voice-memo-sync
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。
安装 / 下载方式
TotalClaw CLI推荐
totalclaw install github:LeoYeAI~openclaw-master-skills~voice-memo-synccURL直接下载，无需登录
curl -fsSL https://skills.taituai.com/api/skills/github%3ALeoYeAI~openclaw-master-skills~voice-memo-sync/file -o voice-memo-sync.md# Voice Memo Sync 🎙️
Intelligent voice/video transcription and organization system.
智能语音/视频转录与整理系统。
---
## Quick Start / 快速开始
```bash
# 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
---
## 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
---
## 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 |
```bash
# 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 |
---
## 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 │
└─────────────────────────────────────┘
```
---
## 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
```
---
## 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]
```
---
## 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
```json
{
"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
```bash
# 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