Musique IA
moseca
Separate vocals and instruments in the browser with Demucs.
★ 397 étoilesMIT
Voir le guide →moises-light est un projet de musique IA open source, créé par crlandsc (crlandsc/moises-light sur GitHub). Il compte 36 étoiles sur GitHub et 1 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 1er mai 2026.
A resource-efficient music source separation model in PyTorch.
Decide what you want the project to do before installing anything.
Grab the source and skim the README for setup requirements.
Install dependencies and add any API keys or settings it needs.
Launch it and follow the example workflow from the docs.
Splits tracks into isolated stems.
Compose and edit with MIDI workflows.
Faithful ports of published models.
Tweak models and pipelines freely.
Describe a track and get audio back.
Generate music without cloud APIs.
Download the source and move into the project folder.
git clone https://github.com/crlandsc/moises-light.git
cd moises-lightCreate a virtual environment and install the requirements.
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtStart the app or CLI and follow the README examples.
python main.py --helpIt is an open-source project at crlandsc/moises-light with 1 forks. Check the repo’s contributors and recent commits to judge how active maintenance is.
Yes. It is MIT licensed, so you can use it commercially with attribution.
Typically 44.1–48kHz stereo — good enough for demos and remixes.
Separation and generation run much faster on GPU, but CPU works for short tracks.
Check the model's license — the code license and model weights may differ.
Separate vocals and instruments in the browser with Demucs.
Loss functions for training audio source separation models.
A cross-platform desktop GUI for music source separation.
Real-time, low-latency music source separation in PyTorch.
Run splifft locally: lightweight utilities for music source separation and t.
A Python inference package for music source separation models.
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Stable Audio Tools — training and inference code for Stability's audio generation models, including Stable Audio Open, MIT
Projets de la catégorie Musique IA proches dans le catalogue, les plus proches en étoiles de moises-light — continuez à explorer de part et d'autre.
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Split songs into stems on Apple Silicon with MLX-powered Demucs.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.