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

# Sortformer

> NVIDIA's end-to-end streaming speaker diarization model.

## Overview

Sortformer is NVIDIA's end-to-end streaming speaker diarization model, converted to CoreML. Unlike the pyannote pipeline (segmentation + clustering), Sortformer is a single neural network with 4 fixed speaker slots.

Model: [FluidInference/diar-streaming-sortformer-coreml](https://huggingface.co/FluidInference/diar-streaming-sortformer-coreml)

## Key Properties

* 4 fixed speaker slots with real-time inference
* No separate segmentation + clustering stages
* Streaming only (no offline mode)
* Best for scenarios with 4 or fewer speakers

## When to Use Sortformer vs Pyannote

Sortformer can beat pyannote on benchmarks with certain configs, but benchmark DER does not always reflect production performance. In practice:

| Scenario                            | Recommendation       | Why                                                     |
| ----------------------------------- | -------------------- | ------------------------------------------------------- |
| Noisy / background noise            | **Sortformer**       | More robust to non-speech audio                         |
| 4 or fewer speakers                 | **Sortformer**       | Designed for this — single model, no clustering         |
| 5+ speakers                         | **Pyannote offline** | Sortformer only has 4 speaker slots, will miss speakers |
| Overlapping speech (5+ people)      | **Pyannote offline** | Sortformer breaks down with heavy crosstalk beyond 4    |
| Best overall accuracy               | **Pyannote offline** | 15% DER vs 32% — more consistent in production          |
| Streaming required, simple meetings | **Sortformer**       | Single model, no clustering overhead                    |

<Warning>
  Benchmarks are not always consistent with production usage. Pyannote's offline pipeline with aggressive tuning can score lower DER on AMI, but those configs may not generalize. Sortformer's 32% DER is more representative of real-world performance on meetings with 4 or fewer speakers.
</Warning>

## Benchmarks

[AMI SDM](https://groups.inf.ed.ac.uk/ami/corpus/) (16 meetings, single distant microphone). Audio length: 30.4s chunks (NVIDIA high-latency config):

| Metric       | Value  |
| ------------ | ------ |
| Average DER  | 31.7%  |
| Average Miss | 21.5%  |
| Average FA   | 0.5%   |
| Average SE   | 9.7%   |
| Average RTFx | 126.7x |

See [full benchmarks](/reference/benchmarks) for per-meeting breakdown.

## CLI

```bash theme={null}
swift run fluidaudio sortformer-benchmark \
  --nvidia-high-latency --hf --auto-download
```