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Storia in 2 fonti

Build real-time voice applications with Amazon SageMaker AI and vLLM | Amazon Web Services

Voice agents, live captioning, contact center analytics, and accessibility tools all depend on real-time speech-to-text, where your application streams audio in and receives transcription back simultaneously over a single persistent connection. Traditional request-response inference falls short here because transcription cannot begin until the entire audio recording has been received, adding latency that breaks the real-time […]

Raccontata datogether.aiaws.amazon.com

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2 prospettive sulla stessa storia
AI · summaries
aws.amazon.comStai leggendo1 mesi fa

Build real-time voice applications with Amazon SageMaker AI and vLLM | Amazon Web Services

Voice agents, live captioning, contact center analytics, and accessibility tools all depend on real-time speech-to-text, where your application streams audio in and receives transcription back simultaneously over a…

originale
together.ai1 mesi fa

Announcing the fastest inference for realtime voice AI agents

Together AI launches the fastest voice AI stack: streaming Whisper STT, serverless open-source TTS (Orpheus & Kokoro), and Voxtral transcription. Sub-second latency for production voice agents.

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Timeline cronologica

  1. mercoledì 20 maggio 2026·together.ai

    Announcing the fastest inference for realtime voice AI agents

    Together AI launches the fastest voice AI stack: streaming Whisper STT, serverless open-source TTS (Orpheus & Kokoro), and Voxtral transcription. Sub-second latency for production…

  2. mercoledì 20 maggio 2026·aws.amazon.com

    Build real-time voice applications with Amazon SageMaker AI and vLLM | Amazon Web Services

    Voice agents, live captioning, contact center analytics, and accessibility tools all depend on real-time speech-to-text, where your application streams audio in and receives…

originale