---
license: cc-by-4.0
task_categories:
- audio-classification
- speaker-diarization
language:
- en
pretty_name: Sortformer Diarization Test Set
size_categories:
- <1K
---

# Sortformer Diarization Test Set

100 real speech samples extracted from LibriSpeech test-clean for speaker diarization testing and benchmarking with NVIDIA Sortformer 4spk-v2 ONNX models.

## Dataset Description

- **Samples**: 100 audio files (WAV, 16 kHz mono)
- **Total size**: ~60 MB
- **Source**: [LibriSpeech ASR corpus](https://www.openslr.org/12) — test-clean subset
- **Speakers**: 40 unique speakers from LibriSpeech test-clean
- **Purpose**: Diarization model evaluation, regression testing, ONNX model validation

## Usage with Sortformer ONNX

```python
from huggingface_hub import snapshot_download
import soundfile as sf

# Download the test set
dataset_path = snapshot_download("DimQ1/sortformer-diarization-test-set")

# Load audio
audio, sr = sf.read(f"{dataset_path}/audio/ls_real_000.wav")
```

## Diarization Models

Compatible ONNX models available on HuggingFace:

| Model | Size | Speed | Repo |
|-------|------|-------|------|
| Sortformer FP32 | 470 MB | 16× real-time | `DimQ1/sortformer-4spk-v2-onnx-fp32-cpu` |
| Sortformer INT8 | 129 MB | 29× real-time | `DimQ1/sortformer-4spk-v2-onnx-int8-cpu` |
| Sortformer INT4 | 73 MB | 33× real-time | `DimQ1/sortformer-4spk-v2-onnx-int4-cpu` |

## Ground Truth RTTM

Speaker diarization ground truth annotations are provided in `rttm/` directory (NIST RTTM format).

## Similar Datasets

For larger-scale diarization training and evaluation:

| Dataset | Description | Source |
|---------|-------------|--------|
| **LibriSpeech** | 1000h English read speech | [openslr.org/12](https://www.openslr.org/12) |
| **VoxCeleb 1&2** | 7000+ celebrity speakers | [robots.ox.ac.uk/~vgg/data/voxceleb](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/) |
| **AMI Corpus** | 100h meeting recordings | [groups.inf.ed.ac.uk/ami/corpus](https://groups.inf.ed.ac.uk/ami/corpus/) |
| **CALLHOME** | Multilingual telephone speech | [catalog.ldc.upenn.edu/LDC97S42](https://catalog.ldc.upenn.edu/LDC97S42) |
| **DIHARD III** | Challenging diarization benchmark | [dihardchallenge.github.io/dihard3](https://dihardchallenge.github.io/dihard3/) |
| **MUSAN** | Music/speech/noise for augmentation | [openslr.org/17](https://www.openslr.org/17/) |

## License

Derived from LibriSpeech (CC BY 4.0). See [LibriSpeech license](https://www.openslr.org/12) for details.