Create app.py

app.py

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+import gradio as gr
+import torch
+from audiocraft.models import MusicGen
+from audiocraft.data.audio import audio_write
+import numpy as np
+import tempfile
+import os
+
+# Load the MusicGen model
+model = MusicGen.get_pretrained('small')
+model.set_generation_params(duration=30)  # Set maximum duration to 30 seconds
+
+def enhance_audio(audio_file):
+    # Load and process the audio file
+    waveform = model.compression_model.encode(audio_file)
+    
+    # Apply AI-based enhancement (this is a simplified example)
+    enhanced_waveform = model.compression_model.decode(waveform)
+    
+    # Convert to numpy array and normalize
+    enhanced_audio = enhanced_waveform.squeeze().cpu().numpy()
+    enhanced_audio = enhanced_audio / np.max(np.abs(enhanced_audio))
+    
+    # Save the enhanced audio to a temporary file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
+        audio_write(temp_file.name, enhanced_audio, model.sample_rate, strategy="loudness", loudness_compressor=True)
+        output_path = temp_file.name
+    
+    return output_path
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=enhance_audio,
+    inputs=gr.Audio(type="filepath", label="Upload your audio file"),
+    outputs=gr.Audio(type="filepath", label="Enhanced Audio"),
+    title="AI Music Mastering and Enhancement",
+    description="Upload an audio file to apply AI-based mastering and enhancement.",
+)
+
+# Launch the app
+iface.launch()