Moshi AI vs Noise Eraser

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.

Noise Eraser pulls apart vocals, wind, traffic, music, and air conditioning noise so you can keep the sounds you want and drop the rest. Upload an audio or video file, pick a separation target, and fine-tune the mix with sliders. DeepWave offers it as a browser studio and a mobile app for creators who need cleaner speech without rebuilding the track in a full DAW.

Most denoisers apply one blanket filter across the whole clip. Noise Eraser splits individual sound types so you can keep the voice while dropping wind, or mute traffic without touching the music bed. The free tier handles vocal separation only; paid plans unlock up to five separation models with stereo output on Pro+.

Podcasters, vloggers, and social media editors use it to salvage clips recorded outdoors, in cars, or near HVAC units. The mobile app supports full video files end to end, which matters when you are fixing audio straight from a phone recording rather than exporting stems first.