How to transcribe a recording without uploading it to anybody

Drop the audio or the video into SCRIBE, choose the spoken language or let it work that out, and transcribe. The model is Whisper — the small, fast size of it — running inside the tab through WebAssembly; the first run fetches the model once from this site and keeps it on your device, and after that the page works with the network off. The transcript comes out as plain text or as an .srt subtitle file with timestamps. The recording is never uploaded, and for a great many recordings that is not a preference but a requirement.

What is actually running

Whisper is a speech recognition model that was trained on a very large amount of multilingual audio, and it is unusually robust: accents, background noise, overlapping speech and bad microphones degrade it far more gently than the systems that came before. It comes in sizes, and the size decides both the accuracy and whether it can run on your laptop at all.

SCRIBE runs the smallest of them. That is a deliberate trade and it is the most important thing on this page: a small model is meaningfully less accurate than the large one a paid service runs on a server rack. Clear speech in a quiet room comes out very well. A four-person meeting on a laptop microphone comes out usable but wrong in places. Anyone telling you a browser tab matches a datacentre is selling something.

What it does well

• One person, close to the microphone, in a quiet room. Interviews, voice notes, dictation, a recorded lecture. This is the good case and it is most of what people transcribe.
• Thirteen languages, and it will detect which one rather than making you say.
• Timestamps. The .srt it writes drops straight into a video editor or a player as subtitles.
• Video as well as audio. The sound is taken out of the file for you.
• Long recordings, given time. It is not fast — a small model on a laptop is roughly real-time or a bit better, so an hour of audio is a coffee, not a second.

Where it goes wrong

• It does not label who is speaking. There is no diarisation: a two-person interview comes out as one column of text and you mark up the turns yourself. This is the gap people notice first.
• Names, jargon and figures. Proper nouns and technical terms are the first thing a small model loses, and it will confidently write something plausible instead. Read the numbers against the audio, always.
• Crosstalk. Two people at once produces one of them, or a blend.
• Heavy accents and poor recordings degrade it faster than they would the large model.
• It can invent. Whisper models are known to fill silence or noise with fluent, entirely fabricated sentences — this is documented behaviour, not a bug in this page. Long pauses and music are where it happens. Do not submit a transcript anywhere consequential without reading it against the recording.

Getting a better transcript

Most of the quality is decided before any software runs. A recorder near the speaker beats a better model further away; a hard-surfaced room is worse than a soft one; and the single biggest improvement available to most people is recording each speaker on a separate track, which also gives you the speaker labels the model will not. If a recording is long and only part of it matters, cut that part out with SNIP first — you will get the answer sooner and read it more carefully.

Why these recordings in particular should stay put

Think about what actually gets transcribed. A grievance hearing. A doctor's appointment. A therapy session. A research interview under an ethics approval that names who may hear the audio. A call with a client under legal privilege. A police interview. A journalist's source.

For every one of those, uploading the file is the disclosure — not a risk of one, the thing itself. The recording is on somebody else's machine, under their retention policy, subject to their jurisdiction and their subpoenas, and in more than one case used to train their next model. A university ethics committee will ask which processor you used and where the audio was stored. "It never left the machine" is the only answer that does not need a paragraph after it.

That is also the reason to be able to check rather than trust: load the page, disconnect the network, and transcribe. It still works.

Afterwards

A transcript is a document. If it is going anywhere, it may need the same care the recording did: ALIAS replaces names consistently before you paste anything into an AI, SENTINEL looks for card numbers and other things that should not be in it, and HUSH goes back to the recording itself and bleeps out the words you do not want in the copy you share. Subtitles that need retiming go to SUBS.

The short version