| from speechbrain.inference.separation import SepformerSeparation as separator | |
| import torchaudio | |
| import gradio as gr | |
| model = separator.from_hparams(source="speechbrain/sepformer-wsj02mix", savedir='pretrained_models/sepformer-wsj02mix') | |
| def speechbrain(aud): | |
| est_sources = model.separate_file(path=aud) | |
| torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 8000) | |
| torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000) | |
| return "source1hat.wav", "source2hat.wav" | |
| title = "Speech Seperation" | |
| description = "Gradio demo for Speech Seperation by SpeechBrain. To use it, simply upload 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://arxiv.org/abs/2010.13154' target='_blank'>Attention is All You Need in Speech Separation</a> | <a href='https://github.com/speechbrain/speechbrain/tree/develop/recipes/WSJ0Mix/separation' '_blank'>Github Repo</a></p>" | |
| examples = [ | |
| ['samples_audio_samples_test_mixture.wav'] | |
| ] | |
| demo = gr.Interface( | |
| fn=speechbrain, | |
| inputs=gr.Audio(type="filepath"), | |
| outputs=[ | |
| gr.Audio(label="Output Audio One", type="filepath"), | |
| gr.Audio(label="Output Audio Two", type="filepath") | |
| ], | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples | |
| ) | |
| demo.launch() | |