https://github.com/jhurliman/music-interpolation
Mix between music tracks using machine learning
https://github.com/jhurliman/music-interpolation
Last synced: about 2 months ago
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Mix between music tracks using machine learning
- Host: GitHub
- URL: https://github.com/jhurliman/music-interpolation
- Owner: jhurliman
- License: mit
- Created: 2023-08-10T16:40:37.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-16T19:54:36.000Z (almost 2 years ago)
- Last Synced: 2025-03-26T16:38:58.848Z (2 months ago)
- Language: Python
- Size: 140 KB
- Stars: 10
- Watchers: 2
- Forks: 3
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# music-interpolation
> Mix between music tracks using machine learning
## Introduction
This repository is a sandbox to experiment with machine learning techniques for smoothly transitioning between music tracks.
## Development
1. `git clone https://github.com/jhurliman/music-interpolation.git`
2. `cd music-interpolation`
3. Install pipenv if you have not already: `pip install pipenv`
4. Install dependencies: `pipenv sync --python 3.10 --dev`
5. Run tests: `pipenv run pytest`
## Usage
```python
import librosa
from IPython.display import Audio
from music_interpolation import EncodecInterpolation
# Instantiate the interpolation class, fetching and loading the pre-trained
# Encodec model
interp = EncodecInterpolation()
# Load two audio tracks, resampling to the sampling rate of the Encodec model
# (defaults to 48kHz)
audio_a, _ = librosa.load('audio_a.mp3', sr=interp.sampling_rate, mono=False)
audio_b, _ = librosa.load('audio_b.mp3', sr=interp.sampling_rate, mono=False)
# Transition between two audio tracks using linear interpolation between
# embedding vectors generated by a pre-trained Encodec model
audio_c = interp.interpolate(audio_a, audio_b)
Audio(data=audio_c, rate=interp.sampling_rate)
```
## Contributing
Contributions are welcome! Open a pull request to fix a bug, or open an issue to discuss a new feature or change.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.