audio-transcribelisted

# audio-transcribe Local audio file -> Whisper transcript as .md. The script does everything itself: ffmpeg conversion (16 kHz mono opus), chunking above 20 MB, upload to the configured endpoint, retries, temp cleanup. It imports the proven pipeline of the video-watch skill instead of duplicating it, so both skills need to be installed side by side (they ship in the same repo). **Setup (once):** same `.env` as video-watch (in the video-watch repo root or skill folder; both locations are read): `WHISPER_API_URL`, plus `WHISPER_API_KEY` for hosted APIs. See `.env.example`. `ffmpeg` must be on PATH. ## Workflow 1. **Transcribe:** ``` python3 <skill-path>/scripts/audio-transcribe.py "<path>" [--vocab "Term1, Term2"] [--out <folder>] [--json] ``` - `--vocab`: proper nouns and jargon likely to occur (derive from file name and context). Improves Whisper accuracy noticeably. - Several files in one call are fine; `--out` must then be a folder. - `--json`: additionally write raw segments (finer timestamps) as .json, only for machine post-processing. 2. **Output:** `<name>-transcript-YYYY-MM-DD.md` next to the source file, paragraphs with `[m:ss]` timestamps. If the source sits in an awkward place (Downloads, Desktop), use `--out` to write into the matching project folder instead, never into a workspace root. 3. Answer follow-ups ("summarize", "what do I say about X") from the .md; never transcribe twice. Cost depends on your endpoint; at Groq's whisper-large-v3 p