Update app.py

app.py

@@ -26,26 +26,29 @@ ui.theme = "peach"
 ui.article = """<h2>Pre-trained model Information</h2>
                 <h3>Automatic Speech Recognition</h3>
                 <p style='text-align: justify'>The model used for the ASR part of this space is from                
-                <a href=\"https://huggingface.co/facebook/hubert-large-ls960-ft"></a> which is pretrained and fine-tuned on <b>960 hours of 
+                <a href=\"https://huggingface.co/facebook/hubert-large-ls960-ft">hubert-large-ls960-ft</a> which is pretrained and fine-tuned on <b>960 hours of 
                 Librispeech</b> on 16kHz sampled speech audio. This model has a self-reported <b>word error rate (WER)</b> of <b>1.9 
                 percent</b> and ranks first in <i>paperswithcode</i> for ASR on Librispeech. More information can be 
                 found on its website at <a href=\"https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-
                 recognition-
-                generation-and-compression"></a> and 
-                original model is under <a href=\"https://github.com/pytorch/fairseq/tree/main/examples/hubert"></a>.</p>
+                generation-and-compression">hubert-self</a> and 
+                original model is under <a href=\"https://github.com/pytorch/fairseq/tree/main/examples/hubert">pytorch/fairseq</a>.</p>
                 <h3>Text Translator</h3>
                 <p style='text-align: justify'>The English to Spanish text translator pre-trained model is from 
-                <a href=\"https://huggingface.co/Helsinki-NLP/opus-mt-en-es"></a> which is part of the <b>The Tatoeba Translation Challenge 
+                <a href=\"https://huggingface.co/Helsinki-NLP/opus-mt-en-es">Helsinki-NLP/opus-mt-en-es</a> which is part of the <b>The 
+                Tatoeba Translation Challenge 
                 (v2021-08-07)</b> as seen from its github repo at 
-                <a href=\"https://github.com/Helsinki-NLP/Tatoeba-Challenge"></a>. This project aims to develop machine 
+                <a href=\"https://github.com/Helsinki-NLP/Tatoeba-Challenge">Helsinki-NLP/Tatoeba-Challenge</a>. This project aims to develop 
+                machine 
                 translation in real-world 
                 cases for many languages. </p>
                 <h3>Text to Speech</h3>
                 <p style='text-align: justify'> The TTS model used is from <a href=\"https://huggingface.co/facebook/tts_transformer-es-
-                css10"></a>. 
+                css10">facebook/tts_transformer-es-
+                css10</a>. 
                 This model uses the <b>Fairseq(-py)</b> sequence modeling toolkit for speech synthesis, in this case, specifically TTS 
                 for Spanish. More information can be seen on their git at 
-                <a href=\"https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis"></a>. </p>
+                <a href=\"https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis">speech_synthesis</a>. </p>
             """