nn~ for puredata, RAVE can be used in realtime on embedded platforms !\n\n[![RAVE x nn~](http://img.youtube.com/vi/jAIRf4nGgYI/mqdefault.jpg)](https://www.youtube.com/watch?v=jAIRf4nGgYI)\n\n# Funding\n\nThis work is led at IRCAM, and has been funded by the following projects\n\n- [ANR MakiMono](https://acids.ircam.fr/course/makimono/)\n- [ACTOR](https://www.actorproject.org/)\n- [DAFNE+](https://dafneplus.eu/) N\u00b0 101061548\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "acids-rave", "package_url": 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