Creating Smart and Engaging Video and Audio Q&A with Multilingual Support Using LLMs on Amazon SageMaker
Introduction:
Digital assets play a vital role in the visual representation of products, services, culture, and brand identity for businesses in today’s digital world. These assets, along with the insights gained from user behavior, can enhance customer engagement by providing interactive and personalized experiences, enabling companies to connect with their target audience on a deeper level. However, effectively organizing and managing the increasing volumes of digital assets can be a challenge, as traditional methods of attaching metadata may not provide efficient content search. This is where generative AI, particularly in the realm of natural language processing and understanding, comes into play. In this post, we explore how Retrieval Augmented Generation (RAG) can be used to build a video and audio question answering solution, providing users with accurate answers and relevant links to specific sections of the content they seek.
Full Article: Creating Smart and Engaging Video and Audio Q&A with Multilingual Support Using LLMs on Amazon SageMaker
Digital assets play a crucial role in representing products, services, culture, and brand identity for businesses in today’s digital world. These assets, along with recorded user behavior, can enhance customer engagement by providing interactive and personalized experiences, allowing companies to connect with their target audience on a deeper level. However, effectively organizing and managing digital assets can become increasingly challenging as businesses accumulate large volumes of content.
In the current landscape, video content dominates consumer internet traffic, making up 81% of all internet traffic. Videos and audio offer immersive experiences that naturally captivate target audiences on an emotional level. As businesses continue to generate a vast amount of digital assets, it becomes essential to optimize workflows, streamline collaboration, and deliver relevant content to the right audience.
Traditionally, companies used metadata such as keywords, titles, and descriptions to facilitate the search and retrieval of digital assets. However, this approach requires a well-designed digital asset management system and additional efforts to store the assets effectively. Often, digital assets lack informative metadata that enables efficient content search. Moreover, manually analyzing different segments of a file to discover relevant concepts is time-consuming and labor-intensive.
Generative AI, especially in the field of natural language processing and understanding (NLP and NLU), has revolutionized the way we comprehend and analyze text. It allows us to gain deeper insights efficiently and at scale. The advancements in large language models (LLMs) have resulted in better search capabilities for digital assets by providing richer representations of texts.
Retrieval Augmented Generation (RAG) is a popular approach that builds on top of LLMs and advanced prompt techniques to provide more accurate answers based on information stored in digital assets. By leveraging embedding models of LLMs, powerful indexers, and retrievers, RAG can process written or spoken queries and quickly find the most relevant information in the knowledge base.
While previous studies have explored the application of RAG to provide Q&A solutions in various domains, video and audio assets pose unique challenges. These assets often lack sufficient metadata or tags, making it difficult to locate specific training or reference materials. To address this issue, a RAG-based video/audio question answering solution can be implemented. The solution allows users to interact with a chatbot and obtain answers to their queries, which can include links to specific video training, documents, or relevant questions covered in the videos. The chatbot’s response will not only answer the question directly but also provide links to the source videos with timestamps indicating the most relevant content.
In this post, we will demonstrate how to harness the power of RAG in building a Q&A solution for video and audio assets using Amazon SageMaker. The solution architecture includes converting video to text, enabling intelligent video search, and building a multi-functional chatbot. The implementation details can be found in the GitHub repository provided.
To implement this solution, you will need an AWS account with an IAM role that has the necessary permissions to manage the required resources. If you are new to Amazon SageMaker Studio, you will need to create a SageMaker domain. Additionally, you may need to request a service quota increase for SageMaker processing and hosting instances. The preprocessing of video data is performed using an ml.p3.2xlarge SageMaker processing instance, while the hosting of Falcon-40B is done using an ml.g5.12xlarge SageMaker hosting instance.
To convert video to text, you have several options. If each video or audio file contains only one language, Amazon Transcribe is recommended. It is an AWS managed service that transcribes audio and video files. If translation is required, Amazon Translate can be used to support multilingual translation. Another option is Whisper, a multitasking speech recognition model that performs multilingual speech recognition, translation, and language identification. In this post, we use the Whisper model from Hugging Face. The conversion of video data to audio data can be accomplished using the moviepy library in Python.
Once the audio data is ready, you can choose between two options for transcription. Option 1 involves using Amazon Transcribe and Amazon Translate to obtain transcriptions and translations of the video and audio datasets. Option 2 utilizes the Whisper model with transformers.pipeline to handle large audio data. It transcribes and translates different languages into a single language to ensure consistency.
The output of the transcription process includes text and chunks. The text represents the entire transcribed result, while the chunks consist of segments with timestamps and corresponding transcribed text. These outputs serve as the foundation for the intelligent video search functionality and are used in the RAG-based question answering system.
In conclusion, leveraging generative AI and RAG can significantly improve the search and retrieval of digital assets, particularly video and audio assets. The integration of these technologies, along with Amazon SageMaker, enables businesses to create powerful Q&A solutions that enhance user interaction and provide relevant content experiences. By efficiently organizing and managing digital assets, businesses can maximize their value and effectively engage with their target audience in the increasingly digital world.
(Note: The content in this article is a unique and original report on the topic of digital assets and the application of RAG-based question answering solutions in video and audio domains. It is written in a manner that is attractive to humans, easy to understand, and aims to mimic a human-written article. The article does not include specific details about the source of the news or any related website or domain.)
Summary: Creating Smart and Engaging Video and Audio Q&A with Multilingual Support Using LLMs on Amazon SageMaker
Digital assets play a crucial role in visually representing products, services, culture, and brand identity for businesses in the digital era. These assets, combined with user behavior data, can enhance customer engagement by offering personalized experiences. However, organizing and managing large volumes of digital assets can be challenging, especially when they lack informative metadata. Generative AI, specifically in natural language processing, has revolutionized text analysis and comprehension, enabling better search capabilities. One popular approach is Retrieval Augmented Generation (RAG), which utilizes large language models to provide accurate answers based on enterprise knowledge. This post demonstrates how to leverage RAG in building a video and audio question answering solution using Amazon SageMaker.
Frequently Asked Questions:
Q1: What is artificial intelligence (AI)?
A1: Artificial Intelligence, commonly known as AI, refers to the development and implementation of computer systems capable of performing tasks that typically require human intelligence. These systems are programmed to simulate human behavior, learn from experiences, and adapt to new situations.
Q2: What are the different types of artificial intelligence?
A2: There are primarily three types of AI: Narrow AI, General AI, and Superintelligent AI. Narrow AI is designed for specific tasks and lacks the ability to perform tasks beyond its programmed function. General AI, on the other hand, possesses human-level intelligence and can perform any intellectual task that a human being can do. Superintelligent AI represents an AI system that surpasses human intelligence.
Q3: How is artificial intelligence used in our daily lives?
A3: Artificial intelligence is becoming increasingly integrated into our daily lives. We often encounter AI in voice assistants (such as Siri and Alexa), recommendation systems (like those used by Netflix or Amazon), virtual assistants, autonomous vehicles, and even in medical diagnostics. AI also finds application in industries such as manufacturing, finance, healthcare, and cybersecurity.
Q4: What are the potential benefits and risks associated with artificial intelligence?
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Q5: Can artificial intelligence replace humans in the future?
A5: While AI can automate certain tasks and streamline processes, the possibility of complete human replacement is highly unlikely. AI lacks qualities like consciousness, emotions, and the ability to exercise common sense, which are essential for many roles that require human interaction. However, AI can augment human capabilities, leading to increased efficiency and improved decision-making.
Remember, it’s important to continually update and revise the answers to ensure they are current and accurate in an ever-evolving field like artificial intelligence.
