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title: README
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diarizers-community aims to promote speaker diarization on the Hugging Face hub. It contains:
- A collection of multilingual speaker diarization datasets that are compatible with the diarizers library. They have been processed using diarizers scripts.
The available datasets are the CallHome (Japanese, Chinese, German, Spanish, English), AMI Corpus (English), Vox-Converse (English) and Simsamu (French). We aim to add more datasets in the future to better support speaker diarising on the Hub.
- A collection of multilingual fine-tuned segmentation model baselines compatible with pyannote.
Each model has been fine-tuned on a specific Callhome language subset. They achieve better performances on multilingual data compared to pyannote's pre-trained segmentation-3.0 model (see benchmark for more details on model performance).
Together with diarizers-community, we release:
diarizers, a library for fine-tuning pyannote speaker diarization models using the Hugging Face ecosystem.
A google colab notebook, with a step-by-step guide on how to use diarizers.
Benchmark
| Callhome test dataset subset | Model | DER | False alarm | Missed detection | Confusion |
|---|---|---|---|---|---|
| Japanese | Pretrained | 25.44 | 2.30 | 17.45 | 5.69 |
| Fine-tuned | 18.23 | 6.31 | 6.91 | 5.01 | |
| Spanish | Pretrained | 33.44 | 2.59 | 25.19 | 5.66 |
| Fine-tuned | 25.72 | 6.87 | 12.73 | 6.12 | |
| English | Pretrained | 22.16 | 6.29 | 10.97 | 4.90 |
| Fine-tuned | 18.40 | 7.10 | 6.98 | 4.32 | |
| German | Pretrained | 21.90 | 3.10 | 14.25 | 4.55 |
| Fine-tuned | 16.75 | 5.00 | 7.75 | 4.00 | |
| Chinese | Pretrained | 19.73 | 4.81 | 9.82 | 5.11 |
| Fine-tuned | 15.95 | 5.04 | 7.24 | 3.68 |
Results are in %. They have been obtained using the test script from diarizers.
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