This application performs a speech-to-text transcription using OpenAI's Whisper-tiny and Whisper-base model on the Hailo-8/8L/10H AI accelerators, on a Raspberry Pi 5

Example of Python code to process chunks of sounds on a application loop

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification.

Suppose you are running a Python systemd service that opens some file descriptors (these can be regular files, named pipes, sockets, and so on). When the service is stopped, it needs to finish cleanly by closing and/or removing the associated resources. This post presents two manners in which the service may be stopped gracefully.

Because I had been previously using my Pi as a headless server, I hadn’t installed a desktop environment. I wanted to add a lightweight heads-up display to the server, and I wanted a graphical application that could operate without X, the usual graphical system on Linux.
Writing such a lightweight application required understanding how to interact more directly with my new screen, the learnings from which I’ll share here. This article is a summary of multiple sources I pieced together.

Raspberry Pi controlling small LED array. Here is using a LED strip, but the chip may be the same to move the wished module:
https://amzn.eu/d/iVcOwJG

Example in Python of a TTS server for Catalan ONNX models

Piper is a fantastic tool for providing very realistic sounding TTS while also being efficient and fast. There are some publicly available pre-trained models, but if you’re here, that means you want to create your own voice. This guide aims to walk you through the steps.

The tool/function calling equivalent for Ollama, looking quite similar to Gemini

The offline Model library for Python