Moshi AI vs AudioStack

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.

AudioStack turns a creative brief or block of raw text into finished radio spots, ads, and long-form audio without booking a studio. The company rebranded from Aflorithmic; the old domain still redirects to audiostack.ai. You can work in a self-serve console or embed the production engine through an API that returns white-labeled, broadcast-ready files.

Where most text-to-speech tools hand you a voice clip, AudioStack runs the full chain: script generation, voice casting across providers like ElevenLabs and OpenAI, music and SFX mixing, duration targeting for 6-second or 30-minute specs, mastering, and automated QA for loudness and brand rules. A case study on the site cites 1,500 hyperlocal ads produced in three days and a catalog of 2,600+ voices across 17 providers.

Agencies, ad-tech platforms, radio publishers, and audiobook producers are the core buyers. Products split between InstantCreative and DynamicCreative for self-serve campaign work and CreativeEngine and StoryEngine for API embedding inside other platforms.