PaksaTalker: Advanced AI-Powered Talking Head Video Generation

PaksaTalker is an enterprise-grade AI framework for generating hyper-realistic talking head videos with perfect lip-sync, natural facial expressions, and life-like gestures. Built on cutting-edge AI research, it seamlessly integrates multiple state-of-the-art models to deliver production-ready video synthesis.

PaksaTalker is an advanced AI-powered platform that creates hyper-realistic talking avatars with synchronized facial expressions and natural body gestures. The system combines multiple state-of-the-art AI models including Qwen for language processing, SadTalker for facial animation, and PantoMatrix/EMAGE for full-body gesture generation, delivering production-ready video synthesis with unprecedented realism.

🌟 Key Features

🎭 Natural Animation

• Precise Lip-Sync: Frame-accurate audio-visual synchronization
• Expressive Faces: Emotionally aware facial animations
• Natural Gestures: Context-appropriate head movements and expressions
• High Fidelity: 4K resolution support with minimal artifacts

🛠️ Technical Capabilities

• Multi-model architecture (SadTalker, Wav2Lip, Qwen)
• GPU-accelerated processing
• Batch processing support
• Real-time preview
• RESTful API for easy integration

🧩 Extensible Architecture

• Modular design for easy model swapping

• Plugin system for custom integrations

• Support for custom voice models

• Multi-language support

• Precise Lip-Sync: Frame-accurate audio-visual synchronization using SadTalker

• Expressive Faces: Emotionally aware facial animations with micro-expressions

• Full-Body Gestures: Context-appropriate body language and hand movements

• High Fidelity: 4K resolution support with DSLR-quality rendering

🛠️ Technical Capabilities

• Multi-Model Architecture: Integrates Qwen LLM, SadTalker, and PantoMatrix
• GPU-Accelerated: Optimized for NVIDIA GPUs with CUDA support
• Modular Design: Swappable components for customization
• High-Quality Output: 1080p+ resolution with advanced rendering
• RESTful API: Easy integration with existing systems

🧩 Extensible Architecture

• Modular Pipeline: Independent components for face, body, and voice
• Custom Avatars: Support for 3D models and 2D images
• Plugin System: Extend with custom models and effects
• Multi-Language: Support for multiple languages and accents
• Customization: Fine-tune animation styles and rendering parameters

🚀 Getting Started

Prerequisites

• Python 3.8+

• CUDA 11.3+ (for GPU acceleration)

• ffmpeg 4.4+

• 8GB+ VRAM recommended

• Python 3.9+ with pip

• Node.js 16+ and npm 8+ (for web interface)

• CUDA 11.8+ (for GPU acceleration)

• ffmpeg 4.4+ for video processing

• NVIDIA GPU with 16GB+ VRAM recommended

• Docker (optional, for containerized deployment

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/paksatalker.git
   cd paksatalker

2. Set up Python environment:

   # Create and activate virtual environment
   python -m venv venv
   # On Windows:
   .\venv\Scripts\activate
   # On macOS/Linux:
   # source venv/bin/activate
   
   # Install Python dependencies
   pip install -r requirements.txt

3. Install AI Models:

   # Download SadTalker models
   python -c "from models.sadtalker import download_models; download_models()"
   
   # Download PantoMatrix/EMAGE models
   python -c "from models.gesture import download_models; download_models()"
   
   # Download Qwen model weights (optional, can use API)
   # python -c "from models.qwen import download_models; download_models()"

4. Set up frontend (for web interface):

   cd frontend
   npm install
   npm run build
   cd ..
   

🖥️ Quick Start

Command Line Interface

# Basic usage
python -m PaksaTalker.cli \
    --image input/face.jpg \
    --audio input/speech.wav \
    --output output/result.mp4 \
    --enhance_face True \
    --expression_intensity 0.8

# Advanced options
python -m PaksaTalker.cli \
    --image input/face.jpg \
    --audio input/speech.wav \
    --output output/result.mp4 \
    --resolution 1080 \
    --fps 30 \
    --background blur \
    --gesture_level medium

