#118 · Primary category: Speech & Audio

rhino

entity-resolution intent-inference natural-language-understanding nlu on-device slot-filling slu speech-recognition spoken-language-understanding voice-assistant voice-command voice-command-control voice-commands voice-control voice-recognition voice-ui voice-user-interface vui

On-device Speech-to-Intent engine powered by deep learning

Project last updated:08/12/26

GitHub Stars

709

Forks

94

Contributors

26

License

Apache-2.0

Why we included this project

Rhino takes a different route from most voice assistants: instead of transcribing speech to text and then parsing that text, it reads intent straight from the audio. Say "a small double-shot espresso" and it returns a structured result with the relevant values already extracted. That directness is what makes it attractive for embedded work, since it runs on Arm Cortex-M, Raspberry Pi, Android, iOS, and in browsers, all on-device with no network call. You define your own command contexts and slot types in the Picovoice Console, then wire them in through SDKs for Python, Node, Flutter, React Native, and more. For teams building smart-home controls, kiosks, or other voice UIs where the command set is fixed and latency matters, the small footprint and real-time behavior are the main reasons to try it.

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