kyutai-labs/moshi

Health v2 · maintenance only

Adoption not part of the score

10949 stars · 1017 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Moshi is a speech-text foundation model and full-duplex spoken dialogue framework from Kyutai, built around the Mimi streaming neural audio codec. It ships three inference stacks (PyTorch for research, MLX for on-device Mac/iPhone, Rust for production) and achieves real-time conversational latency around 200ms.

Use cases

• build a real-time voice chatbot that can talk and listen simultaneously
• run a spoken dialogue model locally on a Mac with MLX
• deploy low-latency speech-to-speech conversation on a GPU server
• experiment with streaming neural audio codecs like Mimi
• create a voice assistant with natural full-duplex conversation
• integrate speech-text foundation model inference into a Rust application

When to choose

• you need real-time, full-duplex spoken dialogue rather than turn-based speech recognition plus TTS
• you want on-device voice AI on Apple silicon via the MLX stack
• you need a production-grade low-latency voice model with a Rust inference server
• you are researching speech-text foundation models and streaming audio codecs

When to avoid

• you only need simple batch speech-to-text or text-to-speech without real-time dialogue
• you have no GPU or Apple silicon and need CPU-only inference
• you need extensive fine-tuning tooling out of the box (fine-tuning lives in a separate repo)
• you need a mature managed API with SLAs rather than self-hosted open-source models

Facets

library · maturity active

speech-recognition tts llm-inference machine-learning audio-processing artificial-intelligence speech-processing large-language-models python rust full-duplex-dialogue neural-audio-codec streaming-inference voice-assistant foundation-model mlx audio macos gpu web-server

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markdown · JSON · MCP: product_card(name="kyutai-labs/moshi")