<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Whisper on Landry Zetam - Tech Blog</title><link>https://blog.landryzetam.net/tags/whisper/</link><description>Recent content in Whisper on Landry Zetam - Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Sat, 20 Dec 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.landryzetam.net/tags/whisper/index.xml" rel="self" type="application/rss+xml"/><item><title>From Transcripts to Summaries: Completing the Audio Pipeline</title><link>https://blog.landryzetam.net/posts/transcripts-to-summaries-audio-pipeline/</link><pubDate>Sat, 20 Dec 2025 00:00:00 +0000</pubDate><guid>https://blog.landryzetam.net/posts/transcripts-to-summaries-audio-pipeline/</guid><description>&lt;p>Remember that audio transcription pipeline I built? Raw recordings flow in, get compressed, transcribed by Whisper, and stored as JSON. Great for archival, but 204 transcript files sitting on NFS storage aren&amp;rsquo;t exactly useful. Today I&amp;rsquo;ll show you how I completed the loop—automatically summarizing transcripts with an LLM and publishing everything to my Obsidian vault.&lt;/p>
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&lt;h2 id="part-1-the-problem-with-raw-transcripts">Part 1: The Problem with Raw Transcripts&lt;/h2>
&lt;p>The original pipeline worked beautifully. Every morning, new transcripts appeared as JSON files:&lt;/p></description></item><item><title>Building an Automated Audio Transcription Pipeline on Kubernetes</title><link>https://blog.landryzetam.net/posts/audio-transcription-pipeline/</link><pubDate>Fri, 12 Dec 2025 00:00:00 +0000</pubDate><guid>https://blog.landryzetam.net/posts/audio-transcription-pipeline/</guid><description>&lt;p>Every day, raw audio recordings land on an SFTP server. By morning, they&amp;rsquo;re compressed, transcribed, and stored as searchable JSON - all without human intervention. Here&amp;rsquo;s how I built this pipeline on my homelab Kubernetes cluster.&lt;/p>
&lt;h2 id="the-problem">The Problem&lt;/h2>
&lt;p>I had a growing collection of audio recordings that needed transcription. Manual processing wasn&amp;rsquo;t sustainable: download files, compress them, run through a transcription service, organize the output. This needed automation.&lt;/p>
&lt;p>The requirements were straightforward:&lt;/p></description></item></channel></rss>