Upload app.py

app.py

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+import gradio as gr
+from speechbrain.pretrained import SepformerSeparation as separator
+import torchaudio
+
+def predict_song(audio_path):
+    est_sources = model.separate_file(path=audio_path)
+    torchaudio.save("enhanced_wham.wav", est_sources[:, :, 0].detach().cpu(), 8000)
+    return "enhanced_wham.wav"
+
+import gradio as gr
+
+# Create title, description and article strings
+title = "Denoise Audio Using Sepformer"
+description = "Using SepFormer model implemented with SpeechBrain"
+article = "Tham khao Hunggingface [speechbrain/sepformer-wsj02mixt](https://huggingface.co/speechbrain/sepformer-wsj02mix)."
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict_song, # mapping function from input to output
+                    inputs=gr.Audio(type="filepath"), # what are the inputs?
+                    outputs=gr.File(file_count="multiple", file_types=[".wav"]), # our fn has two outputs, therefore we have two outputs
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch()