I built a small on-device voice front-end for local assistants: type any wake word, keep only your voice before STT (5 models, 7.5 MB, Apache-2.0, early preview)
The second is a real-time voice agent in the browser: client-side VAD and denoising, an STT fallback chain, a small decision model judging each segment for end of turn, barge-in and "stop", and TTS read sentence by sentence.
Proof1 community thread
Key points
- So I started working on that audio layer, the part that runs before speech-to-text and the LLM, and I'm sharing it as an early preview.
- It's called Alkove: five small models that run on the device and are meant to work together.
- Lex (1.8 MB) turns a typed word into its likely pronunciations, so a wake word is just text: no training, no recording session.
- Cue (1.1 MB) detects speech every 30 ms.
Sources (1)
- [1]I built a small on-device voice front-end for local assistants: type any wake word, keep only your voice before STT (5 models, 7.5 MB, Apache-2.0, early preview)r/LocalLLaMA (top, daily) · Oct 21, 12:59 PM
The second is a real-time voice agent in the browser: client-side VAD and denoising, an STT fallback chain, a small decision model judging each segment for end of turn, barge-in and "stop", and TTS read sentence by sentence.
So I started working on that audio layer, the part that runs before speech-to-text and the LLM, and I'm sharing it as an early preview.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 20, 2026Voxtral 3
- Oct 20, 2026Do Diffusion LLMs Scale? Testing Mercury 3 + Decide
- Oct 19, 2026The Model Swap Workshop: GPT-5.5 vs Kimi vs DeepSeek vs Mistral
- Oct 19, 2026NVIDIA Open Sources Run:ai Under Apache 2.0 for Kubernetes GPU Orchestration
- Oct 18, 2026Google Open-Sources Harness2, a Recursive Agent Harnessing Framework
- Oct 17, 2026Perplexity Open-Sources SPACE, a Firecracker MicroVM Sandbox for AI Agents