القائمة
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。
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| 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)"}]}} |
Intelligent voice/video transcription and organization system.
智能语音/视频转录与整理系统。
# 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:
memory/voice-memos/ / 创建数据目录config/voice-memo-sync.yaml / 创建配置文件✅ USE this skill when user:
❌ DO NOT use when:
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 |
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 │
└─────────────────────────────────────┘
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
The skill reads USER.md, SOUL.md, and MEMORY.md to provide personalized analysis:
处理时会读取 USER.md、SOUL.md 和 MEMORY.md 提供个性化分析：
🎙️ [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]
Apple Voice Memos on iOS/macOS 14+ uses .qta (QuickTime Audio) files that embed native transcription directly in the file metadata.
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)
{
"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 characterilst/data atomextract-apple-transcript.py to reliably extract# 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
| 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 |
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
| Script | Purpose / 用途 | Usage / 用法 |
|---|---|---|
install.sh | Initialize setup | ./install.sh |
process.sh | Unified processing | ./process.sh <input> |
extract-apple-transcript.py | Extract Apple native transcription | python3 extract-apple-transcript.py <file> |
create-apple-note.sh | Create Apple Notes | ./create-apple-note.sh <title> <content> |
sync-icloud-recordings.sh | Sync iCloud directory | ./sync-icloud-recordings.sh |
When user sends audio/video or URL, follow these steps:
当用户发送音视频或URL时，按以下步骤处理：
YouTube URL → summarize extract
Bilibili URL → yt-dlp download + whisper
.qta/.m4a → Apple transcript extraction
Other audio/video → whisper transcription
.txt/.md file → direct read
.doc/.docx → textutil convert
# Record to memory/voice-memos/sources/
echo '{"input":"...", "type":"...", "date":"YYYY-MM-DD"}' > sources/xxx.json
# Save to memory/voice-memos/transcripts/YYYY-MM-DD_source_title.md
# Include: source info + full raw transcript
Read USER.md and MEMORY.md, combining user context.
**MODE SELECTION (Auto-detect or Manual Override) / 模式选择:**
┌─────────────────────────────────────────────────────────────────┐
│ Mode A: Solo Memo (Default) / 短语音 │
│ Trigger: < 5 min, single speaker, casual │
│ Output: Clean text + Key points + TODOs + Connections │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ Mode B: Deep Meeting / 深度会议 │
│ Trigger: 15-60 min, multi-speaker with labels │
│ Output: │
│ 1. Executive Summary (1 paragraph) │
│ 2. Chronological Detail by time blocks │
│ 3. Debate Flow (who said what, conflicts) │
│ 4. Decision Matrix (Issue → Decision → Rationale) │
│ 5. Action Items with owners │
│ 6. Vital Quotes (preserve Voice) │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ Mode C: Lecture / Talk / 讲座模式 (NEW) │
│ Trigger: Single speaker, 30min-3hr, structured presentation │
│ Output: │
│ 1. Executive Summary (1 paragraph) │
│ 2. **Argument Structure (论点层级)**: │
│ - Core Thesis (核心论点) │
│ - Supporting Arguments (分论点 1, 2, 3...) │
│ - Key Evidence/Examples for each argument │
│ - Counter-arguments addressed (if any) │
│ 3. Key Definitions (关键定义/概念) │
│ 4. Notable Quotes (金句, with timestamps if available) │
│ 5. Connections to User's Work (个人关联) │
│ 6. Questions Raised / Gaps (讲座未解决的问题) │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ Mode D: Lecture + Q&A / 讲座+问答 (NEW) │
│ Trigger: First part monologue, second part Q&A │
│ Output: │
│ **Part I: Lecture Section** (use Mode C structure) │
│ **Part II: Q&A Section** │
│ - Group questions by theme/topic (not chronological) │
│ - Format: Q1 → A1 (summary), Q2 → A2... │
│ - Highlight: Best Questions, Surprising Answers │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ Mode E: Long-form No-Speaker-Label / 超长无标注会议 (NEW) │
│ Trigger: > 90 min, NO speaker diarization (text is a blob) │
│ Strategy: │
│ 1. **Chunking**: Split into ~30min segments for processing │
│ 2. **Topic Detection**: Identify topic shift points │
│ (Don't force time blocks; use semantic breaks) │
│ 3. **Abandon Attribution**: Don't guess who said what │
│ Output: │
│ 1. Executive Summary │
│ 2. **Topic Blocks** (not time blocks): │
│ - Topic 1: [Summary] + [Key points] + [Quotes] │
│ - Topic 2: ... │
│ 3. Unresolved Issues / Open Questions │
│ 4. Action Items (may lack owners) │
│ 5. Full Cleaned Transcript (appended or linked) │
└─────────────────────────────────────────────────────────────────┘
**TWO-PASS PROCESSING for Long Content (> 60 min):**
- Pass 1 (Quick Scan): Identify structure type, speaker presence, topic shifts
- Pass 2 (Deep Process): Apply appropriate mode to each segment
**OUTPUT DENSITY LEVELS (User can request):**
- Level 1: Executive Only (1 page, for busy stakeholders)
- Level 2: Structured Summary (5-10 pages, default)
- Level 3: Full Annotated Transcript (everything, with margin notes)
# Save to memory/voice-memos/processed/YYYY-MM-DD_source_title.md
⚠️ CRITICAL: This step is MANDATORY. Never skip it.
⚠️ 关键：此步骤必须执行，不可跳过。
⚠️ Apple Notes requires HTML format, NOT Markdown!
⚠️ Apple Notes 需要 HTML 格式，不能直接用 Markdown！
Correct workflow / 正确流程:
# 1. Convert Markdown to HTML using pandoc (REQUIRED)
pandoc /path/to/processed.md -f markdown -t html -o /tmp/note-content.html
# 2. Create note with HTML content via AppleScript
osascript <<'EOF'
set htmlContent to do shell script "cat /tmp/note-content.html"
set noteTitle to "🎙️ Note Title"
tell application "Notes"
set folderName to "Voice Memos"
set targetFolder to missing value
repeat with f in folders
if name of f is folderName then
set targetFolder to f
exit repeat
end if
end repeat
if targetFolder is missing value then
make new folder with properties {name:folderName}
delay 1
set targetFolder to folder folderName
end if
tell targetFolder
make new note with properties {name:noteTitle, body:htmlContent}
end tell
end tell
EOF
Common mistakes to avoid / 常见错误:
memo notes -a interactively → 无法自动化remindctl add --title "TODO" --list "Reminders" --due "YYYY-MM-DD"
# Append record to memory/voice-memos/INDEX.md
⚠️ Privacy-First Design:
brew install openai-whisper
# Update yt-dlp
brew upgrade yt-dlp
# Or use proxy
export ALL_PROXY=http://127.0.0.1:7890
# Manually create via AppleScript
osascript -e 'tell application "Notes" to tell account "iCloud" to make new folder with properties {name:"Voice Memos"}'
# Use larger model for better accuracy
# Edit config: whisper_model: "medium" or "large"
--with-timestamps option for detailed time-aligned output.