Whisperx Transcribe — AI skill for Claude Code

WhisperX Transcribe — a Claude Code skill for meeting & video transcription

[](LICENSE) [](https://code.claude.com/docs/en/skills) [](https://github.com/m-bain/whisperX)

A [Claude Code](https://claude.com/claude-code) skill that transcribes local audio and video files — meetings, interviews, podcasts, lectures, standups, recorded calls — into a clean, speaker-labeled **Markdown transcript**, using [WhisperX](https://github.com/m-bain/whisperX) (Whisper + forced alignment + speaker diarization) under the hood.

The problem this solves: a one-hour meeting recording is far too long to hand an LLM directly, and raw Whisper/WhisperX output is a flat wall of timestamped JSON segments that's expensive and awkward for an LLM to read. This skill lets Claude offload the actual transcription to WhisperX, then hands itself back a tidy, skimmable Markdown document it can summarize, search, or quote from — without ever touching the raw audio or the raw JSON.

# Transcript: Q3 Planning Call
**Duration:** 05:50
**Language:** en
**Speakers:** 2 (Priya, Sam)

### [00:00] Priya
Hey everyone, thanks for joining the call today...

### [00:09] Sam
Sounds good, can you share your screen?

Why this exists

• Long recordings don't fit in an LLM's context window as raw audio or raw transcripts. Transcribing first and handing back structured Markdown lets Claude work with an hour-long meeting the same way it'd work with any other document.
• Raw WhisperX/Whisper JSON is not LLM-friendly. It's a flat list of 3–10 second segments with no higher-level structure — no sense of "who was talking", no skimmable sections, just timestamps and text.
• Speaker diarization turns a transcript into a conversation. Knowing that Speaker