  This paper presents OmniDataComposer, an innovative approach for multimodal
data fusion and unlimited data generation with an intent to refine and
uncomplicate interplay among diverse data modalities. Coming to the core
breakthrough, it introduces a cohesive data structure proficient in processing
and merging multimodal data inputs, which include video, audio, and text.
  Our crafted algorithm leverages advancements across multiple operations such
as video/image caption extraction, dense caption extraction, Automatic Speech
Recognition (ASR), Optical Character Recognition (OCR), Recognize Anything
Model(RAM), and object tracking. OmniDataComposer is capable of identifying
over 6400 categories of objects, substantially broadening the spectrum of
visual information. It amalgamates these diverse modalities, promoting
reciprocal enhancement among modalities and facilitating cross-modal data
correction. \textbf{The final output metamorphoses each video input into an
elaborate sequential document}, virtually transmuting videos into thorough
narratives, making them easier to be processed by large language models.
  Future prospects include optimizing datasets for each modality to encourage
unlimited data generation. This robust base will offer priceless insights to
models like ChatGPT, enabling them to create higher quality datasets for video
captioning and easing question-answering tasks based on video content.
OmniDataComposer inaugurates a new stage in multimodal learning, imparting
enormous potential for augmenting AI's understanding and generation of complex,
real-world data.
