Building ultra-low-latency voice AI applications on AWS
Learn how Cartesia, an AWS Partner, uses purpose-built State Space Models and AWS infrastructure to deliver sub-100ms text-to-speech and speech-to-text at global scale, and how to deploy its Sonic model on Amazon SageMaker AI or self-managed Amazon EC2 GPU instances.
Voice AI is rapidly reshaping human-computer interaction. Conversational AI applications like virtual assistants, contact centers, telehealth consultations, gaming, accessibility tools, and sales agents rely on near-instantaneous voice processing. However, delivering responsive, high-quality speech remains a technical challenge.
Traditional transformer-based text-to-speech (TTS) models face a fundamental limitation: processing entire sequences before outputting any audio. This architecture creates a trade-off between quality and latency. A new class of models called State Space Models (SSMs) resolves this problem. SSMs process sequences step-by-step and maintain a compact internal state, avoiding the quadratic computational complexity of transformers.
This efficiency allows SSMs to handle long audio sequences with minimal latency, making them ideal for real-time voice applications.
Cartesia, an AWS Partner specializing in real-time voice AI, leverages SSM technology to deliver sub-100ms text-to-speech and speech-to-text at global scale. Sonic, Cartesia's text-to-speech model, generates audio with almost instantaneous latency (first audio byte in ~90ms) and produces ultra-realistic speech in over 44 languages.
Sonic supports streaming-native audio synthesis, context-aware prosody, and voice cloning from just 10 seconds of sample audio. Ink, Cartesia's speech-to-text model, offers streaming transcription with native turn detection, context-aware end-pointing, and multi-speaker handling. By deploying Cartesia on AWS, developers can build conversational AI applications that feel indistinguishable from human conversation, meeting the growing demand for real-time voice AI across industries.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
