UVR Maestro

An AI stem separation and audio restoration partner for Ultimate Vocal Remover (UVR5). Speak in natural language to extract vocals, drums, bass, guitar and piano, combine separation models and clean up recordings, then bring the resulting stems back to your DAW. Your audio stays on your computer.

Stay in the creative flow

Finding the right separation often means trying several models, repeating settings and moving files between processing steps. That work can interrupt the listening that tells you whether a result is useful.

UVR Maestro acts like an assistant engineer beside you in the studio:

• Start with your musical goal: Ask for a vocal, an instrumental, drum stems or a cleanup pass in ordinary studio language.

• Try a small section first: Compare models on an excerpt before processing the whole recording.

• Build repeatable workflows: Choose model settings, combine compatible models and route stems through cleanup chains in one request.

• Keep the source timeline: Lossless exports preserve the selected duration and source sample rate, or use a rate you choose. Excerpts report their position in the original recording.

Maestro uses UVR's engine in a separate local worker and can reuse your installed models. You listen to the results and decide what belongs in the production.

Related MCP server: StemSplit Vocal Remover & Stem Separator

How it feels in the studio

1. Find the right vocal model

You: "Try Kim Vocal 2 and a compatible VR model on the first 20 seconds of the chorus. Keep both results so I can compare them."

Maestro checks which models are installed, processes the same excerpt and returns the vocal and complementary files with their model settings and source offset. Audition the results, choose a model, then use it for the full song.

2. Work with your own drum models

You: "Extract drums with htdemucs, then run my registered DrumSep model on that stem. Keep the intermediate drums too."

Maestro routes the drum output into a compatible custom MDX23C model and keeps the requested intermediate. The resulting audio files can go into any DAW. Independent Audio Maestro can estimate MIDI from the files; Live Maestro handles pad loading and DAW placement.

3. Clean up a recording

You: "Reduce reverb in this vocal, then denoise it. Keep each step so I can hear whether the cleanup goes too far."

Maestro checks the installed cleanup models, routes each selected output into the next step and returns the intermediate and final files. Inspect their levels, then listen for lost detail before choosing the result.

What you can do

Which stems can you separate?

Each model has a trained set of outputs. Choose a model for the sources you need; an ensemble combines model estimates, and a chain can split a resulting stem further.

Piano does not cover every keyboard instrument, and backing vocals do not guarantee a separate choir or an individual singer. Declaring a new output label does not teach a model to extract that source. See model workflows and the Demucs model documentation for model-specific outputs and limitations.

Why Maestro is reliable

• Checked output files: Completed separation results verify decoded frames, sample rate, channels and finite samples. WAV/FLAC exports strip container tags.

• Traceable processing: Saved manifests fingerprint the input, model artifacts and output files, with the actual settings and backend.

• Clear job state: A queued job is still processing. Only state: succeeded includes checked outputs; failures and cancellations remain visible.

• Concrete change previews: Batch processing and destructive model/profile operations require confirm=True. Without it, Maestro reports the proposed action.

• Respect for existing files: Desktop model files are reused read-only. Outputs receive unique run directories.

• Honest backend reporting: GPU availability, the selected device and CPU fallback reasons are reported rather than inferred from the machine's hardware.

Quick start

You need uv. An existing UVR5 installation lets Maestro reuse its models and audio utilities; models can also be downloaded explicitly through Maestro.

1. Install it

uv tool install --managed-python --python 3.12 git+https://github.com/romanstark/uvr-maestro.git

2. Set up the local engine

uvr-maestro-setup --confirm

Setup downloads pinned UVR source and provisions a separate Python 3.11 inference environment. Your desktop UVR installation alone does not replace this step. Use uv-managed Python: Microsoft Store Python redirects AppData writes into a private folder that the separate engine cannot share.

For an NVIDIA system using the CUDA 12.4 build:

uvr-maestro-setup --acceleration cuda --confirm

3. Connect your AI assistant

Add the stdio server to your MCP client's configuration:

{
  "mcpServers": {
    "uvr-maestro": {
      "command": "uvr-maestro"
    }
  }
}

Use the command's absolute path if your client does not inherit your shell's PATH. Examples for agents, Claude, Codex, Cursor and VS Code are included; see MCP client examples for configuration and skill export.

