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import gradio as gr
#Get models
#ASR model for input speech
speech2text = gr.Interface.load("huggingface/facebook/hubert-large-ls960-ft",
inputs=gr.inputs.Audio(label="Record Audio", type="filepath", source = "microphone"))
#translates English to Spanish text
translator = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-en-es",
outputs=gr.outputs.Textbox(label="English to Spanish Translated Text"))
#TTS model for output speech
text2speech = gr.Interface.load("huggingface/facebook/tts_transformer-es-css10",
outputs=gr.outputs.Audio(label="English to Spanish Translated Audio"),
allow_flagging="never")
translate = gr.Series(speech2text, translator) #outputs Spanish text translation
en2es = gr.Series(translate, text2speech) #outputs Spanish audio
ui = gr.Parallel(translate, en2es) #allows transcription of Spanish audio
#gradio interface
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 = """<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
<a href=\"https://huggingface.co/facebook/hubert-large-ls960-ft">hubert-large-ls960-ft</a> which is pretrained and fine-tuned on <b>960 hours of
Librispeech</b> on 16kHz sampled speech audio. This model has a self-reported <b>word error rate (WER)</b> of <b>1.9
percent</b> and ranks first in <i>paperswithcode</i> for ASR on Librispeech. More information can be
found on its website at <a href=\"https://ai.facebook.com/blog/hubert-self-supervised-representation-learning-for-speech-
recognition-
generation-and-compression">hubert-self</a> and
original model is under <a href=\"https://github.com/pytorch/fairseq/tree/main/examples/hubert">pytorch/fairseq</a>.</p>
<h3>Text Translator</h3>
<p style='text-align: justify'>The English to Spanish text translator pre-trained model is from
<a href=\"https://huggingface.co/Helsinki-NLP/opus-mt-en-es">Helsinki-NLP/opus-mt-en-es</a> which is part of the <b>The
Tatoeba Translation Challenge
(v2021-08-07)</b> as seen from its github repo at
<a href=\"https://github.com/Helsinki-NLP/Tatoeba-Challenge">Helsinki-NLP/Tatoeba-Challenge</a>. 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 <a href=\"https://huggingface.co/facebook/tts_transformer-es-
css10">facebook/tts_transformer-es-
css10</a>.
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
<a href=\"https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis">speech_synthesis</a>. </p>
"""
ui.launch(inbrowser=True)
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