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Moshi

Moshi

Moshi · Family: Moshi
Kyutai’s open real-time voice model (full-duplex speech-to-speech): Helium 7B backbone + Mimi codec, ~160 ms latency.
✓ Active✓ Public access⚖ Open sourceAudioAudio📁 Moshi
Parameters
≈8B (backbone Helium 7B)
parameters
Release date
18 September 2024
Access:DownloadHostedDeployment:💻 Local📱 On-device☁ Cloud

Overview

Moshi is an open real-time voice AI model developed by the Paris lab Kyutai and open-sourced on 18 September 2024 (first demo in July 2024). It is a speech-to-speech dialogue system that processes speech directly, without an intermediate text conversion, and operates in full duplex — listening and speaking at the same time.

Its architecture combines the Helium language backbone (7B parameters, trained on 2.1T tokens), the Mimi streaming neural audio codec (12 Hz, 1.1 kbps) and the “Inner Monologue” method, in which the model predicts text tokens as a prefix to audio tokens, improving the linguistic quality of speech. The model has about 8 billion parameters.

Moshi achieves a theoretical latency of 160 ms (about 200 ms in practice), enabling natural real-time conversation; it also understands emotions and non-verbal aspects of communication. It takes audio (24 kHz, mono) as input and produces speech plus time-aligned text as output. It was trained on about 7 million hours of audio and 20,000 hours of synthetic dialogue.

The model is open: weights under CC-BY-4.0 and code under Apache-2.0 (GitHub, Hugging Face). Variants include Moshiko (male voice) and Moshika (female voice). Kyutai is a non-profit AI lab based in Paris, funded by Iliad, CMA CGM and Schmidt Sciences, among others.

Classification
AudioAudio
Family: Moshi
Access & deployment
DownloadHosted
LocalOn-deviceCloud
Weights: Open source
Key parameters
🧩 Parameters: ≈8B (backbone Helium 7B)
✓ Fine-tuning
📥 Input: audio

Technical specification

Parameters
≈8B (backbone Helium 7B)
parameters
License
Wagi CC-BY-4.0; kod Apache-2.0
Hardware requirements
Open source; runs on GPU, with quantized variants (MLX) also available to run locally/on-device (e.g. Apple Silicon).
Features:Fine-tuning
Modalities
⬇ Input
audio
⬆ Output
audiotext

Capabilities and applications

Native model capabilities
Voice Conversation
Ability to conduct multi-turn real-time voice conversations with context retention and natural speech pacing.
Category: speech
Natural conversation
Conducting a conversation with a tone close to human: a warmer voice, empathy in emotional responses, humour, and avoiding the stiff 'AI assistant jargon'. Introduced as a deliberate improvement in GPT-5.1 Instant.
Category: language
Real-time inference
The model's ability to generate responses with very low latency (>1000 tokens/sec) on specialized inference hardware (e.g. Cerebras WSE), enabling interactive, turn-by-turn collaboration with a human.
Category: coding
Speech to text
Category: speech
Text to speech
Category: speech
Streaming Speech-to-Text
Real-time conversion of speech to text with immediate output as the speaker is talking.
Category: speech
Application domains

Technical architecture

Deployment and security

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