🎭 AvatarAI — Real-Time AI Avatar Platform

Upload a photo · Clone a voice · Talk to any face in real time

Quick Start · Features · Architecture · GPU / AWS Deploy · API · Roadmap

The most complete open-source AI avatar / digital human system. Real-time talking-head lip-sync · Zero-shot voice cloning · Multi-LLM · Runs 100% locally or on AWS.

🎬 What is AvatarAI?

AvatarAI is an open-source, production-ready platform for building photorealistic AI avatar conversations. Upload any face photo, clone a voice from a 5-second audio clip, and have a real-time conversation — with lip-sync video generated on every single response.

hljs language-css

[mic] → Whisper STT → Claude / GPT / Ollama (streaming) → Chatterbox TTS → MuseTalk lip-sync → [video]
                              < 2–4 s to first video chunk on AWS GPU >

What makes AvatarAI different:

🎤 Zero-shot voice cloning — 10 seconds of audio is all you need (Chatterbox Multilingual)
🎭 Any face, any language — upload a JPEG, pick from 23 languages, start talking
⚡ Token-streaming pipeline — the LLM streams live tokens while TTS + lip-sync run per sentence; the first video chunk plays before the model finishes its reply
✋ Barge-in — speak (or hit stop) mid-reply and the avatar yields instantly, like a real conversation
🔒 100% local mode — local storage, local Whisper, local LLM via Ollama: nothing leaves your machine
🔌 Multi-LLM — Claude (with prompt caching), GPT-4o, or any local model via Ollama / vLLM / LM Studio
🚀 AWS GPU deployment — one-command deploy to g5.xlarge for true real-time (~30 FPS)
🏗️ Production-grade — JWT + httpOnly-cookie auth, per-user rate limiting, Postgres + Alembic, S3/CloudFront, Prometheus, CI, a real test suite — the only project in this niche you can ship as a product, not just a demo

⚔️ How AvatarAI compares

	AvatarAI	Duix-Avatar	Linly-Talker	AIAvatarKit
Real-time conversation	✅ WebSocket streaming	❌ offline video gen	✅ (Gradio / WebRTC spin-off)	✅
Lip-sync video	✅ MuseTalk V1.5	✅ proprietary models	✅ multiple engines	❌ (drives external avatars)
Voice cloning	✅ 10 s, 23 languages	✅	✅	❌
Barge-in / interruption	✅	❌	✅ (stream variant)	✅
Local / free LLM	✅ Ollama, vLLM	❌	✅	✅
Web app with auth & history	✅ Next.js + JWT + Postgres	❌ Windows client	❌ Gradio demo UI	❌ library
Rate limiting, CI, tests, IaC	✅	❌	❌	❌
License	MIT	custom	MIT	Apache-2.0

Toolkits like Linly-Talker are great research playgrounds; Duix ships a Windows product. AvatarAI is the one you can deploy as a real multi-user web service.

✨ Features

Category	Details
🤖 LLM Backends	Claude (prompt-cached) · GPT-4o · Ollama / vLLM / LM Studio (local, free)
🎤 Voice Cloning	Record 10–60 s → Chatterbox Multilingual zero-shot cloning
🗣️ Speech-to-Text	Whisper (`faster-whisper`, CUDA), decodes browser WebM natively
🎬 Lip-Sync Video	MuseTalk V1.5 persistent worker (30 FPS on GPU) · FFmpeg fallback (CPU)
⚡ Streaming Pipeline	Live LLM tokens + per-sentence video chunks over WebSocket
✋ Barge-In	Speak or hit stop mid-reply — in-flight turn cancels in ms
🔉 TTS Fallback Chain	chatterbox → edge-tts (free neural voices) → gTTS — never silent
😊 Emotion Detection	Live emotion badges per message
🌍 23 Languages	Whisper multilingual STT + Chatterbox multilingual TTS
🏠 Local-First Storage	`USE_LOCAL_STORAGE=true` — no AWS needed for dev
🔐 Auth & Sessions	JWT authentication, conversation history, persistent sessions
📊 Observability	Prometheus · Celery Flower · Sentry · structured logging
🧪 Tested	Full pytest suite — users, avatars, sessions, health checks
🚀 AWS GPU Deploy	One-command `g5.xlarge` deploy with CUDA 11.8 + float16

