hn-podcast-transcriber

Automatically fetch, transcribe, and archive Hacker News podcast episodes (Hacker News Morning Brief). Use when the user wants to set up a podcast transcription pipeline, archive HN podcast episodes as searchable text, transcribe podcast audio to markdown, or schedule periodic HN podcast ingestion. Also use for any podcast RSS feed transcription workflow.

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Install skill "hn-podcast-transcriber" with this command: npx skills add terrycarter1985/hn-podcast-transcriber

HN Podcast Transcriber

Fetch new episodes from the Hacker News Morning Brief podcast RSS feed, transcribe with Whisper, and archive as searchable markdown.

Prerequisites

  • whisper CLI installed (pip install openai-whisper)
  • ffmpeg on PATH (required by whisper; download from https://ffmpeg.org)
  • python3 with standard library (no extra deps for the fetch script)
  • Disk space for audio files (~5-10 MB per episode)

Quick Start

Run the main script to fetch and transcribe all new episodes:

bash scripts/fetch_and_transcribe.sh --archive ~/hn-podcast-archive

First run processes all episodes. Subsequent runs only process new ones (tracked via state.json).

Options

FlagDefaultDescription
--feed URLHN Morning Brief RSSPodcast RSS feed URL
--archive DIR./hn-podcast-archiveArchive root directory
--model MODELturboWhisper model (tiny/base/small/medium/large/turbo)
--limit N0 (all)Max new episodes to process per run

Custom Feeds

Point at any podcast RSS feed:

bash scripts/fetch_and_transcribe.sh --feed "https://example.com/podcast/feed.xml" --archive ./my-podcast-archive

Scheduling

Set up an OpenClaw cron job for daily checks:

  1. Create an isolated cron job that runs the script
  2. Or add a heartbeat check in HEARTBEAT.md

Archive Structure

See references/archive-layout.md for directory layout and state.json schema.

Workflow Summary

  1. Download RSS feed → parse <item> entries
  2. Skip already-processed episodes (state.json lookup)
  3. Download audio (mp3/m4a) to episode directory
  4. Run whisper to produce .txt transcript
  5. Generate cleaned transcript.md with title + date header
  6. Update state.json with processed episode ID

Notes

  • Whisper models cache to ~/.cache/whisper after first download
  • Use --model tiny for speed, --model large for best accuracy
  • Average episode (~6 min) takes ~1-2 min with turbo model on CPU
  • For GPU acceleration, install ffmpeg with CUDA support

Source Transparency

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