Update app.py

app.py

@@ -51,25 +51,14 @@ import subprocess
 
 import whisper
 model1 = whisper.load_model("small")
-os.system('pip install voicefixer --upgrade')
-from voicefixer import VoiceFixer
-voicefixer = VoiceFixer()
-import openai
-import torchaudio
-from speechbrain.pretrained import SpectralMaskEnhancement
 
-enhance_model = SpectralMaskEnhancement.from_hparams(
-source="speechbrain/metricgan-plus-voicebank",
-savedir="pretrained_models/metricgan-plus-voicebank",
-run_opts={"device":"cuda"},
-)
+import openai
 
 mes = [
     {"role": "system", "content": "You are my personal assistant. Try to be helpful. Respond to me only in Chinese."}
 ]
 
 
-
 '''
 from google.colab import drive
 drive.mount('/content/drive')
@@ -231,19 +220,7 @@ def voice_conversion(ta, ra, da):
   # print("Reference Audio after decoder:")
   # IPython.display.display(Audio(ref_wav_voc, rate=ap.sample_rate))
 
-    voicefixer.restore(input=ref_wav_voc, # input wav file path
-                    output="audio1.wav", # output wav file path
-                    cuda=True, # whether to use gpu acceleration
-                    mode = 0) # You can try out mode 0, 1, or 2 to find out the best result
-    
-    noisy = enhance_model.load_audio(
-    "audio1.wav"
-    ).unsqueeze(0)
-
-    enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.]))
-    torchaudio.save("enhanced.wav", enhanced.cpu(), 16000)
-
-    return "enhanced.wav"
+    return (ap.sample_rate, ref_wav_voc)
     
 block = gr.Blocks()