Update app.py

app.py

@@ -11,7 +11,7 @@ from PIL import Image
 import os
 
 # Set device to 'cpu' or 'cuda' if available
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device('cpu')
 
 # Parameters
 sample_rate = 44100  # 44.1kHz stereo sounds
@@ -105,19 +105,6 @@ class Generator(nn.Module):
         return generated_spectrogram
 
 
-
-# Function to save audio
-def save_audio(audio, path, sample_rate=44100):
-    # Ensure audio is in stereo by checking the channels
-    if audio.dim() == 1:
-        audio = audio.unsqueeze(0).repeat(2, 1)  # Convert mono to stereo
-    elif audio.size(0) == 1:
-        audio = audio.repeat(2, 1)  # Convert mono to stereo
-
-    # Save audio to a file
-    torchaudio.save(path, audio, sample_rate)
-
-
 # Function to generate and save audio from a test image using the pre-trained GAN model
 def test_model(generator, test_img_path, output_audio_path, device):
     # Load and preprocess test image
@@ -134,9 +121,6 @@ def test_model(generator, test_img_path, output_audio_path, device):
     # Convert the generated spectrogram to audio
     generated_audio = spectrogram_to_audio(generated_spectrogram.squeeze(0).cpu())  # Remove batch dimension
 
-    # Save the generated audio
-    save_audio(generated_audio, output_audio_path)
-
     print(f"Generated audio saved to {output_audio_path}")
 
 # Load the pre-trained GAN model