Condense phone conversation transcriptions safely with Amazon Transcribe and Amazon Bedrock Guardrails | AWS Machine Learning Blog

Automating Audio Recordings in Business Environments

Given the volume of meetings, interviews, and customer interactions in modern business environments, audio recordings play a crucial role in capturing valuable information. Manually transcribing and summarizing these recordings can be a time-consuming and tedious task. Fortunately, advancements in generative AI and automatic speech recognition (ASR) have paved the way for automated solutions that can streamline this process.

Streamlining Call Recording Analysis

Customer service representatives receive a high volume of calls each day. Previously, calls were recorded and manually reviewed later for compliance, regulations, and company policies. Call recordings had to be transcribed, summarized, and then redacted for personal identifiable information (PII) before analyzing calls, resulting in delayed access to insights. Redacting PII is a critical practice in security for several reasons.

Utilizing Amazon Transcribe and Amazon Bedrock

In this post, we show you how to use Amazon Transcribe to get near real-time transcriptions of calls sent to Amazon Bedrock for summarization and sensitive data redaction. We’ll walk through an architecture that uses AWS Step Functions to orchestrate the process, providing seamless integration and efficient processing. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading model providers such as AI21 Labs, Anthropic, Cohere, Meta, Stability AI, Mistral AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

Designing a Scalable Architecture

The architecture of this solution is designed to be scalable, efficient, and compliant with privacy regulations. It includes key components like AWS Step Functions for orchestration and Amazon Bedrock Guardrails for redacting sensitive information such as PII found in call transcription summaries.

Customizing the Solution

After setting up the solution using an AWS CloudFormation template, there are opportunities to customize it for specific use cases. Potential ideas include adding Lambda layers for updated Boto3 versions and uploading different file formats for processing.

Managing Resources and Data

To clean up resources and manage data, follow the necessary steps to avoid incurring costs. Deleting the stack doesn’t remove associated S3 assets, so additional steps may be required. Businesses should also be aware of data retention policies and manually delete transcription jobs if necessary.

Conclusion

As organizations leverage data for decision-making, automating audio recording analysis with generative AI and AWS services like Amazon Transcribe and Amazon Bedrock becomes crucial. This solution not only saves time and effort but also ensures compliance with data protection regulations. Organizations can benefit from streamlined workflows, improved insights, and enhanced decision-making capabilities by implementing solutions like these in their operations.


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