Voice Memo Organizer

Turn hundreds of untitled Apple Voice Memos into a searchable, organized archive with descriptive titles, summaries, themes, and key quotes.

When to Use

• User wants to organize their voice memos
• User mentions having a lot of recordings they can't find or search
• User wants to transcribe voice memos in bulk
• User asks about finding Apple Voice Memos files on macOS

What It Does

1. Finds all Apple Voice Memos on macOS (they're hidden in a non-obvious path)
2. Extracts metadata from the SQLite database (dates, durations, labels)
3. Transcribes every recording locally using whisper.cpp (no API keys needed)
4. Summarizes each transcript with a descriptive title, themes, key quotes, and type
5. Builds a searchable master index document

Prerequisites

• macOS with Voice Memos app (recordings synced via iCloud from iPhone)
• Full Disk Access for Terminal: System Settings > Privacy & Security > Full Disk Access > enable your terminal app
• Homebrew — if not installed:

  /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Step-by-Step Process

Step 1: Access Voice Memos

Voice Memos are stored at:

~/Library/Group Containers/group.com.apple.VoiceMemos.shared/Recordings/

This path requires Full Disk Access. Verify access:

ls ~/Library/Group\ Containers/group.com.apple.VoiceMemos.shared/Recordings/ | head -5

If you get "Operation not permitted", the user needs to enable Full Disk Access for Terminal in System Settings > Privacy & Security > Full Disk Access.

If the directory doesn't exist or is empty, the user may need to open the Voice Memos app on their Mac first and wait for iCloud to sync their recordings.

The folder also contains

CloudRecordings.db

sqlite3 ~/Library/Group\ Containers/group.com.apple.VoiceMemos.shared/Recordings/CloudRecordings.db \
  "SELECT COUNT(*), ROUND(SUM(ZDURATION)/3600,1) as hours FROM ZCLOUDRECORDING WHERE ZPATH IS NOT NULL;"

Step 2: Copy Recordings & Export Metadata

mkdir -p ~/Documents/Voice-Memos-Organized/{transcripts,summaries,models}
mkdir -p ~/Documents/Voice-Memos-Raw

# Copy all recordings
cp ~/Library/Group\ Containers/group.com.apple.VoiceMemos.shared/Recordings/*.m4a ~/Documents/Voice-Memos-Raw/ 2>/dev/null
cp ~/Library/Group\ Containers/group.com.apple.VoiceMemos.shared/Recordings/*.qta ~/Documents/Voice-Memos-Raw/ 2>/dev/null

# Export metadata
sqlite3 -header -csv ~/Library/Group\ Containers/group.com.apple.VoiceMemos.shared/Recordings/CloudRecordings.db \
  "SELECT Z_PK as id, ZCUSTOMLABEL as label, ZPATH as filename, ROUND(ZDURATION,1) as duration_secs, datetime(ZDATE + 978307200, 'unixepoch', 'localtime') as recorded_date FROM ZCLOUDRECORDING WHERE ZPATH IS NOT NULL AND ZPATH != '' ORDER BY ZDATE ASC;" \
  > ~/Documents/Voice-Memos-Organized/recordings-metadata.csv

Step 3: Install Transcription Tools

# Install ffmpeg for audio conversion
brew install ffmpeg

# Download whisper.cpp base.en model (~150MB, good speed/accuracy for English)
curl -L -o ~/Documents/Voice-Memos-Organized/models/ggml-base.en.bin \
  "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin"

For the whisper binary:

# Install whisper.cpp via brew (recommended)
brew install whisper-cpp

# The binary is called whisper-cli (not whisper-cpp):
which whisper-cli  # /opt/homebrew/bin/whisper-cli (Apple Silicon) or /usr/local/bin/whisper-cli (Intel)

Step 4: Batch Transcribe

For each audio file:

1. Convert to 16kHz mono WAV using ffmpeg
2. Run whisper.cpp with the base.en model
3. Save transcript as .txt

WHISPER="$(command -v whisper-cli)"  # auto-detect path
MODEL="$HOME/Documents/Voice-Memos-Organized/models/ggml-base.en.bin"

for audiofile in ~/Documents/Voice-Memos-Raw/*; do
    [ -f "$audiofile" ] || continue
    BASENAME=$(basename "$audiofile" | sed 's/\.[^.]*$//')
    TEMP_WAV="/tmp/vm_${BASENAME}.wav"

    # Skip if already transcribed
    [ -s ~/Documents/Voice-Memos-Organized/transcripts/"${BASENAME}.txt" ] && continue

    # Convert to WAV (skip on failure to avoid stale data)
    if ! ffmpeg -y -i "$audiofile" -ar 16000 -ac 1 -c:a pcm_s16le "$TEMP_WAV" 2>/dev/null; then
        echo "SKIP: Could not convert $BASENAME"
        continue
    fi

    # Transcribe
    "$WHISPER" -m "$MODEL" -f "$TEMP_WAV" --output-txt \
      -of ~/Documents/Voice-Memos-Organized/transcripts/"$BASENAME" -t 8 --no-timestamps 2>/dev/null

    rm -f "$TEMP_WAV"
done

Performance note: ~1 minute of audio transcribes in ~1 second on Apple Silicon. 67 hours takes roughly 1 hour.

Step 5: Summarize Each Transcript

For each transcript, generate a JSON summary with:

• title: Short descriptive title (5-10 words)
• summary: 2-3 sentence summary of the content
• themes: Array of tags (e.g. "business", "personal", "health", "brainstorm")
• key_quotes: Array of 1-3 notable verbatim quotes
• type: One of "conversation", "brainstorm", "voice_note", "meeting", "personal", "phone_call"

Save each as a JSON file in the summaries/ folder.

Process in batches of 10-20 transcripts at a time using parallel agents for speed. Skip transcripts that are too short (<10 words) or contain only silence/noise.

Step 6: Build Master Index

Create

voice-memos-master-index.md

• Stats header (count, date range, total hours)
• Theme breakdown
• Type breakdown
• Chronological entries with: date, title, duration, type, themes, summary, key quotes, filename
• "Best Quotes" section at the end

Output Structure

~/Documents/Voice-Memos-Organized/
├── voice-memos-master-index.md    ← searchable master document
├── transcripts/                   ← full text of every recording
├── summaries/                     ← JSON summaries with titles/themes/quotes
├── recordings-metadata.csv        ← database export
└── models/                        ← whisper model

~/Documents/Voice-Memos-Raw/       ← original audio files

Tips

• The master index works great in Obsidian, VS Code, or any text editor with Cmd+F search
• For non-English memos, use

  ggml-base.bin

  (multilingual) instead of

  ggml-base.en.bin

• For higher accuracy on important recordings, use

  ggml-medium.en.bin

  (~500MB)
• The SQLite database also contains folder info if the user organized memos into folders in the app