Transcribing 33,000 Danish voice logs on home GPUs — the local pipeline (2026)
Business phone calls had been recorded for 13 months — ~33,000 Danish mp3s, ~570 hours of 32 kbps phone audio. The job: transcribe everything, name the speakers, summarize per call/day/week, browsable on a site, and do it locally with no LLM API. This is the build: a benchmark of Danish ASR models, a dual-model + Claude-fusion transcription pipeline, 'phone-first' speaker identification from metadata, self-healing infrastructure across two GPUs, and Claude Code subagents as the (API-less) summary layer. Plus what it teaches about applying the same pipeline to a customer-service function.