Why not automate speaker labels?

AI diarization is about 80% accurate under good conditions. On real recordings — overlapping speech, background noise, similar voices — it’s worse. Fixing wrong labels takes longer than labeling from scratch.

How do I label efficiently?

Skim the transcript. Every time a new person starts talking, add a name. Usually 2-3 minutes for a 1-hour meeting.

Can I ask an LLM to add labels after the fact?

Yes, and it helps for long interviews. Feed the transcript + a short description of each speaker (“A is the host, B is the guest CEO”) and it does a reasonable first pass.

Is there a format that makes this easier?

Plain text with a line break between voice changes. Then you just prepend the name.

How to Transcribe a Video With Multiple Speakers and Label Who Said What

Updated 24 Apr 2026 · TranscriptX editorial

Quick answer: Transcribe the video. Do a 2-minute first-read and drop speaker names inline. It’s faster and more accurate than fixing AI labels — we intentionally don’t auto-label because the errors are worse than the help.

Labeling speakers sounds automatable. It isn’t — not reliably. This is a short human task that beats a long AI cleanup.

The 60-second answer

Transcribe the video on TranscriptX. First read-through, drop the speaker name at every voice change. Done.

Step-by-step

1) Transcribe

Paste your meeting recording link on transcriptx.xyz.

2) Open the transcript with voice breaks visible

Our default export uses a blank line between voice changes.

3) Read fast, label inline

Prepend each block with the speaker’s name:

Sarah: We need to talk about the pricing page.
Alex: Agreed. What’s the current conversion?
Sarah: About 2.1% — lower than I thought.

4) Optional — LLM-assisted pass

For longer interviews, paste the unlabeled transcript + a one-line speaker description into Claude or ChatGPT. Ask for labeled output. Not perfect but gets you to 90% on a 1-hour recording in seconds.

Common things that break

Trusting AI labels. Never publish them without a human check.
Overlapping speech. Nothing handles it well. Pick the dominant speaker.
Similar voices. Same-gender / same-accent confuses both AI and humans. Keep a short voice sample in mind.
4+ people on voice-only recording. Hard. Record per-speaker audio channels next time.

Related guides

Try it

3 free transcripts a month. Paste a multi-person recording.

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