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Transcribe video

Skill AlemTuzlak/skills/skills/transcribe-video

Personal AI agent skills for Claude Code, Copilot, Codex, Gemini & Cursor — turn a PR or idea into marketing content, technical Slidev presentations, courses, changelogs, and rendered videos.

Install
npx -y skills add AlemTuzlak/skills --skill transcribe-video

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Use when the user wants to transcribe a video or audio file to text with word-level timestamps — spins up a self-contained local Whisper (whisper.cpp) Docker service bundled in this skill and returns transcript.txt, transcript.srt, and word-level transcript.words.json. Also used as a building block by /produce-video. Triggers on "transcribe this video", "get a transcript", "transcribe-video".

SKILL.md

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Transcribe Video

Transcribes any video/audio file locally using a bundled whisper.cpp service. No external project or cloud API. Returns word-level timestamps (needed for synced overlays/captions).

When to use

  • "Transcribe this clip / video / audio", "get me a transcript with timestamps".
  • As a sub-step of /produce-video.

How it runs

Everything is driven by the bundled runner — never ask the user to manage Docker:

node scripts/transcribe.mjs <path-to-video-or-audio> [--out <dir>] [--port 9111] [--language en] [--no-word-ts] [--task transcribe|translate]
node scripts/transcribe.mjs --stop      # stop the warm container

The runner:

  1. Checks the Docker daemon is reachable (fails loud with guidance if not).
  2. Builds the transcribe-video-whisper image from assets/whisper-service/ if it is missing (first build is slow: it compiles whisper.cpp and bakes the ggml-base.en.bin model).
  3. Starts the transcribe-video-whisper container (host port 9111 → container 9001); reuses it if already running, docker starts it if stopped.
  4. Waits for GET /healthz to report ok.
  5. Extracts a 16 kHz mono WAV from the input with local ffmpeg (whisper.cpp only decodes WAV), then POSTs it to /transcribe with word_ts=true.
  6. Writes transcript.txt, transcript.srt, transcript.words.json to --out (default: the input file's directory), and prints a JSON result line to stdout.

Outputs

  • transcript.txt — plain text.
  • transcript.srt — subtitle text.
  • transcript.words.json — [{ "word", "start", "end" }], seconds, time-ordered. This is the sync source for overlays/captions.

stdout result line (for programmatic callers like /produce-video):

{ "ok": true, "outDir": "...", "wordCount": 38, "segmentCount": 2, "files": { "txt": "transcript.txt", "srt": "transcript.srt", "words": "transcript.words.json" } }

Requirements

  • Docker is REQUIRED — the Whisper service runs in a container, so Docker Desktop must be installed AND running. The runner checks this first and fails loud with install/start guidance if Docker is missing or its daemon is down; there is no non-Docker fallback.
  • ffmpeg is REQUIRED (used to extract a 16 kHz mono WAV for Whisper) — the runner checks for it up front and fails loud if it is not on PATH.
  • Node ≥ 22.

Notes

  • The container is left running (--restart unless-stopped) for warm reuse; stop with --stop.
  • If word timestamps are requested but none come back, the runner fails loud (overlay sync depends on them).
  • See references/ for the Docker lifecycle, the API shape, and the vendoring provenance.

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