🏗️ Architecture

hljs language-scss

┌──────────────────────────────────────────────────────────────┐
│                        Browser / Client                       │
│  ┌─────────────┐  ┌──────────────┐  ┌──────────────────────┐ │
│  │Avatar Studio│  │ Voice Studio │  │   Chat Interface     │ │
│  │  (upload)   │  │  (cloning)   │  │ Idle anim + chunks   │ │
│  └──────┬──────┘  └──────┬───────┘  └──────────┬───────────┘ │
└─────────┼───────────────┼─────────────────────┼─────────────┘
          │ REST           │ REST                │ WebSocket
          ▼                ▼                     ▼
┌──────────────────────────────────────────────────────────────┐
│                       FastAPI Backend                         │
│  ┌──────────────────────────────────────────────────────┐    │
│  │                  WebSocket Manager                    │    │
│  │  split sentences → TTS → MuseTalk → stream chunks    │    │
│  └──────────────────────────────────────────────────────┘    │
│  ┌──────────┐ ┌───────────┐ ┌──────────┐ ┌───────────────┐  │
│  │  Whisper │ │Claude/GPT │ │ XTTS v2  │ │  MuseTalk     │  │
│  │   STT    │ │  / Llama  │ │   TTS    │ │  (GPU/CPU)    │  │
│  └──────────┘ └───────────┘ └──────────┘ └───────────────┘  │
│  ┌──────────┐ ┌──────────┐  ┌──────────┐ ┌───────────────┐  │
│  │PostgreSQL│ │  Redis   │  │  Celery  │ │ Local FS / S3 │  │
│  └──────────┘ └──────────┘  └──────────┘ └───────────────┘  │
└──────────────────────────────────────────────────────────────┘

Real-Time Data Flow (one conversation turn)

hljs language-css

[User types / speaks]
        │
        ▼
  Whisper STT ─────────────────► transcript
        │
        ▼
  Claude / GPT / Llama ────────► full response text
        │
        ▼
  Split into sentences ────────► ["Hello!", "How are you?", ...]
        │
        ├── sentence 1 → XTTS → MuseTalk → video_chunk WS → browser plays
        ├── sentence 2 → XTTS → MuseTalk → video_chunk WS → queued
        └── sentence N → XTTS → MuseTalk → video_chunk WS → queued

📁 Project Structure

hljs language-bash

ai-avatar-system/
├── backend/                    # FastAPI application
│   ├── app/
│   │   ├── api/v1/             # REST endpoints (users, avatars, sessions, messages)
│   │   ├── services/           # Core services (LLM, TTS, STT, animator, storage)
│   │   ├── models/             # SQLAlchemy DB models
│   │   └── websocket.py        # Real-time WebSocket handler + sentence streaming
│   ├── alembic/                # Database migrations
│   ├── models/MuseTalk/        # MuseTalk V1.5 (lip-sync engine)
│   │   └── scripts/
│   │       └── musetalk_worker.py  # Persistent worker (models loaded once)
│   ├── tests/                  # pytest suite
│   ├── Dockerfile              # CUDA 11.8 base image
│   └── requirements.txt
├── frontend/                   # Next.js 14 application
│   ├── app/                    # App Router pages
│   ├── components/             # React components (ChatInterface, IdleAvatar, etc.)
│   ├── lib/api.ts              # Axios API client
│   └── store/                  # Zustand global state
├── nginx/
│   └── nginx.conf              # Reverse proxy (HTTP → backend/frontend, WebSocket)
├── infrastructure/
│   ├── main.tf                 # AWS Terraform (ECS, RDS, ElastiCache, S3, CloudFront)
│   └── variables.tf
├── scripts/
│   ├── setup_musetalk.sh       # Download MuseTalk models (~9 GB)
│   └── deploy-aws.sh           # One-command EC2 GPU deployment
├── docker-compose.yml          # Development (CPU) — all services
├── docker-compose.prod.yml     # Production overrides (GPU, no bind mounts, logging)
├── deploy.sh                   # ECR push + Terraform deploy (ECS path)
├── .env.example                # Development env template
└── .env.prod.example           # Production env template

