Why it is worth attention

Combines local AI transcription with word-level editing and filler removal, all private and offline, making it a unique tool for fast, text-driven video editing.

Who it is for

Video editors seeking a privacy-focused workflow
Content creators who want to remove filler words easily
Developers interested in browser-based media processing
Journalists or educators needing quick transcript-based cuts

Use cases

Removing verbal fillers like 'um' and 'uh' from recordings
Creating short clips from long videos by deleting unwanted sections in the transcript
Editing interview footage with speaker labels for quick cuts

Strengths

Runs entirely offline after initial model cache, ensuring privacy
Word-level editing directly from transcript simplifies cutting
One-click filler removal saves time
Exports frame-accurate MP4 using ffmpeg.wasm in the browser

Considerations

Requires Chromium-based browser with SharedArrayBuffer support
Initial download of ~90 MB AI models needed
Only supports videos with audio tracks; no built-in video effects or transitions

README quick start

Rescript

Edit videos like you edit text — fully offline, in your browser.

✨ Try it now: rescript.aashuu.tech

Rescript is an open-source, transcript-based video editor. Drop in a video 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 video. Export the final cut to MP4 — without your video ever leaving your device.

🔒 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
🧹 Filler removal — one-click cut of "um", "uh", and similar fillers (use Verbatim mode to keep them in the transcript)
🗣️ Speaker diarization — the transcript is grouped by speaker
🎬 Timeline — waveform, word labels, cut regions, playhead, zoom
⚡ Live preview — playback skips your cuts in real time
📦 In-browser export — frame-accurate MP4 render with ffmpeg.wasm

Stack

Piece	Tech
App	Next.js + React + TypeScript + Tailwind
Transcription	transformers.js running `whisper-base_timestamped` (WebGPU with WASM fallback) in a Web Worker
Speaker labels	`pyannote-segmentation-3.0` (ONNX)
Media processing	ffmpeg.wasm (multi-threaded) for audio extraction and export
State	zustand

Development

npm install     # also copies ffmpeg/onnxruntime WASM into public/vendor
npm run dev     # dev server
npm run build   # production build
npm run lint    # eslint

Open http://localhost:3000 and drop in a video with an audio track.

Note on "offline": the AI models (~90 MB total) are downloaded from the Hugging Face Hub the first time you transcribe, then cached in browser 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

Description

🎬 Open source, transcript-based video editor that lives in the browser.

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