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| import torch | |
| import torchaudio | |
| import gradio as gr | |
| import stempeg | |
| torch.hub.download_url_to_file('https://github.com/AK391/open-unmix-pytorch/blob/master/test.wav?raw=true', 'test.wav') | |
| use_cuda = torch.cuda.is_available() | |
| device = torch.device("cuda" if use_cuda else "cpu") | |
| # loading umxhq four target separator | |
| separator = torch.hub.load('sigsep/open-unmix-pytorch', 'umxhq') | |
| def inference(audio): | |
| audio, rate = stempeg.read_stems( | |
| audio, | |
| sample_rate=44100 | |
| ) | |
| audio = torch.as_tensor(audio).float().T | |
| audio = audio[None] | |
| estimates = separator(audio) | |
| estimates = separator.to_dict(estimates) | |
| estimates_numpy = {} | |
| for target, estimate in estimates.items(): | |
| estimates_numpy[target] = torch.squeeze(estimate).detach().cpu().numpy().T | |
| target_path = str("target.wav") | |
| stempeg.write_stems( | |
| target_path, | |
| estimates_numpy, | |
| sample_rate=rate, | |
| writer=stempeg.FilesWriter(multiprocess=True, output_sample_rate=44100), | |
| ) | |
| return 'vocals.wav', 'drums.wav', 'bass.wav', 'other.wav' | |
| inputs = gr.inputs.Audio(label="Input Audio", type="filepath") | |
| outputs = [gr.outputs.Audio(label="Vocals", type="file"), | |
| gr.outputs.Audio(label="Drums", type="file"), | |
| gr.outputs.Audio(label="Bass", type="file"), | |
| gr.outputs.Audio(label="Other Audio", type="file")] | |
| title = "OPEN-UNMIX" | |
| description = "gradio demo for OPEN-UNMIX, reference implementation 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." | |
| article = "<p style='text-align: center'><a href='https://joss.theoj.org/papers/10.21105/joss.01667'>Open-Unmix - A Reference Implementation for Music Source Separation</a> | <a href='https://github.com/sigsep/open-unmix-pytorch'>Github Repo</a></p>" | |
| examples = [['test.wav']] | |
| gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples).launch(enable_queue=True) | |