vailabel/vailabel-studio has 68 GitHub stars and 25 forks. Star changes are not measured yet. Last checked 2026-11-13 22:39 UTC. Last push 2026-10-31.
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概要
Vailabel Studio - a local-first desktop studio that labels your data with an offline AI copilot. Private by default. Windows, macOS, Linux.
開発ツールREADME.md
オリジナルVailabel Studio
Local-first, AI-assisted data labeling — a cross-platform desktop studio for images, video, text & audio.
Download · Features · Quick start · Architecture · Contributing
Table of Contents
- Features
- Download
- Keyboard shortcuts
- Development
- Project structure
- Architecture
- Contributing
- License
Overview
Vailabel Studio is an open-source desktop annotation tool in the spirit of LabelMe and Label Studio — but it runs entirely on your machine. Your data lives in a local SQLite database, images are referenced in place (no base64 copies), and everything works offline. On top of that familiar labeling workflow it adds an AI layer: one-click auto-labeling, click-to-segment, and an on-device assistant.
It's built as a native desktop app with Tauri (Rust) + React, so you get a small, fast binary with real filesystem access instead of a browser tab.
✨ Features
Label anything
- Images — bounding boxes, polygons, keypoints, lines, polylines, circles, and free-draw.
- Video — frame extraction, scene detection, and object labeling across time (FFmpeg pipeline).
- Text — named-entity spans, relations, classification, Q&A, and translation.
- Audio — waveform region labeling, diarization, and transcription.
- Custom — define your own projects with Label-Studio-style JSON configs; a config-driven editor renders the right tools automatically.
AI on top
- Auto-label — run a YOLO detection model on the current image and review the suggestions.
- Smart Segment — click an object to segment it with SAM (Segment Anything).
- Copilot (experimental) — an offline, on-device vision assistant for label suggestions.
Built for real datasets
- Local-first — a single SQLite database; fully offline, your data never leaves your machine.
- Reference in place — point at a folder of images; nothing is copied or duplicated.
- Import / export — LabelMe sidecars, JSONL, and dataset exports for model training.
- Cloud storage (optional) — connect S3, Azure Blob, or Google Cloud Storage; credentials stay in your OS keychain.
- Label-Studio-style workspace — a file queue, center canvas with a tool rail, and a docked Classes / Regions / AI / Copilot panel.
Project status: Vailabel Studio is under active development. Core image/text/audio/video labeling and YOLO/SAM assistance are usable today; the on-device Copilot and embedded training runtime are evolving — expect rough edges and breaking changes.
⬇️ Download
Grab the latest installer for your platform from the Releases page:
| Platform | Installer |
|---|---|
| Windows | .exe (setup) or .msi |
| macOS | .dmg — Apple Silicon (aarch64) and Intel (x64) |
| Linux | .AppImage, .deb, or .rpm |
macOS builds are unsigned for now — if Gatekeeper blocks the app, right-click → Open the first time (or
xattr -dr com.apple.quarantine /Applications/Vailabel\ Studio.app).
🛠️ Development
Prerequisites
- Node.js 20+ and Yarn (Classic / v1)
- Run
nvm usefrom the repository root to match the project's Node.js version - Rust (stable toolchain) — install via rustup
- The platform build dependencies for Tauri v2 — see the Tauri prerequisites guide
Run it
# install workspace dependencies
yarn install
# run the full desktop app (Tauri + React)
yarn dev # alias: yarn desktop
# run just the React UI in the browser (no Rust backend)
yarn studio
# tests, lint, typecheck
yarn test
yarn lint
yarn typecheck
# build production bundles
yarn build # web + studio frontend
yarn release # native desktop installers (tauri build)
🧱 Project structure
This is a Yarn-workspaces monorepo:
apps/
├── studio/ # the Tauri desktop app
│ ├── src/ # React frontend — feature-sliced
│ │ ├── app/ # router (lazy routes), providers, layout shell
│ │ ├── features/ # studio · projects · ai-models · labels · …
│ │ └── shared/ # ui (shadcn-style) · lib · services · ipc · types
│ └── src-tauri/ # Rust backend — Tauri commands + DDD modular crates
│ ├── crates/ # per-domain crates (pure domain + Diesel/SQLite)
│ └── migrations/ # the canonical SQLite schema
└── web/ # marketing / documentation site
🏗️ Architecture
- Frontend — React 19 + Vite + TypeScript + Tailwind v4, organized feature-first
(
app/+features/*+shared/*); routes are code-split withReact.lazy. - Desktop shell — Tauri v2; the React UI talks to Rust over a typed IPC bridge.
- Backend — Rust as a DDD modular monolith: each domain is its own crate with a pure core and a typed Diesel repository over a shared SQLite connection. Embedded migrations are the single source of truth for the schema.
🤝 Contributing
Contributions are welcome! Please read CONTRIBUTE.md to get set up, and our Code of Conduct before opening a pull request. Found a security issue? See SECURITY.md — please don't file it as a public issue.
📄 License
Vailabel Studio is licensed under the MIT License.