Update app.py

app.py

@@ -26,20 +26,26 @@ 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                
-                https://huggingface.co/facebook/wav2vec2-base-960h which is pretrained and fine-tuned on <b>960 hours of 
+                [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) which is pretrained and fine-tuned on 
+                <b>960 hours of 
                 Librispeech</b> on 16kHz sampled speech audio. This model has a <b>word error rate (WER)</b> of <b>8.6 percent on 
                 noisy speech</b> and <b>5.2 percent on clean speech</b> on the standard LibriSpeech benchmark. More information can be 
-                found on its website at https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio and 
-                original model is under https://github.com/pytorch/fairseq/tree/main/examples/wav2vec .</p>
+                found on its website at [wav2vec](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio) 
+                and 
+                original model is under [pytorch/fairseq](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec).</p>
                 <h3>Text Translator</h3>
-                <p style='text-align: justify'>The English to Spanish text translator pre-trained model is from <Helsinki-NLP/opus-
-                mt-en-es> which is part of the <b>The Tatoeba Translation Challenge (v2021-08-07)</b> as seen from its github repo at 
-                https://github.com/Helsinki-NLP/Tatoeba-Challenge . This project aims to develop machine translation in real-world 
+                <p style='text-align: justify'>The English to Spanish text translator pre-trained model is from [Helsinki-NLP/opus-
+                mt-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-en-es) which is part of the <b>The Tatoeba Translation Challenge 
+                (v2021-08-07)</b> as seen from its github repo at 
+                [Helsinki-NLP/Tatoeba-Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge). 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 https://huggingface.co/facebook/tts_transformer-es-css10 . 
+                <p style='text-align: justify'> The TTS model used is from [facebook/tts_transformer-es-css10]
+                (https://huggingface.co/facebook/tts_transformer-es-css10). 
                 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 https://github.com/pytorch/fairseq . </p>
+                for Spanish. More information can be seen on their git at [speech_synthesis]
+                (https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis). </p>
             """