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BytesAgain β€Ί Skills β€Ί YouTube Transcribe
πŸ¦€ ClawHub

YouTube Transcribe

by @iml885203

Transcribe YouTube videos with smart fallback: extracts captions first (fast, free), falls back to local Whisper transcription when no captions available. Au...

Versionv1.0.0
Downloads592
Installs5
#writing#video#audio#legal#communication
TERMINAL
clawhub install youtube-transcribe

πŸ“– About This Skill

name: youtube-transcribe description: "Transcribe YouTube videos with smart fallback: extracts captions first (fast, free), falls back to local Whisper transcription when no captions available. Auto-detects best Whisper backend (MLX/faster-whisper/openai-whisper) and model size based on hardware. Use when the user shares a YouTube link and wants to know what it says, get a transcript, summarize, or analyze video content. Keywords: YouTube, transcribe, transcript, subtitles, captions, speech-to-text, whisper, mlx, video to text." license: MIT metadata: { "openclaw": { "emoji": "🎬", "requires": { "bins": ["python3", "yt-dlp"], "optionalBins": ["ffmpeg", "mlx_whisper"], }, "install": [ { "id": "yt-dlp", "kind": "brew", "formula": "yt-dlp", "bins": ["yt-dlp"], "label": "Install yt-dlp (brew)", }, { "id": "ffmpeg", "kind": "brew", "formula": "ffmpeg", "bins": ["ffmpeg"], "label": "Install ffmpeg (brew)", }, ], }, }

YouTube Transcribe

Smart YouTube video transcription with automatic fallback: 1. Captions first β€” extracts existing subtitles (manual or auto-generated) via yt-dlp. Fast, free, no compute. 2. Whisper fallback β€” 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 β€” for caption extraction and audio download
  • python3

    • For Whisper fallback (when no captions available):

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

    Usage

    Basic β€” 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)

    1. MLX Whisper β€” detected via import mlx_whisper. Best for Apple Silicon. 2. faster-whisper β€” detected via import faster_whisper. Best for CUDA GPU, good on CPU. 3. OpenAI Whisper β€” detected via import whisper. Universal fallback.

    Model Size (based on available RAM)

    | RAM | Model | VRAM/RAM Usage | |-----|-------|----------------| | β‰₯16GB | large-v3 | ~6-10GB | | β‰₯8GB | medium | ~5GB | | β‰₯4GB | small | ~2.5GB | | <4GB | base | ~1.5GB |

    Caption Language Priority

    When --language is not specified, captions are searched in this order: 1. Video's original language 2. Chinese variants: zh-Hant, zh-Hans, zh-TW, zh-CN, zh 3. English: en 4. 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.

    ⚑ When to Use

    TriggerAction
    - 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

    πŸ’‘ Examples

    Basic β€” 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

    βš™οΈ Configuration

    | 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 |