HeyGem Open-Source Digital Human: A Comprehensive Guide from Local Deployment to API Integration
Project Overview
HeyGem is an open-source digital human solution developed by Silicon Intelligence, enabling rapid cloning of human appearances and voices through a 10-second video sample. Users can generate lip-synced broadcast videos by inputting text scripts or uploading audio files. The project offers local deployment and API integration modes to meet diverse development and enterprise needs.
Core Features Breakdown
1. Precision Cloning Technology
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Appearance Replication: Utilizes AI algorithms to capture facial contours and features, constructing high-precision 3D models -
Voice Cloning: Extracts vocal characteristics with adjustable parameters, achieving over 95% similarity to original voices
2. Multi-Modal Control System
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Text-driven: Converts scripts to natural speech via NLP -
Audio-driven: Analyzes rhythm and intonation for corresponding facial animations -
Multi-language Support: 8 languages including EN, ZH, JP, KO
3. Offline Video Synthesis
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Full local processing ensures data privacy -
Intelligent AV synchronization (<0.1s error) -
Supports batch processing and long-form video generation
Hardware Configuration Guide
Windows Requirements
| Component | Minimum | Recommended |
|---|---|---|
| CPU | i5-10400F | i5-13400F |
| RAM | 16GB DDR4 | 32GB DDR5 |
| GPU | RTX 3060 8G | RTX 4070 12G |
| Storage | 120GB SSD | 1TB NVMe SSD |
Ubuntu Special Requirements
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Requires NVIDIA Container Toolkit -
Kernel version ≥6.8.0-52-generic -
CUDA 12.0+ environment mandatory
Step-by-Step Deployment Tutorial
Windows Installation Process
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Pre-Installation Checks
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Verify ≥30GB free space on D drive -
Confirm NVIDIA driver version ≥535.98 -
Check WSL status: wsl --list --verbose
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Core Component Installation
# Install WSL subsystem wsl --install # Update Docker environment wsl --update -
Server Deployment
cd /deploy docker-compose up -d-
≈70GB data download required -
Initial startup takes ≈30 minutes
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Client Configuration
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Download latest installer from GitHub Releases -
Default storage path: D:\heygem_data
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Ubuntu Optimization Guide
# Configure NVIDIA runtime
sudo nvidia-ctk runtime configure --runtime=docker
# Launch Linux-specific image
docker-compose -f docker-compose-linux.yml up -d
API Development Documentation
Model Training Interface
POST http://127.0.0.1:18180/v1/preprocess_and_tran
{
"format": ".wav",
"reference_audio": "train_data/voice_sample.wav",
"lang": "zh"
}
Video Synthesis Workflow
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Audio Preprocessing
# Get ASR results response = requests.post(preprocess_url, json=voice_params) asr_audio = response.json()['asr_format_audio_url'] -
Video Generation
video_params = { "audio_url": "output/audio_final.wav", "video_url": "models/base_avatar.mp4", "code": str(uuid.uuid4()) } -
Progress Monitoring
GET http://127.0.0.1:8383/easy/query?code=3b6a5d8e-7c12-4feb
Performance Optimization Strategies
GPU Memory Management
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Use lite version: docker-compose -f docker-compose-lite.yml -
Reduce resolution: 1080p→720p saves 40% VRAM -
Maintain ≥5min intervals between batch jobs
RTX 5090 Optimization
cd /deploy
docker-compose -f docker-compose-5090.yml up -d
Commercial Applications
Enterprise Solutions
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E-commerce: 24/7 AI-powered live streaming -
Education: Multilingual tutorial generation -
Customer Service: Intelligent virtual agents
Licensing Terms
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Free tier: <100K users & <$10M annual revenue -
Commercial license: Customized service agreement
Developer Ecosystem
Open-Source Collaboration Program
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Tutorial incentives: $20-$50 for quality content -
Monthly MVP rewards: Blockchain-based digital badge -
Dev community: Scan QR to join core group
Troubleshooting Guide
Service Initialization Issues
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Verify Docker status: docker ps -a | grep heygem -
Check GPU drivers: nvidia-smi -
Review logs: Get-Content "D:\heygem_data\logs\service.log" -Tail 100
Video Rendering Optimization
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Lower resolution to 720p -
Close GPU-intensive applications -
Update to latest GPU drivers
Technical Architecture
Core Stack
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Speech Processing: FunASR + Fish-Speech -
Visual Engine: PyTorch3D + OpenCV -
Animation System: Progressive Growing GANs
Algorithm Performance
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Lip-sync accuracy: 92.7% -
Frame rendering: ≤35ms (RTX 4070) -
Audio compensation: ±80ms dynamic adjustment
Platform Integration
Coze Marketplace
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Pre-built plugin: Silicon Digital Human Plugin -
No-code workflow builder -
Multi-platform publishing support
Roadmap Updates
Development Timeline
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Mobile SDK (Q3 release) -
Real-time mode (≤500ms latency) -
200+ micro-expressions library
Contribution Guidelines
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Priority for PRs with test cases -
Major features require CLA signing -
Document translators get special badge
Learning Resources
Official Documentation
Community Tutorials
Project Repository: https://github.com/GuijiAI/HeyGem.ai
Business Inquiry: james@duix.com
License: MIT License