Edit video and audio like you edit text — fully offline, on your device.

✨ Try it in the browser wassgha.github.io/rescript or download the Desktop App

Rescript is an open-source, transcript-based media editor. Drop in a video or audio file and it is transcribed locally with per-word timestamps and speaker labels. Delete words in the transcript and the corresponding clip is cut from the media. Export the final cut — without your file ever leaving your device.

See the Releases page. Desktop builds auto-update from GitHub Releases. Prefer the browser? Use the web app — same editor, no install.

• 🔒 Private by design — no server, no auth, no uploads; all media processing happens on-device
• 📝 Word-level editing — select words, press ⌫, the cut follows the text
• 📥 Import your own transcript — skip Whisper and edit with an SRT, VTT, or JSON caption file
• 🧹 Filler removal — one-click cut of "um", "uh", and similar fillers
• 🗣️ Speaker diarization — the transcript is grouped by speaker
• 🎬 Timeline — waveform, wordbar with draggable timing handles, Split, cut regions, playhead; scroll to zoom, side-scroll to pan
• ✂️ Split & trim — blade clips at the playhead; drag clip edges to refine cuts beyond word boundaries
• 🎯 Word timing — zoom in and drag a word's edges when ASR alignment is off
• 🔴 Cut edges — drag either edge of a cut to trim independently of Whisper timestamps; double-click to reset
• ⚡ Live preview — playback skips your cuts in real time
• 📦 In-browser / desktop export — frame-accurate MP4 (video) or M4A (audio) with ffmpeg.wasm
• 🎧 Audio files — edit podcasts, voice notes, and interviews the same way as video
• 🖥️ Desktop app — macOS, Windows, and Linux via Electron (signed + notarized on Mac)

npm install     # also copies ffmpeg/onnxruntime WASM into public/vendor
npm run dev     # Next.js web app (http://localhost:3000)
npm run electron:dev   # Electron shell + Next.js dev server
npm run build   # production web build
npm run dist    # unsigned desktop installers into dist/
npm run lint    # eslint

Open http://localhost:3000 and drop in a video with an audio track. For desktop packaging, signing, and cutting releases, see RELEASING.md.

Note on "offline": the AI models (Whisper Base ~200 MB, or Small ~600 MB, plus a small speaker model) are downloaded from the Hugging Face Hub the first time you transcribe, then cached in browser / app storage. After that, everything — transcription, editing, export — works with the network fully disconnected. Your media and transcript never leave the device; the only third-party request the app makes is anonymous page analytics (Google Analytics), which fails silently when offline.

1. Extract — ffmpeg.wasm decodes the audio track to mono 16 kHz PCM.
2. Transcribe — Whisper runs in a Web Worker with return_timestamps: "word", streaming text as it goes; pyannote assigns a speaker to every word. Choose Whisper Base, Whisper Small, or Import transcript (SRT / VTT / JSON) on the homepage.
3. Edit — deleting words produces "cut ranges" of the original media. The preview player skips them in real time and the timeline shows them in red. Remove fillers cuts every detected "um" / "uh" / etc. in one click.
4. Export — the kept ranges are trimmed and concatenated with an ffmpeg filter graph and re-encoded (libx264/aac), so cuts are word-accurate.

A Chromium-based browser is recommended for the web app. It requires SharedArrayBuffer (served with COOP/COEP headers) and uses WebGPU for inference when available, falling back to WASM otherwise. The desktop app bundles Chromium via Electron and sets the same isolation headers on its app:// protocol.

MIT

Built by @wassgha — follow along on X for updates, or come say hi in the Discord.