Moshi AI vs Ermine.ai

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.

Ermine.ai transcribes audio from your device microphone entirely in the browser with no server upload. You click to start, speak, and get a live transcript plus downloadable audio and text files when you finish. The transcription model loads client-side on first use (about 50 MB download) and caches locally so later sessions start faster.

Cloud transcription services send your audio to remote servers for processing. Ermine.ai runs the model on your machine via transformers.js, which means recordings never leave your device. That privacy trade-off comes with limits: English-only transcription and a one-time model download that can take a few minutes on first load.

Journalists, students, and privacy-conscious professionals use Ermine.ai when they need quick voice notes without signing up for an account or sending audio to a third party. It fits anyone who wants a free, no-login dictation page that works offline after the model caches.