Kyutai TTS

Streaming speech models whose TTS starts speaking while an LLM is still generating its reply.

Open SourceSelf HostedOffline CapableGPU Required
0.0 (0)

About

Kyutai's delayed-streams-modeling repository carries the production releases of the technique behind Moshi: aligned audio and text streams are modeled framewise with a deliberate delay between them, which turns both speech-to-text and text-to-speech into streaming problems. The TTS models consume text as a stream, so audio playback can begin while an upstream LLM is still generating its reply, removing the usual wait for a complete sentence; the STT side offers a roughly 1B parameter English and French model with 0.5 second latency and semantic voice activity detection, plus a 2.6B English model. Kyutai ships three implementations: PyTorch for research, a Rust server with WebSocket streaming for production, where a single H100 can serve about 400 realtime streams, and MLX for Apple silicon down to iPhone class devices. Installation goes through the moshi Python package with pip or uv. Code is MIT for Python and Apache-2.0 for Rust, and the model weights are CC-BY-4.0, so commercial use is permitted with attribution, an unusually permissive stance for streaming speech models of this quality.

Should you use Kyutai TTS?

Pick it when

Pick Kyutai TTS when you are wiring an LLM to speech and want playback to start while text is still streaming in, served at scale from its Rust WebSocket server or on Apple devices via MLX, with weights usable commercially under CC-BY-4.0.

Look elsewhere when

Skip it if you only render audio files offline, where a lighter model like Kokoro is simpler, or if the CC-BY-4.0 attribution requirement is a problem. RealtimeTTS is an easier layer if you want to swap engines freely.

Alternatives to Kyutai TTS

  • Dia2

    Also starts speaking after the first words and handles multi-turn dialogue under Apache-2.0 with no attribution clause, but it is research-grade.

  • Moshi

    Kyutai's full-duplex model that listens and speaks at once, for end-to-end voice dialogue rather than TTS behind an LLM, but it needs 24 GB.

  • Orpheus TTS

    Apache-2.0 streaming at about 200 ms with emotion tags and cloning, served through vLLM instead of a dedicated Rust streaming server.

  • RealtimeTTS

    Python glue that streams LLM output to 20-plus engines with fallback, easier to adopt, but adds no model of its own and latency depends on the engine.

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