github.com/stemdeckapp/stemdeck @v0.8.0-alpha.4 sqlite

Free, local stem separation. No account. No upload. No subscription.

_{JOIN THE COMMUNITY}

Drop in an MP3, WAV, or FLAC file, or paste a YouTube URL, and StemDeck splits the audio into up to six stems (vocals, drums, bass, guitar, piano, other). Play them back in a DAW-style multitrack mixer: mute, solo, balance levels, zoom the waveform, loop a region, and export individual stems or a custom mix. Everything runs locally on your own machine.

What is this? StemDeck is a stem separation tool, not a downloader. Its main job is processing audio you already own: drag an MP3, WAV, or FLAC onto the import bar and go. YouTube support is a convenience for content you have the right to process. StemDeck does not store, cache, or redistribute any downloaded content. Everything happens locally and nothing leaves your machine.

StemDeck is a free, open alternative to cloud stem-splitters like Moises and LALAL.AI: no account, no quota, no uploads, no subscription. If you want stems for personal study and prefer to keep things local and free, StemDeck has you covered. If you need the polish, a mobile app, or deeper musician tooling, the commercial products are a better fit.

We Recommend

StemDeck is free and does not accept any money, sponsorship, or funding - not from users, not from anyone listed below. We share these makers and artists purely for the joy of pointing you toward wonderful people doing beautiful work. Go meet them ❤️

Name	What they do	Link
Dlima Guitars	Custom guitars and basses	@dlimaguitars
Lisbon Guitar Works	Guitar building	dlimaguitars.com
Joao Gaspar	Producer/Film Scorer, Touring/Session Musician	@jay_glaspar
Kris Luthier	Luthier and Musical Instrument Repair, Lisboa	@krisluthier
Thomann	Online Music Store	@thomann.music
Analog4Lyfe	Analog music gear	@analog4lyfe
Empress Effects	Effects pedals	empresseffects.com

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Features

6-stem separation via Demucs htdemucs_6s, with auto-detection of the best Torch device (CUDA on NVIDIA, MPS on Apple Silicon, CPU fallback).

YouTube and local file import. Paste a YouTube URL or drop an MP3 or WAV directly onto the import bar.

DAW-style waveform editor with min/max sample rendering across all stems, shared normalization, zoom in/out/Fit, loop drag on the ruler, gold playhead overlay, and stem-aligned lanes.

Stem subset extraction. Click stem chips to choose which stems to keep. Clicking from "all selected" snaps to "only this one"; subsequent clicks add or remove.

"Original" backing track. When you pick a subset, a 7th lane contains the complement (full song minus selected stems), perfect for A/B reference without doubling.

Downloadable selected mix. A single mix.wav of just your selected stems, summed via ffmpeg amix.

Per-stem mixer with volume fader, mute, solo, and "monitor" (solo-only) per stem. State syncs between the preview mixer and the stems sidebar.

Live VU meters per stem. Post-gain RMS via Web Audio analysers with peak hold and slow falloff.

Song analysis including BPM (librosa beat tracker), key, scale, and confidence (Albrecht-Shanahan profiles), integrated LUFS (BS.1770), and sample peak in dBFS.

Cancellable jobs. Cancel mid-pipeline and the runner terminates the active subprocess immediately, deletes the partial job dir, and returns to ready.

Library panel with folder-based track organisation, drag-and-drop, search, and trash.

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Honest Comparison

StemDeck is not trying to compete with commercial stem-separation products. It covers the core use case well and stops there. This table exists so you can make an informed choice rather than discover the gaps after the fact.

	StemDeck	Moises / LALAL.AI / similar
Price	Free, forever	Freemium; credits or subscription required for regular use
Hosting	Runs entirely on your machine	Cloud; audio must be uploaded to their servers
Account / login	None	Required
Internet required	Only for YouTube download and first model fetch (~170 MB, cached after)	Always; no offline use
Privacy	Audio never leaves your machine	Audio is uploaded and processed on third-party servers
Data retention	You control it; delete anytime	Governed by their privacy policy and retention period
Stem model	Demucs htdemucs_6s (open source, Meta AI)	Proprietary models, regularly updated, generally higher quality
Stem count	6 (vocals, drums, bass, guitar, piano, other)	Up to 10 depending on service and plan
Input formats	YouTube URL, MP3, WAV	MP3, WAV, FLAC, M4A, and more depending on service
Processing speed	Depends on your hardware; fast with a GPU, slow on CPU only	Fast regardless of your hardware (runs on their servers)
Batch processing	One job at a time	Yes, on paid plans
Mobile app	No	iOS and Android
Extra features	No (no pitch shift, chord detection, lyrics, click track, BPM tap)	Yes, varies by product
Polish	Functional, hobby-grade UI	Polished, production-grade apps
Source code	Open source, forkable, self-hostable	Closed source

If you need speed, quality, mobile access, or the extra musician tooling, the commercial products are worth the money. If you want stems for personal study, prefer to keep audio private, or just want something that runs locally with no strings attached, StemDeck is enough.

