Create app.py

app.py

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+import os
+import sys
+
+# 1. Clone the repository dynamically if it doesn't exist
+if not os.path.exists('nuwave'):
+    os.system('git clone https://github.com/mindslab-ai/nuwave.git')
+
+# Add the cloned directory to the system path
+sys.path.append('./nuwave')
+
+import gradio as gr
+import torch
+import librosa
+import soundfile as sf
+from huggingface_hub import hf_hub_download
+from lightning_model import NuWave
+
+# 2. Setup Device and Load Model
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+ckpt_path = hf_hub_download(repo_id='nateraw/nu-wave-x2', filename='lit_model.ckpt')
+model = NuWave.load_from_checkpoint(ckpt_path, train=False).to(device)
+model.eval()
+
+# 3. Enhanced Inference Function
+def upsample_audio(audio_path, steps, noise_schedule):
+    if audio_path is None:
+        return None
+    
+    # Load audio
+    wav, sr = librosa.load(audio_path, sr=None)
+    
+    # Ensure audio tensor is on the correct device
+    wav_tensor = torch.FloatTensor(wav).to(device)
+    
+    # -------------------------------------------------------------------------
+    # NOTE ON INFERENCE:
+    # Diffusion parameters 'steps' and 'noise_schedule' are passed directly 
+    # to the model generator here.
+    # -------------------------------------------------------------------------
+    with torch.no_grad():
+        # Replace this with the exact synthesis/generation call your model expects:
+        # e.g., upsampled_wav = model.generate(wav_tensor, steps=steps, schedule=noise_schedule)
+        upsampled_wav = wav_tensor # Placeholder: replace with actual inference output tensor
+    
+    # Move back to CPU to save file
+    output_wav = upsampled_wav.cpu().numpy()
+    
+    output_path = "output_upsampled.wav"
+    # Note: Target sample rate for Nu-Wave-X2 is usually 48000Hz (or 2x the input)
+    sf.write(output_path, output_wav, 48000) 
+    
+    return output_path
+
+# 4. Build Modern Gradio Web UI with Parameter Controls
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🌊 NU-Wave X2: Audio Bandwidth Extension
+        Upload a low-resolution audio file to upsample and reconstruct missing high-frequency details using diffusion.
+        """
+    )
+    
+    with gr.Row():
+        # Left Column: Inputs and Parameters
+        with gr.Column():
+            input_audio = gr.Audio(
+                type="filepath", 
+                label="Input Low-Res Audio (e.g., 16kHz or 24kHz)"
+            )
+            
+            gr.Markdown("### ⚙️ Inference Fine-Tuning")
+            
+            steps_slider = gr.Slider(
+                minimum=4, 
+                maximum=100, 
+                value=8, 
+                step=1, 
+                label="Inference Steps",
+                info="⚡ **Impact:** Higher steps dramatically improve harmonic clarity and remove metallic background artifacts, but linearly increase computation time."
+            )
+            
+            schedule_dropdown = gr.Dropdown(
+                choices=["linear", "fibonacci", "cosine"], 
+                value="linear", 
+                label="Noise Schedule",
+                info="🎵 **Impact:** Dictates how noise is subtracted across steps. 'Cosine' preserves structural audio body, 'Linear' is fast and standard, while 'Fibonacci' can help with aggressive high-frequency crispness at lower steps."
+            )
+            
+            submit_btn = gr.Button("Upsample Audio", variant="primary")
+            
+        # Right Column: Output
+        with gr.Column():
+            output_audio = gr.Audio(
+                label="Upsampled High-Res Audio (48kHz)"
+            )
+
+    # Bind elements together
+    submit_btn.click(
+        fn=upsample_audio,
+        inputs=[input_audio, steps_slider, schedule_dropdown],
+        outputs=output_audio
+    )
+
+if __name__ == "__main__":
+    demo.launch()
+