Information retrieval systems like search engines and databases have revolutionized the digital age with their ability to manage and analyze vast amounts of data rapidly and efficiently, largely based on keywords and fields in data files. With increasing volumes of audio and video files, retrieving pertinent data could be challenging as it commonly involves manual insertion of text-based metadata such as timestamps into these files – a process that is difficult to scale.
Artificial intelligence (AI) solutions can now accurately transcribe audio and execute semantic searches, enhancing the efficiency of content queries from these audio files. Amazon Transcribe, from Amazon Web Services (AWS), conveniently converts speech into text. On the other hand, Amazon Bedrock, a managed service, offers select high-performing foundation models from leading AI firms through a secure, privacy-focused single API, backed by several capabilities to develop responsible generative AI applications.
This article demonstrates using AWS services to catalog, analyze, and search content in audio files stored in mp3 format on Amazon Simple Storage Service (S3). Amazon Transcribe transcribes these files into JSON format, which Amazon stores on S3. Tagging each JSON file with the respective episode title allows later retrieval of the title for each query result.
Amazon Bedrock then creates numerical representations of the file content, stored as vectors within a vector database for future queries. Amazon Bedrock facilitates Foundation Models (FMs) from esteemed AI startups and Amazon through an API. While the Knowledge Bases for Amazon Bedrock splits the data files on S3 into portions, Amazon Titan – a robust selection of FMs by Amazon – creates embeddings of each chunk.
When a user queries the contents of the audio files, it triggers an API call to Knowledge Bases for Amazon Bedrock, which in turn calls the vector database to perform a semantic search. The returned results are augmented to the user’s original query, and sent to the large language model which returns customized results that are highly relevant and accurate.
The only prerequisite to using AWS services is having access to them via the AWS Management Console. For making API calls to Amazon Bedrock from a generative AI application, Python version 3.11.4 and the AWS SDK for Python (Boto3) is essential.
Being a pay-as-you-go service, Amazon Transcribe charges only for the services used, hence thoroughly cost-efficient. Amazon Bedrock only charges for what is used, making the overall charges contingent on resource consumption.
In conclusion, with rising volumes of audio files, cataloging, analyzing, and finding relevant content could be difficult. However, services such as Amazon Transcribe and Knowledge Bases for Amazon Bedrock can automate this process and make it scalable. The AI services enable expansion of our knowledge bases.
