YouTube Transcribe

Smart YouTube video transcription with automatic fallback:

Captions first — extracts existing subtitles (manual or auto-generated) via yt-dlp. Fast, free, no compute.
Whisper fallback — when no captions exist, downloads audio and transcribes locally with the best available Whisper backend.

When to Use

Use this skill when the user wants to:

Get a transcript or text version of a YouTube video
Understand what a YouTube video says without watching it
Summarize, analyze, or take notes from a YouTube video
Extract subtitles or captions from a video

Triggers

"transcribe this YouTube video"
"what does this video say"
"get the transcript of [YouTube URL]"
"summarize this YouTube video" (transcribe first, then process)
Any YouTube URL shared with a request to understand its content

Requirements

Required:

yt-dlp — for caption extraction and audio download
python3

For Whisper fallback (when no captions available):

ffmpeg — for audio processing
One of these Whisper backends (auto-detected in priority order):
1. mlx-whisper — Apple Silicon native, fastest on Mac (pip install mlx-whisper)
2. faster-whisper — CTranslate2 backend, fast on CUDA/CPU (pip install faster-whisper)
3. openai-whisper — Original Whisper, universal fallback (pip install openai-whisper)

Usage

Basic — transcribe a video

python3 {baseDir}/scripts/transcribe.py "https://www.youtube.com/watch?v=VIDEO_ID"

Specify language for captions

python3 {baseDir}/scripts/transcribe.py "URL" --language zh

Force Whisper (skip caption check)

python3 {baseDir}/scripts/transcribe.py "URL" --force-whisper

JSON output

python3 {baseDir}/scripts/transcribe.py "URL" --format json

Save to file

python3 {baseDir}/scripts/transcribe.py "URL" --output transcript.txt

Options

Flag	Default	Description
`--language`	auto	Preferred subtitle/transcription language (e.g. `zh`, `en`, `ja`)
`--format`	`text`	Output format: `text`, `json`, `srt`, `vtt`
`--output`	stdout	Save transcript to file
`--force-whisper`	false	Skip caption extraction, go straight to Whisper
`--backend`	auto	Whisper backend: `auto`, `mlx`, `faster-whisper`, `whisper`
`--model`	auto	Whisper model size: `auto`, `large-v3`, `medium`, `small`, `base`, `tiny`

Environment Variables

Variable	Description
`YT_WHISPER_BACKEND`	Override Whisper backend selection
`YT_WHISPER_MODEL`	Override Whisper model size

Auto-Detection

Whisper Backend (priority order)

MLX Whisper — detected via import mlx_whisper. Best for Apple Silicon.
faster-whisper — detected via import faster_whisper. Best for CUDA GPU, good on CPU.
OpenAI Whisper — detected via import whisper. Universal fallback.

Model Size (based on available RAM)

RAM	Model	VRAM/RAM Usage
≥16GB	`large-v3`	~6-10GB
≥8GB	`medium`	~5GB
≥4GB	`small`	~2.5GB
<4GB	`base`	~1.5GB

Caption Language Priority

When --language is not specified, captions are searched in this order:

Video's original language
Chinese variants: zh-Hant, zh-Hans, zh-TW, zh-CN, zh
English: en
Any available language

Output Formats

text (default)

Plain text transcript, one continuous block.

json

{
  "video_id": "ZSnYlbIYpjs",
  "title": "Video Title",
  "channel": "Channel Name",
  "duration": 708,
  "language": "zh",
  "method": "captions",
  "transcript": [
    {"start": 0.0, "end": 5.2, "text": "..."},
    ...
  ],
  "full_text": "Complete transcript as single string"
}

srt / vtt

Standard subtitle formats with timestamps.

youtube-transcribe

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