import os import tempfile import gradio as gr import torch import torchaudio from loguru import logger from typing import Optional, Tuple import random import numpy as np import requests import json # Simplified working version without loading large models def create_demo_audio(video_file, text_prompt: str, duration: float = 5.0) -> str: """Create a simple demo audio file""" sample_rate = 48000 duration_samples = int(duration * sample_rate) # Generate a simple tone as demo t = torch.linspace(0, duration, duration_samples) frequency = 440 # A note audio = 0.3 * torch.sin(2 * 3.14159 * frequency * t) # Add some variation based on text prompt length if text_prompt: freq_mod = len(text_prompt) * 10 audio += 0.1 * torch.sin(2 * 3.14159 * freq_mod * t) # Save to temporary file temp_dir = tempfile.mkdtemp() audio_path = os.path.join(temp_dir, "demo_audio.wav") torchaudio.save(audio_path, audio.unsqueeze(0), sample_rate) return audio_path def process_video_demo(video_file, text_prompt: str, guidance_scale: float, inference_steps: int, sample_nums: int) -> Tuple[list, str]: """Working demo version that generates simple audio""" if video_file is None: return [], "❌ Please upload a video file!" if text_prompt is None: text_prompt = "" try: logger.info(f"Processing video in demo mode: {video_file}") logger.info(f"Text prompt: {text_prompt}") # Generate simple demo audio video_outputs = [] for i in range(min(sample_nums, 3)): # Limit to 3 samples demo_audio = create_demo_audio(video_file, f"{text_prompt}_sample_{i+1}") # For demo, just return the audio file path # In a real implementation, this would be merged with video video_outputs.append(demo_audio) success_msg = f"""✅ Demo Generation Complete! 📹 **Processed**: {os.path.basename(video_file) if hasattr(video_file, 'name') else 'Video file'} 📝 **Prompt**: "{text_prompt}" ⚙️ **Settings**: CFG={guidance_scale}, Steps={inference_steps}, Samples={sample_nums} 🎵 **Generated**: {len(video_outputs)} demo audio sample(s) ⚠️ **Note**: This is a working demo with synthetic audio. For real AI-generated Foley audio, run locally with the full model: https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley""" return video_outputs, success_msg except Exception as e: logger.error(f"Demo processing failed: {str(e)}") return [], f"❌ Demo processing failed: {str(e)}" def create_working_interface(): """Create a working Gradio interface""" css = """ .gradio-container { font-family: 'Inter', sans-serif; background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%); } .main-header { text-align: center; padding: 2rem; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 20px; margin-bottom: 2rem; color: white; } .demo-notice { background: #e8f4fd; border: 2px solid #1890ff; border-radius: 10px; padding: 1rem; margin: 1rem 0; color: #0050b3; } """ with gr.Blocks(css=css, title="HunyuanVideo-Foley Demo") as app: # Header with gr.Column(elem_classes=["main-header"]): gr.HTML("""

🎵 HunyuanVideo-Foley

Working Demo Version

""") # Demo Notice gr.HTML("""
🎯 Working Demo: This version generates synthetic audio to demonstrate the interface. Upload a video and try the controls to see how it works!
For real AI audio: Visit the original repository
""") with gr.Row(): # Input Section with gr.Column(scale=1): gr.Markdown("### 📹 Video Input") video_input = gr.Video( label="Upload Video", info="Upload any video file to test the interface" ) text_input = gr.Textbox( label="🎯 Audio Description", placeholder="Describe the audio you want (affects demo tone)", lines=3 ) with gr.Row(): guidance_scale = gr.Slider( minimum=1.0, maximum=10.0, value=4.0, step=0.1, label="🎚️ CFG Scale" ) inference_steps = gr.Slider( minimum=10, maximum=100, value=50, step=5, label="⚡ Steps" ) sample_nums = gr.Slider( minimum=1, maximum=3, value=1, step=1, label="🎲 Samples" ) generate_btn = gr.Button("🎵 Generate Demo Audio", variant="primary") # Output Section with gr.Column(scale=1): gr.Markdown("### 🎵 Generated Audio") audio_output_1 = gr.Audio(label="Sample 1", visible=True) audio_output_2 = gr.Audio(label="Sample 2", visible=False) audio_output_3 = gr.Audio(label="Sample 3", visible=False) status_output = gr.Textbox( label="Status", interactive=False, lines=6 ) # Event handlers def update_visibility(sample_nums): return [ gr.update(visible=True), # Sample 1 always visible gr.update(visible=sample_nums >= 2), gr.update(visible=sample_nums >= 3) ] def process_demo(video_file, text_prompt, guidance_scale, inference_steps, sample_nums): audio_files, status_msg = process_video_demo( video_file, text_prompt, guidance_scale, inference_steps, int(sample_nums) ) # Prepare outputs outputs = [None, None, None] for i, audio_file in enumerate(audio_files[:3]): outputs[i] = audio_file return outputs[0], outputs[1], outputs[2], status_msg # Connect events sample_nums.change( fn=update_visibility, inputs=[sample_nums], outputs=[audio_output_1, audio_output_2, audio_output_3] ) generate_btn.click( fn=process_demo, inputs=[video_input, text_input, guidance_scale, inference_steps, sample_nums], outputs=[audio_output_1, audio_output_2, audio_output_3, status_output] ) # Footer gr.HTML("""

🎭 Demo Version: Generates synthetic audio for interface demonstration

🚀 Full Version: GitHub Repository

""") return app if __name__ == "__main__": # Setup logging logger.remove() logger.add(lambda msg: print(msg, end=''), level="INFO") logger.info("Starting HunyuanVideo-Foley Working Demo...") # Create and launch app app = create_working_interface() logger.info("Demo app ready - will generate synthetic audio for testing") app.launch( server_name="0.0.0.0", server_port=7860, share=False, debug=False, show_error=True )