Build real-time voice applications with vLLM-Omni on SageMaker AI – Part 1
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This article provides a technical tutorial on deploying real-time voice applications using the AWS vLLM-Omni Deep Learning Container on Amazon SageMaker AI. It focuses on streaming text-to-speech capabilities by utilizing the Qwen3-TTS model, which allows audio generation to begin before the full response is complete. The solution leverages SageMaker's bidirectional streaming over HTTP/2 WebSockets to send text input and receive 24 kHz PCM audio chunks through a persistent connection. Readers are guided through cloning a sample repository, configuring instance pools for GPU resources, and deploying a real-time endpoint. A Gradio application is used to demonstrate the workflow, enabling users to generate streamed speech from text inputs. The tutorial emphasizes that this approach complements previous speech-to-text examples, creating a full duplex voice pipeline. Finally, the authors provide instructions for cleaning up the deployed resources to avoid unnecessary charges.
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