transcribe-summarizelisted
Install: claude install-skill kevin-burns/claude-skills
# transcribe-summarize
Audio in, a transcript out, and optionally a set of meeting notes. The default
path runs entirely on the user's machine.
**Two things make this different from calling Whisper yourself**, and both came
out of a day of measurement rather than design:
1. **Whisper invents speech over silence, and a bigger model does not help.** On
a real call with 40 s of joining silence, `large-v3` produced an 18-second
segment of one word repeated 55 times. This skill filters those out and keeps
them, with the metrics that rejected them, in a sidecar so the decision is
auditable.
2. **Cleaning the audio beats choosing a model.** Normalising and silence-trimming
took invented segments from 2 to 0 and fixed a word both `large-v3` and `turbo`
had wrong. It is on by default.
## Setup
`ffmpeg` and `ffprobe` must be on PATH — macOS `brew install ffmpeg`, Windows
`winget install Gyan.FFmpeg`, Linux `apt install ffmpeg`.
Everything else is stdlib. The **backend** brings its own library, and only the
one selected, so pick it with `uv run --with`:
| backend | platform | `--with` spec |
|---|---|---|
| `mlx-whisper` — default on Apple Silicon | macOS arm64 only | `'mlx-whisper>=0.4.2'` |
| `faster-whisper` — default elsewhere | mac / Windows / Linux | `'faster-whisper>=1.2'` |
| `parakeet` — opt-in | Apple Silicon verified | `'parakeet-mlx'` |
| `groq`, `openai`, `elevenlabs`, `gemini` — opt-in, **network** | any | nothing to install |
If the library is mi