🎬 VideoLingo: Netflix‑Level Subtitles & Dubbing Made Simple

In today’s global media landscape, creating high‑quality subtitles and dubbing for every language can feel like a full‑time job. VideoLingo cuts through that complexity by turning a handful of click‑through steps into a complete, end‑to‑end workflow that produces Netflix‑standard subtitles, translations and even voice‑cloned dubbing.

Why VideoLingo?

• Open‑source and battle‑tested with 15.7k stars on GitHub • Single‑line, auto‑aligned subtitles that keep viewer focus • Built‑in WhisperX transcription, GPT‑SoVITS voice cloning, and any OpenAI‑style LLM • Dockerizable, GPU‑accelerated, and fully scriptable • Automatic Translate‑Reflect‑Adapt cycle for theatrical‑grade quality

🚀 Core Features Explained

🛠️ Quick Start Guide

Below is a minimal setup that will have you spinning subtitles in 10 minutes.

1️⃣ Clone & Create Environment (Python 3.10+)

git clone https://github.com/Huanshere/VideoLingo.git
cd VideoLingo
conda create -n videolingo python=3.10 -y
conda activate videolingo
pip install -r requirements.txt

2️⃣ Optional: GPU & CUDA

• Windows – Install CUDA 12.6 and cuDNN 9.3.0, then add C:/Program Files/NVIDIA/CUDNN/v9.3/bin/12.6 to PATH.
• Linux/macOS – Add export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH.

3️⃣ Install Dependencies

python install.py

Tip – On Windows you can run the bundled OneKeyStart.bat if you prefer a GUI installer.

4️⃣ Launch the Streamlit UI

streamlit run st.py

The application will automatically open in your browser at http://localhost:8501. From there, upload a video, choose your target language, tweak the translation model, and hit Start!

5️⃣ Docker (Optional)

docker build -t videolingo .
docker run -d -p 8501:8501 --gpus all videolingo

Docker guarantees reproducibility, especially on servers without conda.

🔎 How It Works Under the Hood

1. Download – yt-dlp pulls the video and FFmpeg strips the audio for WhisperX.
2. Transcribe – WhisperX performs low‑illusion, word‑aligned transcription and outputs a JSON timeline.
3. Segment – A custom NLP pipeline determines the optimal subtitle boundaries to keep a single line.
4. Translate – The “Translate‑Reflect‑Adapt” chain uses an OpenAI‑or‑compatible LLM to translate, check, and polish the text—ensuring it feels natural in the target language.
5. Dub – If a dubbing option is selected, GPT‑SoVITS or a chosen TTS engine synthesizes speech, then FFmpeg merges the new audio with the video.
6. Export – Subtitles are saved in .srt / .vtt, and, if requested, a dubbed MP4 is exported.

The workflow is fully automated, but you can override any step with custom configuration or pass‑through options.

🌍 Real‑World Use Cases

📚 Getting Help & Contributing

• Documentation – Visit the official docs at https://docs.videolingo.io for detailed tutorials.
• Slack/Discord – Join the community for quick support.
• GitHub Issues – Report bugs, request features, or propose improvements.
• Contributions – All contributions are welcome; the repo follows an OCT‑clean workflow and has detailed guidelines for pull requests.

📈 Future Roadmap (What’s Next?)

• Support for Additional TTS Engines – Edge‑TTS, AWS Polly, Google Cloud, etc.
• Multi‑Character Dubbing – Enhancing WhisperX speaker diarization for separate character voices.
• Advanced Custom Terminology – Automatic extraction of industry‑specific vocab from source material.
• AI‑Driven Quality Assurance – Automated checks for alignment errors or mistranslations.

Wrap‑Up

VideoLingo is more than a subtitle generator; it’s an all‑in‑one suite that transforms raw video into a multi‑language, audience‑ready product in minutes. Whether you’re a content creator, educator, or developer, the combination of WhisperX, GPT‑SoVITS, and a thoughtful UI puts production quality at your fingertips.

Ready to give your videos global reach? Clone the repo, drop in a video, and watch Netflix‑grade subtitles appear instantly.