Speaker-labeled transcription with WhisperX on SageMaker AI
Amazon Web Services Ayush Sharma
AWS published a guide for deploying the WhisperX speech-to-text deep learning container on Amazon SageMaker AI to produce transcripts with word-level timestamps and speaker labels. The real-time endpoint must return within a 60-second response cap, while long audio should use the asynchronous endpoint. This changes transcription outputs and deployment workflow by adding per-word alignment and diarization labels and by routing requests through either synchronous or S3-backed async inference on SageMaker.
Why it matters
The AWS WhisperX Deep Learning Container packages Whisper, wav2vec2 forced alignment, and speaker diarization into a GPU-ready image. Learn how to deploy it to Amazon SageMaker AI real-time and asynchronous endpoints for word-level, speaker-labeled transcription, plus the production details that matter: the GPU AMI pin, scaling, and cost controls.