Python API

from PaksaTalker import PaksaTalker

### Generate a Talking Avatar from Text

```bash
# Generate speech and animate avatar from text
python -m cli.generate \
    --text "Hello, I'm your AI assistant. Welcome to PaksaTalker!" \
    --image assets/avatars/default.jpg \
    --voice en-US-JennyNeural \
    --output output/welcome.mp4 \
    --gesture-style natural \
    --resolution 1080

Animate with Custom Audio

# Animate avatar with existing audio
python -m cli.animate \
    --image assets/avatars/presenter.jpg \
    --audio input/presentation.wav \
    --output output/presentation.mp4 \
    --expression excited \
    --background blur \
    --lighting studio

Advanced Options

# Full pipeline with custom settings
python -m cli.pipeline \
    --prompt "Explain quantum computing in simple terms" \
    --avatar assets/avatars/scientist.jpg \
    --voice en-US-ChristopherNeural \
    --style professional \
    --gesture-level high \
    --output output/quantum_explainer.mp4 \
    --resolution 4k \
    --fps 30 \
    --enhance-face \
    --background office

🐍 Python API

Basic Usage

from paksatalker import Pipeline

# Initialize the pipeline
pipeline = Pipeline(
    model_dir="models",
    device="cuda"  # or "cpu" if no GPU
)

# Generate a talking avatar video
result = pipeline.generate(
    text="Welcome to PaksaTalker, the future of digital avatars.",
    image_path="assets/avatars/host.jpg",
    voice="en-US-JennyNeural",
    output_path="output/welcome.mp4",
    gesture_style="casual",
    resolution=1080
)

print(f"Video generated at: {result['output_path']}")

Advanced Usage

from paksatalker import (
    TextToSpeech,
    FaceAnimator,
    GestureGenerator,
    VideoRenderer
)

# Initialize components
tts = TextToSpeech(voice="en-US-ChristopherNeural")
animator = FaceAnimator(model_path="models/sadtalker")
gesture = GestureGenerator(model_path="models/pantomatrix")
renderer = VideoRenderer(resolution=1080, fps=30)

# Process pipeline
text = "Let me show you how this works..."
audio = tts.generate(text)
face_animation = animator.animate("assets/avatars/assistant.jpg", audio)
body_animation = gesture.generate(audio, style="presentation")

# Render final video
video = renderer.combine(
    face_animation=face_animation,
    body_animation=body_animation,
    audio=audio,
    output_path="output/demo.mp4"
)

from pathlib import Path

Initialize with custom settings

pt = PaksaTalker( device="cuda", # or "cpu" model_dir="models/", temp_dir="temp/" )

Generate video with enhanced settings

result = pt.generate( image_path="input/face.jpg", audio_path="input/speech.wav", output_path="output/result.mp4", config={ "resolution": 1080, "fps": 30, "expression_scale": 0.9, "head_pose": "natural", "background": { "type": "blur", "blur_strength": 0.7 }, "post_processing": { "denoise": True, "color_correction": True, "stabilization": True } } )

## 🏗️ Architecture

PaksaTalker/ ├── api/ # REST API endpoints │ ├── routes/ # API route definitions │ ├── schemas/ # Pydantic models │ └── utils/ # API utilities │ ├── config/ # Configuration management │ ├── init.py │ └── config.py │ ├── core/ # Core functionality │ ├── engine.py # Main processing pipeline │ ├── video.py # Video processing │ └── audio.py # Audio processing │ ├── integrations/ # Model integrations │ ├── sadtalker/ # SadTalker implementation │ ├── wav2lip/ # Wav2Lip integration │ ├── qwen/ # Qwen language model │ └── gesture/ # Gesture generation │ ├── models/ # Model architectures │ ├── base.py # Base model interface │ └── registry.py # Model registry │ ├── static/ # Static files │ ├── css/ │ ├── js/ │ └── templates/ │ ├── tests/ # Test suite │ ├── unit/ │ └── integration/ │ ├── utils/ # Utility functions │ ├── audio_utils.py │ ├── video_utils.py │ └── face_utils.py │ ├── app.py # Main application ├── cli.py # Command-line interface └── requirements.txt # Dependencies

🏃‍♂️ Usage

Development Mode

1. Start the development servers:

   # In the project root directory
   python run_dev.py

   This will start:
   • Frontend at http://localhost:5173
   • Backend API at http://localhost:8000
   • API Docs at http://localhost:8000/api/docs

Production Build

1. Build the frontend:

   cd frontend
   npm run build
   cd ..

2. Start the production server:

   uvicorn app:app --host 0.0.0.0 --port 8000

   The application will be available at http://localhost:8000

Command Line (Direct API)

python app.py --input "Hello world" --output output/video.mp4

🔧 Configuration

PaksaTalker is highly configurable. Here's an example configuration:

# config/config.yaml
models:
  sadtalker:
    checkpoint: "models/sadtalker/checkpoints"
    config: "models/sadtalker/configs"

  wav2lip:
    checkpoint: "models/wav2lip/checkpoints"

  qwen:
    model_name: "Qwen/Qwen-7B-Chat"

processing:
  resolution: 1080
  fps: 30
  batch_size: 4
  device: "cuda"

api:
  host: "0.0.0.0"
  port: 8000
  workers: 4
  debug: false

🧪 Testing

Run the test suite:

# Install test dependencies
pip install -r requirements-test.txt

# Run tests
pytest tests/

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more information.

📚 Documentation

For detailed documentation, please visit our Documentation.

Project Structure

paksatalker/
├── frontend/           # React + TypeScript frontend
│   ├── src/            # Source files
│   ├── public/         # Static files
│   └── package.json    # Frontend dependencies
├── api/                # API endpoints
├── config/             # Configuration files
├── models/             # AI models
├── static/             # Static files (served by FastAPI)
├── app.py              # Main application entry point
├── requirements.txt    # Python dependencies
└── README.md           # This file

Environment Variables

Create a .env file in the project root with the following variables:

# Backend
DEBUG=True
PORT=8000

# Database
DATABASE_URL=sqlite:///./paksatalker.db

# JWT
SECRET_KEY=your-secret-key
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

For development, you can also create a .env.development file in the frontend directory.

API Documentation

Once the server is running, visit /api/docs for interactive API documentation (Swagger UI).

For detailed documentation, please visit our documentation website.

📧 Contact

Project Link: https://github.com/yourusername/paksatalker

🙏 Acknowledgments

• SadTalker - For the amazing talking head generation
• Wav2Lip - For lip-sync technology
• Qwen - For advanced language modeling
• All contributors and open-source maintainers who made this project possible

Quick Start (Stable Server)

Run the bundled stable server with fallbacks and background asset prefetch:

python stable_server.py
# API: http://localhost:8000
# Swagger UI: http://localhost:8000/api/docs

Optionally force asset ensure:

curl -X POST http://localhost:8000/api/v1/assets/ensure

Fusion Background & Green‑Screen

POST /api/v1/generate/fusion-video supports optional background parameters:

• backgroundMode: none|blur|portrait|cinematic|color|image|greenscreen
• backgroundColor: hex (for color/greenscreen)
• backgroundImage: file (for image/greenscreen)
• chromaColor, similarity, blend: green‑screen chroma key tuning

AI Style Suggestions (MVP)

Suggest presets matching your context:

curl -X POST http://localhost:8000/api/v1/style-presets/suggest \
  -F prompt="energetic keynote" -F cultural_context=GLOBAL -F formality=0.7