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https://github.com/1aienthusiast/audiocraft-infinity-webui
https://github.com/1aienthusiast/audiocraft-infinity-webui
agplv3 artificial-intelligence audiocraft generation machine-learning ml music music-generation musicgen open-source python web-ui webui
Last synced: 5 days ago
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- Host: GitHub
- URL: https://github.com/1aienthusiast/audiocraft-infinity-webui
- Owner: 1aienthusiast
- License: agpl-3.0
- Created: 2023-06-12T14:22:55.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-08-14T02:55:04.000Z (about 1 year ago)
- Last Synced: 2024-08-02T16:51:55.429Z (3 months ago)
- Topics: agplv3, artificial-intelligence, audiocraft, generation, machine-learning, ml, music, music-generation, musicgen, open-source, python, web-ui, webui
- Language: Python
- Homepage:
- Size: 101 KB
- Stars: 154
- Watchers: 3
- Forks: 20
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Audiocraft Infinity WebUI
Adds generation of songs with a length of over 30 seconds.
Adds the ability to continue songs.
Adds a seed option.
Adds ability to load locally downloaded models.
### Adds training (Thanks to chavinlo's repo https://github.com/chavinlo/musicgen_trainer)
Adds MacOS support.
Adds queue (on the main-queue branch: https://github.com/1aienthusiast/audiocraft-infinity-webui/tree/main-queue)
Batching (**run webuibatch.py instead of webui.py**)
Disables (hopefully) the gradio analytics.
## Note! Project is currently not actively maintained but accepts PRs.
## Installation
Python 3.9 is recommended.
1. Clone the repo:
`git clone https://github.com/1aienthusiast/audiocraft-infinity-webui.git`
2. Install pytorch:
`pip install 'torch>=2.0'`
3. Install the requirements:
`pip install -r requirements.txt`
4. Clone my fork of the Meta audiocraft repo and chavinlo's MusicGen trainer inside the `repositories` folder:
```
cd repositories
git clone https://github.com/1aienthusiast/audiocraft
git clone https://github.com/chavinlo/musicgen_trainer
cd ..
```
## Note!
If you already cloned the Meta audiocraft repo you have to remove it then clone the provided fork for the seed option to work.
```
cd repositories
rm -rf audiocraft/
git clone https://github.com/1aienthusiast/audiocraft
git clone https://github.com/chavinlo/musicgen_trainer
cd ..
```
## Usage
```python webui.py```
```python webuibatch.py``` - with batching support
## Updating
Run `git pull` inside the root folder to update the webui, and the same command inside `repositories/audiocraft` to update audiocraft.
## Models
Meta provides 4 pre-trained models. The pre trained models are:
- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small)
- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium)
- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody)
- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large)
**Needs a GPU!**
I recommend 12GB of VRAM for the large model.
## Training
### Dataset Creation
Create a folder, in it, place your audio and caption files. **They must be WAV and TXT format respectively.**
**Place the folder in `training/datasets/`.**
### Important: Split your audios in 35 second chunks. Only the first 30 seconds will be processed. Audio cannot be less than 30 seconds.
In this example, segment_000.txt contains the caption "jazz music, jobim" for wav file segment_000.wav
### Options
- `dataset_path` - path to your dataset with WAV and TXT pairs.
- `model_id` - MusicGen model to use. Can be `small`/`medium`/`large`. Default: `small` - model it will be finetuned on
- `lr`: Float, learning rate. Default: `0.0001`/`1e-4`
- `epochs`: Integer, epoch count. Default: `5`
- `use_wandb`: Integer, `1` to enable wandb, `0` to disable it. Default: `0` = Disabled
- `save_step`: Integer, amount of steps to save a checkpoint. Default: None
### Models
Once training finishes, the model (and checkpoints) will be available under the `models/` directory.
### Loading the finetuned models
Model gets saved to models/ as `lm_final.pt`
1) Place it in models/DIRECTORY_NAME/
2) In the Inference tab choose `custom` as the model and enter DIRECTORY_NAME into the input field.
3) In the Inference tab choose the model it was finetuned on
## Colab
For google colab you need to use the `--share` flag.
## License
* The code in this repository is released under the AGPLv3 license as found in the [LICENSE file](LICENSE).