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Download

Pre-built installers and zips are attached to each GitHub Release.

macOS

DMG	GPU	Chip
StemDeck-macOS-arm64.dmg	Apple Silicon (MPS)	M1 and later
StemDeck-macOS-x64.dmg	CPU only	Intel

Open the DMG, drag StemDeck to Applications, and launch it. On first launch the setup screen downloads the Python runtime (~500 MB), FFmpeg, and the Demucs model (~170 MB). Subsequent launches skip setup and start in seconds. No Python or system dependencies required.

macOS may show a Gatekeeper prompt on first open — right-click the app and choose Open to bypass it.

Windows

Zip	GPU	Approx. size
StemDeck-Windows-x64.zip	CPU only	~700 MB
StemDeck-Windows-x64.NVIDIA.zip	NVIDIA CUDA	~1.6 GB

Extract the zip anywhere, run StemDeck.exe. On first launch the app verifies the bundled Python runtime and downloads FFmpeg and the Demucs model (~170 MB). Subsequent launches skip this and start in seconds. Everything is self-contained; no Python or system dependencies required.

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Technologies

StemDeck is built on Python 3.12 managed via uv, with a FastAPI backend serving REST and Server-Sent Events. Stem separation uses Demucs (htdemucs_6s), Meta AI's open-source 6-stem neural network. YouTube audio is fetched via yt-dlp; transcoding and mixing use FFmpeg. BPM detection and key analysis run on librosa; loudness measurement uses pyloudnorm (ITU-R BS.1770). The macOS and Windows desktop shells are Tauri v2 (Rust/WKWebView on macOS, Rust/WebView2 on Windows). The frontend is vanilla JS with the Web Audio API, no framework and no build step; waveforms are rendered on <canvas> using min/max sample rendering.

Thanks to the creators and maintainers of all the open-source libraries that make StemDeck possible.

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Build from Source

macOS Native App

Requires Rust, Node.js, and Python 3.12. Builds a self-contained .app that downloads its own runtime on first launch.

# First time only — add the cross-compilation targets
rustup target add aarch64-apple-darwin   # Apple Silicon
rustup target add x86_64-apple-darwin    # Intel

# Build Apple Silicon
ARCH=arm64 scripts/macos/make-runtime-pack.sh
ARCH=arm64 scripts/macos/make-app.sh
ARCH=arm64 scripts/macos/make-dmg.sh

# Build Intel (requires Rosetta 2 and an x86_64 Python)
ARCH=x64 scripts/macos/make-runtime-pack.sh
ARCH=x64 scripts/macos/make-app.sh
ARCH=x64 scripts/macos/make-dmg.sh

The .app lands at desktop/src-tauri/target/<target>/release/bundle/macos/StemDeck.app. The DMG lands at .build/macos-dist/StemDeck-macOS-<arch>.dmg.

To run a fresh build directly without the DMG:

open desktop/src-tauri/target/aarch64-apple-darwin/release/bundle/macos/StemDeck.app

If macOS blocks the app with a Gatekeeper prompt, run:

xattr -dr com.apple.quarantine desktop/src-tauri/target/aarch64-apple-darwin/release/bundle/macos/StemDeck.app

Note: To test a clean first-launch during development, you can wipe previous app data first: rm -rf ~/Library/Application\ Support/StemDeck. Don't do this on a real install.

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Web Server (macOS / Linux / Windows with Python 3.12+)

Prerequisites

Python 3.12 or newer, ffmpeg on your PATH, and uv. Around 170 MB of free disk for the Demucs model, which downloads automatically on first run.

macOS / Linux (one-shot)

git clone https://github.com/stemdeckapp/stemdeck stemdeck && cd stemdeck
./run.sh setup     # installs ffmpeg + uv, runs uv sync
./run.sh start

Open http://localhost:8000.

setup uses Homebrew on macOS and apt-get on Debian/Ubuntu. For other Linux distros, install ffmpeg and uv manually, then run uv sync followed by ./run.sh start.

Windows (PowerShell)

Install prerequisites: - uv — winget install astral-sh.uv - ffmpeg — winget install Gyan.FFmpeg (or Chocolatey: choco install ffmpeg)

git clone https://github.com/stemdeckapp/stemdeck stemdeck; cd stemdeck
uv sync
uv run uvicorn app.main:app --host 127.0.0.1 --port 8000

Open http://localhost:8000.

run.sh is macOS/Linux only. On Windows use the PowerShell commands above, or run inside WSL.

NVIDIA GPU (CUDA): install the CUDA-enabled torch build before starting:

uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
$env:STEMDECK_DEMUCS_DEVICE = "cuda"
uv run uvicorn app.main:app --host 127.0.0.1 --port 8000

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Manual (any platform)

git clone https://github.com/stemdeckapp/stemdeck stemdeck && cd stemdeck
uv sync
uv run uvicorn app.main:app --reload

Docker

docker compose -f build/docker-compose.yml up --build

Stems land in ./jobs/ on the host. Demucs weights are cached in a named volume so they don't re-download on rebuild. Note: no GPU passthrough on macOS Docker.

run.sh control script

```sh ./run.sh setup # one-shot: install ffmpeg + uv, then