Transcribe

transcribe

Fetch a spoken-word transcript from a video URL. YouTube URLs try the uploader's captions first (seconds, no model download). Everything else — and YouTube videos without captions — falls through to audio extraction + mlx-whisper on Apple Silicon, with a headless-Playwright fallback for Threads and other yt-dlp-unsupported platforms.

When to invoke

• The user gives you a YouTube, Shorts, Instagram, TikTok, or Threads URL and asks for a transcript, summary, analysis, or quote
• Another skill needs the spoken content of a video to do its job (e.g. research-scout analyzing a reference video)

How to invoke

From the Bash tool:

transcribe <url>                  # default: captions-first for YouTube, whisper otherwise
transcribe <url> --json
transcribe <url> --force-whisper  # skip YouTube captions, always run whisper

The transcribe executable is on PATH once the plugin is installed.

From another skill, use the Skill tool:

Skill({ skill: "transcribe:transcribe", args: "<url>" })

Output

Default (markdown):

# {title}

**Source:** {url}
**Platform:** {YouTube|Instagram|TikTok|Threads}
**Duration:** {mm:ss or "unknown"}
**Model:** mlx-community/whisper-large-v3-mlx

{transcript}

With --json: structured object with title, url, platform, duration_seconds, model, tier (youtube-captions-manual, youtube-captions-auto, ytdlp, or playwright), text, and segments[] with timestamps. The captions tiers return an empty segments[] — segment timing is only available when whisper runs.

Dependencies

Install with:

bash ${CLAUDE_PLUGIN_ROOT}/scripts/install.sh

Apple Silicon only in v0.1. Install deps: yt-dlp, ffmpeg, Node.js ≥ 20, Playwright Chromium, mlx-whisper.

Model

Default model: mlx-community/whisper-large-v3-mlx (~3GB, downloaded on first use). Override per-call with the WHISPER_MODEL environment variable, for example:

WHISPER_MODEL=mlx-community/whisper-base-mlx transcribe <url>

Smaller models trade accuracy for speed. Use whisper-base-mlx (~140MB) for quick checks.

Tiers

1. YouTube captions (tier 0) — yt-dlp --write-sub --write-auto-sub fetches the uploader's captions directly, no audio download, no whisper. Manual captions preferred; auto-generated is the fallback within this tier. YouTube URLs only. Seconds per video.
2. yt-dlp + mlx-whisper (tier 1) — downloads audio, converts to 16kHz WAV, transcribes locally. Used for non-YouTube platforms, YouTube videos without any captions, or when --force-whisper is set. Minutes per video.
3. Playwright + mlx-whisper (tier 2) — headless-browser extraction for Threads and other platforms yt-dlp can't reach.

The Model: line in the output reports which tier ran: youtube-manual-captions, youtube-auto-captions, or the whisper model name.

Known limitations

• Apple Silicon only for tiers 1 and 2. Tier 0 (YouTube captions) works anywhere with yt-dlp + python3. Tier 1/2 need mlx-whisper, which is Apple Silicon only.
• YouTube auto-captions are noisy. Auto-generated captions miss punctuation, mis-transcribe names, and don't attribute speakers. Use --force-whisper when accuracy matters more than speed.
• Silent videos hallucinate (whisper tiers only). Whisper will invent text on silent or music-only audio. The plugin returns the transcript regardless but adds a stderr warning when repeated segments are detected.
• Login-gated posts. Public content only unless the user has signed into the persistent Playwright profile at ~/.cache/transcribe-plugin/playwright-profile/.
• No batching. One URL per call. Use a shell loop for multiple.
• Threads is flaky. Threads post pages also render a feed of suggested videos, and Playwright's network capture can land on a neighbor instead of the target post. The extractor anchors to the <video> element's currentSrc and filters to segments sharing its CDN asset id, but expect occasional failures or mismatches. Retry or fall back to manual download for critical Threads transcripts.

See also

• references/integration-guide.md — how other skills should invoke transcribe