An Efficient Transformer-Based System for Text-Based Video Segment Retrieval Using FAISS

Abstract

An efficient system for text-based video segment retrieval is presented, leveraging transformer- based embeddings and the FAISS library for similarity search. The sys- tem enables users to perform real-time, scalable searches over video datasets by converting video segments into combined text and image embeddings. Key components include video segmentation, speech-to-text transcription using Wav2Vec 2.0, frame extraction, embedding generation using Vision Transformers and Sentence Transformers, and efficient similarity search using FAISS. Experimental results demonstrate the system’s applicability in media archives, education, and content discovery, even when applied to a small dataset.