> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pyannote.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Feature overview

> Explore the key features of the pyannoteAI API

## Speaker diarization

Automatically detect each speaker in multi-speaker audio recordings.

```json Example diarization output theme={null}
[
  {
    "speaker": "SPEAKER_00",
    "start": 10.0,
    "end": 15.0
  },
  {
    "speaker": "SPEAKER_01",
    "start": 12.5,
    "end": 14.0
  }
]
```

**Key input parameters:**

* `num_speakers`: Expected number of speakers, leave empty for automatic detection
* `min_speakers`/`max_speakers`: Range for speaker detection
* `exclusive`: Enable exclusive diarization mode, equivalent to diarization but without overlapping speech. Useful for easier reconciliation with STT/ASR results.
* `model`: Choose diarization model
* `confidence`: Include confidence scores

**[Learn how to diarize an audio file <Icon icon="arrow-right" iconType="solid" />](/tutorials/how-to-diarize-audio)**

***

## Speaker Identification vs. Diarization

**Diarization** answers "who spoke when?" with generic labels (`SPEAKER_00`, `SPEAKER_01`, etc.).

**Identification** answers "who is speaking?" by recognizing specific known voices using voiceprints.

***

## Voiceprint

Captures a speaker's voice to identify that person in other audio recordings.

**Best practices:**

* Use clear, high-quality audio (max 30 seconds)
* One voiceprint per speaker

**[Learn how to identify speakers with voiceprints <Icon icon="arrow-right" iconType="solid" />](/tutorials/identification-with-voiceprints)**

***

## Confidence scores

Receive confidence scores for each speaker segment to assess reliability and perform human in the loop correction. Set the `confidence` parameter to `true` in your diarization or identification request.

**[Understanding confidence scores <Icon icon="arrow-right" iconType="solid" />](/tutorials/confidence-scores)**

***

## Overlapped speech detection

Detect when multiple speakers talk over each other and attribute overlapping speech to the correct speakers.

Find overlapping speech by comparing timestamps of segments from different speakers. For example:

```json Example diarization output theme={null}
[
  {
    "speaker": "SPEAKER_00",
    "start": 10.0,
    "end": 15.0
  },
  {
    "speaker": "SPEAKER_01",
    "start": 12.5,
    "end": 14.0
  }
]
```

In this example, both `SPEAKER_00` and `SPEAKER_01` are talking between `12.5`-`14.0` seconds.

You can also use the segment timestamps to calculate statistics such as total
speaker time per speaker, total overlap duration, and percentage of overlapped
speech, etc.

***

## STT Orchestration: Speaker-attributed transcripts

We host open-source transcription models like [Nvidia Parakeet-tdt-0.6b-v3](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) and [OpenAI whisper-large-v3-turbo](https://huggingface.co/dropbox-dash/faster-whisper-large-v3-turbo) with specialized STT + diarization reconciliation logic for speaker-attributed transcripts.

To use this feature, make a request to the diarize API endpoint with the `transcription: true` flag. If you want to configure other parameters, like the transcription model to use, refer to the [API reference](/api-reference/diarize).

**[Learn more about speech to text with diarization <Icon icon="arrow-right" iconType="solid" />](/tutorials/speech-to-text-diarization)**

**Already have your own transcript?** Merge it with our diarization results using [this tutorial](/tutorials/diarization-asr-merge).