Update app.py

app.py

@@ -1,10 +1,41 @@
+import torch 
+import torchaudio
 import gradio as gr
+import requests
 
+# Download and load the HuBERT content encoder
+hubert = torch.hub.load("bshall/hubert:main", "hubert_soft", trust_repo=True).cuda()
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Assuming similar steps for downloading and loading the acoustic model
+acoustic_model = torch.hub.load("bshall/acoustic-model:main", "hubert_soft", trust_repo=True).cuda()
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Load the HiFiGAN vocoder (if used in the notebook)
+vocoder = torch.hub.load("bshall/hifigan:main", "hifigan", trust_repo=True).cuda()
 
+def voice_conversion(input_audio):
+    # Load input audio
+    waveform, sample_rate = torchaudio.load(input_audio)
+    
+    # Process the audio using the models
+    with torch.no_grad():
+        units = hubert(waveform.cuda())
+        mel_spec = acoustic_model.generate(units)
+        audio_out = vocoder(mel_spec)
+    
+    # Save the output audio
+    output_path = "output.wav"
+    torchaudio.save(output_path, audio_out.cpu(), sample_rate)
+    
+    return output_path
 
+# Define Gradio interface
+iface = gr.Interface(
+    fn=voice_conversion,
+    inputs=gr.inputs.Audio(source="upload", type="filepath"),
+    outputs=gr.outputs.Audio(type="file"),
+    title="Voice Conversion Demo",
+    description="Upload an audio file to convert its voice using HuBERT and other models."
+)
+
+# Launch the interface
+iface.launch()