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Speaker Diarization
Audio AI

Speaker Diarization

Make multi-person recordings usable by separating speech into speaker-labeled segments. Designed for meetings, interviews, panels, and podcasts, diarization improves clarity and reduces time spent manually identifying who said what. An editable timeline supports fast corrections, and the resulting structure improves downstream tasks like dubbing, captioning, and accurate summaries.

Key Features

  • Reliable speaker separation: Groups speech by speaker for clear “who said what” transcripts.
  • Editable speaker timeline: Merge/split speaker segments for fast correction when needed.
  • Built for real conversations: Handles interruptions and rapid back-and-forth more effectively than basic tools.
  • Unlocks better dubbing/captions: Improves multi-speaker dubbing and subtitle accuracy by clarifying speaker turns.

What you'll create

Teams cleaning up meeting recordings

Turn a messy multi-person call into a labeled transcript so you can see exactly who said what without scrubbing the audio yourself.

Journalists transcribing interviews

Separate your questions from each source's answers automatically, making long interviews far faster to quote, fact-check, and write up.

Podcasters producing show notes

Get a turn-by-turn timeline of hosts and guests, so building chapter markers, quotes, and accurate show notes takes minutes instead of hours.

Researchers analyzing panels and focus groups

Attribute speech to the right participant across overlapping conversation, giving your qualitative analysis clean, per-speaker structure.

How it will work

  1. 1

    Add your multi-speaker recording

    Bring a meeting, interview, panel, or podcast file. The tool is built for real conversations with interruptions and rapid back-and-forth.

  2. 2

    Speakers get separated and labeled

    Speech is grouped by speaker into a clear "who said what" transcript, with each turn placed on a timeline you can review.

  3. 3

    Correct and export the result

    Merge or split segments on the editable timeline to fix any mix-ups, then export a structure that also improves downstream dubbing and captions.

Frequently asked questions

What audio formats does speaker diarization support?
MP3, WAV, M4A, AAC, OGG, and FLAC — the formats meeting, interview, and podcast recordings usually arrive in.
How many speakers can it tell apart?
The tool is designed for real multi-person conversations—meetings, interviews, panels, and podcasts—rather than just two people, and it aims to handle interruptions and rapid back-and-forth.
Can I fix it when a speaker is labeled wrong?
Yes. Rename any speaker, move a turn to a different speaker, or split a turn onto a new one when two people were merged — and your corrections are saved with the transcript.
Is diarization the same as transcription?
Not quite. Transcription turns speech into text; diarization figures out who spoke when and labels each turn. Together they produce a clear, speaker-attributed transcript.
Can I export the transcript?
Yes — download it as TXT, SRT, or VTT, so the same speaker-labeled result works for documents, subtitles, and captions.

Guides & resources