Музыка с ИИ
moises-light
A resource-efficient music source separation model in PyTorch.
★ 36 звёздMIT
Смотреть руководство →moseca — проект с открытым исходным кодом из категории «Музыка с ИИ» от fabiogra (fabiogra/moseca на GitHub). У него 397 звёзд на GitHub и 57 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 28 февраля 2026.
Separate vocals and instruments in the browser with Demucs.
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.
A browser-based interface built with Streamlit.
Compose and edit with MIDI workflows.
Faithful ports of published models.
Tweak models and pipelines freely.
Splits songs into vocals, drums, bass, and more.
Describe a track and get audio back.
Download the source and move into the project folder.
git clone https://github.com/fabiogra/moseca.git
cd mosecaCreate 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 fabiogra/moseca with 57 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.
A resource-efficient music source separation model in PyTorch.
Loss functions for training audio source separation models.
A cross-platform desktop GUI for music source separation.
Separate music tracks into stems in a web app.
Split songs into stems on Apple Silicon with MLX-powered Demucs.
DiffRhythm — blazingly fast, embarrassingly simple full-length song generation with latent diffusion: complete songs with vocals from lyrics and a style prompt, Apache-2.0
Другие проекты из категории «Музыка с ИИ» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если moseca вам не подходит.
YuE2: frontier music generation with symbolic planning, zero-shot covers, and agentic music editing.
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ACE-Step — the open foundation model for music generation: full songs with vocals and lyrics in 19 languages, up to 4 minutes in about 20 seconds, Apache-2.0
Official DiffSinger code: diffusion-based singing voice synthesis and text-to-speech (AAAI 2022)
Stable Audio Tools — training and inference code for Stability's audio generation models, including Stable Audio Open, MIT
Соседние проекты из категории «Музыка с ИИ» в каталоге, ближайшие по числу звёзд к moseca — продолжайте изучать полку по обе стороны от него.
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