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| import sys | |
| sys.path.append('.') | |
| import gradio as gr | |
| import os | |
| os.system('pip install -U torchtext==0.8.0') | |
| os.system('./separate_scripts/download_checkpoints.sh') | |
| def inference(audio): | |
| os.system('./separate_scripts/separate_vocals.sh ' + audio.name + ' "sep_vocals.mp3"') | |
| os.system('./separate_scripts/separate_accompaniment.sh ' + audio.name + ' "sep_accompaniment.mp3"') | |
| return 'sep_vocals.mp3', 'sep_accompaniment.mp3' | |
| title = "Music Source Separation" | |
| 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. Currently supports .wav files" | |
| 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>" | |
| examples = [['example.wav']] | |
| gr.Interface( | |
| inference, | |
| gr.inputs.Audio(type="file", label="Input"), | |
| [gr.outputs.Audio(type="file", label="Vocals"),gr.outputs.Audio(type="file", label="Accompaniment")], | |
| title=title, | |
| description=description, | |
| article=article, | |
| enable_queue=True, | |
| examples=examples | |
| ).launch(debug=True) |