Musique IA
moseca
Separate vocals and instruments in the browser with Demucs.
★ 397 étoilesMIT
Voir le guide →torch-l1-snr est un projet de musique IA open source, créé par crlandsc (crlandsc/torch-l1-snr sur GitHub). Il compte 45 étoiles sur GitHub et 0 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 1er septembre 2026.
Loss functions for training audio source separation models.
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.
Generate music without cloud APIs.
Studio-grade sample rates and formats.
Runs on CPU, CUDA, ROCm, Metal, or Vulkan.
Compose and edit with MIDI workflows.
Faithful ports of published models.
Download the source and move into the project folder.
git clone https://github.com/crlandsc/torch-l1-snr.git
cd torch-l1-snrCreate 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/torch-l1-snr with 0 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.
A resource-efficient music source separation model in PyTorch.
A cross-platform desktop GUI for music source separation.
An audio quality metric and loss for training cleaner models.
Stable Audio Tools — training and inference code for Stability's audio generation models, including Stable Audio Open, MIT
A curated MIDI dataset built for training precise music AI models.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.