doc: add link to pyannote official repo

README.md

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 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 diarization on the Hub.
 
-- A collection of multilingual [fine-tuned segmentation model](https://huggingface.co/collections/diarizers-community/models-66261d0f9277b825c807ff2a) baselines compatible with pyannote.
+- A collection of multilingual [fine-tuned segmentation model](https://huggingface.co/collections/diarizers-community/models-66261d0f9277b825c807ff2a) baselines compatible with [pyannote](https://github.com/pyannote/pyannote-audio).
 
-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 (see benchmark for more details on model performance). 
+Each model has been fine-tuned on a specific Callhome language subset. They achieve better performances on multilingual data compared to [pyannote](https://github.com/pyannote/pyannote-audio)'s pre-trained [segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) model (see benchmark for more details on model performance). 
 
 Together with diarizers-community, we release: 
 
-- [diarizers](https://github.com/huggingface/diarizers/tree/main), a library for fine-tuning pyannote speaker diarization models using the Hugging Face ecosystem.
+- [diarizers](https://github.com/huggingface/diarizers/tree/main), a library for fine-tuning [pyannote](https://github.com/pyannote/pyannote-audio) 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.