AI-Driven Text-to-Multimedia Content Generation: Enhancing Modern Content Creation

This research investigates the use of artificial intelligence for the creation of multimedia content from textual sources, such as press articles, stories, and scripts. The system utilizes Natural Language Processing and image generation techniques to create relevant visuals and voiceovers, enhancing content creation efficiency without sacrificing quality. The system processes various input formats, using Optical Character Recognition (OCR) for non-text formats and advanced large language models (LLMs) like LLaMA2 and Gemini for text summarization. It automatically extracts keywords and uses them to generate synchronized visual and audio content, utilizing tools like Google Text-to-Speech (GTTS) and AI-based image generation models such as DALL-E and Stable Diffusion. The multimedia content is assembled and rendered using MoviePy, ensuring seamless integration of text, audio, visuals, and subtitles. Applications in journalism, marketing, and education are examined to showcase how this technology improves accessibility and audience engagement. Overall, the study demonstrates the potential of AI in automating media content creation, streamlining the process while maintaining narrative coherence and enhancing the user experience.