fine-tuned with the rest of the weights on speech data. Now, at its core, Moshi is a speech-to-speech architecture. But, the inner monologue makes it more versatile. If we delay the text stream by a couple of seconds, the model learns to transcribe the audio it just heard, making it a speech-to-text model. The other way around, if we delay the audio stream, we can train a text-to-speech model. Of course, at inference time, the TTS model will read user text rather than its own inner monologue. So, with a single model architecture, we get three different behaviors. Moshi really paved the new way for voice models. It was trained on about 100 H100 GPUs for about a month, which might sound like a lot, but for Frontier Labs, that's just a rounding error. Yet, Moshi responds faster than the average human, and if you quantize it, it even runs on your MacBook. So, if full duplex models