Update app.py

app.py

@@ -26,26 +26,25 @@ 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                
-                [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) which is pretrained and fine-tuned on 
+                [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 [wav2vec](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio) 
+                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 [pytorch/fairseq](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec).</p>
+                original model is under [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](https://huggingface.co/Helsinki-NLP/opus-mt-en-es) which is part of the <b>The Tatoeba Translation Challenge 
+                <p style='text-align: justify'>The English to Spanish text translator pre-trained model is from 
+                [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 
+                [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 [facebook/tts_transformer-es-css10]
-                (https://huggingface.co/facebook/tts_transformer-es-css10). 
+                <p style='text-align: justify'> The TTS model used is from [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 [speech_synthesis]
-                (https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis). </p>
+                for Spanish. More information can be seen on their git at 
+                [https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis]. </p>
             """