Transcripteur audio
/SKILLTransformez vos enregistrements audio en documentation Markdown de qualité professionnelle grâce à des résumés intelligents, grâce à l'intégration d'u
name: audio-transcriber
description: "Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration"
category: content
risk: safe
source: community
tags: "[audio, transcription, whisper, meeting-minutes, speech-to-text]"
date_added: "2026-02-27"
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)"
fiIf 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 "✅ Installation successful! Proceeding with transcription..."
else
echo "❌ Installation failed. Please install manually."
exit 1
fi
else
echo ""
echo "📦 Manual installation required:"
echo ""
echo "Recommended (fastest):"
echo " pip install faster-whisper"
echo ""
echo "Alternative (original):"
echo " pip install openai-whisper"
echo ""
echo "Optional (format conversion):"
echo " brew install ffmpeg # macOS"
echo " apt install ffmpeg # Linux"
echo ""
exit 1
fiThis 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:
- Accept file path or URL from user:
- Local file:
meeting.mp3 - URL:
https://example.com/audio.mp3(download to temp directory)
- Verify file exists:
if [[ ! -f "$AUDIO_FILE" ]]; then
echo "❌ File not found: $AUDIO_FILE"
exit 1
fi- Extract metadata using ffprobe or file utilities:
# Get file size
FILE_SIZE=$(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")