🚀 Quick Start

Prerequisites

Docker & Docker Compose v2+ (recommended)
OR: Python 3.10+, Node.js 18+, FFmpeg, PostgreSQL, Redis

Option A — Docker / CPU (development)

hljs language-bash

git clone https://github.com/PunithVT/ai-avatar-system.git
cd ai-avatar-system
cp .env.example .env          # add your ANTHROPIC_API_KEY (or OPENAI_API_KEY)
docker compose up -d

Service	URL
🖥️ Frontend	http://localhost:3000
⚙️ Backend API	http://localhost:8000
📖 Swagger Docs	http://localhost:8000/docs
🌸 Celery Flower	http://localhost:5555

No AWS required. Set USE_LOCAL_STORAGE=true (default) — uploads saved to backend/uploads/.

Want something to talk to immediately? Seed three ready-made demo avatars (AI-generated faces + personalities):

hljs language-bash

backend/venv/bin/python scripts/seed_demo.py            # or any python with `requests`
backend/venv/bin/python scripts/seed_demo.py --with-voices   # + cloned demo voices

Prebuilt images are also published on every release — ghcr.io/punithvt/ai-avatar-system-backend and …-frontend.

Option B — Manual (development)

hljs language-bash

# Backend
cd backend
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
cp ../.env.example ../.env
alembic upgrade head
uvicorn main:app --reload --port 8000

# Frontend (new terminal)
cd frontend
npm install
npm run dev

Option C — Enable MuseTalk Lip-Sync

hljs language-bash

# Download models (~9 GB, one-time)
bash scripts/setup_musetalk.sh

# Set in .env
AVATAR_ENGINE=musetalk

# Restart
docker compose restart backend

🚀 GPU & AWS Deployment

MuseTalk achieves 30 FPS at 256×256 on a V100-class GPU (source: MuseTalk paper). On CPU it is 30–50× slower. Deploying on AWS gets you genuine real-time performance.

Recommended Instance

Instance	GPU	VRAM	Spot $/hr	MuseTalk FPS
`g4dn.xlarge`	T4	16 GB	~$0.16	~15–20 FPS
`g5.xlarge`	A10G	24 GB	~$0.30	~30 FPS ✓
`g6.xlarge`	L4	24 GB	~$0.24	~30 FPS ✓

Recommended: g5.xlarge Spot (~$72/mo at 8 hrs/day).

One-Command EC2 Deploy

hljs language-bash

# 1. Launch g5.xlarge with Ubuntu 22.04 LTS, SSH in, then:
bash <(curl -fsSL https://raw.githubusercontent.com/PunithVT/ai-avatar-system/main/scripts/deploy-aws.sh)

# 2. Fill in API keys:
nano /opt/ai-avatar-system/.env.prod

# 3. Redeploy with your keys:
bash /opt/ai-avatar-system/scripts/deploy-aws.sh --update

The script automatically:

Installs Docker + nvidia-docker2
Verifies GPU is accessible
Downloads MuseTalk models (~9 GB)
Starts all services with GPU passthrough + float16 (2× faster via Tensor Cores)

Manual Production Docker

hljs language-bash

cp .env.prod.example .env.prod   # fill in your values
docker compose -f docker-compose.yml -f docker-compose.prod.yml up -d

What docker-compose.prod.yml adds over development:

GPU reservation (nvidia driver, count=1) for backend + celery-worker
float16 inference enabled automatically on CUDA → ~2× speedup
Persistent musetalk_models volume (survive container restarts)
No source-code bind mounts (runs from built image)
Log rotation (100 MB max, 5 files)
Flower disabled (security)

Verify GPU is Working

hljs language-bash

# Check GPU is visible in container
docker exec avatar-backend python -c "
import torch
print('CUDA:', torch.cuda.is_available())
print('GPU:', torch.cuda.get_device_name(0))
print('VRAM:', round(torch.cuda.get_device_properties(0).total_memory/1024**3,1), 'GB')
"

# Expected on g5.xlarge:
# CUDA: True
# GPU: NVIDIA A10G
# VRAM: 24.0 GB

# Live GPU utilisation
docker exec avatar-backend nvidia-smi

AWS Terraform (ECS Path)

