A Privacy-Preserving Universal Multimodal Framework for Real-Time Any-to-Any Transformation

We propose a unified multimodal framework, Universal Multi-Modal Generation Enabling Any-to-Any Transformation, that enables seamless transformation between text, audio, and image inputs and outputs. The system integrates three core capabilities: speech understanding using Whisper, visual understanding through LLaVA, and speech synthesis via PyTorch-based text-to-speech models. All modules are deployed on-premise using Docker, providing a privacy-centric execution environment and reducing operational overhead associated with cloud processing. The framework supports advanced workflows including document/PDF-to-text extraction, text-to-speech conversion, and image-driven description generation, thereby enabling accessible and interactive multimodal content pipelines. The implementation emphasizes efficient orchestration and inference to meet real-time constraints. Experimental results across multiple cross-modal tasks demonstrate robust accuracy and consistently low latency, suggesting that local, containerized multimodal systems can deliver scalable performance for practical applications. The proposed approach is particularly relevant to accessibility, education, and content creation, where rapid modality conversion and data privacy are essential.