Voice Memo Sync

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name: voice-memo-sync description: | Sync, transcribe, and intelligently organize voice memos, audio/video files, and URLs. 同步、转录、智能整理语音备忘录、音视频文件和视频链接。 version: 1.6.1 author: Ying Wen homepage: https://github.com/ying-wen/voice-memo-sync license: MIT metadata: openclaw: emoji: "🎙️" os: ["darwin"] requires: bins: ["ffmpeg", "python3"] optional_bins: ["whisper-cli", "whisper", "yt-dlp", "remindctl", "summarize"] install: - id: init kind: script command: "./scripts/install.sh" label: "Initialize Voice Memo Sync" - id: ffmpeg kind: brew formula: ffmpeg bins: ["ffmpeg"] label: "Install FFmpeg (required)" - id: whisper-cpp kind: brew formula: whisper-cpp bins: ["whisper-cli"] label: "Install whisper-cpp (Metal GPU - recommended)" - id: whisper kind: brew formula: openai-whisper bins: ["whisper"] label: "Install Whisper (CPU fallback)" - id: yt-dlp kind: brew formula: yt-dlp bins: ["yt-dlp"] label: "Install yt-dlp (for video URLs)"

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Voice Memo Sync 🎙️

Intelligent voice/video transcription and organization system. 智能语音/视频转录与整理系统。

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Quick Start / 快速开始

# Run installation script / 运行安装脚本
cd ~/.openclaw/workspace/skills/voice-memo-sync
./scripts/install.sh

What it does / 安装内容: 1. Creates data directory memory/voice-memos/ / 创建数据目录 2. Creates config file config/voice-memo-sync.yaml / 创建配置文件 3. Creates Apple Notes folder "Voice Memos" / 创建 Apple Notes 文件夹 4. Checks dependencies and prompts installation / 检查依赖并提示安装

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When to Use / 何时使用

✅ USE this skill when user:

Sends voice/audio/video files / 发送语音/音频/视频文件

Sends YouTube/Bilibili URLs / 发送 YouTube/B站 链接

Sends transcript text files / 发送转录文本文件

Says "sync voice memos", "process recording", "organize this video"

说「同步语音备忘录」「处理录音」「整理这个视频」

❌ DO NOT use when:

User just wants to play audio/video / 用户只想播放音视频

User asks about music/podcasts without transcription needs / 询问音乐/播客但不需要转录

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Supported Formats / 支持格式

⚡ Metal GPU Acceleration (NEW)

On Apple Silicon, whisper-cpp provides 15-20x faster transcription:

| Audio | CPU (openai-whisper) | Metal GPU (whisper-cpp) | |-------|---------------------|------------------------| | 5 min | ~5 min | ~20 sec | | 30 min | ~30 min | ~2 min | | 60 min | ~60 min | ~4 min |

# Install for Metal acceleration (recommended)
brew install whisper-cpp

The skill auto-detects and uses Metal when available.

| Type / 类型 | Formats / 格式 | Processing / 处理方式 | |-------------|----------------|----------------------| | Voice Memos | .qta, .m4a | Apple native (QTA metadata) → Whisper fallback | | Audio | .mp3, .wav, .aac, .flac | Whisper local transcription | | Video | .mp4, .mov, .mkv, .webm | ffmpeg extract → Whisper | | YouTube | URL | summarize CLI → yt-dlp fallback | | Bilibili | URL | yt-dlp download → Whisper | | Text | .txt, .md | Direct read, skip transcription | | Documents | .doc, .docx | textutil convert → process | | Structured | .json, .csv | Parse and extract text | | iCloud | Configured paths | Scheduled sync |

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Processing Pipeline / 处理流程

Input (File/URL/Text)
        │
        ▼
┌─────────────────────────────────────┐
│     1. Source Detection            │
│     来源识别                        │
│  Voice Memo / URL / File / Text    │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│     2. Save Source Metadata        │
│     保存源信息                      │
│  → memory/voice-memos/sources/     │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│     3. Transcription               │
│     转录提取                        │
│  Priority: Apple > Text > summarize│
│           > Whisper-local > API    │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│     4. Save Raw Transcript         │
│     保存原始转录                    │
│  → memory/voice-memos/transcripts/ │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│     5. LLM Deep Processing         │
│     LLM深度整理                     │
│  • Read USER.md & MEMORY.md        │
│  • Clean up spoken language        │
│  • Extract key points & insights   │
│  • Identify TODOs & connections    │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│     6. Save Processed Result       │
│     保存处理结果                    │
│  → memory/voice-memos/processed/   │
└─────────────────┬───────────────────┘
                  │
          ┌───────┴───────┐
          ▼               ▼
┌─────────────────┐ ┌─────────────────┐
│ 7a. Apple Notes │ │ 7b. Reminders  │
│ Structured note │ │ Create TODOs   │
│ with #hashtags  │ │ 创建提醒       │
└────────┬────────┘ └────────┬───────┘
         │                   │
         └─────────┬─────────┘
                   ▼
┌─────────────────────────────────────┐
│     8. Update Index                │
│     更新索引                        │
│  → memory/voice-memos/INDEX.md     │
└─────────────────────────────────────┘

