git:20260518.471df3d to git:20260710.2925510
14 added, 25 removed. Audit A to B.
---
- name: "Audio Stem Separator with Demucs"
- slug: "audio-stem-separator-demucs"
- description: "Separates audio tracks into individual stems (vocals, drums, bass, other) using Meta's Demucs neural network model via the demucs Python package. Supports batch processing of WAV and MP3 files, outputs isolated stems in FLAC or WAV format, and integrates with FFmpeg for format conversion and loudness matching post-separation."
- github_stars: 2507
- verification: "listed"
+ title: "Audio Stem Separator with Demucs"
+ description: "Separates audio tracks into individual stems (vocals, drums, bass, other) using Meta’s Demucs neural network model via the demucs Python package. Supports batch processing of WAV and MP3 files, outputs isolated stems in FLAC or WAV format, and integrates with FFmpeg for format conversion and loudness matching post-separation."
+ verification: "security_reviewed"
source: "https://github.com/adefossez/demucs"
author: "adefossez"
- category: "Media & Transcription"
- framework: "MCP"
+ category:
+ - "Media & Transcription"
+ framework:
+ - "MCP"
tool_ecosystem:
github_repo: "adefossez/demucs"
github_stars: 2507
---
# Audio Stem Separator with Demucs
- Separates audio tracks into individual stems (vocals, drums, bass, other) using Meta's Demucs neural network model via the demucs Python package. Supports batch processing of WAV and MP3 files, outputs isolated stems in FLAC or WAV format, and integrates with FFmpeg for format conversion and loudness matching post-separation.
+ Separates audio tracks into individual stems (vocals, drums, bass, other) using Meta’s Demucs neural network model via the demucs Python package. Supports batch processing of WAV and MP3 files, outputs isolated stems in FLAC or WAV format, and integrates with FFmpeg for format conversion and loudness matching post-separation.
## Installation
- Use the upstream install or setup path that matches your environment:
- - conda env update -f environment-cpu.yml # if you don't have GPUs
- - conda env update -f environment-cuda.yml # if you have GPUs
- - conda activate demucs
- - pip install -e .
-
- Requirements and caveats from upstream:
- - requires custom CUDA code that is not ready for release yet.
- - You will need at least Python 3.8. See requirements_minimal.txt for requirements for separation only,
- - Everytime you see python3, replace it with python.exe. You should always run commands from the
-
- Basic usage or getting-started notes:
- - and environment-[cpu|cuda].yml (or requirements.txt) if you want to train a new model.
- - ### For Windows users
- - Anaconda console.
+ Choose whichever fits your setup:
- - Source: https://github.com/adefossez/demucs
- - Extracted from upstream docs: https://raw.githubusercontent.com/adefossez/demucs/HEAD/README.md
+ 1. Copy this skill folder into your local skills directory.
+ 2. Clone the repo and symlink or copy the skill into your agent workspace.
+ 3. Add the repo as a git submodule if you manage shared skills centrally.
+ 4. Install it through your internal provisioning or packaging workflow.
+ 5. Download the folder directly from GitHub and place it in your skills collection.
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/audio-stem-separator-demucs/)