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deeplearning
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Create app.py
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app.py
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import os
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os.system('pip install gradio==2.3.0a0')
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os.system('pip install voicefixer --upgrade')
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from voicefixer import VoiceFixer
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import gradio as gr
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voicefixer = VoiceFixer()
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def inference(audio,mode):
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voicefixer.restore(input=audio.name, # input wav file path
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output="output.wav", # output wav file path
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cuda=False, # whether to use gpu acceleration
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mode = int(mode)) # You can try out mode 0, 1 to find out the best result
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return 'output.wav'
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inputs = [gr.inputs.Audio(type="file", label="Input Audio"),gr.inputs.Radio(choices=['0','1','2'], type="value", default='0', label='mode')]
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outputs = gr.outputs.Audio(type="file",label="Output Audio")
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title = "Voice Fixer"
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description = "Gradio demo for VoiceFixer: Toward General Speech Restoration With Neural Vocoder. To use it, simply add your audio, or click one of the examples to load them. Read more at the links below."
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.13731' target='_blank'>VoiceFixer: Toward General Speech Restoration With Neural Vocoder</a> | <a href='https://github.com/haoheliu/voicefixer_main' target='_blank'>Github Repo</a></p>"
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examples=[['bruce.wav','2']]
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gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, enable_queue=True).launch()
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