Digital assets play a crucial role in representing products, services, culture, and brand identity for businesses in the digital era. They enable interactive and personalized experiences, enhancing customer engagement and deepening connections with the target audience. Efficiently organizing and searching for specific content within digital assets is essential for optimizing workflows, streamlining collaboration, and delivering relevant content to the right audience.
Videos have become the dominant form of consumer internet traffic, accounting for 81% of all online traffic by 2021. Video and audio assets offer immersive experiences and engage audiences on an emotional level. However, managing and organizing large volumes of digital assets, especially videos and audio, can be challenging due to the lack of informative metadata.
Generative AI, particularly in natural language processing and understanding (NLP and NLU), has transformed how we comprehend and analyze text. This advancement enables us to gain deeper insights efficiently and at scale. Retrieval Augmented Generation (RAG), built on top of large language models (LLMs), provides accurate answers based on information stored in digital asset repositories.
In this context, a video/audio question answering solution based on RAG can address the challenge of locating training and reference materials in non-text formats. By interacting with a chatbot, users can ask questions and receive direct answers along with links to relevant video training or documents. The chatbot leverages the power of RAG and indexing techniques to retrieve the most relevant information from the knowledge base.
The solution architecture involves converting video/audio assets to text using speech-to-text models, enabling intelligent video search using RAG, and building a multi-functional chatbot using LLMs. The video/audio content is stored in Amazon S3, and the chatbot is deployed on Amazon SageMaker.
To convert video/audio to text, options such as Amazon Transcribe, Amazon Translate, or Whisper can be used. Amazon Transcribe is recommended for single-language content, while Whisper is suitable for multilingual videos. The converted audio data is transcribed using the chosen method and processed further for organization and analysis.
Finally, the solution allows users to search for specific information within video/audio assets using natural language queries. The chatbot generates answers based on LLMs and provides links to relevant sources with timestamps. This enables efficient access to training materials and facilitates knowledge sharing within the organization.
Overall, the integration of RAG and NLP techniques with video/audio assets provides a powerful solution for organizing, searching, and retrieving information from digital asset repositories. Businesses can leverage this technology to optimize workflows, improve collaboration, and deliver personalized experiences to their target audience.