Update app.py

app.py

@@ -1,41 +1,10 @@
-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()
 
-# 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()
+def greet(name):
+    return "Hello " + name + "!!"
 
-# Load the HiFiGAN vocoder (if used in the notebook)
-vocoder = torch.hub.load("bshall/hifigan:main", "hifigan", trust_repo=True).cuda()
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.launch()
 
-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()