Update README.md

README.md

@@ -4045,13 +4045,13 @@ language:
  [XEUS - A Cross-lingual Encoder for Universal Speech]()
  
 
- XEUS is a large-scale multilingual speech encoder by Carnegie Mellon University's [WAVLab]() that covers over **4000** languages. It is pre-trained on over 1 million hours of publicly available speech datasets. It requires fine-tuning to be used in downstream tasks such as Speech Recognition or Translation. Its hidden states can also be used with k-means for semantic Speech Tokenization. XEUS uses the [E-Branchformer]() architecture and is trained using [HuBERT]()-style masked prediction of discrete speech tokens extracted from [WavLabLM](). During training, the input speech is also augmented with acoustic noise and reverberation, making XEUS more robust. The total model size is 577M parameters.
+ XEUS is a large-scale multilingual speech encoder by Carnegie Mellon University's [WAVLab](https://www.wavlab.org/) that covers over **4000** languages. It is pre-trained on over 1 million hours of publicly available speech datasets. It requires fine-tuning to be used in downstream tasks such as Speech Recognition or Translation. Its hidden states can also be used with k-means for semantic Speech Tokenization. XEUS uses the [E-Branchformer]() architecture and is trained using [HuBERT]()-style masked prediction of discrete speech tokens extracted from [WavLabLM](). During training, the input speech is also augmented with acoustic noise and reverberation, making XEUS more robust. The total model size is 577M parameters.
 
  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630438615c70c21d0eae6613/BBRKYvTjJmx2B5oyWBLcZ.png)
  
- XEUS tops the [ML-SUPERB]() multilingual speech recognition leaderboard, outperforming [MMS](), [w2v-BERT 2.0](), and [XLS-R](). XEUS also sets a new state-of-the-art on 4 tasks in the monolingual [SUPERB]() benchmark.
+ XEUS tops the [ML-SUPERB](https://arxiv.org/abs/2305.10615) multilingual speech recognition leaderboard, outperforming [MMS](https://arxiv.org/abs/2305.13516), [w2v-BERT 2.0](https://arxiv.org/abs/2312.05187), and [XLS-R](https://arxiv.org/abs/2111.09296). XEUS also sets a new state-of-the-art on 4 tasks in the monolingual [SUPERB]() benchmark.
 
- More information about XEUS, including ***download links for our crawled 4000-language dataset***, can be found in the [project page]().
+ More information about XEUS, including ***download links for our crawled 4000-language dataset***, can be found in the [project page](https://www.wavlab.org/activities/2024/xeus/).
 
 
 ## Requirements
@@ -4067,7 +4067,7 @@ git lfs install
 git clone https://huggingface.co/espnet/XEUS
 ```
 
-XEUS supports [Flash Attention](), which can be installed as follows:
+XEUS supports [Flash Attention](https://github.com/Dao-AILab/flash-attention), which can be installed as follows:
 
 ```
 pip install flash-attn --no-build-isolation