Google's Gemma Translator Runs Offline Voice Translation on an $80 Computer
Google's Creative Lab open-sources a fully offline, Raspberry Pi-powered voice translator built with Gemma 4, Moonshine ASR, and 3D-printed hardware
- What: Google Creative Lab open-sourced Gemma Translator, a fully offline, DIY voice translation device.
- Hardware: Raspberry Pi 5 (8GB RAM), portable battery, touchscreen, speaker, mini keyboard -- all held in a custom 3D-printed case.
- AI stack: Gemma 4 E2B via LiteRT-LM for translation, Moonshine for speech recognition, Kokoro for text-to-speech -- all on-device.
- Performance: Gemma 4 E2B runs at ~6 tokens/sec on the Pi 5, under 1.5GB RAM; Moonshine is up to 5x faster than Whisper Tiny.
- Deployment: One-command Pi setup via
deploy-pi.sh; also runs on laptops and Android devices. - License: Apache 2.0, free to clone and build -- STL files for the case are included in the repo.
Google's Creative Lab just published an open-source project called Gemma Translator: a handheld, fully offline voice translation device you can build yourself. It combines a Raspberry Pi 5, off-the-shelf components, and a stack of on-device AI models to translate speech between languages with no internet connection, no API keys, and no cloud costs.
The project is free to clone on GitHub. The hardware list is intentionally approachable: a Raspberry Pi 5 with 8GB RAM, a portable battery pack, a small touchscreen display, a mini speaker, and a micro keyboard with a rotary knob for language selection. A custom 3D-printed shell holds everything together, and STL files are included in the repo.
Three models, zero cloud calls
The entire pipeline runs locally. Speech recognition, translation, and text-to-speech each map to a distinct AI component:
- Translation: LiteRT-LM, Google's open-source edge inference framework, runs the
gemma4-e2bmodel. This is Gemma 4's smallest variant, a 2-billion effective parameter model using mixed 2-bit/4-bit/8-bit quantization to stay under 1.5GB of RAM. - Speech recognition (ASR): Moonshine, an open-source ASR model from Useful Sensors, handles transcription. Built for edge hardware, it benchmarks at up to 5x faster than Whisper Tiny with no increase in word error rate.