For a fully managed ECS deployment with RDS + ElastiCache + CloudFront:

hljs language-bash

cd infrastructure
terraform init
terraform apply -var="environment=production"
bash deploy.sh production

🎤 Voice Cloning

Go to Voice tab → Clone Voice
Record 10–60 s of clear speech (or upload a WAV/MP3/WebM)
Name it → Clone → select it for your session

Every TTS response then uses your cloned voice.

hljs language-bash

# REST API
curl -X POST http://localhost:8000/api/v1/voices/clone \
  -F "audio=@my_voice.wav" -F "name=My Voice" -F "language=en"

📡 API Reference

Authentication

hljs language-bash

POST /api/v1/users/register   { "email": "...", "username": "...", "password": "..." }
POST /api/v1/users/login      form: username=... password=...   → { "access_token": "..." }

# All protected routes:
Authorization: Bearer <access_token>

Avatars

hljs language-bash

POST   /api/v1/avatars/upload        Upload photo (multipart: file + name)
GET    /api/v1/avatars/              List avatars
DELETE /api/v1/avatars/{id}          Delete avatar
PUT    /api/v1/avatars/{id}/voice    Assign voice to avatar

Sessions & Messages

hljs language-bash

POST   /api/v1/sessions/create       { "avatar_id": "..." }
POST   /api/v1/sessions/{id}/end
GET    /api/v1/messages/session/{id}

WebSocket

hljs language-bash

WS  /ws/session/{session_id}

Client → Server:

hljs language-json

{ "type": "text",         "text": "Hello!" }
{ "type": "audio",        "audio": "<base64-webm>" }
{ "type": "stop" }                                  // barge-in: cancel the in-flight reply
{ "type": "set_voice",    "voice_id": "<uuid>" }    // attach a cloned voice (owner-checked)
{ "type": "set_language", "language": "es" }
{ "type": "ping" }

Server → Client:

hljs language-json

{ "type": "token",            "token": "Hel" }       // live LLM stream
{ "type": "transcription",    "text": "Hello!" }
{ "type": "message",          "content": "Hi!", "role": "assistant" }
{ "type": "video_chunk_start","total_chunks": -1 }   // -1 = streaming, total unknown
{ "type": "video_chunk",      "chunk_index": 0, "video_url": "...", "text": "Hi!" }
{ "type": "video_chunk_end",  "sent_chunks": 3 }
{ "type": "status",           "message": "Animating…", "stage": "animation" }
{ "type": "tts_fallback",     "engine": "edge-tts", "voice_cloned": false, "message": "…" }
{ "type": "interrupted",      "message": "Previous response interrupted" }
{ "type": "error",            "message": "Something went wrong" }

⚙️ Configuration

Key .env variables:

hljs language-bash

# LLM
LLM_PROVIDER=anthropic            # anthropic | openai | ollama (local & free)
LLM_MODEL=claude-sonnet-4-6       # or gpt-4o · llama3.1 · qwen2.5 …
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_BASE_URL=                  # e.g. http://localhost:11434/v1 for Ollama / vLLM / LM Studio

# Avatar engine
AVATAR_ENGINE=musetalk            # musetalk (GPU recommended) | simple (CPU fallback)
MUSETALK_PATH=models/MuseTalk

# TTS — automatic fallback chain: chatterbox → edge-tts → gtts
TTS_PROVIDER=chatterbox

# STT
WHISPER_MODEL=large-v3-turbo      # tiny | base | small | medium | large-v3 | large-v3-turbo

# Storage
USE_LOCAL_STORAGE=true            # false → AWS S3 (+ presigned URLs / CloudFront)
S3_BUCKET_NAME=...

# Auth (≥32 chars enforced at boot)
SECRET_KEY=$(python -c "import secrets; print(secrets.token_hex(32))")
JWT_SECRET_KEY=$(python -c "import secrets; print(secrets.token_hex(32))")
JWT_EXPIRATION_HOURS=24

🛠️ Tech Stack

Frontend

Library	Purpose
Next.js 14 + React 18	App framework
TypeScript 5	Type safety
Tailwind CSS	Styling
Zustand	Global state

