Moshi AI vs Audio Note

Moshi AI is a speech-native conversational model from Kyutai, a Paris-based open-science research lab. Instead of chaining speech recognition, text generation, and text-to-speech, Moshi processes audio directly and holds full-duplex voice conversations with minimal latency.

Its multi-stream design runs separate channels for the user, Moshi's spoken output, and an Inner Monologue text stream that improves coherence. That setup lets Moshi listen and talk at the same time, handle overlaps, interruptions, and backchanneling like a real conversation rather than rigid speaker turns.

Moshi is built on Helium, a 7B language model, and Mimi, Kyutai's neural audio codec. Weights and inference code ship for PyTorch, Rust, and MLX, and you can try it in the browser at moshi-chat.kyutai.org. Researchers, voice AI developers, and anyone building real-time spoken interfaces will find the most value here.

Audio Note turns voice recordings and typed thoughts into clean, structured text. Speak into your browser or upload an audio file, and the tool produces a transcript you can reshape into notes, emails, lists, or meeting recaps without retyping anything.

It sits between a quick voice memo and a finished piece of writing. You record once, then pick from seven output formats such as a plain note, journal entry, meeting summary, action items, or LinkedIn post. The homepage demo shows a rambling client call turned into a tight summary with follow-ups and deadlines called out.

The product is built for people who think faster out loud than on a keyboard. Consultants, managers, and creators can capture ideas while walking or between meetings, then polish them for follow-up emails, posts, or personal journaling.

Audio Note runs in the browser and installs as a progressive web app on iPhone, Android, Mac, and Windows, so you can start recording without downloading a native app.