Stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI

A customer asks an AI voice agent a question and waits. The agent already has an answer, it’s midway through generating a long, multi-paragraph response, but the customer hears nothing for five seconds. By the time an answer arrives, the customer is frustrated, confused, and no longer paying attention.

Text-to-speech integrations create exactly that gap. A one-sentence response might generate in 600 milliseconds, but the full response takes five seconds, and the integration waits for all of it before synthesis starts.

This post shows how to close that gap. We stream each sentence to a hosted Kokoro-82M text-to-speech (TTS) model on Amazon SageMaker AI as soon as an agent produces it, so synthesis begins on sentence one instead of the finished response. Getting that audio back to a browser needs its own solution too, so we also cover both transports for doing it and what each costs in practice.

This solution closes the gap by streaming synthesis alongside generation instead of waiting for it to finish. As the agent produces each sentence, that sentence streams immediately to a hosted Kokoro-82M text-to-speech model, so audio playback can start before the agent finishes speaking its full response. Kokoro-82M holds only 82 million parameters, small enough to run cheaply on a single mid-tier GPU instance. That keeps both latency and inference cost low for a voice agent handling real traffic.

speech-infra deploy provisions the Kokoro-82M endpoint. This includes the foundation and model-cache stacks that create the underlying IAM roles and sync the model’s weights into Amazon Simple Storage Service (Amazon S3). The endpoint stack then builds the container image and creates the SageMaker AI model, endpoint configuration, and endpoint. agent-infra deploy provisions the Amazon Bedrock AgentCore runtime hosting the Strands agent, which calls the endpoint you just deployed, so deploy the endpoint first.

The fuller, end-to-end case is a live, generative agent composing its response one token at a time, with no way to know sentence four until sentence one exists. This example runs as a Strands agent, backed by Amazon Bedrock, deployed to Amazon Bedrock AgentCore runtime, calling the Kokoro endpoint with the pattern from Step 4. Three communication legs connect in this design: the browser-facing leg carrying Server-Sent Events back to the caller, the agent’s streaming call to Amazon Bedrock for text generation, and the agent’s bidirectional session with the Kokoro endpoint for synthesis.

Figure 3: The three communication legs connecting the caller, Amazon Bedrock, and the Kokoro endpoint

Kokoro-82M runs in a custom container on a SageMaker AI real-time endpoint, on an ml.g5.xlarge GPU instance. The container exposes two routes: POST /invocations , which streams a response for one complete input, and WS /invocations-bidirectional-stream , which keeps one connection open across many messages in both directions.

Figure 1: Request path from the client to the SageMaker AI real-time endpoint over a bidirectional stream

The walkthrough first deploys the endpoint and agent, then works through the client-side changes that make incremental streaming work end to end.