# Audiolla

A self-hosted MCP server running inside Docker for local audio tasks. It gives coding assistants tools for stem splitting and speech cleanup without external cloud calls.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed · ★ 5
- Author: psyb0t
- License: WTFPL · policy=link-only · description only; the file stays with the author
- Source: https://github.com/psyb0t/docker-audiolla
- Updated: 2026-10-19
- HTML: https://affpapa.org/en/nevibecode/mcp/audiolla

## What it does

Audiolla packages thirty audio engines into a single Docker image exposed through an HTTP API and an MCP endpoint. It includes Demucs for stem isolation, basic-pitch for polyphonic note detection, and DeepFilterNet with pyannote for vocal enhancement. The container accepts an audio file and returns extracted stems, analytical JSON data, or rendered MIDI files.

The container isolates system dependencies and avoids Python library clashes across models. Heavy tasks run in the background. They report completion via `async_job=true` and webhook callbacks. Running this server requires a local Docker engine with enough resources to pull and run `psyb0t/audiolla`.

## Requirements
- Needs: Docker
- Works with: Docker

## Install

### Claude Code (native platform)

**Install**
- The MCP registry has no launch command for it: see the repository README

## When to use it
- Separate mixed tracks into individual stems using Demucs
- Transcribe recorded instruments into MIDI sequences
- Remove room reverberation, background noise, and electrical hum
- Measure BPM, musical key, and integrated LUFS loudness
- Label speaker segments in conversation files with pyannote

## Example prompts
- Split the file `track.mp3` into stems and output the vocal track
- Convert the guitar recording in `solo.wav` into a MIDI file
- Clean the background noise from `speech.wav` and normalize its LUFS level

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