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Update app.py

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  1. app.py +14 -8
app.py CHANGED
@@ -26,20 +26,26 @@ ui.theme = "peach"
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  ui.article = """<h2>Pre-trained model Information</h2>
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  <h3>Automatic Speech Recognition</h3>
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  <p style='text-align: justify'>The model used for the ASR part of this space is from
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- https://huggingface.co/facebook/wav2vec2-base-960h which is pretrained and fine-tuned on <b>960 hours of
 
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  Librispeech</b> on 16kHz sampled speech audio. This model has a <b>word error rate (WER)</b> of <b>8.6 percent on
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  noisy speech</b> and <b>5.2 percent on clean speech</b> on the standard LibriSpeech benchmark. More information can be
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- found on its website at https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio and
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- original model is under https://github.com/pytorch/fairseq/tree/main/examples/wav2vec .</p>
 
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  <h3>Text Translator</h3>
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- <p style='text-align: justify'>The English to Spanish text translator pre-trained model is from <Helsinki-NLP/opus-
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- mt-en-es> which is part of the <b>The Tatoeba Translation Challenge (v2021-08-07)</b> as seen from its github repo at
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- https://github.com/Helsinki-NLP/Tatoeba-Challenge . This project aims to develop machine translation in real-world
 
 
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  cases for many languages. </p>
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  <h3>Text to Speech</h3>
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- <p style='text-align: justify'> The TTS model used is from https://huggingface.co/facebook/tts_transformer-es-css10 .
 
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  This model uses the <b>Fairseq(-py)</b> sequence modeling toolkit for speech synthesis, in this case, specifically TTS
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- for Spanish. More information can be seen on their git at https://github.com/pytorch/fairseq . </p>
 
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  """
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  ui.article = """<h2>Pre-trained model Information</h2>
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  <h3>Automatic Speech Recognition</h3>
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  <p style='text-align: justify'>The model used for the ASR part of this space is from
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+ [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) which is pretrained and fine-tuned on
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+ <b>960 hours of
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  Librispeech</b> on 16kHz sampled speech audio. This model has a <b>word error rate (WER)</b> of <b>8.6 percent on
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  noisy speech</b> and <b>5.2 percent on clean speech</b> on the standard LibriSpeech benchmark. More information can be
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+ found on its website at [wav2vec](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio)
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+ and
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+ original model is under [pytorch/fairseq](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec).</p>
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  <h3>Text Translator</h3>
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+ <p style='text-align: justify'>The English to Spanish text translator pre-trained model is from [Helsinki-NLP/opus-
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+ mt-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-en-es) which is part of the <b>The Tatoeba Translation Challenge
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+ (v2021-08-07)</b> as seen from its github repo at
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+ [Helsinki-NLP/Tatoeba-Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge). This project aims to develop machine
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+ translation in real-world
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  cases for many languages. </p>
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  <h3>Text to Speech</h3>
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+ <p style='text-align: justify'> The TTS model used is from [facebook/tts_transformer-es-css10]
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+ (https://huggingface.co/facebook/tts_transformer-es-css10).
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  This model uses the <b>Fairseq(-py)</b> sequence modeling toolkit for speech synthesis, in this case, specifically TTS
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+ for Spanish. More information can be seen on their git at [speech_synthesis]
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+ (https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis). </p>
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  """
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