app.py

import gradio as gr
import torch
from transformers import AutoProcessor
from elastic_models.transformers import MusicgenForConditionalGeneration
import scipy.io.wavfile
import numpy as np
import subprocess
import sys
import os

# def setup_flash_attention():
#     """One-time setup for flash-attention with special flags"""
#     # Check if flash-attn is already installed
#     try:
#         import flash_attn
#         print("flash-attn already installed")
#         return
#     except ImportError:
#         pass
    
#     # Check if we've already tried to install it in this session
#     if os.path.exists("/tmp/flash_attn_installed"):
#         return
        
#     try:
#         print("Installing flash-attn with --no-build-isolation...")
#         subprocess.run([
#             sys.executable, "-m", "pip", "install", 
#             "flash-attn==2.7.3", "--no-build-isolation"
#         ], check=True)
        
#         # Uninstall apex if it exists
#         subprocess.run([
#             sys.executable, "-m", "pip", "uninstall", "apex", "-y"
#         ], check=False)  # Don't fail if apex isn't installed
        
#         # Mark as installed
#         with open("/tmp/flash_attn_installed", "w") as f:
#             f.write("installed")
            
#         print("flash-attn installation completed")
        
#     except subprocess.CalledProcessError as e:
#         print(f"Warning: Failed to install flash-attn: {e}")
#         # Continue anyway - the model might work without it

# Run setup once when the module is imported
# setup_flash_attention()

# Load model and processor
@gr.cache()
def load_model():
    """Load the musicgen model and processor"""
    processor = AutoProcessor.from_pretrained("facebook/musicgen-large")
    model = MusicgenForConditionalGeneration.from_pretrained(
                "facebook/musicgen-large",
                torch_dtype=torch.float16,
                device="cuda",
                mode="S",
                __paged=True,
            )
    return processor, model

def generate_music(text_prompt, duration=10, temperature=1.0, top_k=250, top_p=0.0):
    """Generate music based on text prompt"""
    try:
        processor, model = load_model()
        
        # Process the text prompt
        inputs = processor(
            text=[text_prompt],
            padding=True,
            return_tensors="pt",
        ).to("cuda")
        # Generate audio
        with torch.no_grad():
            audio_values = model.generate(
                **inputs,
                max_new_tokens=duration * 50,  # Approximate tokens per second
                do_sample=True,
                temperature=temperature,
                top_k=top_k,
                top_p=top_p,
                cache_implementation="paged"
            )
        
        audio_data = audio_values[0, 0].cpu().numpy().astype(np.float32)
        sample_rate = model.config.sample_rate
        
        # Normalize audio
        audio_data = audio_data / np.max(np.abs(audio_data))
        
        return sample_rate, audio_data
        
    except Exception as e:
        print(f"Error: {str(e)}")
        return None

# Create Gradio interface
with gr.Blocks(title="MusicGen Large - Music Generation") as demo:
    gr.Markdown("# 🎵 MusicGen Large Music Generator")
    gr.Markdown("Generate music from text descriptions using Facebook's MusicGen Large model.")
    
    with gr.Row():
        with gr.Column():
            text_input = gr.Textbox(
                label="Music Description",
                placeholder="Enter a description of the music you want to generate (e.g., 'upbeat jazz with piano and drums')",
                lines=3
            )
            
            with gr.Row():
                duration = gr.Slider(
                    minimum=5,
                    maximum=30,
                    value=10,
                    step=1,
                    label="Duration (seconds)"
                )
                temperature = gr.Slider(
                    minimum=0.1,
                    maximum=2.0,
                    value=1.0,
                    step=0.1,
                    label="Temperature (creativity)"
                )
            
            with gr.Row():
                top_k = gr.Slider(
                    minimum=1,
                    maximum=500,
                    value=250,
                    step=1,
                    label="Top-k"
                )
                top_p = gr.Slider(
                    minimum=0.0,
                    maximum=1.0,
                    value=0.0,
                    step=0.1,
                    label="Top-p"
                )
            
            generate_btn = gr.Button("🎵 Generate Music", variant="primary")
        
        with gr.Column():
            audio_output = gr.Audio(
                label="Generated Music",
                type="numpy"
            )
            
            gr.Markdown("### Tips:")
            gr.Markdown("""
            - Be specific in your descriptions (e.g., "slow blues guitar with harmonica")
            - Higher temperature = more creative/random results
            - Lower temperature = more predictable results
            - Duration is limited to 30 seconds for faster generation
            """)
    
    # Example prompts
    gr.Examples(
        examples=[
            ["upbeat jazz with piano and drums"],
            ["relaxing acoustic guitar melody"],
            ["electronic dance music with heavy bass"],
            ["classical violin concerto"],
            ["reggae with steel drums and bass"],
            ["rock ballad with electric guitar solo"],
            ["test example"],

        ],
        inputs=text_input,
        label="Example Prompts"
    )
    
    # Connect the generate button to the function
    generate_btn.click(
        fn=generate_music,
        inputs=[text_input, duration, temperature, top_k, top_p],
        outputs=audio_output
    )

if __name__ == "__main__":
    demo.launch()