README.md

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title: README
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[diarizers-community](https://huggingface.co/diarizers-community) aims to promote speaker diarization on the Hugging Face hub. It contains: 

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

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](https://huggingface.co/collections/diarizers-community/models-66261d0f9277b825c807ff2a) 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](https://huggingface.co/pyannote/segmentation-3.0) model: 


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Note: Results have been obtained using the [test script](https://github.com/kamilakesbi/diarizers/blob/main/test_segmentation.py) from diarizers. 

Together with diarizers-community, we release: 

- [diarizers](https://github.com/kamilakesbi/diarizers/tree/main), a library for fine-tuning pyannote speaker diarization models using the Hugging Face ecosystem.

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


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