Update app.py

app.py

@@ -23,24 +23,24 @@ ui.title = "English to Spanish Speech Translator"
 ui.description = """<center>A useful tool in translating English to Spanish audio. All pre-trained models are found in huggingface.</center>"""
 ui.examples = [['ljspeech.wav'],['ljspeech2.wav',]]
 ui.theme = "peach"
-ui.article = 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 
-                       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>
-                       <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 
-                       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>. 
-                       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>
-                    ""   
+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 
+                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>
+                <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 
+                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>. 
+                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>
+            """           
                        
                        
 ui.launch(inbrowser=True)