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Building a Meeting Summarizer Backend with Python FastAPI, AWS Transcribe and AWS Bedrock
- The tutorial focuses on building a meeting summarizer backend using FastAPI, AWS Transcribe, and AWS Bedrock for structured summaries with sentiment analysis and issue detection.
- Key features include audio transcription using AWS Transcribe, speaker labeling, summarization with AWS Bedrock's Titan model, and sentiment analysis.
- The tech stack consists of FastAPI, AWS Transcribe, AWS Bedrock, Amazon S3, and Jinja2.
- Project setup involves installing prerequisites, setting up AWS S3 and Bedrock, cloning the repository, installing dependencies, and configuring AWS credentials.
- The backend components include audio upload and transcription, text processing, and summarization with AWS Bedrock.
- Summarization involves prompt engineering with a Jinja2 template and using AWS Bedrock for generating meeting summaries.
- The article provides code snippets for FastAPI implementation, transcription processing, and summarization using AWS Bedrock.
- To run the application, you start the FastAPI server, test the API using cURL, and receive JSON responses with meeting summaries and sentiment analysis.
- Challenges include transcription accuracy, summarization accuracy, processing time, scalability issues, and prompt engineering complexity.
- The author concludes by highlighting the potential of AWS Bedrock models and plans to deploy the API using AWS Lambda or EKS for better summarization accuracy.
- Next steps involve enhancing prompt engineering and exploring advanced models for further application development.
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