juulaii commited on
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1 Parent(s): feba911

Update app.py

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  1. app.py +18 -18
app.py CHANGED
@@ -23,24 +23,24 @@ ui.title = "English to Spanish Speech Translator"
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  ui.description = """<center>A useful tool in translating English to Spanish audio. All pre-trained models are found in huggingface.</center>"""
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  ui.examples = [['ljspeech.wav'],['ljspeech2.wav',]]
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  ui.theme = "peach"
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- ui.article = 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.launch(inbrowser=True)
 
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  ui.description = """<center>A useful tool in translating English to Spanish audio. All pre-trained models are found in huggingface.</center>"""
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  ui.examples = [['ljspeech.wav'],['ljspeech2.wav',]]
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  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.launch(inbrowser=True)