SceneLine: AI-Powered Dubbing Practice Platform 🎬

I recently released a new project called SceneLine — an AI-powered dubbing practice platform that enables language learners to practice dubbing through immersive film/TV scene dialogues.

Why I Built This

The hardest part of language learning is often not vocabulary or grammar, but language sense — that natural, authentic way of expression. Traditional listening and reading exercises struggle to develop this, but dubbing practice is a great solution:

• Immersive scenarios — Real movie/TV dialogues, not textbook sentences
• Multi-character interactions — Different tones, emotions, and pacing
• Instant feedback — Know whether your pronunciation is accurate

But traditional dubbing practice has a pain point: no feedback. You say the lines along with the video, but don’t know if you’re doing it right.

SceneLine aims to solve this.

Core Features

🎙️ Real-time ASR (Speech Recognition)

• Uses FunASR for speech recognition
• Resident process mode, 10x performance optimization
• Real-time comparison of your pronunciation against the original

🔊 40+ TTS Voices

• Microsoft Edge TTS support
• 40+ voice options
• Filter by gender/locale to find the best reference voice

🎭 Multi-character Dialogue Practice

• Supports multi-role scenes (like Friends dialogues)
• Individual scoring for each character
• Practice multiple roles by yourself

📊 Practice History & Statistics

• Three view modes: Overview / By Script / Details
• Track your progress curve
• Identify weak areas

📝 Smart Deduplication

• Content hash-based script deduplication
• Automatically merges identical content to avoid repetition

Tech Stack

Frontend

• React + Vite — Fast development experience
• Tailwind CSS — Clean UI design
• TypeScript — Type safety

Backend

• Express + TypeScript — API service
• FunASR — Core speech recognition
• node-edge-tts — TTS wrapper

AI/ML

• FunASR — Alibaba DAMO Academy’s open-source ASR framework
• ModelScope — Model hub
• faster-whisper — Accelerated Whisper ASR

Quick Start

One-click Launch (Recommended)

git clone https://github.com/hugcosmos/SceneLine.git
cd SceneLine
./start.sh

First launch will:

• Ask if you’re in mainland China (auto-configures mirror sources)
• Download ASR model (~2GB, takes 6-9 minutes first time)

Then visit http://localhost:5000

Docker Deployment

docker-compose up -d

System Requirements

• Node.js: 20+
• Python: 3.9-3.11 (ASR dependencies, torch doesn’t support 3.12+)
• Memory: Minimum 4GB (ASR model uses ~2GB)
• Disk: 3GB+ free space
• FFmpeg: For audio format conversion

Project Structure

sceneline/
├── server/          # Backend (Express + TypeScript)
│   ├── lib/         # Core libraries (ASR, TTS)
│   └── routes/      # API routes
├── client/          # Frontend (React + Vite + Tailwind)
│   └── src/pages/   # Page components
├── shared/          # Shared type definitions
├── models/          # ASR model cache
├── tts-cache/       # TTS audio cache
└── docker-compose.yml

License

MIT License — Fully open source, contributions welcome.

Future Plans

• Multi-user mode — Support multiple users practicing simultaneously with real-time comparison
• Streaming ASR — Faster real-time recognition with lower latency
• Intelligent scoring system — More systematic and human-friendly scoring mechanism
• TTS upgrade — Support for richer voice options
• Multiple TTS providers — Integration with more API providers (ElevenLabs, iFlytek, Baidu, etc.)

Links

• GitHub: github.com/hugcosmos/SceneLine
• Online Demo: (Coming soon)

Discuss

💬 Join the discussion on GitHub

If you’re also learning languages or interested in AI + education, give it a try and share your feedback!

Made with 💙 by Nicky & AI