Transcripteur audio

--- name: audio-transcriber description: "Convert 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: - The user needs to transcribe audio/video files into text - The user wants meeting minutes automatically generated from recordings - The user requires speaker identification (diarization) in conversations - The user needs subtitles/captions (SRT, VTT formats) - The user wants executive summaries of long audio content - The user asks variations of “transcribe this audio,” “convert audio to text,” or “generate meeting notes from a recording” - The 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: ``bash # 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: bash 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 fi `` This ensures users can install dependencies with a single confirmation, or choose to install them manually if they prefer. If a transcription tool is found: Proceed to Step 0b (Detection of CLI). ### Step 1: Validate Audio File Objective: Verify that the file exists, check its format, and extract metadata. Actions: 1. Accept file path or URL