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# AudioCraft Plus | |
![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg) | |
![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg) | |
![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg) | |
AudioCraft is a PyTorch library for deep learning research on audio generation. AudioCraft contains inference and training code | |
for two state-of-the-art AI generative models producing high-quality audio: AudioGen and MusicGen. | |
<a target="_blank" href="https://colab.research.google.com/github/camenduru/MusicGen-colab/blob/main/MusicGen_ClownOfMadness_plus_colab.ipynb"> | |
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> | |
</a> | |
<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> | |
<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/> | |
</a> | |
<br> | |
<br> | |
![image](https://github.com/GrandaddyShmax/audiocraft_plus/assets/52707645/043fc037-54a9-48c4-bb5c-bf9b7440d146) | |
## Features | |
AudioCraft Plus is an all-in-one WebUI for the original AudioCraft, adding many quality features on top. | |
- AudioGen Model | |
- Multiband Diffusion | |
- Custom Model Support | |
- Generation Metadata and Audio Info tab | |
- Mono to Stereo | |
- Multiprompt/Prompt Segmentation with Structure Prompts | |
- Video Output Customization | |
- Music Continuation | |
## Installation | |
If you are updating from the previous version of AudioCraft Plus, do the following steps in the AudioCraft Plus folder: | |
```shell | |
git pull | |
pip install transformers --upgrade | |
pip install torchmetrics --upgrade | |
``` | |
#### Otherwise: Clean Installation | |
AudioCraft requires Python 3.9, PyTorch 2.0.0. To install AudioCraft, you can run the following: | |
```shell | |
# Best to make sure you have torch installed first, in particular before installing xformers. | |
# Don't run this if you already have PyTorch installed. | |
pip install 'torch>=2.0' | |
# Then proceed to one of the following | |
pip install -U audiocraft # stable release | |
pip install -U git+https://git@github.com/GrandaddyShmax/audiocraft_plus#egg=audiocraft # bleeding edge | |
pip install -e . # or if you cloned the repo locally (mandatory if you want to train). | |
``` | |
We also recommend having `ffmpeg` installed, either through your system or Anaconda: | |
```bash | |
sudo apt-get install ffmpeg | |
# Or if you are using Anaconda or Miniconda | |
conda install 'ffmpeg<5' -c conda-forge | |
``` | |
Installation video thanks to Pogs Cafe: | |
[![Untitled](http://img.youtube.com/vi/WjGk4bcbUOI/0.jpg)](http://www.youtube.com/watch?v=WjGk4bcbUOI "Installing MusicGen+ Locally") | |
Additional installation guide by [radaevm](https://github.com/radaevm) can be found [HERE](https://github.com/GrandaddyShmax/audiocraft_plus/discussions/31) | |
## Models | |
At the moment, AudioCraft contains the training code and inference code for: | |
* [MusicGen](./docs/MUSICGEN.md): A state-of-the-art controllable text-to-music model. | |
* [AudioGen](./docs/AUDIOGEN.md): A state-of-the-art text-to-sound model. | |
* [EnCodec](./docs/ENCODEC.md): A state-of-the-art high fidelity neural audio codec. | |
* [Multi Band Diffusion](./docs/MBD.md): An EnCodec compatible decoder using diffusion. | |
## Training code | |
AudioCraft contains PyTorch components for deep learning research in audio and training pipelines for the developed models. | |
For a general introduction of AudioCraft design principles and instructions to develop your own training pipeline, refer to | |
the [AudioCraft training documentation](./docs/TRAINING.md). | |
For reproducing existing work and using the developed training pipelines, refer to the instructions for each specific model | |
that provides pointers to configuration, example grids and model/task-specific information and FAQ. | |
## API documentation | |
We provide some [API documentation](https://facebookresearch.github.io/audiocraft/api_docs/audiocraft/index.html) for AudioCraft. | |
## FAQ | |
#### Is the training code available? | |
Yes! We provide the training code for [EnCodec](./docs/ENCODEC.md), [MusicGen](./docs/MUSICGEN.md) and [Multi Band Diffusion](./docs/MBD.md). | |
#### Where are the models stored? | |
Hugging Face stored the model in a specific location, which can be overriden by setting the `AUDIOCRAFT_CACHE_DIR` environment variable. | |
## License | |
* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE). | |
* The models weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights). | |
## Citation | |
For the general framework of AudioCraft, please cite the following. | |
``` | |
@article{copet2023simple, | |
title={Simple and Controllable Music Generation}, | |
author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, | |
year={2023}, | |
journal={arXiv preprint arXiv:2306.05284}, | |
} | |
``` | |
When referring to a specific model, please cite as mentioned in the model specific README, e.g | |
[./docs/MUSICGEN.md](./docs/MUSICGEN.md), [./docs/AUDIOGEN.md](./docs/AUDIOGEN.md), etc. | |