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You can build a standalone voice translator from Google’s Gemma Translator project: it runs Gemma 4 E2B locally through LiteRT-LM, with Moonshine components for speech recognition and speech output. The project documents a Raspberry Pi 5 with 8GB RAM, a microphone or USB audio interface, an output device, and a display. Its README says internet is not required after setup; downloading software and model files still requires connectivity. This is a documented build path, not a guarantee of translation quality or a report of independent testing.
How the translator works
The project separates voice translation into three stages. Moonshine speech-to-text (STT) transcribes the microphone input; Gemma translates the text; moonshine-voice text-to-speech (TTS) speaks the translated result. A web front end is designed for small displays, while a Python API server handles the service side. The repository identifies LiteRT-LM as the runtime for its local gemma4-e2b model. See the Gemma Translator README for the current architecture and instructions.
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“Standalone” here means local inference after setup, not a machine that can be prepared without downloads. The README says the running translator needs no internet after setup, but obtaining dependencies and model files is part of the setup process.
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What you need
Hardware
- Raspberry Pi 5 with 8GB RAM
- A microphone or USB audio capture interface
- A speaker or headphones for translated speech
- A monitor or touchscreen
The README’s hardware list is a project requirement, not a claim that a board or starter kit is a complete translator. Check that the chosen audio and display devices connect to the Pi and are supported by the operating-system image you plan to use.
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Software environment
- Python 3.10 or later
- Node.js 18 or later; the README recommends Node.js 20 LTS
- Linux or macOS for the documented development environment
For the exact Pi deployment flow and package requirements, use the repository’s live instructions. They describe making scripts executable, running setup, downloading or importing the model, and starting services. A separate deployment script is described as installing packages, preparing the environment, fetching the LiteRT model, and configuring a kiosk-style service. These are documented steps, not a guarantee that scripts work unchanged on every system image.
Build the documented Raspberry Pi version
- Check the live README first. Confirm the current model package, runtime steps, operating-system assumptions, and any licensing or terms that apply to your intended use.
- Prepare the Pi and peripherals. Connect the microphone or audio capture interface, speaker or headphones, and display. Confirm the operating system recognizes the devices.
- Install the project environment. Follow the repository’s setup instructions, including its executable-script and dependency steps. Use the documented Python and Node.js versions.
- Obtain and import the model. Follow the repository’s model-download and import instructions while online. Keep the model and required dependencies available locally for offline use.
- Start the services. Use the README’s commands to launch the Python API and web front end, or its Pi deployment script if appropriate for your system.
- Test the full voice path. Speak into the microphone, verify the transcript, check the translated text, and listen to the spoken output. Test the actual languages, accents, room noise, and phrases you expect to use.
Because the repository may change, its current commands and model packaging are authoritative; no command sequence here should be treated as a substitute for those live instructions.
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Choose a model and runtime for your device
The Pi project specifies Gemma 4 E2B with LiteRT-LM. That exact project configuration should not be confused with Google’s broader model-selection guidance or with translation-specific models. Google’s current getting-started guide maps Gemma 4 E2B to mobile devices, E4B to mobile devices and laptops, 12B to laptops, desktops, and small servers, A4B to desktops and small servers, and 31B to large servers or clusters. These are suggested device classes, not promises about translation speed or quality on a particular setup. Google’s Gemma getting-started guide provides the model-family overview.
Runtime choice also depends on the device and model format. Google lists LM Studio and Ollama for local chat interfaces; LiteRT-LM, llama.cpp, and MLX for efficient local or edge inference; and Transformers, Keras, Tunix, and Unsloth for building or tuning. Verify that the selected framework supports the format of the model you intend to run. Google’s inference guidance explains these options and hardware considerations.
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For mobile and edge use, Google documents LiteRT-LM as an on-device framework with CPU, GPU, and NPU support on Android and iOS. The Gemma Translator project’s Pi setup is a specific implementation, so check its current package and runtime steps rather than assuming every LiteRT-LM model or device combination follows the same instructions.
Alternatives to the Pi kiosk
| Approach | What the cited documentation establishes | What to evaluate yourself |
|---|---|---|
| Raspberry Pi kiosk | The Gemma Translator README specifies a Pi 5 with 8GB RAM, Gemma 4 E2B through LiteRT-LM, Moonshine speech components, and a small-display web front end. | Compatibility with your peripherals and OS image, setup behavior, responsiveness, and results for your language pair. |
| Phone or tablet | Google describes Gemma 3n as designed for local operation on phones, tablets, and laptops, with audio, text, image, and video input capabilities and transcription/translation support. | Whether a suitable app and model package support your device, languages, and offline requirements. The Gemma Translator README does not identify Gemma 3n as its model. |
| Laptop or desktop | Google’s model and inference guides list device-appropriate model families and several local runtimes and developer frameworks. | Model-format support, hardware fit, setup complexity, and performance on your actual workload. The cited sources provide no like-for-like setup-time or speed comparison. |
Google DeepMind’s Gemma 3n page says it “Enables developers to build intelligent, interactive features that respect user privacy and work reliably offline.” That is the product page’s description of Gemma 3n, not independent validation of the Pi translator or every offline configuration. Read Google DeepMind’s Gemma 3n overview.
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How much translation quality can you expect?
Gemma Translator’s README establishes an implementation path, but it does not establish results for every language pair or speaking environment. Translation-focused evidence exists separately: the TranslateGemma technical report describes Gemma 3-based models fine-tuned for translation using supervised fine-tuning on synthetic and human-translated parallel data, followed by reinforcement learning with reward models.
The report authors describe human evaluation on WMT25 across 10 language pairs and automatic evaluation on WMT24++ across 55 language pairs. Those numbers measure evaluation breadth, not an accuracy score or a guarantee for your conversations. The report’s abstract says the models improved over baseline Gemma 3 models, but the cited summary does not provide pair-by-pair scores or a complete operational test of this Raspberry Pi voice workflow. Read the TranslateGemma technical report.
Quick Recap
Best Value
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- Test both directions for the language pair you need; quality may differ by direction.
- Try representative speech, including names, numbers, idioms, and domain-specific terms.
- Assess transcription errors separately from translation errors, then check whether spoken output is understandable.
- Test in the room and on the hardware where you plan to use it, including latency and background noise.
- Do not rely on an unverified result for important medical, legal, safety, or emergency communication.
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