updated app.py

app.py

@@ -28,7 +28,7 @@ def preprocess_audio(waveform):
 #   processed_waveform_np = rms_normalize(peak_normalize(waveform_np))
     return torch.from_numpy(waveform_np).unsqueeze(0).to(device)
 
-#@spaces.GPU(10)
+@spaces.GPU(duration=10)
 def generate_drum_sample():
     model = MusicGen.get_pretrained('pharoAIsanders420/micro-musicgen-jungle')
     model.set_generation_params(duration=10)
@@ -41,7 +41,7 @@ def generate_drum_sample():
 
     return filename_with_extension
 
-#@spaces.GPU(10)
+@spaces.GPU(duration=10)
 def continue_drum_sample(existing_audio_path):
     # Load the existing audio
     existing_audio, sr = torchaudio.load(existing_audio_path)
@@ -85,7 +85,7 @@ def continue_drum_sample(existing_audio_path):
 
     return combined_file_path
 
-@spaces.GPU(90)
+@spaces.GPU(duation=90)
 def generate_music(wav_filename, prompt_duration, musicgen_model, output_duration):
     # Load the audio from the passed file path
     song, sr = torchaudio.load(wav_filename)
@@ -121,7 +121,7 @@ def generate_music(wav_filename, prompt_duration, musicgen_model, output_duratio
 
     return filename_with_extension
 
-@spaces.GPU(90)
+@spaces.GPU(duration=90)
 def continue_music(input_audio_path, prompt_duration, musicgen_model, output_duration):
     # Load the audio from the given file path
     song, sr = torchaudio.load(input_audio_path)