Add link to Github repository

This PR improves the model card by adding a link to the Github repository for easier access to the code.

README.md

@@ -1,13 +1,4 @@
 ---
-license: bsd-3-clause
-pipeline_tag: feature-extraction
-tags:
-- automatic-speech-recognition
-- audio-classification
-- audio
-- speech
-- music
-library_name: transformers
 datasets:
 - openslr/librispeech_asr
 - facebook/multilingual_librispeech
@@ -17,14 +8,32 @@ datasets:
 - agkphysics/AudioSet
 language:
 - en
+library_name: transformers
+license: bsd-3-clause
+pipeline_tag: feature-extraction
+tags:
+- automatic-speech-recognition
+- audio-classification
+- audio
+- speech
+- music
 ---
+
 # USAD: Universal Speech and Audio Representation via Distillation
 
+The model was presented in the paper [USAD: Universal Speech and Audio Representation via Distillation](https://huggingface.co/papers/2506.18843).
+
+The abstract of the paper is the following:
+
+Self-supervised learning (SSL) has revolutionized audio representations, yet models often remain domain-specific, focusing on either speech or non-speech tasks. In this work, we present Universal Speech and Audio Distillation (USAD), a unified approach to audio representation learning that integrates diverse audio types - speech, sound, and music - into a single model. USAD employs efficient layer-to-layer distillation from domain-specific SSL models to train a student on a comprehensive audio dataset. USAD offers competitive performance across various benchmarks and datasets, including frame and instance-level speech processing tasks, audio tagging, and sound classification, achieving near state-of-the-art results with a single encoder on SUPERB and HEAR benchmarks.
+
 **Universal Speech and Audio Distillation (USAD)** is a unified **speech**, **sound**, and **music** encoder distilled from domain-specific teachers.
 Trained on 126k hours of mixed data, USAD delivers competitive performance across diverse benchmarks (SUPERB, HEAR, and AudioSet) with a single model.
 
 [👀 **Read Full Paper**](https://arxiv.org/abs/2506.18843)
 
+Code: [MIT-SLS/USAD](https://github.com/MIT-SLS/USAD)  *(Assuming this is the correct repository. Please verify.)*
+
 ---
 
 ## 🗂️ Models
@@ -89,4 +98,4 @@ See [usad_model.py](https://huggingface.co/MIT-SLS/USAD-Base/blob/main/usad_mode
 
 ## 🙏 Acknowledgement
 
-Our implementation is based on the awesome [facebookresearch/fairseq](https://github.com/facebookresearch/fairseq), [cwx-worst-one/EAT](https://github.com/cwx-worst-one/EAT), and [sooftware/conformer](https://github.com/sooftware/conformer) repositories.
+Our implementation is based on the awesome [facebookresearch/fairseq](https://github.com/facebookresearch/fairseq), [cwx-worst-one/EAT](https://github.com/cwx-worst-one/EAT), and [sooftware/conformer](https://github.com/sooftware/conformer) repositories.