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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()
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