What is Kyutai TTS?
Kyutai TTS is a high-performance, open-source text-to-speech model engineered to solve a critical challenge in modern applications: latency. Designed for developers and builders, it enables you to create truly responsive, real-time voice experiences by generating audio as text is created, not after. This eliminates the awkward pauses common in other systems, paving the way for more natural and fluid human-computer interaction.
Key Features
⚡ True Text Streaming for Instant Audio Unlike models that only stream audio after receiving the full text, Kyutai TTS streams in both text and audio. You can pipe in words as they’re generated by an LLM, and the model begins producing audio with a latency of just 220ms. This is made possible by our innovative "Delayed Streams Modeling" architecture, which processes text and audio in a time-aligned manner for genuinely immediate output.
🗣️ High-Fidelity Voice Cloning Using just a 10-second audio sample, Kyutai TTS accurately captures the unique characteristics of a source voice, including its intonation, pacing, and even recording quality. To ensure ethical use, we provide a repository of voices from consensual datasets and do not release the core voice embedding model, protecting against unauthorized cloning.
⚙️ Production-Ready Performance & Scalability Kyutai TTS is built for real-world deployment. It ships with a robust Rust server and a Dockerfile for easy, reproducible setup. On a single L40S GPU, our server can handle up to 32 simultaneous requests with a real-world latency of 350ms, ensuring your application can scale efficiently.
⏱️ Precise Word-Level Timestamps Alongside the audio stream, the model outputs the exact start and end times for every word it speaks. This capability is essential for building advanced features like real-time subtitles or, as demonstrated in our Unmute tool, creating AI agents that know precisely where they were interrupted and can resume a conversation intelligently.
How Kyutai TTS Solves Your Problems:
For Conversational AI & Virtual Assistants: Build AI agents that respond instantly, without the unnatural delay between when they "think" of a response and when they speak. This creates conversations that feel more fluid, engaging, and human.
For Live Content Narration: Power real-time narration for live-streamed events, dynamic data visualizations, or breaking news feeds. As text content updates, Kyutai TTS can vocalize it on the fly, keeping the audio perfectly in sync with the information.
For Accessible Technology: Develop highly responsive screen readers and accessibility tools that can vocalize text as it appears on a screen, providing immediate auditory feedback to users and dramatically improving the user experience.
Unique Advantages
The Delayed Streams Modeling Architecture: This is the core technical advantage that sets Kyutai TTS apart. By modeling text and audio as parallel, time-aligned streams, we fundamentally solve the latency problem that constrains traditional TTS. This architecture is also what enables other powerful features like batching and precise word-level timestamps, all from a single, unified model.
Verifiably State-of-the-Art Quality: Our claims are backed by clear data. In comparative benchmarks against leading models, Kyutai TTS demonstrates a significantly lower Word Error Rate (WER) and superior speaker similarity in both English and French. This means you get not only incredible speed but also highly accurate and natural-sounding speech.
Conclusion:
Kyutai TTS is more than just another text-to-speech engine; it's a foundational tool for the future of real-time voice interaction. By providing true text streaming, production-grade performance, and high-fidelity output, it gives you the power to build faster, smarter, and more natural voice-enabled applications.
Explore how Kyutai TTS can transform your projects. Check out the live demo at Unmute.sh or dive into the code on GitHub to get started!
More information on Kyutai TTS
Launched
2023-11
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Free
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Kyutai TTS was manually vetted by our editorial team and was first featured on 2025-07-05.
