Use HF papers (#38184)

* Use hf papers

* Hugging Face papers

* doi to hf papers

* style

docs/source/en/model_doc/seamless_m4t.md

@@ -132,7 +132,7 @@ Use `return_intermediate_token_ids=True` with [`SeamlessM4TModel`] to return bot
 
 SeamlessM4T features a versatile architecture that smoothly handles the sequential generation of text and speech. This setup comprises two sequence-to-sequence (seq2seq) models. The first model translates the input modality into translated text, while the second model generates speech tokens, known as "unit tokens," from the translated text.
 
-Each modality has its own dedicated encoder with a unique architecture. Additionally, for speech output, a vocoder inspired by the [HiFi-GAN](https://arxiv.org/abs/2010.05646) architecture is placed on top of the second seq2seq model.
+Each modality has its own dedicated encoder with a unique architecture. Additionally, for speech output, a vocoder inspired by the [HiFi-GAN](https://huggingface.co/papers/2010.05646) architecture is placed on top of the second seq2seq model.
 
 Here's how the generation process works: