This is a pure-MicroPython driver for the ST7789 / ST7735 / ILI9471 / ILI9472 display drivers, designed in order to use very little memory.

This graphic library is dedicated to :
Lilygo T4-S3 AMOLED Lilygo T-Display S3 AMOLED Lilygo 1.43 inches SH8601 AMOLED Lilygo 1.75 inches SH8601 AMOLED Waveshare ESP32-S3 1.8 inches AMOLED Touch Waveshare ESP32-S3 2.41 inches AMOLED Touch This Micropython driver is created on behalf of nspsck RM67162 driver. It is also convergent with russhugues ST7789 driver. I also would like to thanks lewisxhe. Your advices helped me a lot. Finally, I must talk of tomolt. The libschrift TTF lightweight library truelly rocks ! My main goal was to adapt a driver library that would give the same functions thant ST7789 driver, in order to be able to get my micropythons projects working whether on PICO + ST7789 or ESP32 + RM690B0 or ESP32 + RM67162 The driver involves a frame buffer of 600x450, requiring 540ko of available ram (T4-S3 version). The driver involves a frame buffer of 536x240, requiring 280ko of available ram (TDisplay S3 version). In a more general way, requirements are WIDTH x HEIGHT x 2 bytes or ram.

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

Hailo AI forum page from March 2025 announcing Speech to Text through Whisper by using the Hailo AI accelerator included also in the AI HAT+2

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

This page provides instructions for running AI models powered by Hailo NPUs on Raspberry Pi 5. The Hailo NPU is an AI accelerator chip designed to run neural networks; instead of Raspberry Pi’s CPU doing the AI work, the NPU handles it more efficiently.

AI HATs are add-on boards for Raspberry Pi 5 that come with a built-in AI accelerator chip: the Hailo neural processing unit (NPU). The Hailo NPU allows Raspberry Pi 5 to run hardware-accelerated AI models locally, removing the need to send data to a remote cloud server for processing. This edge AI approach improves performance, reduces latency, and helps to keep data private.

Using **fbi** to show a custom splash screen during boot. Take a look at the comments for further configuration to tune it up more.

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.