How do I add speaker labels to an audio transcript?

To add speaker labels to an audio transcript, you typically need speaker diarization — the process of identifying who spoke when. Then you merge those speaker segments with the transcript.

Basic workflow

1. Transcribe the audio

   • Use a speech-to-text tool to get the words and timestamps.
   • Preferably get word-level timestamps or at least sentence/segment timestamps.

2. Run speaker diarization

   • This splits the audio into chunks by speaker.
   • Output looks like:
     • Speaker 1: 00:00–00:12
     • Speaker 2: 00:12–00:20

3. Align transcript text with speakers

   • Match each transcript segment/word timestamp to the diarization segments.
   • Assign a label like Speaker 1, Speaker 2, etc.

4. Format the transcript

   • Example:

     Speaker 1: Welcome everyone.
     Speaker 2: Thanks, glad to be here.
     Speaker 1: Let’s get started.
     

Common tools

All-in-one transcription + diarization

• OpenAI Whisper + diarization externally
• AssemblyAI
• Google Cloud Speech-to-Text
• AWS Transcribe
• Azure Speech

Diarization tools

• pyannote.audio (popular open-source choice)
• NVIDIA NeMo

If you’re doing this programmatically

A common approach is:

• Transcribe audio with timestamps
• Diarize the audio
• For each transcript segment, find which speaker overlaps that time range the most
• Label the text with that speaker

Example logic

If a transcript segment is from 00:10.2–00:13.8 and diarization says:

• Speaker A from 00:09–00:12
• Speaker B from 00:12–00:15

Then the segment may be split or assigned based on overlap:

• Speaker A: ...
• Speaker B: ...

Important notes

• Diarization is not perfect, especially with:
  • overlapping speech
  • background noise
  • phone calls
  • many speakers
• If you know the speakers’ names, you can map Speaker 1 / Speaker 2 to actual names manually.

Quickest practical option

If you want the easiest route, use a service that already supports diarization, like:

• AssemblyAI
• AWS Transcribe
• Google Cloud Speech-to-Text
• Azure Speech

If you want, I can also show you:

• a Python example
• a Whisper + pyannote setup
• or a step-by-step workflow in a specific tool