Create app.py

app.py

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+# Import necessary libraries
+from audiocraft.models import MusicGen  # Import the MusicGen model for music generation
+from audiocraft.data.audio import audio_write  # Import audio_write function for saving audio
+import gradio as gr  # Import Gradio for creating a web interface
+import torch  # Import PyTorch for deep learning operations
+
+# Load the pretrained MusicGen model (large)
+model = MusicGen.get_pretrained("large")
+
+# Set generation parameters for the model (e.g., duration of 8 seconds)
+model.set_generation_params(duration=8)
+
+# Function to generate music from text descriptions
+def generate_music(description):
+    # Generate music based on the provided description
+    wav = model.generate([description])
+
+    # Save the generated audio as "output.wav" with loudness normalization at -14 dB LUFS.
+    audio_write("output.wav", wav[0].cpu(), model.sample_rate, strategy="loudness")
+
+    return "Audio generated and saved as 'output.wav'"
+
+# Define a Gradio interface
+iface = gr.Interface(
+    fn=generate_music,  # Use the generate_music function for processing input
+    inputs="text",  # Accept text input from the user
+    outputs="text",  # Display a text output message
+    title="Music Generation from Descriptions",  # Set the title of the web interface
+    description="Generate music based on descriptions.",  # Provide a description
+    live=False  # Set to False if you don't want real-time updates (for beginner-friendly interaction)
+)
+
+# Start the Gradio interface
+iface.launch(debug=True)