abubakarsiddik31

Whisperx Transcribe — AI skill for Claude Code

AI community

Claude Code skill/plugin: transcribe audio & video into clean, speaker-labeled Markdown via WhisperX, so an LLM can read a long meeting without processing raw audio.

How to install Whisperx Transcribe

This entry records only its repository, not the path inside it, so there is no exact command to give. Open abubakarsiddik31/whisperx-transcribe and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Whisperx Transcribe does

Claude Code skill/plugin: transcribe audio & video into clean, speaker-labeled Markdown via WhisperX, so an LLM can read a long meeting without processing raw audio.

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README

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

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) [![Claude Code Skill](https://img.shields.io/badge/Claude%20Code-Skill-blueviolet)](https://code.claude.com/docs/en/skills) [![Powered by WhisperX](https://img.shields.io/badge/Powered%20by-WhisperX-orange)](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