GenNarrate: AI-Powered Story Synthesis with Visual and Audio Outputs

Abstract: The emergence of generative artificial intelligence has redefined the boundaries of digital content creation, particularly in the domain of computational storytelling. This paper presents GenNarrate, a modular, multi-modal generative AI system engineered to synthesize coherent narratives augmented with corresponding visual and auditory elements. The architecture leverages advanced machine learning models, including LLaMA2 for text generation, DALL·E for image synthesis, and a combination of Google Text-to-Speech (GTTS) and AudioLDM for expressive audio narration and sound design. GenNarrate facilitates user-driven content generation by accepting configurable parameters-such as genre, tone, character elements, and desired multimedia outputs-through an interactive front-end interface. These inputs are orchestrated through a Flask-based backend pipeline, which integrates the constituent modules and produces downloadable outputs comprising narrated stories, image-enhanced documents, and synchronized audio tracks. The proposed system demonstrates a novel approach to narrative automation, emphasizing cross-modal coherence, scalability, and personalization. This study further situates GenNarrate within the broader context of AI-enhanced storytelling technologies, offering comparative insights with existing open-source models such as GPT-3 and Stable Diffusion. Potential applications are explored across educational content delivery, therapeutic interventions, creative industries, and interactive media. The findings underscore the transformative potential of multi-modal AI systems in facilitating immersive, user-centric storytelling experiences, while also identifying avenues for future development in real-time interaction, fine-grained customization, and adaptive content generation