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belschakbelschak/video-watch/audio-transcribe/SKILL.md

audio-transcribe

Skill1 starsChanged 4 months agoMITView on GitHub

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.

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    1. sonichi/sutando★ 396same name
    2. eze-is/eze-skills★ 74same name
    3. SongXy99/audio-transcriber-memory★ 8same name
    4. iurysza/agent-skills★ 5same name
    5. Apprentice-Geo/skills★ 3same name
    6. itqanlab/agent-toolkit★ 1same name
    7. jay1803/audio-transcribe★ 0same name
    8. sunchaser15/claude-skills★ 0same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

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SKILL.md

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

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