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

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 MODEL``turboWhisper 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

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