Update app.py

app.py

@@ -12,23 +12,33 @@ from stable_audio_tools.inference.generation import generate_diffusion_cond
 
 # Load the model outside of the GPU-decorated function
 def load_model():
+    print("Loading model...")
     model, model_config = get_pretrained_model("stabilityai/stable-audio-open-1.0")
+    print("Model loaded successfully.")
     return model, model_config
 
 # Function to set up, generate, and process the audio
 @spaces.GPU(duration=120)  # Allocate GPU only when this function is called
 def generate_audio(prompt, seconds_total=30, steps=100, cfg_scale=7):
+    print(f"Prompt received: {prompt}")
+    print(f"Settings: Duration={seconds_total}s, Steps={steps}, CFG Scale={cfg_scale}")
+
     device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Using device: {device}")
 
     # Fetch the Hugging Face token from the environment variable
     hf_token = os.getenv('HF_TOKEN')
+    print(f"Hugging Face token: {hf_token}")
 
     # Use pre-loaded model and configuration
     model, model_config = load_model()
     sample_rate = model_config["sample_rate"]
     sample_size = model_config["sample_size"]
 
+    print(f"Sample rate: {sample_rate}, Sample size: {sample_size}")
+
     model = model.to(device)
+    print("Model moved to device.")
 
     # Set up text and timing conditioning
     conditioning = [{
@@ -36,8 +46,10 @@ def generate_audio(prompt, seconds_total=30, steps=100, cfg_scale=7):
         "seconds_start": 0,
         "seconds_total": seconds_total
     }]
+    print(f"Conditioning: {conditioning}")
 
     # Generate stereo audio
+    print("Generating audio...")
     output = generate_diffusion_cond(
         model,
         steps=steps,
@@ -49,17 +61,23 @@ def generate_audio(prompt, seconds_total=30, steps=100, cfg_scale=7):
         sampler_type="dpmpp-3m-sde",
         device=device
     )
+    print("Audio generated.")
 
     # Rearrange audio batch to a single sequence
     output = rearrange(output, "b d n -> d (b n)")
+    print("Audio rearranged.")
 
     # Peak normalize, clip, convert to int16
     output = output.to(torch.float32).div(torch.max(torch.abs(output))).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
+    print("Audio normalized and converted.")
 
     # Generate a unique filename for the output
     unique_filename = f"output_{uuid.uuid4().hex}.wav"
+    print(f"Saving audio to file: {unique_filename}")
+
     # Save to file
     torchaudio.save(unique_filename, output, sample_rate)
+    print(f"Audio saved: {unique_filename}")
 
     # Return the path to the generated audio file
     return unique_filename
@@ -70,7 +88,7 @@ interface = gr.Interface(
     inputs=[
         gr.Textbox(label="Prompt", placeholder="Enter your text prompt here"),
         gr.Slider(0, 47, value=30, label="Duration in Seconds"),
-        gr.Slider(10, 300, value=100, step=10, label="Number of Diffusion Steps"),
+        gr.Slider(10, 150, value=100, step=10, label="Number of Diffusion Steps"),
         gr.Slider(1, 15, value=7, step=0.1, label="CFG Scale")
     ],
     outputs=gr.Audio(type="filepath", label="Generated Audio"),