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import sys |
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sys.path.append('.') |
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import gradio as gr |
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import os |
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os.system('pip install -U torchtext==0.8.0') |
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os.system('./separate_scripts/download_checkpoints.sh') |
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def inference(audio): |
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os.system('./separate_scripts/separate_vocals.sh ' + audio.name + ' "sep_vocals.mp3"') |
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os.system('./separate_scripts/separate_accompaniment.sh ' + audio.name + ' "sep_accompaniment.mp3"') |
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return 'sep_vocals.mp3', 'sep_accompaniment.mp3' |
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title = "Music Source Separation" |
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description = "Gradio demo for Music Source Separation. 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://github.com/bytedance/music_source_separation'>Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation</a> | <a href='https://github.com/bytedance/music_source_separation'>Github Repo</a></p>" |
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gr.Interface( |
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inference, |
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gr.inputs.Audio(type="file", label="Input"), |
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[gr.outputs.Audio(type="file", label="Vocals"),gr.outputs.Audio(type="file", label="Accompaniment")], |
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title=title, |
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description=description, |
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article=article, |
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enable_queue=True |
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).launch(debug=True) |