Speaker-Diarized Call Transcription on a CPU — No Cloud, No GPU, No Hugging Face Token

Tue, 18 Aug 2026 00:00:00 +0000

I wanted a speaker-labeled, timestamped transcript of a two-person phone call — the kind an LLM can actually reason about (“what did the customer commit to?”, “when did the price come up?”) — without shipping the audio to a cloud speech-to-text service.

One hard constraint shaped every decision: it all had to run on hardware already in the rack. No new GPU, no API keys, no “just use the cloud for the hard part.” A 35-watt desktop chip that already earns its keep running other things.

It worked. And, as usual, the interesting parts weren’t in the happy path — they were in why the whisper I already had running couldn’t do the job, how to diarize speakers on a CPU with no Hugging Face token, and a genuinely great debugging story that ends with a 114-byte file quietly corrupting every timestamp in the system.

A note on the transcript examples below: the real test call involved an actual third-party business and named individuals who never agreed to be quoted. Every name, company, and phone number in the excerpts here is synthetic — invented for illustration. In many US states, Colorado included, recording calls and reusing what was said carries real legal weight. If you build one of these, redact before you publish. I’m practicing what I preach.

Teaching an AI to Develop My RAW Photos: A Self-Hosted Linux Pipeline

Tue, 18 Aug 2026 00:00:00 +0000

I wanted to answer a specific question: could an AI agent do the boring, careful parts of my photography workflow — the parts I actually skip because they take too long?

Not “make my photos look good” in the Instagram-filter sense. I mean the tedious, correct-but-thankless work: pulling the right RAW off my server, applying the exact lens-correction data for the exact lens I used, denoising only the frames that need it, keeping the EXIF intact, filing everything under the right date, and getting a print onto photo paper without fighting a driver. The stuff a careful darkroom tech would do and a busy human never quite gets around to.

So I built it. A local, self-hosted pipeline where Claude Code can reach into my Immich library, pull original Canon CR3 RAW files, develop them in darktable with camera- and lens-specific corrections, retouch in GIMP, print on a networked Canon photo printer, and push finished JPEGs back into Immich as new assets — all driven through the Model Context Protocol (MCP).

Projects on Linux Colorado

Speaker-Diarized Call Transcription on a CPU — No Cloud, No GPU, No Hugging Face Token

Teaching an AI to Develop My RAW Photos: A Self-Hosted Linux Pipeline