AI-Powered Automated Video Dubbing System with Multi-Language Support and Lip Synchronization

Abstract

The exponential expansion of digital multimedia across international platforms necessitates efficient multilingual dubbing solutions. Conventional dubbing methodologies prove resource-intensive and economically prohibitive for widespread content localization. This research introduces an intelligent automated dubbing framework integrating advanced neural architectures for speech processing, translation, and synthesis. The system employs Whisper for acoustic modeling, NLLB-200 for cross-lingual translation, XTTS v2 for voice cloning, and Wav2Lip GAN for visual synchronization. A novel segment-based processing approach ensures temporal precision between synthesized audio and source video. Experimental validation demonstrates superior naturalness and synchronization accuracy compared to existing methodologies. The framework addresses critical applications in educational technology, digital entertainment, corporate communication, and accessibility enhancement.