Freeze gradio

app.py

@@ -1,20 +1,15 @@
-from speechbrain.pretrained import SepformerSeparation as separator
+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) 
+  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"
 
-inputs = gr.inputs.Audio(label="Input Audio", type="filepath")
-outputs =  [
-  gr.outputs.Audio(label="Output Audio One", type="filepath"),
-  gr.outputs.Audio(label="Output Audio Two", type="filepath")
-]
 
 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."
@@ -22,4 +17,18 @@ article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2010.131
 examples = [
     ['samples_audio_samples_test_mixture.wav']
 ]
-gr.Interface(speechbrain, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()
+
+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()
+

requirements.txt

@@ -1,2 +1,3 @@
 speechbrain
-torchaudio
+torchaudio
+gradio==3.50.2