<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>experiment on NNDI Blog</title><link>https://blog.nndi.cloud/tags/experiment/</link><description>Recent content in experiment on NNDI Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>hello@nndi.cloud (NNDI)</managingEditor><webMaster>hello@nndi.cloud (NNDI)</webMaster><lastBuildDate>Mon, 10 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.nndi.cloud/tags/experiment/index.xml" rel="self" type="application/rss+xml"/><item><title>Running Chichewa Speech to Text (STT) inference via ONNX</title><link>https://blog.nndi.cloud/beaker/ai/running-chichewa-speech-inference-via-onnx/</link><pubDate>Mon, 10 Aug 2026 00:00:00 +0000</pubDate><author>hello@nndi.cloud (NNDI)</author><guid>https://blog.nndi.cloud/beaker/ai/running-chichewa-speech-inference-via-onnx/</guid><description>In this article, I will show how we converted Dunstan Matekenya&amp;rsquo;s Chichewa Whisper model to ONNX and running it within Python. Chichewa is one of the national languages of Malawi, fondly known as the warm heart of Africa. Our home. Chichewa is considered a low-resource language and there are not that many resources around transcription and audio based models for it.
Fortunately, there is an ongoing effort to change this status quo, which we are eager to support by putting into practical implementation.</description></item></channel></rss>