Module B / Guide / 10-20 min
Speaker diarization: getting Speaker 1 and Speaker 2 right
Identify speakers reliably in interviews, panels, and podcasts.
TL;DR
Use this lesson to identify speakers reliably in interviews, panels, and podcasts. Treat it as practical guidance, not a rigid rulebook.
Why it matters
Captions make clips understandable without sound, searchable after publishing, and reviewable by editors before export. The goal is to help you make a stronger clip without taking away your creative freedom.
What you will learn
Prerequisites
- An audio file, video file, URL, or exported clip
- A target output format such as SRT, VTT, burned-in MP4, or transcript text
What you need
Core concept
Caption work is part accuracy and part design. The workflow only works if viewers can read the result quickly on a phone.
Example
Scenario
Auto-captions are mostly correct, but the clip contains names, numbers, jargon, or fast speech.
Move
Apply the workflow to a short section first and proofread the result at phone size.
Result
The caption pass becomes readable and accurate enough that sound-off viewers can follow the clip.
How to do it
- 1Use diarization when two or more speakers appear and captions or transcripts need correct speaker labels.
- 2Check the first speaker switch manually; early mistakes often repeat through the transcript.
- 3Rename generic labels like Speaker 1 and Speaker 2 once you know who is speaking.
- 4Correct crosstalk and short interjections manually because AI labels are weakest there.
- 5Do not show speaker labels in the final clip unless they help viewers follow the edit.
Expected output
A caption or transcript artifact that is proofread, timed, readable on a phone, and matched to the target platform.
Practice task
Produce a clean caption pass
- 1Take a 20-30 second section of a real clip.
- 2Apply the caption or transcript workflow from this lesson.
- 3Proofread it with sound on, then watch it again with sound off at phone size.