Підхід до автоматизації конверсії відео та аудіо матеріалів у текст

Abstract

The automation of media content conversion, particularly video-to-text transformation, presents challenges related to accuracy and information loss. Automated transcription systems often struggle with factors such as audio quality, speech clarity, and domain-specific terminology. A two-step approach, combining initial rough transcription with AI-driven contextual enhancement, improves recognition accuracy. The first stage generates a draft transcript, while the second refines it using additional metadata and linguistic models. Contextual adaptation significantly enhances precision. Although this method increases processing costs, it optimizes transcription quality, making automated media conversion more reliable and applicable across various domains.