Multilingual Video Translation and Speech Synthesis: A Deep Learning Approach for Seamless Language Adaptation

This paper introduces an efficient a n d scalable pipeline for multilingual video translation, incorporating state-of-the-art modules for audio extraction, automatic speech recognition (ASR), machine translation, text-to-speech (TTS), and lip synchronization. The system directly translates video content across multiple languages, leveraging FFmpeg for audio processing, OpenAI’s Whisper model for robust ASR, and MarianMT models fine-tuned o n t he O PUS100 dataset for high-quality multilingual translations. Additionally, gTTS provides natural speech synthesis using Google’s Text-to-Speech API, while Wav2Lip precisely aligns lip movements with synthesized speech, delivering realistic video outputs. The approach has been proven successful for both high and medium-resource languages and forms a complete solution for video translation in any language. Future work will focus on extending support for low-resource languages and improving the capability of real-time processing.