README.md

---
title: audiocraft_plus
app_file: app.py
sdk: gradio
sdk_version: 3.47.1
---
# 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.