video-summarizer

name

video-summarizer

description

Download videos from 1800+ platforms (YouTube, Bilibili, Twitter/X, TikTok, Vimeo, Instagram, etc.) and generate complete resource package with video, audio, subtitles, and AI summary. Actions: summarize, download, transcribe, extract video content. Platforms: youtube.com, bilibili.com, twitter.com, x.com, tiktok.com, vimeo.com, instagram.com, twitch.tv. Outputs: MP4 video, MP3 audio, VTT subtitles with timestamps, TXT transcript, MD AI summary. Auto-installs uv, yt-dlp, ffmpeg. Python dependencies managed by uv.

Video Summarizer

Overview

Download videos from any platform and generate a complete resource package including:

• Original video file (mp4)
• Audio file (mp3)
• Subtitle file (with timestamps, vtt/srt format)
• Summary file (summary.md)

Supports all 1800+ websites supported by yt-dlp.

Trigger Conditions

When the user:

• Provides a video link and asks for a summary
• Says "summarize this video", "what's in this video"
• Asks to "extract video content", "transcribe video"
• Says "download this video"
• Provides a link from YouTube/Bilibili/Twitter/Vimeo/TikTok etc.

Supported Platforms

• YouTube (youtube.com, youtu.be)
• Bilibili (bilibili.com, b23.tv)
• Twitter/X (x.com, twitter.com)
• Vimeo (vimeo.com)
• TikTok (tiktok.com)
• Instagram (instagram.com)
• Twitch (twitch.tv)
• And 1800+ other platforms (all sites supported by yt-dlp)

Output Structure

All files are saved to downloads/<video-title>/ in the current working directory:

./downloads/
└── <video-title>/
    ├── video.mp4          # Original video
    ├── audio.mp3          # Extracted audio
    ├── subtitle.vtt       # Subtitles with timestamps
    ├── transcript.txt     # Plain text transcript (no timestamps)
    └── summary.md         # Structured summary

Workflow

Step 1: Install Dependencies

Run the install script to check and install all dependencies:

bash "$SKILL_DIR/scripts/install_deps.sh"

This installs: uv (Python package manager), ffmpeg, yt-dlp, and checks Python version. faster-whisper will be automatically managed by uv.

Step 2: Get Video Info and Create Output Directory

# Get video title (sanitize special characters for folder name)
TITLE=$(yt-dlp --print "%(title)s" "VIDEO_URL" | sed 's/[/:*?"<>|]/_/g' | cut -c1-80)
DURATION=$(yt-dlp --print "%(duration)s" "VIDEO_URL")

# Create output directory
OUTPUT_DIR=./downloads/"$TITLE"
mkdir -p "$OUTPUT_DIR"

Step 3: Download Video and Audio

# Download video (mp4 format, best quality up to 1080p)
yt-dlp -f "bestvideo[height<=1080][ext=mp4]+bestaudio[ext=m4a]/best[height<=1080][ext=mp4]/best" \
  --merge-output-format mp4 \
  -o "$OUTPUT_DIR/video.%(ext)s" "VIDEO_URL"

# Extract audio (mp3 format)
yt-dlp -x --audio-format mp3 -o "$OUTPUT_DIR/audio.%(ext)s" "VIDEO_URL"

Step 4: Get Subtitles

Priority order:

1. Try downloading manual subtitles (best quality)

yt-dlp --write-subs --sub-lang zh,en,zh-Hans,zh-Hant --skip-download \
  -o "$OUTPUT_DIR/subtitle" "VIDEO_URL"

2. Try downloading auto-generated subtitles

yt-dlp --write-auto-subs --sub-lang zh,en --skip-download \
  -o "$OUTPUT_DIR/subtitle" "VIDEO_URL"

3. Use faster-whisper transcription when no subtitles available

uv run "$SKILL_DIR/scripts/parallel_transcribe.py" \
  --input "$OUTPUT_DIR/audio.mp3" \
  --output-dir "$OUTPUT_DIR" \
  --model small \
  --language auto

The script automatically:

• Splits long audio files at silence points
• Uses multiple CPU cores for parallel transcription
• Outputs both subtitle.vtt and transcript.txt

Transcription Options:

Option	Default	Description
--model	small	tiny/base/small/medium/large-v3
--language	auto	Language code or 'auto'
--workers	CPU/2	Number of parallel workers
--min-segment	60	Min duration (sec) to enable splitting

Step 5: Generate Plain Text Transcript

If subtitles were downloaded (not transcribed), convert to plain text:

if [[ ! -f "$OUTPUT_DIR/transcript.txt" ]]; then
  SUBTITLE_FILE=$(ls "$OUTPUT_DIR"/*.vtt "$OUTPUT_DIR"/*.srt 2>/dev/null | head -1)
  if [[ "$SUBTITLE_FILE" == *.vtt ]]; then
    sed '/^[0-9]/d; /^$/d; /-->/d; /^WEBVTT/d; /^Kind:/d; /^Language:/d; /^NOTE/d' \
      "$SUBTITLE_FILE" > "$OUTPUT_DIR/transcript.txt"
  elif [[ "$SUBTITLE_FILE" == *.srt ]]; then
    sed '/^[0-9]/d; /^$/d; /-->/d' "$SUBTITLE_FILE" > "$OUTPUT_DIR/transcript.txt"
  fi
fi

Step 6: Generate Summary File

1. Read prompt template from $SKILL_DIR/reference/summary-prompt.md
2. Replace placeholders: {{TITLE}}, {{PLATFORM}}, {{URL}}, {{DURATION}}, {{LANGUAGE}}, {{DOWNLOAD_TIME}}, {{TRANSCRIPT}}
3. Generate summary and save to $OUTPUT_DIR/summary.md

Platform-Specific Handling

Bilibili

# Prioritize Chinese subtitles
yt-dlp --sub-lang zh-Hans,zh-Hant,zh ...
# If login required
yt-dlp --cookies-from-browser chrome "VIDEO_URL"

Platforms Requiring Login

yt-dlp --cookies-from-browser chrome "VIDEO_URL"
# or firefox
yt-dlp --cookies-from-browser firefox "VIDEO_URL"

Error Handling

Cannot Get Subtitles

Use the parallel transcription script (Step 4, option 3).

Video Too Long (>1 hour)

1. Ask user if they only need partial content
2. The parallel script handles long files automatically

Unsupported Platform

yt-dlp --list-extractors | grep -i "platform-name"

Notes

1. Storage: Files saved to ./downloads/ in current working directory
2. Copyright: For personal learning use only
3. Network: Some platforms may require proxy
4. First Run: Whisper model download required (~244MB for small)
5. Parallel Processing: Long audio (>60s) auto-splits at silence points