Start with: "Check my UVR setup and show me the installed vocal models." Maestro uses uvr_status and find_model to establish readiness before separation.

Your music stays yours

• Local audio processing: Your recordings, stems and settings remain on your disk. Inference runs on your computer.

• No telemetry: UVR Maestro collects no analytics and uploads no audio, prompts or settings.

• Explicit network access: Runtime setup, catalog refresh, model downloads and update checks use the network when requested. Separation does not fetch missing weights.

Model downloads can use publishers' Hugging Face repositories or Meta's public-file host. Maestro does not bundle model weights.

What stays in your hands

Compatibility and limits

The packaged catalog snapshot contains 104 models. Classic MelBand/BandSplit Roformer use an optional separate runtime; modified modern Roformer/Bandit and legacy Demucs v1/v2 remain unsupported. Catalog presence does not establish algorithm support or model quality.

Native coverage includes VR, MDX, MDX23C, Demucs v3/v4 and classic Roformer on Windows, with CPU/CUDA availability depending on the architecture. Apple Silicon coverage includes VR v5, MDX and MDXC on CPU/MPS, and Demucs v4 on CPU. Demucs v4 uses an explicit CPU fallback for the pinned Torch MPS convolution limit. See the compatibility matrix for backend coverage and limits.

Single-model, ensemble and chain jobs support direct excerpts, source-rate or selected-rate lossless output, per-member options and intermediate retention. See audio workflows for frame guarantees, read-only custom models, Roformer setup and independent consumers.

Models have separate installation and verification states. An unknown upstream size/hash is reported as unknown integrity, not a successful trusted-checksum comparison. Manifests fingerprint the actual inputs, model artifacts and outputs. A model-selection score is a task heuristic, not a quality benchmark.

MCP tools and resources

The server exposes 34 tools, three static resources and one resource template: uvr://catalog, uvr://settings, uvr://status, uvr://skill/{name}. Two prompts, separate_stems and restore_recording, guide the complete workflows.

Five client-neutral skills cover setup, model choice, separation, restoration and audio utilities. Read them through uvr_skills or export them with uvr-maestro-skills.

Paths and audio utilities

Maestro's own data directory contains models, profiles, cache, jobs, journal and runtime. The default is %LOCALAPPDATA%/uvr-maestro on Windows, ~/Library/Application Support/uvr-maestro on macOS and $XDG_DATA_HOME/uvr-maestro or ~/.local/share/uvr-maestro on Linux.

FFmpeg is required for MP3 export and additional input codecs. Rubber Band is required for pitch/time utilities. Both are discovered on PATH or in the existing Windows/macOS UVR installation. Maestro does not bundle those executables.

An existing UVR5 desktop installation supplies reusable models and executables. Maestro additionally provisions its own pinned headless engine; the desktop installation alone does not replace that setup step.

Related Maestro projects

• Dorico Maestro controls notation workflows in Steinberg Dorico.

• Live Maestro controls Ableton Live, musical timing, MIDI and sample placement.

Each server works independently. Use ordinary MIDI, audio and JSON files when combining their workflows.

Development

From a local checkout:

uv venv --managed-python --python 3.12
uv pip install --python .venv/Scripts/python.exe -e ".[dev]"
.venv/Scripts/python -m pytest
.venv/Scripts/ruff check src scripts tests
.venv/Scripts/pyright
.venv/Scripts/pyrefly check
uv build

On macOS and Linux, use the corresponding commands under .venv/bin/. The unit suite requires neither Torch nor UVR. CI checks the light server on Windows, Linux and macOS with Python 3.11 through 3.14, then installs wheels and extracted sdists away from the checkout.

• Architecture

• Catalog and ownership

• Operational limits

• Compatibility and validation

• Audio workflows

• Contributing

• Third-party notices

• AGPL-3.0-or-later license

UVR Maestro is independent of the upstream projects and their trademark owners.