Backend

Library	Purpose
FastAPI	Async REST API + WebSocket
SQLAlchemy 2 (async)	ORM with asyncpg
PostgreSQL 15	Primary database
Alembic	Migrations
Redis 7	Cache + Celery broker
Celery	Background tasks

AI / ML

Model	Purpose
Claude / GPT-4o / Ollama (local)	LLM conversation
Whisper (`faster-whisper`)	Speech-to-text
Chatterbox Multilingual (Resemble AI)	TTS + zero-shot voice cloning, 23 languages
Edge TTS → gTTS	Free no-GPU fallback voices
MuseTalk V1.5	Photorealistic lip-sync (30 FPS on GPU)

🧪 Running Tests

hljs language-bash

cd backend
pytest -v                           # all tests
pytest tests/test_health.py         # single module
pytest --cov=app --cov-report=html  # HTML coverage

📰 What's New

2026-06 — Edge-TTS neural fallback chain · local LLMs via Ollama/vLLM · demo avatar seeding · prebuilt GHCR images · cascade-delete + WebM-STT + 429 fixes · SEO/metadata pass
2026-05 — httpOnly-cookie auth (XSS-safe) · conversation resume from history · end-to-end WebSocket tests · perf indexes (migration 0002)
2026-03 — Chatterbox Multilingual replaces XTTS v2 (23 languages) · MuseTalk persistent worker (models load once) · barge-in interruption · live token streaming

🗺️ Roadmap

❓ FAQ

Do I need a GPU?

No — everything runs on CPU. MuseTalk takes 30–90 s/sentence on CPU (the simple engine is instant). For real-time lip-sync, use an AWS g5.xlarge (~$0.30/hr spot) or any 16 GB+ NVIDIA card.

Can I run it with no API key, fully offline?

Yes — set LLM_PROVIDER=ollama, run Ollama (ollama run llama3.1), and you have a fully local, free conversation stack: Whisper STT, local LLM, Chatterbox TTS, MuseTalk video.

How do I get something to talk to quickly?

Run python scripts/seed_demo.py — it creates three demo avatars (AI-generated faces, distinct personalities) and optionally cloned demo voices with --with-voices.

How do I get MuseTalk models?

Run bash scripts/setup_musetalk.sh — downloads ~9 GB of models automatically.

Why does the first response take longer?

The MuseTalk persistent worker loads all models into GPU VRAM on the first request (~60 s on GPU, ~5 min on CPU). Subsequent requests reuse the loaded models.

What happens if the TTS model can't load?

The pipeline degrades gracefully: chatterbox → edge-tts (free Microsoft neural voices) → gTTS. The UI shows a one-time notice when a cloned voice couldn't be applied.

What avatar photo works best?

A clear, well-lit frontal face photo (JPEG/PNG/WebP). Avoid sunglasses or heavy occlusion.

🤝 Contributing

Contributions welcome! Read CONTRIBUTING.md before opening a PR.

hljs language-bash

git clone https://github.com/PunithVT/ai-avatar-system.git
git checkout -b feat/my-feature
# make changes + tests
git commit -m "feat(backend): add my feature"
git push origin feat/my-feature

📄 License

If AvatarAI saves you time or inspires your project, please ⭐ star the repo.

_{Built with FastAPI · Next.js · MuseTalk V1.5 · Chatterbox · Whisper · Claude AI}

ai-avatar-system

🎭 AvatarAI — Real-Time AI Avatar Platform

🎬 What is AvatarAI?

⚔️ How AvatarAI compares

✨ Features

🏗️ Architecture

Real-Time Data Flow (one conversation turn)

📁 Project Structure

🚀 Quick Start

Prerequisites

Option A — Docker / CPU (development)

Option B — Manual (development)

Option C — Enable MuseTalk Lip-Sync

🚀 GPU & AWS Deployment

Recommended Instance

One-Command EC2 Deploy

Manual Production Docker

Verify GPU is Working

AWS Terraform (ECS Path)

🎤 Voice Cloning

📡 API Reference

Authentication

Avatars

Sessions & Messages

WebSocket

⚙️ Configuration

🛠️ Tech Stack

Frontend

Backend

AI / ML

🧪 Running Tests

📰 What's New

🗺️ Roadmap

❓ FAQ

🤝 Contributing

📄 License

Similar Packages

Tags

Original Author

Publisher