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title: README
emoji: 🏃
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colorTo: purple
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pinned: false
---

[diarizers-community](https://huggingface.co/diarizers-community) aims to promote speaker diarization on the Hugging Face hub. It comes with [diarizers](https://github.com/kamilakesbi/diarizers), a library for fine-tuning pyannote speaker diarzaition models that is compatible with the Hugging Face ecosystem.

This organization contains: 

- A collection of [multilingual speaker diarization datasets](https://huggingface.co/collections/diarizers-community/speaker-diarization-datasets-66261b8d571552066e003788) that are compatible with diarizers. They have been processed using [diarizers scripts](https://github.com/kamilakesbi/diarizers/blob/main/datasets/README.md).

The currently available datasets are the CallHome (Japanese, Chinese, German, Spanish, English), the AMI Corpus (English), Vox-Converse (English) and Simsamu (French). We aim at adding more datasets in the future to support speaker diarization on the Hub. 

- A collection of [5 fine-tuned segmentation model](https://huggingface.co/collections/diarizers-community/models-66261d0f9277b825c807ff2a) baselines that can be used in a pyannote speaker diarization pipeline.

- Each model has been fine-tuned on a specific language of the Callhome dataset. Compared to the pre-trained pyannote [segmentation model](https://huggingface.co/pyannote/segmentation-3.0), they obtain better performances on each language: 


** ADD BENCHMARK ** 

Note: Results have been obtained using the `test_segmentation.py` script from diarizers. 

Together with this organisation, we release: 

- The diarizers library, to fine-tune pyannote segmentation models and use them back in a pyannote speaker diarization pipeline.

- A google colab [notebook](https://colab.research.google.com/github/kamilakesbi/notebooks/blob/main/fine_tune_pyannote.ipynb), whith a step-by-step guide on how to use diarizers. 


Edit this `README.md` markdown file to author your organization card.