izwi diarize
Speaker diarization — identify and separate multiple speakers in audio.
Synopsis
izwi diarize <FILE> [OPTIONS]
Description
Analyzes audio to identify different speakers and when they spoke. Optionally includes transcription with speaker labels.
Arguments
| Argument | Description |
|---|
<FILE> | Audio file to analyze |
Options
| Option | Description | Default |
|---|
-m, --model <MODEL> | Diarization model | sortformer-4spk |
-n, --num-speakers <N> | Expected number of speakers | Auto-detect |
-f, --format <FORMAT> | Output format: text, json, verbose_json | text |
-o, --output <PATH> | Output file (default: stdout) | — |
--transcribe | Include transcription with speaker labels | — |
--asr-model <MODEL> | ASR model for transcription | qwen3-asr-0.6b |
Examples
Basic diarization
izwi diarize meeting.wav
With known speaker count
izwi diarize meeting.wav --num-speakers 3
With transcription
izwi diarize meeting.wav --transcribe
JSON output
izwi diarize meeting.wav --format json --output diarization.json
Full pipeline with custom models
izwi diarize interview.wav \\ --transcribe \\ --asr-model qwen3-asr-1.7b \\ --format verbose_json \\ --output interview_transcript.json
Output Formats
Text
[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.
JSON
{
"segments": [
{"speaker": "Speaker 1", "start": 0.0, "end": 5.2},
{"speaker": "Speaker 2", "start": 5.5, "end": 12.1}
],
"num_speakers": 2
}Verbose JSON (with transcription)
{
"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
}
Available Models
| Model | Description |
|---|
sortformer-4spk | Streaming Sortformer, up to 4 speakers |
See Also
