Diarization

Run diarization to get who-spoke-when segments, the number of speakers, and confidence scores — without separating the audio. It returns the same diarization JSON that Multi-Speaker Separation includes alongside its stems, so use multi_voice instead when you also need the per-speaker audio.

Create a Task

import requests

API_KEY = "your_api_key"
HEADERS = {"Content-Type": "application/json", "x-api-key": API_KEY}

response = requests.post(
    "https://api.audioshake.ai/tasks",
    headers=HEADERS,
    json={
        "assetId": "your_asset_id",
        "targets": [
            {"model": "diarization", "formats": ["json"]}
        ]
    }
)

task_id = response.json()["id"]
print(f"Task created: {task_id}")

const API_KEY = "your_api_key";
const headers = { "Content-Type": "application/json", "x-api-key": API_KEY };

const createRes = await fetch("https://api.audioshake.ai/tasks", {
  method: "POST",
  headers,
  body: JSON.stringify({
    assetId: "your_asset_id",
    targets: [
      { model: "diarization", formats: ["json"] }
    ]
  })
});

const { id: taskId } = await createRes.json();
console.log(`Task created: ${taskId}`);

curl -X POST "https://api.audioshake.ai/tasks" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $AUDIOSHAKE_API_KEY" \
  -d '{
    "assetId": "your_asset_id",
    "targets": [
      { "model": "diarization", "formats": ["json"] }
    ]
  }'

Check Task status to monitor progress and download results, or use webhooks to be notified when the target completes.

Output

The task returns one diarization JSON output:

diarization.json

{
  "results": {
    "diarization": {
      "segments": [
        { "speaker": "SPEAKER_01", "start": 0.213, "end": 4.673 },
        { "speaker": "SPEAKER_00", "start": 4.813, "end": 12.793 },
        { "speaker": "SPEAKER_01", "start": 11.913, "end": 19.993 }
      ],
      "num_speakers": 2,
      "scores": {
        "scores_per_frame": {
          "assignment_confidence": { "resolution": 0.02, "score": [0.883, 0.946, "..."] }
        },
        "scores": {
          "assignment_confidence": 0.946
        }
      }
    }
  }
}

All fields are relative to results.diarization:

segments — one entry per speech segment, with speaker and start/end in seconds. The timeline is overlap-aware: segments from different speakers can overlap when people talk at once.
num_speakers — number of distinct speakers detected.
scores — assignment_confidence in [0, 1], per-frame at 20 ms resolution and for the whole file: how confident the model is that speech is attributed to the right speaker.

Use cases

Attribute transcripts to speakers without paying for audio separation
Count speakers and measure talk time in meetings, calls, and interviews
Pre-screen recordings to decide which need full Multi-Speaker Separation

Multi-Speaker Separation

Get per-speaker audio stems along with the same diarization output.

Speech Recovery

Denoise and de-reverb speech recordings.

Getting started

Tutorials

Tools

Support

Create a Task

Output

Use cases

Multi-Speaker Separation

Speech Recovery

​Create a Task

​Output

​Use cases

Multi-Speaker Separation

Speech Recovery

Create a Task

Output

Use cases