AWS 推出了一种通过在生成句子时流式传输语音合成来减少 AI 语音代理延迟的方法，而不是等待整个响应。该技术利用托管在 Amazon SageMaker AI 上的 Kokoro-82M 文本转语音模型，该模型足够小，可以在单个 GPU 实例上高效运行。该解决方案涉及在 SageMaker 实时端点上部署自定义容器，并实现用于生成和音频播放的双向流。 AI
影响 通过降低感知延迟，实现与 AI 代理更自然、更具响应性的语音交互。
排序理由 这是一项特定 AWS 服务的技术指南和实现细节，而不是核心 AI 模型发布或重要的行业事件。
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Voice agents often wait for an agent's full response before starting speech synthesis, adding latency users feel directly. This post shows how to stream each sentence to a Kokoro-82M text-to-speech model on Amazon SageMaker AI as the agent produces it, and how to get that audio b…
🤖 Stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI Voice agents often wait for an agent's full response before starting speech synthesis, adding latency users feel directly. This post shows how to stream each sentence to a Kokoro-82M text-to-speech…
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🤖 Stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI Voice agents often wait for an agent's full response before starting speech synthesis, adding latency users feel directly. This post shows how to stream each sentence to a Kokoro-82M text-to-speech…