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Update app.py
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app.py
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@@ -26,26 +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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<a href=\"https://huggingface.co/facebook/hubert-large-ls960-ft"> 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 self-reported <b>word error rate (WER)</b> of <b>1.9
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percent</b> and ranks first in <i>paperswithcode</i> for ASR on Librispeech. More information can be
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found on its website at <a href=\"https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-
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recognition-
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generation-and-compression"> and
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original model is under <a href=\"https://github.com/pytorch/fairseq/tree/main/examples/hubert">.</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
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<a href=\"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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<a href=\"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 <a href=\"https://huggingface.co/facebook/tts_transformer-es-
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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
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<a href=\"https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis">. </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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+
<a href=\"https://huggingface.co/facebook/hubert-large-ls960-ft"></a> 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 self-reported <b>word error rate (WER)</b> of <b>1.9
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percent</b> and ranks first in <i>paperswithcode</i> for ASR on Librispeech. More information can be
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found on its website at <a href=\"https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-
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recognition-
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+
generation-and-compression"></a> and
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original model is under <a href=\"https://github.com/pytorch/fairseq/tree/main/examples/hubert"></a>.</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
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+
<a href=\"https://huggingface.co/Helsinki-NLP/opus-mt-en-es"></a> 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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+
<a href=\"https://github.com/Helsinki-NLP/Tatoeba-Challenge"></a>. 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 <a href=\"https://huggingface.co/facebook/tts_transformer-es-
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css10"></a>.
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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
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<a href=\"https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis"></a>. </p>
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"""
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