How do I install the Audio Transcribe skill?

Run npx skillmds@latest add belschak/audio-transcribe in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Audio Transcribe safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Audio Transcribe?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Audio Transcribe free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Audio Transcribe?

belschak (@belschak) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Audio Transcribe by belschak — Agent Skill

Name: Audio Transcribe
Author: belschak

Audio Transcribe

Transcribe local audio files (voice memos, meeting recordings, dictations, interviews; m4a/mp3/wav/opus/ogg/flac, also the audio track of local video files) in high quality via Whisper (configurable API endpoint) and place the transcript as a dated .md next to the source file. Use whenever the user drops an audio file, mentions a path to a recording, or wants its content as text: "transcribe this", "what do I say in the recording", "turn this into text", "summarize the memo", even without the word "transcribe". NOT for YouTube or podcast URLs; that is the video-watch skill.

belschak 8d3fcd8 2 files · 7.3 KB Updated Sep 17, 2026

File contents

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

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.
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.
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 pricing (as of 2026-07) about $0.111 per audio hour. For YouTube or podcast URLs use the video-watch skill.

---name: audio-transcribedescription: Transcribe local audio files (voice memos, meeting recordings, dictations, interviews; m4a/mp3/wav/opus/ogg/flac, also the audio track of local video files) in high quality via Whisper (configurable API endpoint) and place the transcript as a dated .md next to the source file. Use whenever the user drops an audio file, mentions a path to a recording, or wants its content as text: "transcribe this", "what do I say in the recording", "turn this into text", "summarize the memo", even without the word "transcribe". NOT for YouTube or podcast URLs; that is the video-watch skill.---# audio-transcribeLocal 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.## Workflow1. **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 pricing (as of 2026-07) about $0.111 per audio hour. For YouTube or podcast URLs use the video-watch skill.

Frequently asked questions