Category: Amazon Transcribe

This post, developed through a strategic scientific partnership between AWS and the Instituto de Ciência e Tecnologia Itaú (ICTi), P&D hub maintained by Itaú Unibanco, the largest private bank in Latin America, explores the technical aspects of sentiment analysis for both text and audio. We present experiments comparing multiple machine learning (ML) models and services, discuss the trade-offs and pitfalls of each approach, and highlight how AWS services can be orchestrated to build robust, end-to-end solutions. We also offer insights into potential future directions, including more advanced prompt engineering for large language models (LLMs) and expanding the scope of audio-based analysis to capture emotional cues that text data alone might miss.

In this post, we demonstrate how to implement a predictive maintenance solution using Foundation Models (FMs) on Amazon Bedrock, with a case study of Amazon’s manufacturing equipment within their fulfillment centers. The solution is highly adaptable and can be customized for other industries, including oil and gas, logistics, manufacturing, and healthcare.

This post introduces a serverless meeting summarization system that harnesses the advanced capabilities of Amazon Bedrock and Amazon Transcribe to transform audio recordings into concise, structured, and actionable summaries. By automating this process, organizations can reclaim countless hours while making sure key insights, action items, and decisions are systematically captured and made accessible to stakeholders.

SkillShow, a leader in youth sports video production, films over 300 events yearly in the youth sports industry, creating content for over 20,000 young athletes annually. This post describes how SkillShow used Amazon Transcribe and other Amazon Web Services (AWS) machine learning (ML) services to automate their video processing workflow, reducing editing time and costs while scaling their operations.

In this post, we explore the implementation details of a web application that uses the browser’s Web Audio API and Amazon Transcribe streaming to enable real-time dual-channel transcription. By using the combination of AudioContext, ChannelMergerNode, and AudioWorklet, we were able to seamlessly process and encode the audio data from two microphones before sending it to Amazon Transcribe for transcription.

Today, we’re excited to announce the general availability of Amazon Bedrock Data Automation, a powerful, fully managed capability within Amazon Bedrock that seamlessly transforms unstructured multimodal data into structured, application-ready insights with high accuracy, cost efficiency, and scalability.

As the healthcare industry continues to embrace digital transformation, solutions that combine advanced technologies like audio-to-text translation and LLMs will become increasingly valuable in addressing key challenges, such as patient education, engagement, and empowerment. In this post, we discuss possible use cases for combining speech recognition technology with LLMs, and how the solution can revolutionize clinical trials.

In this post, we discuss how AWS and DXC used Amazon Connect and other AWS AI services to deliver near real-time V2V translation capabilities.

The solution presented in this post demonstrates a powerful pattern for accelerating video and audio review workflows while maintaining human oversight. By combining the power of AI models in Amazon Bedrock with human expertise, you can create tools that not only boost productivity but also maintain the critical element of human judgment in important decision-making processes.

In this post, we explore the approach behind building an AWS AI-powered Chrome extension that aims to revolutionize the live streaming experience by providing real-time transcription, translation, and summarization capabilities directly within your browser.