About

Imported from terminalskills/skills (skills/youtube-transcription/SKILL.md). Install upstream with npx skills add terminalskills/skills --skill youtube-transcription. Copyright stays with the author (Apache-2.0).

YouTube Video Transcription

Name: youtube-transcription
Author: terminalskills

Transcribe YouTube videos to text using OpenAI Whisper and yt-dlp.

Overview

This skill downloads audio from YouTube videos using yt-dlp and transcribes it using OpenAI's Whisper model. Supports multiple output formats (txt, srt, vtt, json) and various model sizes for different accuracy/speed tradeoffs.

Instructions

1. Install dependencies

# Install whisper and yt-dlp
pip install openai-whisper yt-dlp

# Verify ffmpeg is installed (required for audio processing)
ffmpeg -version

If ffmpeg is missing:

macOS: brew install ffmpeg
Ubuntu/Debian: sudo apt install ffmpeg
Windows: Download from https://ffmpeg.org/download.html

2. Download audio from YouTube

# Download best audio quality as WAV
yt-dlp -x --audio-format wav -o "%(title)s.%(ext)s" "YOUTUBE_URL"

# Download as MP3 (smaller file)
yt-dlp -x --audio-format mp3 -o "%(title)s.%(ext)s" "YOUTUBE_URL"

# Download with video ID as filename (safer for special characters)
yt-dlp -x --audio-format wav -o "%(id)s.%(ext)s" "YOUTUBE_URL"

3. Choose Whisper model

Model	Parameters	VRAM	Relative Speed	Use Case
tiny	39M	~1 GB	~32x	Quick drafts, testing
base	74M	~1 GB	~16x	Fast transcription
small	244M	~2 GB	~6x	Good balance
medium	769M	~5 GB	~2x	High accuracy
large	1550M	~10 GB	1x	Best accuracy

English-only models (tiny.en, base.en, small.en, medium.en) are faster for English content.

4. Run transcription

CLI approach:

# Basic transcription (auto-detect language)
whisper audio.wav --model medium

# Specify language for better accuracy
whisper audio.wav --model medium --language en

# Output specific format
whisper audio.wav --model medium --output_format srt

# All formats at once
whisper audio.wav --model medium --output_format all

# Specify output directory
whisper audio.wav --model medium --output_dir ./transcripts

Python approach:

import whisper

# Load model (downloads on first run)
model = whisper.load_model("medium")

# Transcribe
result = model.transcribe("audio.wav", language="en")

# Get plain text
print(result["text"])

# Get segments with timestamps
for segment in result["segments"]:
    print(f"[{segment['start']:.2f} - {segment['end']:.2f}] {segment['text']}")

5. One-liner pipeline

Combine download and transcription:

# Download and transcribe in one command
yt-dlp -x --audio-format wav -o "audio.wav" "YOUTUBE_URL" && whisper audio.wav --model medium --output_format all

6. Alternative: yt-whisper tool

For simpler workflow, use the dedicated yt-whisper package:

# Install
pip install git+https://github.com/m1guelpf/yt-whisper.git

# Transcribe directly from URL
yt_whisper "https://www.youtube.com/watch?v=VIDEO_ID"

# With options
yt_whisper "YOUTUBE_URL" --model medium --language en --output_format srt

Output Formats

Format	Extension	Description
txt	.txt	Plain text transcript
srt	.srt	SubRip subtitle format (with timestamps)
vtt	.vtt	WebVTT subtitle format
tsv	.tsv	Tab-separated values
json	.json	Full data with word-level timestamps

Examples

Guidelines

Use --language flag when you know the spoken language for significantly better accuracy
For long videos (>1 hour), use small or medium model to balance speed and accuracy
English-only models (.en suffix) are faster and more accurate for English content
GPU with CUDA dramatically speeds up transcription; CPU works but is 5-10x slower
If transcription fails, ensure ffmpeg is properly installed and in PATH
For videos with background music, larger models (medium/large) handle it better
Clean up audio files after transcription to save disk space
Use --output_format all to get every format at once, then choose what you need

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/terminalskills-skills-youtube-transcription/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

terminalskills-skills-youtube-transcription.ocm.jsonjson

{
  "ocm": "1",
  "id": "terminalskills-skills-youtube-transcription",
  "kind": "skill",
  "name": "youtube-transcription",
  "description": "Transcribe YouTube videos to text using OpenAI Whisper and yt-dlp. Use when the user wants to get a transcript from a YouTube video, generate subtitles, convert video speech to text, create SRT/VTT captions, or extract spoken content from YouTube URLs.",
  "publisher": "terminalskills",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "youtube",
      "transcription",
      "whisper",
      "subtitles",
      "speech-to-text",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Transcribe YouTube videos to text using OpenAI Whisper and yt-dlp. Use when the user wants to get a transcript from a YouTube video, generate subtitles, convert video speech to text, create SRT/VTT captions, or extract spoken content from YouTube URLs."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/terminalskills/skills",
      "path": "skills/youtube-transcription/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/terminalskills/skills/blob/HEAD/skills/youtube-transcription/SKILL.md",
      "key": "terminalskills/skills/skills/youtube-transcription/SKILL.md"
    },
    "compatibility": "Requires Python 3.8+, ffmpeg, and CUDA GPU recommended for large models. Install: pip install openai-whisper yt-dlp. CPU works but is slower.",
    "license": "Apache-2.0"
  },
  "instructions": "# YouTube Video Transcription\n\nTranscribe YouTube videos to text using OpenAI Whisper and yt-dlp.\n\n## Overview\n\nThis skill downloads audio from YouTube videos using yt-dlp and transcribes it using OpenAI's Whisper model. Supports multiple output formats (txt, srt, vtt, json) and various model sizes for different accuracy/speed tradeoffs.\n\n## Instructions\n\n### 1. Install dependencies\n\n```bash\n# Install whisper and yt-dlp\npip install openai-whisper yt-dlp\n\n# Verify ffmpeg is installed (required for audio processing)\nffmpeg -version\n```\n\nIf ffmpeg is missing:\n- macOS: `brew install ffmpeg`\n- Ubun",
  "cost": {
    "context_tokens": 1324
  }
}

Fetch it by URL: GET /api/v1/registry/terminalskills-skills-youtube-transcription/manifest?version=1.0.0

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