Diarization

Overview

Speaker diarization answers the question "who spoke when?" It segments audio by speaker, making it invaluable for:

• Meeting transcripts — Attribute statements to participants
• Interviews — Separate interviewer and interviewee
• Podcasts — Identify hosts and guests
• Call recordings — Distinguish callers

Getting Started

Download an ASR Model

Diarization uses ASR models with speaker detection:

izwi pull qwen3-asr-0.6b

Start the Server

izwi serve

Using the Web UI

1. Navigate to Diarization in the sidebar
2. Upload an audio file with multiple speakers
3. Click Analyze
4. View the speaker-segmented transcript

Output

The diarization view shows:

• Speaker labels — Speaker 1, Speaker 2, etc.
• Timestamps — When each speaker talks
• Transcript — What each speaker said

Example output:

[00:00 - 00:05] Speaker 1: Welcome to the meeting. [00:05 - 00:12] Speaker 2: Thanks for having me. [00:12 - 00:20] Speaker 1: Let's start with the agenda.

Using the API

Endpoint

POST /v1/audio/diarize

Request (multipart/form-data)

Example (curl)

curl -X POST http://localhost:8080/v1/audio/diarize \ -F "file=@meeting.wav" \ -F "model=qwen3-asr-0.6b"

Response

{
  "segments": [
    {
      "speaker": "Speaker 1",
      "start": 0.0,
      "end": 5.2,
      "text": "Welcome to the meeting."
    },
    {
      "speaker": "Speaker 2",
      "start": 5.5,
      "end": 12.1,
      "text": "Thanks for having me."
    }
  ],
  "num_speakers": 2,
  "duration": 120.5
}

Configuration

Number of Speakers

If you know how many speakers are in the audio, specify it for better accuracy:

# Via API curl -X POST http://localhost:8080/v1/audio/diarize \ -F "file=@meeting.wav" \ -F "num_speakers=3"

Speaker Labels

By default, speakers are labeled "Speaker 1", "Speaker 2", etc. You can rename them in the UI after processing.

Tips for Best Results

1. Quality audio — Clear recordings with minimal background noise
2. Distinct voices — Works best when speakers have different voice characteristics
3. Minimal overlap — Speakers talking over each other reduces accuracy
4. Specify speaker count — If known, helps the algorithm
5. Longer segments — Short utterances are harder to attribute

Limitations

• Similar voices — May confuse speakers with very similar voices
• Overlapping speech — Simultaneous talking is challenging
• Background noise — Reduces speaker detection accuracy
• Very short clips — Need enough audio to identify speaker patterns

Use Cases

Meeting Minutes

Upload a meeting recording to get a transcript with speaker attribution:

1. Record your meeting
2. Upload to Diarization
3. Export the speaker-labeled transcript
4. Edit speaker names as needed

Interview Transcription

Perfect for journalist interviews or research:

1. Record the interview
2. Process with diarization
3. Get clean Q&A format output

Podcast Production

Identify speakers for editing and show notes:

1. Upload raw podcast audio
2. See who spoke when
3. Use timestamps for editing

See Also

• Transcription — Single-speaker transcription
• Voice Mode — Real-time conversations
• CLI Reference — Command documentation