Stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI
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This article demonstrates how to reduce latency in AI voice agents by streaming text-to-speech synthesis concurrently with text generation using the Kokoro-82M model on Amazon SageMaker AI. Instead of waiting for a complete response, the system sends each sentence to the TTS endpoint immediately via a bidirectional WebSocket stream. The solution involves deploying a custom container on an ml.g5.xlarge GPU instance and handling binary WebSocket frames correctly within a Starlette application. A key technical challenge addressed is maintaining a persistent session to avoid reconnection overhead while processing sequential sentences. For transporting audio back to the client, the author compares base64-encoded Server-Sent Events against multiplexing raw binary data over the same connection. Benchmarks show that this incremental approach cuts time to first audio by more than half in isolation and improves end-to-end performance by 2.4 to 3.5 times compared to batch processing. The guide provides specific code examples for sentence chunking, frame decoding, and infrastructure deployment using AWS CDK.
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