YouTube Video Transcription

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 (use a virtual environment)
pip install -U 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: winget install ffmpeg or download from https://ffmpeg.org/download.html

YouTube downloads in current yt-dlp need a JavaScript runtime; only deno is enabled by default (Node.js, Bun or QuickJS via --js-runtimes). Keep yt-dlp current (pip install -U yt-dlp), since YouTube changes often and old versions break.

2. Check for existing captions first

If the video already has subtitles, skip transcription entirely:

yt-dlp --skip-download --write-subs --write-auto-subs --sub-langs "en.*" --convert-subs srt -o "%(id)s.%(ext)s" "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

Auto-generated captions are lower quality than Whisper output; use Whisper when accuracy matters or no captions exist.

2b. Download audio from YouTube

# Download best audio quality as WAV
yt-dlp -x --audio-format wav -o "%(title)s.%(ext)s" "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

# Download as MP3 (smaller file)
yt-dlp -x --audio-format mp3 -o "%(title)s.%(ext)s" "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

# Download with video ID as filename (safer for special characters)
yt-dlp -x --audio-format wav -o "%(id)s.%(ext)s" "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

3. Choose Whisper model

Speeds are relative to large on an A100. turbo is not trained for translation: --task translate returns the original language, so use medium or large for that. turbo is a good default for transcription.

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.%(ext)s" "https://www.youtube.com/watch?v=dQw4w9WgXcQ" && whisper audio.wav --model medium --output_format all

Output Formats

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
• Only download videos you have the right to transcribe; respect YouTube's terms and creators' copyright.
• --word_timestamps True adds word-level timing to JSON output (check whisper --help).
• 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