Update app.py

app.py

@@ -1,38 +1,45 @@
-import tempfile
-from audiocraft.models import MusicGen
-from audiocraft.data.audio import audio_write
-import gradio as gr
-import torch
-import tempfile
-import uuid
-import os
-from scipy.io.wavfile import write
+# Import necessary libraries
+import tempfile  # Library for temporary file operations
+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
+import uuid  # Library for generating unique identifiers (UUIDs)
+import os  # Operating system-related functions
+from scipy.io.wavfile import write  # Function for writing audio to a WAV file
 
 # Load the model and set parameters
-model = MusicGen.get_pretrained("facebook/musicgen-small")
-model.set_generation_params(duration=8)
+model = MusicGen.get_pretrained("facebook/musicgen-small")  # Load a pre-trained MusicGen model
+model.set_generation_params(duration=8)  # Set the duration for audio generation in seconds
 
+# Function to generate music from text descriptions
 def generate_music(description):
-    # Generate audio
-    wav = model.generate([description])
-    audio_array = wav.cpu().numpy().squeeze()
-    sample_rate = model.sample_rate
+    # Generate audio based on the provided description
+    wav = model.generate([description])  # Generate audio using the model
+    audio_array = wav.cpu().numpy().squeeze()  # Convert the audio to a NumPy array
+    sample_rate = model.sample_rate  # Get the audio sample rate (e.g., 44100 Hz)
+
+    # Generate a unique file identifier (UUID) for the temporary audio file
     file_id = uuid.uuid1()
+
+    # Define the path for the temporary audio file using the identifier
     file_path = os.path.join(
-        tempfile.gettempdir(),
-        f'{file_id}.wav'
+        tempfile.gettempdir(),  # Get the temporary directory path
+        f'{file_id}.wav'  # Create a unique file name with the UUID and .wav extension
     )
+
+    # Write the generated audio to the temporary file as a WAV file
     write(file_path, rate=sample_rate, data=audio_array)
+
+    # Return the path to the temporary audio file
     return file_path
 
 # Define Gradio interface with temporary file output
 iface = gr.Interface(
-    fn=generate_music,
-    inputs="text",
-    outputs=gr.components.Audio(type="filepath", label="Audio"),
-    title="Text to Audio Generation",
-    description="Generate audio based on text descriptions.",
-    live=False,
-)
-
-iface.launch(debug=True)
+    fn=generate_music,  # Use the generate_music function for processing input
+    inputs="text",  # Accept text input from the user
+    outputs=gr.components.Audio(type="filepath", label="Audio"),  # Display the generated audio as output
+    title="Text to Audio Generation",  # Set the title of the web interface
+    description="Generate audio based on text descriptions.",  # Provide a description
+    live=False,  # Set to False if you don't want real-time updates (for beginner-friendly interaction)
+)