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:

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 "✅ 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 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:

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

# 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")

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

EXTENSION="${AUDIO_FILE##.}"
SUPPORTED_FORMATS=("mp3" "wav" "m4a" "ogg" "flac" "webm" "mp4")
if [[ ! " ${SUPPORTED_FORMATS[@]} " =~ " ${EXTENSION,,} " ]]; then
    echo "⚠️  Unsupported format: $EXTENSION"
    if command -v ffmpeg &>/dev/null; then
        echo "🔄 Converting to WAV..."
        ffmpeg -i "$AUDIO_FILE" -ar 16000 "${AUDIO_FILE%.}.wav" -y
        AUDIO_FILE="${AUDIO_FILE%.}.wav"
    else
        echo "❌ Install ffmpeg to convert formats: brew install ffmpeg"
        exit 1
    fi
fi

Step 3: Generate Markdown Output

Objective: Create structured Markdown with metadata, transcription, meeting minutes, and summary.

Output Template:

# Audio Transcription Report
📊 Metadata
<div class="overflow-x-auto my-6"><table class="min-w-full divide-y divide-border border border-border"><thead><tr><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Field</th><th class="px-4 py-2 text-left text-sm font-semibold text-foreground bg-muted/50">Value</th></tr></thead><tbody class="divide-y divide-border"><tr><td class="px-4 py-2 text-sm text-foreground"><strong>File Name</strong></td><td class="px-4 py-2 text-sm text-foreground">{filename}</td></tr><tr><td class="px-4 py-2 text-sm text-foreground"><strong>File Size</strong></td><td class="px-4 py-2 text-sm text-foreground">{file_size}</td></tr><tr><td class="px-4 py-2 text-sm text-foreground"><strong>Duration</strong></td><td class="px-4 py-2 text-sm text-foreground">{duration_hms}</td></tr><tr><td class="px-4 py-2 text-sm text-foreground"><strong>Language</strong></td><td class="px-4 py-2 text-sm text-foreground">{language} ({language_code})</td></tr><tr><td class="px-4 py-2 text-sm text-foreground"><strong>Processed Date</strong></td><td class="px-4 py-2 text-sm text-foreground">{process_date}</td></tr><tr><td class="px-4 py-2 text-sm text-foreground"><strong>Speakers Identified</strong></td><td class="px-4 py-2 text-sm text-foreground">{num_speakers}</td></tr><tr><td class="px-4 py-2 text-sm text-foreground"><strong>Transcription Engine</strong></td><td class="px-4 py-2 text-sm text-foreground">{engine} (model: {model})</td></tr></tbody></table></div>

📋 Meeting Minutes
Participants

{speaker_1}

{speaker_2}

...
Topics Discussed

{topic_1} ({timestamp})

   - {key_point_1}
   - {key_point_2}
{topic_2} ({timestamp})

   - {key_point_1}
Decisions Made

✅ {decision_1}

✅ {decision_2}
Action Items

[ ] {action_1} - Assigned to: {speaker} - Due: {date_if_mentioned}

[ ] {action_2} - Assigned to: {speaker}

Generated by audio-transcriber skill v1.0.0  
Transcription engine: {engine} | Processing time: {elapsed_time}s

Implementation:

Use Python or bash with AI model (Claude/GPT) for intelligent summarization:

def generate_meeting_minutes(segments):
    """Extract topics, decisions, action items from transcription."""
    
    # Group segments by topic (simple clustering by timestamps)
    topics = cluster_by_topic(segments)
    
    # Identify action items (keywords: "should", "will", "need to", "action")
    action_items = extract_action_items(segments)
    
    # Identify decisions (keywords: "decided", "agreed", "approved")
    decisions = extract_decisions(segments)
    
    return {
        "topics": topics,
        "decisions": decisions,
        "action_items": action_items
    }
def generate_summary(segments, max_paragraphs=5):
    """Create executive summary using AI (Claude/GPT via API or local model)."""
    
    full_text = " ".join([s["text"] for s in segments])
    
    # Use Chain of Density approach (from prompt-engineer frameworks)
    summary_prompt = f"""
    Summarize the following transcription in {max_paragraphs} concise paragraphs.
    Focus on key topics, decisions, and action items.
    
    Transcription:
    {full_text}
    """
    
    # Call AI model (placeholder - user can integrate Claude API or use local model)
    summary = call_ai_model(summary_prompt)
    
    return summary

Output file naming:

# v1.1.0: Use timestamp para evitar sobrescrever
TIMESTAMP=$(date +%Y%m%d-%H%M%S)
TRANSCRIPT_FILE="transcript-${TIMESTAMP}.md"
ATA_FILE="ata-${TIMESTAMP}.md"
echo "$TRANSCRIPT_CONTENT" > "$TRANSCRIPT_FILE"
echo "✅ Transcript salvo: $TRANSCRIPT_FILE"
if [[ -n "$ATA_CONTENT" ]]; then
    echo "$ATA_CONTENT" > "$ATA_FILE"
    echo "✅ Ata salva: $ATA_FILE"
fi

SCENARIO A: User Provided Custom Prompt

Workflow:

📝 Prompt fornecido pelo usuário:
   ┌──────────────────────────────────┐
   │ [User's prompt preview]          │
   └──────────────────────────────────┘

🔧 Melhorando prompt com prompt-engineer...
   [Invokes: gh copilot -p "melhore este prompt: {user_prompt}"]

