Moshi AI vs Emastered

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.

Emastered masters your tracks online in seconds using an AI engine built with Grammy-winning engineers. Upload an MP3, AIFF, or WAV file and the system analyzes your song, applies EQ, compression, and loudness processing, then returns a polished master ready for streaming. You can preview the result against your original and download from the homepage for free before subscribing.

Unlike generic loudness plugins, Emastered picks processing per track and learns from each master. Paid plans add Mastering Options so you can tweak compression, EQ, stereo width, and volume and save presets. The Studio bundle also bundles stem separation, distribution, ATOM synth, PitchPro vocal tuning, and Linkify in one subscription instead of paying separate tools.

Independent musicians, producers, and bedroom artists use Emastered when they need radio-ready masters without hiring a mastering engineer. The platform reports 3 million plus artists and 30 million plus tracks mastered. Billboard, Vice, and MusicRadar have featured the service.