> ## 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.
# Models
> Choose the right speaker diarization model for your audio processing needs
Highest-accuracy diarization for recordings, with support for voiceprints and speaker identification.
Streaming diarization over WebSocket for live audio.
Open-source diarization for research, prototyping, and self-hosting.
## Choosing the right model
### Precision-2
**Best for:** Startups, SMEs, and enterprises who need the state-of-the-art in speaker diarization accuracy and advanced features like voiceprints and speaker identification.
Precision-2 is 28% more accurate, on average, than Community-1.
Self-hosted options for Precision-2 are available on Enterprise plans.
**Typical use cases:** phone call analytics, meeting transcription with speaker attribution, video dubbing and timestamp-critical workflows, building training data for voice assistants, and more.
**Advanced features:**
* **Speaker identification with voiceprints**: Identify known speakers in your audio using pre-enrolled voiceprints
* **Exclusive diarization mode:** Returns speaker diarization where only one single speaker (the most likely to be transcribed) is active at a time, making STT reconciliation easier
* **Flexible speaker count control:** Set `minSpeakers`, `maxSpeakers` and `numSpeakers` parameters for any number of speakers
* **Human-in-the-loop correction:** Use confidence scores to help streamline manual correction processes
[Learn more about Precision-2 ](https://www.pyannote.ai/blog/precision-2)
***
### Live-1
**Best for:** Teams building live voice products that need speaker labels before a recording finishes: contact centers, meeting tools, broadcast workflows, and real-time voice agents.
**Typical use cases:** live meeting assistants, contact center agent assist, live captioning with speaker attribution, broadcast speaker attribution, and multi-party voice agents that need to track who is speaking.
**Advanced features:**
* **Sub-300ms latency:** Speaker labels arrive fast enough for live captioning and real-time agent assist, tested against noisy, real-world audio rather than clean studio recordings.
* **Native streaming architecture:** Processes audio in 100ms chunks over WebSocket, with a speaker tracking layer that holds consistency across the stream without needing the full conversation.
* **Built for real conditions:** Trained and validated on overlapping speech, background noise, and conversations with more than two participants.
**Technical specs:**
* Up to 8 speakers, up to 5 hours duration per stream
* Input: 16 kHz mono audio, 100ms chunks over WebSocket
* Output: `diarization_speaker_start` and `diarization_speaker_end` events, each with a start or end timestamp and speaker label
[Learn more about Live-1](https://www.pyannote.ai/blog/how-we-built-streaming-diarization)
***
### Community-1 (hosted)
**Best for:** Teams who want the open-source model without managing infrastructure
**Typical use cases:** Prototyping, low-volume production workloads, testing and validation
**Key Benefits:**
* **Cost efficiency:** hosted at cost, ideal for experimentation and low-volume workloads
* **No infrastructure management:** Focus on your application while we handle the deployment
* **Easy migration:** Start with hosted Community-1 and upgrade to Precision-2 when needed
* **Same powerful model:** Access the same Community-1 model through our API without setup complexity
[Learn more about Community-1 ](https://www.pyannote.ai/blog/community-1)
***
### Community-1 (self-hosted with pyannote.audio 4.0)
**Best for:** Researchers, developers, and personal hobby projects who want full control over their diarization models and workflows.
**Typical use cases:** Academic work, product-iteration, prototyping, and custom diarization deployment (e.g., dataset-specific fine-tuning or custom reconciliation with STT).
**Key Benefits:**
* **Best open-source speaker diarization model available** - outperforms pyannote.audio 3.1 across all key metrics
* **Open-source flexibility:** Full transparency into model weights and code allowing local and offline training and inference.
**Trade-offs:**
* Lower accuracy compared to Precision-2
* No support for advanced features like speaker identification and voiceprints
* Requires deploying the model on your own infrastructure
[Learn more about pyannote.audio 4.0 ](https://github.com/pyannote/pyannote-audio)
***
## How to specify a model in diarization requests
When making a diarization request, you can specify which model to use using the `model` parameter:
```bash theme={null}
curl -X POST "https://api.pyannote.ai/v1/diarize" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"url": "https://files.pyannote.ai/marklex1min.wav",
"model": "precision-2"
}'
```
By default, if you do not specify a model, the API will use the Precision-2 model.
### Switch between models
You can easily switch between models by changing the `model` parameter:
* `"model": "community-1"` for Community-1
* `"model": "precision-2"` for Precision-2
**Note:** Speaker identification and voiceprint features are not available for Community-1 models. These advanced features are exclusive to Precision-2
### Compare results between models
To compare performance between models on your specific data:
1. Process the same audio file with both models
2. Compare the diarization results
3. Evaluate which model provides better accuracy for your use case
## Pricing
For detailed pricing information, visit our [pricing page](https://www.pyannote.ai/pricing).