Identify and separate multiple speakers in audio recordings with speaker diarization.

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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
Granite Speech speaker-turn transcripts are documented separately as Speaker Attributed ASR. Use diarization when you need timestamped speaker segments.

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Getting Started

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Download Diarization Pipeline Models

For best results, use a diarization + ASR + aligner pipeline: 
izwi pull diar_streaming_sortformer_4spk-v2.1
izwi pull Parakeet-TDT-0.6B-v3
izwi pull Qwen3-ForcedAligner-0.6B

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Start the Server

izwi serve

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Using the Web UI

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

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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.

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Using the API

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Endpoint

POST /v1/speech-to-text/jobs?job_kind=diarization

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Request (multipart/form-data)

FieldTypeDescription
fileFileAudio file to analyze
modelStringDiarization model (for example diar_streaming_sortformer_4spk-v2.1)
asr_modelStringOptional ASR model override
aligner_modelStringOptional forced aligner model override
llm_modelStringOptional transcript refinement model
min_speakersIntegerOptional minimum expected speakers
max_speakersIntegerOptional maximum expected speakers
min_speech_duration_msNumberOptional VAD speech-duration tuning
min_silence_duration_msNumberOptional VAD silence-duration tuning
enable_llm_refinementBoolean/StringEnable optional transcript refinement

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Example (curl)

curl -X POST "http://localhost:8080/v1/speech-to-text/jobs?job_kind=diarization" \
  -F "file=@meeting.wav" \
  -F "model=diar_streaming_sortformer_4spk-v2.1" \
  -F "asr_model=Parakeet-TDT-0.6B-v3" \
  -F "aligner_model=Qwen3-ForcedAligner-0.6B" \
  -F "min_speakers=2" \
  -F "max_speakers=2"

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Response

The create response is a persisted diarization job. Poll the returned id with GET /v1/speech-to-text/jobs/{record_id}?job_kind=diarization until processing_status is ready. 
{
  "id": "diarization_...",
  "kind": "diarization",
  "processing_status": "pending"
}
Ready job records include segments, words, utterances, speaker_count, duration_secs, alignment_coverage, LLM refinement status, processing metrics, and the formatted transcript. See the API Reference for JSON input and exact response shapes.

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Configuration

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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/speech-to-text/jobs?job_kind=diarization" \
  -F "file=@meeting.wav" \
  -F "min_speakers=3" \
  -F "max_speakers=3"

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Speaker Labels

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

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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

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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

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Use Cases

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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

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Interview Transcription

Perfect for journalist interviews or research:
  1. Record the interview
  2. Process with diarization
  3. Get clean Q&A format output

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Podcast Production

Identify speakers for editing and show notes:
  1. Upload raw podcast audio
  2. See who spoke when
  3. Use timestamps for editing

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See Also