tubescribe

TubeScribe 🎬

Turn any YouTube video into a polished document + audio summary.

Drop a YouTube link → get a beautiful transcript with speaker labels, key quotes, timestamps that link back to the video, and an audio summary you can listen to on the go.

💸 Free & No Paid APIs

• No subscriptions or API keys — works out of the box
• Local processing — transcription, speaker detection, and TTS run on your machine
• Network access — fetching from YouTube (captions, metadata, comments) requires internet
• No data uploaded — nothing is sent to external services; all processing stays on your machine
• Safe sub-agent — spawned sub-agent has strict instructions: no software installation, no network calls beyond YouTube

✨ Features

• 📄 Transcript with summary and key quotes — Export as DOCX, HTML, or Markdown
• 🎯 Smart Speaker Detection — Automatically identifies participants
• 🔊 Audio Summaries — Listen to key points (MP3/WAV)
• 📝 Clickable Timestamps — Every quote links directly to that moment in the video
• 💬 YouTube Comments — Viewer sentiment analysis and best comments
• 📋 Queue Support — Send multiple links, they get processed in order
• 🚀 Non-Blocking Workflow — Conversation continues while video processes in background

🎬 Works With Any Video

• Interviews & podcasts (multi-speaker detection)
• Lectures & tutorials (single speaker)
• Music videos (lyrics extraction)
• News & documentaries
• Any YouTube content with captions

Quick Start

When user sends a YouTube URL:

1. Spawn sub-agent with the full pipeline task immediately
2. Reply: "🎬 TubeScribe is processing — I'll let you know when it's ready!"
3. Continue conversation (don't wait!)
4. Sub-agent notification will announce completion with title and details

DO NOT BLOCK — spawn and move on instantly.

First-Time Setup

Run setup to check dependencies and configure defaults:

python skills/tubescribe/scripts/setup.py

This checks: summarize CLI, pandoc, ffmpeg, Kokoro TTS

Full Workflow (Single Sub-Agent)

Spawn ONE sub-agent that does the entire pipeline:

sessions_spawn(
    task=f"""
## TubeScribe: Process {youtube_url}

⚠️ CRITICAL: Do NOT install any software.
No pip, brew, curl, venv, or binary downloads.
If a tool is missing, STOP and report what's needed.

Run the COMPLETE pipeline — do not stop until all steps are done.

### Step 1: Extract
```bash
python3 skills/tubescribe/scripts/tubescribe.py "{youtube_url}"

Note the Source and Output paths printed by the script. Use those exact paths in subsequent steps.

Step 2: Read source JSON

Read the Source path from Step 1 output and note:

• metadata.title (for filename)
• metadata.video_id
• metadata.channel, upload_date, duration_string

Step 3: Create formatted markdown

Write to the Output path from Step 1:

1. # **<title>**

---

2. Video info block — Channel, Date, Duration, URL (clickable). Empty line between each field.

---

3. ## **Participants** — table with bold headers:

   | **Name** | **Role** | **Description** |
   |----------|----------|-----------------|
   

---

4. ## **Summary** — 3-5 paragraphs of prose

---

5. ## **Key Quotes** — 5 best with clickable YouTube timestamps. Format each as:

   "Quote text here." - [12:34](https://www.youtube.com/watch?v=ID&t=754s)
   
   "Another quote." - [25:10](https://www.youtube.com/watch?v=ID&t=1510s)
   

   Use regular dash -, NOT em dash —. Do NOT use blockquotes >. Plain paragraphs only.

---

6. ## **Viewer Sentiment** (if comments exist)

---

7. ## **Best Comments** (if comments exist) — Top 5, NO lines between them:

   Comment text here.
   
   *- ▲ 123 @AuthorName*
   
   Next comment text here.
   
   *- ▲ 45 @AnotherAuthor*
   

   Attribution line: dash + italic. Just blank line between comments, NO --- separators.

---

8. ## **Full Transcript** — merge segments, speaker labels, clickable timestamps

Step 4: Create DOCX

Clean the title for filename (remove special chars), then:

pandoc <output_path> -o ~/Documents/TubeScribe/<safe_title>.docx

Step 5: Generate audio

Write the summary text to a temp file, then use TubeScribe's built-in audio generation:

# Write summary to temp file (use python3 to write, avoids shell escaping issues)
python3 -c "
text = '''YOUR SUMMARY TEXT HERE'''
with open('<temp_dir>/tubescribe_<video_id>_summary.txt', 'w') as f:
    f.write(text)
"

# Generate audio (auto-detects engine, voice, format from config)
python3 skills/tubescribe/scripts/tubescribe.py \
  --generate-audio <temp_dir>/tubescribe_<video_id>_summary.txt \
  --audio-output ~/Documents/TubeScribe/<safe_title>_summary

This reads ~/.tubescribe/config.json and uses the configured TTS engine (mlx/kokoro/builtin), voice blend, and speed automatically. Output format (mp3/wav) comes from config.

Step 6: Cleanup

python3 skills/tubescribe/scripts/tubescribe.py --cleanup <video_id>

Step 7: Open folder

open ~/Documents/TubeScribe/

Report

Tell what was created: DOCX name, MP3 name + duration, video stats. """, label="tubescribe", runTimeoutSeconds=900, cleanup="delete" )

**After spawning, reply immediately:**
> 🎬 TubeScribe is processing - I'll let you know when it's ready!
Then continue the conversation. The sub-agent notification announces completion.

## Configuration

Config file: `~/.tubescribe/config.json`

