README.md

---
title: Audiocraft_Music-Audio_Generation
app_file: app.py
sdk: gradio
sdk_version: 3.40.1
---
# AudioCraft Plus
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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.

![image](https://github.com/GrandaddyShmax/audiocraft_plus/assets/52707645/c4c5327c-901a-40d8-91be-aa5afcf80b52)

## 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
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
```

## 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.