✨ Versão melhorada:
   ┌──────────────────────────────────┐
   │ Role: Você é um documentador...  │
   │ Instructions: Transforme...      │
   │ Steps: 1) ... 2) ...             │
   │ End Goal: ...                    │
   └──────────────────────────────────┘
   📝 Versão original:
   ┌──────────────────────────────────┐
   │ [User's original prompt]         │
   └──────────────────────────────────┘

💡 Usar versão melhorada? [s/n] (default: s):

from rich.progress import Progress, SpinnerColumn, TextColumn
def process_with_llm(transcript, prompt, cli_tool='claude'):
    full_prompt = f"{prompt}\n\n---\n\nTranscrição:\n\n{transcript}"
    
    with Progress(
        SpinnerColumn(),
        TextColumn("[progress.description]{task.description}"),
        transient=True
    ) as progress:
        progress.add_task(
            description=f"🤖 Processando com {cli_tool}...",
            total=None
        )
        
        if cli_tool == 'claude':
            result = subprocess.run(
                ['claude', '-'],
                input=full_prompt,
                capture_output=True,
                text=True,
                timeout=300  # 5 minutes
            )
        elif cli_tool == 'gh-copilot':
            result = subprocess.run(
                ['gh', 'copilot', 'suggest', '-t', 'shell', full_prompt],
                capture_output=True,
                text=True,
                timeout=300
            )
    
    if result.returncode == 0:
        return result.stdout.strip()
    else:
        return None

🤖 Processando com claude... ⠋
[After completion:]
✅ Ata gerada com sucesso!

💾 Salvando arquivos...
✅ Arquivos criados:
  - transcript-20260203-023045.md  (transcript puro)
  - ata-20260203-023045.md         (processado com LLM)
🧹 Removidos arquivos temporários: metadata.json, transcription.json
✅ Concluído! Tempo total: 3m 45s

💾 Salvando arquivos...
✅ Arquivo criado:
  - transcript-20260203-023045.md
ℹ️  Ata não gerada (processamento LLM recusado pelo usuário)
🧹 Removidos arquivos temporários: metadata.json, transcription.json
✅ Concluído!

echo ""
echo "✅ Transcription Complete!"
echo ""
echo "📊 Results:"
echo "  File: $OUTPUT_FILE"
echo "  Language: $LANGUAGE"
echo "  Duration: $DURATION_HMS"
echo "  Speakers: $NUM_SPEAKERS"
echo "  Words: $WORD_COUNT"
echo "  Processing time: ${ELAPSED_TIME}s"
echo ""
echo "📝 Generated:"
echo "  - $OUTPUT_FILE (Markdown report)"
[if alternative formats:]
echo "  - ${OUTPUT_FILE%.}.srt (Subtitles)"
echo "  - ${OUTPUT_FILE%.}.json (Structured data)"
echo ""
echo "🎯 Next steps:"
echo "  1. Review meeting minutes and action items"
echo "  2. Share report with participants"
echo "  3. Track action items to completion"

Example Usage

Example 1: Basic Transcription

User Input:

copilot> transcribe audio to markdown: meeting-2026-02-02.mp3

Skill Output:

✅ Faster-Whisper detected (optimized)
✅ ffmpeg available (format conversion enabled)
📂 File: meeting-2026-02-02.mp3
📊 Size: 12.3 MB
⏱️  Duration: 00:45:32
🎙️  Processing...
[████████████████████] 100%
✅ Language detected: Portuguese (pt-BR)
👥 Speakers identified: 4
📝 Generating Markdown output...
✅ Transcription Complete!
📊 Results:
  File: meeting-2026-02-02.md
  Language: pt-BR
  Duration: 00:45:32
  Speakers: 4
  Words: 6,842
  Processing time: 127s
📝 Generated:
  - meeting-2026-02-02.md (Markdown report)
🎯 Next steps:
  1. Review meeting minutes and action items
  2. Share report with participants
  3. Track action items to completion

Example 3: Batch Processing

User Input:

copilot> transcreva estes áudios: recordings/.mp3

Skill Output:

📦 Batch mode: 5 files found
  1. team-standup.mp3
  2. client-call.mp3
  3. brainstorm-session.mp3
  4. product-demo.mp3
  5. retrospective.mp3
🎙️  Processing batch...
[1/5] team-standup.mp3 ✅ (2m 34s)
[2/5] client-call.mp3 ✅ (15m 12s)
[3/5] brainstorm-session.mp3 ✅ (8m 47s)
[4/5] product-demo.mp3 ✅ (22m 03s)
[5/5] retrospective.mp3 ✅ (11m 28s)
✅ Batch Complete!
📝 Generated 5 Markdown reports
⏱️  Total processing time: 6m 15s

Example 5: Large File Warning

User Input:

copilot> transcribe audio to markdown: conference-keynote.mp3

Skill Output:

✅ Faster-Whisper detected (optimized)
📂 File: conference-keynote.mp3
📊 Size: 87.2 MB
⏱️  Duration: 02:15:47
⚠️  Large file (87.2 MB) - processing may take several minutes
Continue? [Y/n]:

User: Y

🎙️  Processing... (this may take 10-15 minutes)
[████░░░░░░░░░░░░░░░░] 20% - Estimated time remaining: 12m

This skill is platform-agnostic and works in any terminal context where GitHub Copilot CLI is available. It does not depend on specific project configurations or external APIs, following the zero-configuration philosophy.