How do I install the Youtube Transcribe Local skill?

Run npx skillmds@latest add connect0459/youtube-transcribe-local in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Youtube Transcribe Local safe to use?

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Which AI agents work with Youtube Transcribe Local?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Youtube Transcribe Local free to use?

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Who published Youtube Transcribe Local?

connect0459 (@connect0459) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Youtube Transcribe Local by connect0459

Name: Youtube Transcribe Local
Author: connect0459

Youtube Transcribe Local

Transcribe a local audio or video file using ffmpeg + whisper-transcribe. Accepts any format ffmpeg can decode (mp3, m4a, wav, mp4, mkv, mov, webm, opus, flac, …) — transcodes to 16 kHz mono PCM WAV automatically before passing to the Whisper backend. Default output is an SRT file written next to the input. Use whenever the user wants to transcribe a local media file, generate subtitles, get a transcript from audio/video, or convert speech to text — including phrases like "transcribe this file", "get the transcript", "subtitle this video", "speech to text", "run whisper on", or any time the user references a local audio/video file path and asks for text output. Trigger even when the user doesn't say "whisper" — file extension plus transcription intent is enough. Requires /connect0459-agent-skills:whisper-transcribe-setup to have been run first.

connect0459 Updated Sep 17, 2026

File contents

youtube-transcribe-local

Transcribe a local audio or video file by composing ffmpeg normalization and the whisper-transcribe Rust binary.

Script

<skill-path>/scripts/local-transcribe <input-file> [options]

Options:

--format srt|txt|vtt|json    Output format. Default: srt (file next to input).
--model NAME                 Model name. Default: large-v3-turbo.
--output-prefix PATH         Write output to <PATH>.<ext>. Default: next to input.
--language CODE              Language code (e.g. en, ja). Default: auto-detect.
--gpu                        Force GPU/Metal acceleration.
--no-gpu                     Force CPU mode.
-h, --help                   Show usage.

Workflow

Verify ~/.local/bin/whisper-transcribe and the model exist; exit early with a setup hint if not.
ffmpeg: transcode input → 16 kHz mono PCM WAV (temp file, deleted on exit).
Run whisper-transcribe on the WAV.
For --format srt|vtt|json: write <output-prefix>.<ext> and print wrote <path>. For --format txt: stream plain transcript to stdout.

When to use this skill

The user has a local file and wants a transcript or subtitles.
Prefer this over invoking whisper-transcribe directly: this skill handles the ffmpeg transcoding step and sets the correct output prefix relative to the original input.

Long content

Long transcripts can be tens of KB. This skill runs with context: fork so the transcript stays out of the calling session's prompt cache. Return the artifact path and a short summary to the parent session.

Examples

# SRT subtitles (default)
./scripts/local-transcribe ~/Downloads/lecture.m4a
# wrote /Users/you/Downloads/lecture.srt

# Plain text to stdout
./scripts/local-transcribe ~/Downloads/lecture.m4a --format txt > transcript.txt

# Japanese audio, JSON output
./scripts/local-transcribe interview.mp4 --language ja --format json
# wrote /path/to/interview.json

---name: youtube-transcribe-localdescription: Transcribe a local audio or video file using ffmpeg + whisper-transcribe. Accepts any format ffmpeg can decode (mp3, m4a, wav, mp4, mkv, mov, webm, opus, flac, …) — transcodes to 16 kHz mono PCM WAV automatically before passing to the Whisper backend. Default output is an SRT file written next to the input. Use whenever the user wants to transcribe a local media file, generate subtitles, get a transcript from audio/video, or convert speech to text — including phrases like "transcribe this file", "get the transcript", "subtitle this video", "speech to text", "run whisper on", or any time the user references a local audio/video file path and asks for text output. Trigger even when the user doesn't say "whisper" — file extension plus transcription intent is enough. Requires /connect0459-agent-skills:whisper-transcribe-setup to have been run first.---# youtube-transcribe-localTranscribe a local audio or video file by composing ffmpeg normalization and the `whisper-transcribe` Rust binary.## Script```bash<skill-path>/scripts/local-transcribe <input-file> [options]```Options:```text--format srt|txt|vtt|json Output format. Default: srt (file next to input).--model NAME Model name. Default: large-v3-turbo.--output-prefix PATH Write output to <PATH>.<ext>. Default: next to input.--language CODE Language code (e.g. en, ja). Default: auto-detect.--gpu Force GPU/Metal acceleration.--no-gpu Force CPU mode.-h, --help Show usage.```## Workflow1. Verify `~/.local/bin/whisper-transcribe` and the model exist; exit early with a setup hint if not.2. ffmpeg: transcode input → 16 kHz mono PCM WAV (temp file, deleted on exit).3. Run `whisper-transcribe` on the WAV.4. For `--format srt|vtt|json`: write `<output-prefix>.<ext>` and print `wrote <path>`. For `--format txt`: stream plain transcript to stdout.## When to use this skill- The user has a local file and wants a transcript or subtitles.- Prefer this over invoking `whisper-transcribe` directly: this skill handles the ffmpeg transcoding step and sets the correct output prefix relative to the original input.## Long contentLong transcripts can be tens of KB. This skill runs with `context: fork` so the transcript stays out of the calling session's prompt cache. Return the artifact path and a short summary to the parent session.## Examples```bash# SRT subtitles (default)./scripts/local-transcribe ~/Downloads/lecture.m4a# wrote /Users/you/Downloads/lecture.srt# Plain text to stdout./scripts/local-transcribe ~/Downloads/lecture.m4a --format txt > transcript.txt# Japanese audio, JSON output./scripts/local-transcribe interview.mp4 --language ja --format json# wrote /path/to/interview.json```

Frequently asked questions