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AWS guides how to stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI

Learn how to stream sentences incrementally to a Kokoro text-to-speech model on Amazon SageMaker AI to reduce voice agent latency.

AWS guides how to stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI — image from AWS
Image: AWS

Summary

AWS published a guide on streaming text sentence-by-sentence from an AI agent to a Kokoro text-to-speech model hosted on Amazon SageMaker AI. By starting synthesis on the first sentence instead of waiting for a full response, developers can cut the time to the first audio byte. The post covers endpoint deployment, WebSocket handling, and sentence chunking.

What changed

  • Stream text sentence-by-sentence to a hosted Kokoro-82M text-to-speech model on Amazon SageMaker AI as the agent produces it.
  • Deploy the Kokoro-82M endpoint using AWS CDK commands and an ml.g5.xlarge GPU instance.
  • Configure Starlette WebSocket handlers to accept binary frames from SageMaker AI endpoints.
  • Multiplex raw binary audio and JSON control frames over a session to avoid base64 overhead.

Availability

Available now

“We verified this against a live endpoint sending a three-sentence input over one session.”

Founder takeaway

Small teams building real-time voice agents should implement sentence-level streaming and binary multiplexing to cut audio latency.

Source

AWSStream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI | Amazon Web Services

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