AI音楽
moseca
Separate vocals and instruments in the browser with Demucs.
★ 397 スターMIT
ガイドを見る →torch-l1-snrは、crlandscが公開しているオープンソースのAI音楽プロジェクトです（GitHub: crlandsc/torch-l1-snr）。GitHubスターは45、フォークは0で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年9月1日です。
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.
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