Does OpenAI Whisper do speaker detection out of the box?

No. The vanilla Whisper model is a transcription model — it converts audio to text but doesn't separate who said what. To get 'Speaker 1 / Speaker 2' labels you need a separate speaker diarization step layered on top. The common pairing is Whisper plus pyannote.audio; hosted tools like WhisperAI wire it in for you.

How accurate is Whisper speaker detection?

On clean two- or three-person dialogue with little overlap, modern diarization is reliable. Accuracy drops on heavy crosstalk, conference-room audio with one shared microphone, very short turns, and recordings where two speakers have very similar voices.

How many speakers can it handle?

Two to about six speakers works well in practice. Beyond that you'll see more label swaps. For large meetings, the cleanest result usually comes from separate microphone tracks.

Can I rename 'Speaker 1' to actual names?

Yes. A good editor lets you replace Speaker 1, Speaker 2 and so on with real names once — and the change propagates through the whole transcript.

Will the speaker labels show up in my SRT or VTT export?

Yes, if you leave them on. Cues come out prefixed with the speaker name. For interview videos this is what you want; for monologue, turn diarization off before export.

What recording setup gives the best speaker detection?

One microphone per person beats a single shared mic, distance matters (closer is cleaner), and a quiet room beats any model. If you can record each participant on their own track, diarization is almost trivial.

Does Whisper speaker detection work in languages other than English?

Yes. Diarization is mostly acoustic — it works from voice characteristics rather than the words being said — so it generalises across languages Whisper supports.

Whisper speaker detection, explained honestly.