Hugging Face has detailed a method for running conversations with the Reachy Mini robot entirely on local hardware, removing the previous requirement to send audio data to a remote server. The local stack is driven by a cascaded speech-to-speech pipeline that incorporates voice activity detection, speech-to-text, a large language model, and text-to-speech, exposing a Realtime API-compatible WebSocket server. Users can launch the backend locally and connect the robot through its desktop application. To serve the large language model, the guide outlines options including Hugging Face's llama.cpp, MLX for Mac users, and vanilla transformers. Additionally, the architecture supports external inference engines through the Responses API protocol, enabling integration with vLLM, managed Hugging Face Inference Endpoints, Inference Providers, and OpenAI. According to the guidance, running a localized voice engine gives users complete control over their setup, avoids data leaving the machine, and allows for the customization of individual pipeline stages based on specific speed and quality trade-offs.
Reachy Mini goes fully local
Hugging Face published a guide on running conversations for the Reachy Mini robot fully locally using a speech-to-speech cascaded pipeline. The setup eliminates the need to send audio to a server, keeping all processing on the user's machine without cloud reliance or API keys. The system utilizes a VAD to STT to LLM to TTS pipeline exposed via a Realtime API-compatible WebSocket server.
Why it matters
Running real-time voice interaction stacks entirely on local hardware improves user privacy by keeping data on the machine while offering flexibility in swapping out pipeline components as new models emerge.