Create app.py

app.py

@@ -0,0 +1,30 @@
+import torch
+import torchaudio
+import gradio as gr
+from clearvoice import ClearVoice
+
+myClearVoice = ClearVoice(task='speech_enhancement', model_names=['FRCRN_SE_16K'])
+
+def fn_clearvoice(aud):
+    # Load and add fake batch dimension
+    """
+    noisy = enhance_model.load_audio(
+    aud
+    ).unsqueeze(0)
+    enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.]))
+    """
+    output_wav_dict = myClearVoice(input_path='input.wav', online_write=False)
+    key = next(iter(output_wav_dict))
+    output_wav = output_wav_dict[key]
+    torchaudio.save('enhanced.wav', output_wav.cpu(), 16000)
+    return 'enhanced.wav'
+  
+inputs = gr.Audio(sources=["upload"], label="Input Audio", type="filepath")
+outputs = gr.Audio(label="Output Audio", type="filepath")
+title = "ClearVoice"
+description = "Gradio demo for Speech enhancement with ClearVoice. 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/2206.07293' target='_blank'>FRCRN: Boosting Feature Representation Using Frequency Recurrence for Monaural Speech Enhancement</a> | <a href='https://github.com/speechbrain/speechbrain' target='_blank'>Github Repo</a></p>"
+examples = [
+    ['input.wav']
+]
+gr.Interface(fn_clearvoice, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()