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  4. AWS Machine Learning

    Building Real-Time Speech Applications on SageMaker AI with vLLM-Omni (Part 1)

    AWS has published a tutorial demonstrating how to deploy Qwen3-TTS on Amazon SageMaker AI using the vLLM-Omni Deep Learning Container, enabling text streaming in and 24 kHz PCM audio chunks streaming out through SageMaker bidirectional streaming connections, along with a companion Gradio application. This DLC is based on vLLM-Omni v1.5 and adds the bidirectional streaming capability labels and routing middleware required by SageMaker AI; the example uses the native WebSocket route v1/audio/speech/stream.

    AWS has published a tutorial demonstrating how to deploy Qwen3-TTS on Amazon SageMaker AI using the vLLM-Omni Deep Learning Container, enabling text streaming in and 24 kHz PCM audio chunks streaming out through SageMaker bidirectional streaming connections, along with a companion Gradio application. This DLC is based on vLLM-Omni v1.5 and adds the bidirectional streaming capability labels and routing middleware required by SageMaker AI; the example uses the native WebSocket route v1/audio/speec

    Matched fieldsSummary、Body

    #lab#Tutorials / practice#Speech#English text is being prepared.
  5. AWS Machine Learning

    Generating Images and Videos with vLLM-Omni on SageMaker AI (Part 2)

    On Amazon SageMaker AI, deploy two endpoints using the same AWS vLLM-Omni DLC image: a real-time endpoint running FLUX.2-klein-4B to generate images, and an asynchronous endpoint running Wan2.1-VACE-1.3B for image-conditioned video generation. A text prompt first generates a PNG, which is then sent along with a motion prompt to the video endpoint; the generated MP4 is retrieved from Amazon S3, and the example also provides a command-line workflow and an optional Streamlit interface.

    Matched fieldsSummary、Body

    #lab#Tutorials / practice#English text is being prepared.#Multimodal

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