Animal Language Translator Device using ML and Raspberry Pi

The Animal Language Translator Device operates by capturing acoustic signals from the environment and processing them through an embedded machine learning pipeline on a Raspberry Pi 5. Audio data is initially recorded using a dedicated microphone paired with the AIY Voice Bonnet, which handles high-quality analog-to-digital conversion and audio routing. Utility tools such as SoX and GStreamer manage the real-time audio streams, handling tasks like format conversion, buffering, and pipeline management in the Linux environment. Once captured, the audio features are analyzed locally using the Edge Impulse Linux SDK, which executes a pre-trained machine learning model designed to classify acoustic patterns and map them to specific emotional states or expressions. This entire inference process runs directly at the edge without requiring constant cloud connectivity. Depending on the classification output determined by the model, the system can trigger text-to-speech or playback routines written in Python and Node.js. Finally, the synthesized or mapped human-understandable response is routed back through the AIY Voice Bonnet and outputted via an attached speaker, allowing the device to announce the translated sentiment in real time.