# VideoLingo — Netflix-Level Video Subtitle Translation and Dubbing

> Open-source automated video translation pipeline that delivers subtitle cutting, translation, alignment, and AI dubbing in one click.

## Install

Save in your project root:

# VideoLingo — Netflix-Level Video Subtitle Translation and Dubbing

## Quick Use
```bash
git clone https://github.com/Huanshere/VideoLingo.git
cd VideoLingo
pip install -r requirements.txt
streamlit run st.py
```

## Introduction
VideoLingo is an open-source, one-click automated video subtitle team that handles the full pipeline from transcription to dubbing. It produces Netflix-quality subtitle cutting, multi-language translation, precise alignment, and optional AI voice dubbing, making professional video localization accessible to individual creators and small teams.

## What VideoLingo Does
- Transcribes audio using Whisper with accurate word-level timestamps
- Cuts subtitles at natural sentence boundaries rather than fixed time intervals
- Translates subtitles across dozens of languages with context-aware LLM translation
- Aligns translated subtitles to match original speech timing precisely
- Generates AI dubbing in the target language synchronized to the video

## Architecture Overview
VideoLingo chains several AI models into a sequential pipeline. Whisper handles speech recognition, an LLM (GPT, Claude, or local models via Ollama) performs context-aware translation, and a TTS engine (Azure, OpenAI, or Fish Speech) generates the dubbed audio. A Streamlit web UI orchestrates the workflow and lets users adjust parameters between steps. The subtitle alignment module uses dynamic programming to match translated text duration to source speech segments.

## Self-Hosting & Configuration
- Clone the repository and install Python dependencies with pip
- Configure API keys for your preferred LLM and TTS providers in the web UI
- Supports local models through Ollama for fully offline operation
- Run via Streamlit for the web interface or use CLI scripts for batch processing
- Docker deployment available for containerized setups

## Key Features
- Intelligent subtitle segmentation that respects sentence and clause boundaries
- Multi-step translation with terminology consistency across the entire video
- Supports 80+ languages for both subtitles and AI dubbing
- Built-in quality checks that flag timing mismatches and translation issues
- Batch mode for processing multiple videos in sequence

## Comparison with Similar Tools
- **pyvideotrans** — similar pipeline but VideoLingo focuses on subtitle quality with smarter segmentation
- **Subtitle Edit** — manual subtitle editor; VideoLingo automates the entire flow end-to-end
- **Kapwing** — cloud-based with usage limits; VideoLingo is self-hosted and unlimited
- **Rask AI** — commercial SaaS for video translation; VideoLingo is free and open source
- **WhisperX** — transcription only; VideoLingo adds translation, alignment, and dubbing

## FAQ
**Q: What languages does VideoLingo support?**
A: It supports 80+ languages for subtitles and a growing set for AI dubbing depending on your TTS provider.

**Q: Can I use VideoLingo without any API keys?**
A: Yes, by configuring Ollama for translation and a local TTS model, you can run the entire pipeline offline.

**Q: How long does it take to process a 10-minute video?**
A: Processing time depends on your hardware and chosen models, but typically 5-15 minutes for transcription, translation, and subtitle generation on a modern GPU.

**Q: Does VideoLingo preserve the original audio when adding dubs?**
A: Yes, it can mix the dubbed voice track with the original background audio and music.

## Sources
- https://github.com/Huanshere/VideoLingo
- https://videolingo.io/

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Source: https://tokrepo.com/en/workflows/asset-820af45f
Author: AI Open Source