```json
{
  "output": {
    "folder": "~/Documents/TubeScribe",
    "open_folder_after": true,
    "open_document_after": false,
    "open_audio_after": false
  },
  "document": {
    "format": "docx",
    "engine": "pandoc"
  },
  "audio": {
    "enabled": true,
    "format": "mp3",
    "tts_engine": "mlx"
  },
  "mlx_audio": {
    "path": "~/.openclaw/tools/mlx-audio",
    "model": "mlx-community/Kokoro-82M-bf16",
    "voice": "af_heart",
    "lang_code": "a",
    "speed": 1.05
  },
  "kokoro": {
    "path": "~/.openclaw/tools/kokoro",
    "voice_blend": { "af_heart": 0.6, "af_sky": 0.4 },
    "speed": 1.05
  },
  "processing": {
    "subagent_timeout": 600,
    "cleanup_temp_files": true
  }
}

Output Options

Option	Default	Description
output.folder	~/Documents/TubeScribe	Where to save files
output.open_folder_after	true	Open output folder when done
output.open_document_after	false	Auto-open generated document
output.open_audio_after	false	Auto-open generated audio summary

Document Options

Option	Default	Values	Description
document.format	docx	docx, html, md	Output format
document.engine	pandoc	pandoc	Converter for DOCX (falls back to HTML)

Audio Options

Option	Default	Values	Description
audio.enabled	true	true, false	Generate audio summary
audio.format	mp3	mp3, wav	Audio format (mp3 needs ffmpeg)
audio.tts_engine	mlx	mlx, kokoro, builtin	TTS engine (mlx = fastest on Apple Silicon)

MLX-Audio Options (preferred on Apple Silicon)

Option	Default	Description
mlx_audio.path	~/.openclaw/tools/mlx-audio	mlx-audio venv location
mlx_audio.model	mlx-community/Kokoro-82M-bf16	MLX model to use
mlx_audio.voice	af_heart	Voice preset (used if no voice_blend)
mlx_audio.voice_blend	{af_heart: 0.6, af_sky: 0.4}	Custom voice mix (weighted blend)
mlx_audio.lang_code	a	Language code (a=US English)
mlx_audio.speed	1.05	Playback speed (1.0 = normal, 1.05 = 5% faster)

Kokoro PyTorch Options (fallback)

Option	Default	Description
kokoro.path	~/.openclaw/tools/kokoro	Kokoro repo location
kokoro.voice_blend	{af_heart: 0.6, af_sky: 0.4}	Custom voice mix
kokoro.speed	1.05	Playback speed (1.0 = normal, 1.05 = 5% faster)

Processing Options

Option	Default	Description
processing.subagent_timeout	600	Seconds for sub-agent (increase for long videos)
processing.cleanup_temp_files	true	Remove /tmp files after completion

Comment Options

Option	Default	Description
comments.max_count	50	Number of comments to fetch
comments.timeout	90	Timeout for comment fetching (seconds)

Queue Options

Option	Default	Description
queue.stale_minutes	30	Consider a processing job stale after this many minutes

Output Structure

~/Documents/TubeScribe/
├── {Video Title}.html         # Formatted document (or .docx / .md)
└── {Video Title}_summary.mp3  # Audio summary (or .wav)

After generation, opens the folder (not individual files) so you can access everything.

Dependencies

Required:

• summarize CLI — brew install steipete/tap/summarize
• Python 3.8+

Optional (better quality):

• pandoc — DOCX output: brew install pandoc
• ffmpeg — MP3 audio: brew install ffmpeg
• yt-dlp — YouTube comments: brew install yt-dlp
• mlx-audio — Fastest TTS on Apple Silicon: pip install mlx-audio (uses MLX backend for Kokoro)
• Kokoro TTS — PyTorch fallback: see https://github.com/hexgrad/kokoro

yt-dlp Search Paths

TubeScribe checks these locations (in order):

Priority	Path	Source
1	which yt-dlp	System PATH
2	/opt/homebrew/bin/yt-dlp	Homebrew (Apple Silicon)
3	/usr/local/bin/yt-dlp	Homebrew (Intel) / Linux
4	~/.local/bin/yt-dlp	pip install --user
5	~/.local/pipx/venvs/yt-dlp/bin/yt-dlp	pipx
6	~/.openclaw/tools/yt-dlp/yt-dlp	Tub

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