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Data Structure / 数据结构

memory/voice-memos/           # All data, searchable via memory_search
├── INDEX.md                  # Processing records index / 处理记录索引
├── sources/                  # Original file metadata / 原始文件元数据
│   └── YYYY-MM-DD_xxx.json
├── transcripts/              # Raw transcripts / 原始转录文本
│   └── YYYY-MM-DD_source_title.md
├── processed/                # LLM processed content / LLM处理后内容
│   └── YYYY-MM-DD_source_title.md
└── synced/                   # Sync records / 同步记录
    └── YYYY-MM-DD_source_title.json

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Apple Notes Output Format / 输出格式

The skill reads USER.md, SOUL.md, and MEMORY.md to provide personalized analysis:

Deep insights tailored to user's research/work focus

Connections to active projects and ongoing interests

Actionable recommendations based on user's decision style

Critical thinking that challenges assumptions

处理时会读取 USER.md、SOUL.md 和 MEMORY.md 提供个性化分析：

结合用户研究/工作重点的深度洞察

与活跃项目和持续关注领域的关联

基于用户决策风格的行动建议

挑战假设的批判性思考

🎙️ [Auto-generated Title / 智能生成的标题]
📅 Date | ⏱️ Duration | 👤 Source
🏷️ #tag1 #tag2 #tag3
━━━━━━━━━━━━━━━━━━━━━━
📌 Summary / 核心摘要
[One paragraph summarizing the content]
🎯 Key Points / 关键要点
• Point 1
• Point 2
• Point 3
💡 Deep Analysis & Reflection (For User) / 深度分析与反思
[Personalized analysis connecting to user's:
 - Current research directions (from MEMORY.md)
 - Active projects and interests (from USER.md)
 - Decision-making style and preferences
 - Critical counter-arguments and blind spots]
📋 Action Items / 行动建议
☐ Research: [specific to user's academic work]
☐ Business: [relevant to startup/investment focus]
☐ Content: [ideas for courses/articles]
🔗 Related Connections / 相关联系
• Connection to [project/memory]
• Recommended reading/research
💬 Notable Quotes / 金句摘录
• "Quote 1"
• "Quote 2"
━━━━━━━━━━━━━━━━━━━━━━
📝 Original Transcript (Cleaned) / 原始转录（已整理）
[Full transcript text, cleaned up from spoken language / 完整转录，已整理口语表达]

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QTA File Format / QTA文件格式 (Technical Reference)

Apple Voice Memos on iOS/macOS 14+ uses .qta (QuickTime Audio) files that embed native transcription directly in the file metadata.

Structure

QTA File
├── ftyp (file type marker: "qt  ")
├── wide (extended marker)
├── mdat (audio data, typically 90%+ of file size)
└── moov (metadata container)
    ├── mvhd (movie header)
    └── trak (one or more tracks)
        ├── tkhd (track header)
        ├── mdia (media data)
        └── meta (metadata - TRANSCRIPTION HERE!)
            ├── hdlr (handler: "mdta")
            ├── keys (key list: "com.apple.VoiceMemos.tsrp")
            └── ilst (data list)
                └── data (JSON transcription payload)

Transcription JSON Format

{
  "locale": {"identifier": "zh-Hans_GB", "current": 1},
  "attributedString": {
    "runs": ["字",0,"符",1,"转",2,"录",3,...],
    "attributeTable": [
      {"timeRange": [0.0, 0.5]},
      {"timeRange": [0.5, 0.8]},
      ...
    ]
  }
}

Key Points:

runs array alternates: [text, index, text, index, ...]

attributeTable provides timestamps for each character

JSON is embedded raw in the ilst/data atom

Use extract-apple-transcript.py to reliably extract

Extraction Script

# Extract plain text
python3 scripts/extract-apple-transcript.py recording.qta
Extract with metadata (JSON output)
python3 scripts/extract-apple-transcript.py recording.qta --json
Extract with timestamps
python3 scripts/extract-apple-transcript.py recording.qta --json --with-timestamps

Common Issues

| Issue | Cause | Solution | |-------|-------|----------| | "未找到转录数据" | Recording still processing | Wait 1-2 min, or use Whisper | | "转录标记存在但数据不完整" | Partial transcription | Use Whisper fallback | | JSON parse error | Corrupted file | Try Whisper transcription |

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Location / 位置: ~/.openclaw/workspace/config/voice-memo-sync.yaml

sources:
  voice_memos:
    enabled: true
    path: "~/Library/Group Containers/group.com.apple.VoiceMemos.shared/Recordings/"
  icloud:
    enabled: true
    paths:
      - "~/Library/Mobile Documents/com~apple~CloudDocs/Recordings"
      - "~/Library/Mobile Documents/com~apple~CloudDocs/Meeting Recordings"
    watch_patterns: ["*.m4a", "*.mp3", "*.mp4", "*.wav", "*.mov"]
transcription:
  # Priority order / 优先级顺序
  priority: ["apple", "text", "summarize", "whisper-local"]
  whisper_model: "small"  # tiny/small/medium/large
  language: "auto"        # auto/zh/en/ja/ko/...
notes:
  folder: "Voice Memos"   # Apple Notes folder name
  include_quotes: true
  include_original: true
reminders:
  enabled: true
  list: "Reminders"
  auto_create: true

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Scripts / 脚本

| Script | Purpose / 用途 | Usage / 用法 | |--------|----------------|--------------| | install.sh | Initialize setup | ./install.sh | | process.sh | Unified processing | ./process.sh | | extract-apple-transcript.py | Extract Apple native transcription | python3 extract-apple-transcript.py | | create-apple-note.sh | Create Apple Notes | ./create-apple-note.sh