audio-transcriber

name	audio-transcriber
description	Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
category	Document Processing
source	antigravity
tags	["python","markdown","api","claude","ai","llm","gpt","workflow","template","document"]
url	https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/audio-transcriber

Purpose

This skill automates audio-to-text transcription with professional Markdown output, extracting rich technical metadata (speakers, timestamps, language, file size, duration) and generating structured meeting minutes and executive summaries. It uses Faster-Whisper or Whisper with zero configuration, working universally across projects without hardcoded paths or API keys.

Inspired by tools like Plaud, this skill transforms raw audio recordings into actionable documentation, making it ideal for meetings, interviews, lectures, and content analysis.

When to Use

Invoke this skill when:

• User needs to transcribe audio/video files to text
• User wants meeting minutes automatically generated from recordings
• User requires speaker identification (diarization) in conversations
• User needs subtitles/captions (SRT, VTT formats)
• User wants executive summaries of long audio content
• User asks variations of "transcribe this audio", "convert audio to text", "generate meeting notes from recording"
• User has audio files in common formats (MP3, WAV, M4A, OGG, FLAC, WEBM)

Workflow

Step 0: Discovery (Auto-detect Transcription Tools)

Objective: Identify available transcription engines without user configuration.

Actions:

Run detection commands to find installed tools:

# Check for Faster-Whisper (preferred - 4-5x faster)
if python3 -c "import faster_whisper" 2>/dev/null; then
    TRANSCRIBER="faster-whisper"
    echo "✅ Faster-Whisper detected (optimized)"
# Fallback to original Whisper
elif python3 -c "import whisper" 2>/dev/null; then
    TRANSCRIBER="whisper"
    echo "✅ OpenAI Whisper detected"
else
    TRANSCRIBER="none"
    echo "⚠️  No transcription tool found"
fi

# Check for ffmpeg (audio format conversion)
if command -v ffmpeg &>/dev/null; then
    echo "✅ ffmpeg available (format conversion enabled)"
else
    echo "ℹ️  ffmpeg not found (limited format support)"
fi

If no transcriber found:

Offer automatic installation using the provided script:

echo "⚠️  No transcription tool found"
echo ""
echo "🔧 Auto-install dependencies? (Recommended)"
read -p "Run installation script? [Y/n]: " AUTO_INSTALL

if [[ ! "$AUTO_INSTALL" =~ ^[Nn] ]]; then
    # Get skill directory (works for both repo and symlinked installations)
    SKILL_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
    
    # Run installation script
    if [[ -f "$SKILL_DIR/scripts/install-requirements.sh" ]]; then
        bash "$SKILL_DIR/scripts/install-requirements.sh"
    else
        echo "❌ Installation script not found"
        echo ""
        echo "📦 Manual installation:"
        echo "  pip install faster-whisper  # Recommended"
        echo "  pip install openai-whisper  # Alternative"
        echo "  brew install ffmpeg         # Optional (macOS)"
        exit 1
    fi
    
    # Verify installation succeeded
    if python3 -c "import faster_whisper" 2>/dev/null || python3 -c "import whisper" 2>/dev/null; then
        echo 
    
         
         1
    

     
     
     
     
     
     
     
     
     
     
     
     
     
     1

This ensures users can install dependencies with one confirmation, or opt for manual installation if preferred.

If transcriber found:

Proceed to Step 0b (CLI Detection).

Step 1: Validate Audio File

Objective: Verify file exists, check format, and extract metadata.

Actions:

1. Accept file path or URL from user:

   • Local file: meeting.mp3
   • URL: https://example.com/audio.mp3 (download to temp directory)

2. Verify file exists:

if [[ ! -f "$AUDIO_FILE" ]]; then
    echo "❌ File not found: $AUDIO_FILE"
    exit 1
fi

3. Extract metadata using ffprobe or file utilities:

# Get file size
FILE_SIZE=$(du -h "$AUDIO_FILE" | cut -f1)

# Get duration and format using ffprobe
DURATION=$(ffprobe -v error -show_entries format=duration \
    -of default=noprint_wrappers=1:nokey=1 "$AUDIO_FILE" 2>/dev/null)
FORMAT=$(ffprobe -v error -select_streams a:0 -show_entries \
    stream=codec_name -of default=noprint_wrappers=1:nokey=1 "$AUDIO_FILE" 2>/dev/null)

# Convert duration to HH:MM:SS
DURATION_HMS=$(date -u -r "$DURATION" +%H:%M:%S 2>/dev/null || echo "Unknown")

4. Check file size (warn if large for cloud APIs):

SIZE_MB=$(du -m "$AUDIO_FILE" | cut -f1)
if [[ $SIZE_MB -gt 25 ]]; then
    echo "⚠️  Large file ($FILE_SIZE) - processing may take several minutes"
fi

5. Validate format (supported: MP3, WAV, M4A, OGG, FLAC, WEBM):

EXTEN