The AWS Machine Learning Blog describes the WhisperX Deep Learning Container, which packages Whisper, wav2vec2 forced alignment, and speaker diarization into a single GPU-ready image. The post walks through deploying the container to Amazon SageMaker AI using both real-time and asynchronous endpoints to produce word-level, speaker-labeled transcription. It also addresses production considerations including the GPU AMI pin, scaling, and cost controls.
Why it matters
Combining transcription, forced alignment, and speaker diarization in one container simplifies the setup needed for speaker-labeled, word-level transcripts and provides guidance on operating it in production.
Who should care
Developers and teams building speech transcription workflows on AWS who need speaker-labeled output and want to run it on managed SageMaker AI endpoints.