Audio Transcriber Skill for Claude
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/lingxling/awesome-skills-cn --skill audio-transcriberThe instruction itself
15 sections, as written by the authorPurpose
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 "✅ 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:
- 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 "$DURATION" +%H:%M:%S 2>/dev/null || echo "Unknown")
- Check file size (warn if large for cloud APIs):
SIZE_MB=$(du -m "$AUDIO_FILE" | cut -f1)
if [[ $SIZE_MB -gt 25 ]]; then
echo "⚠️ Large file ($FILE_SIZE) - processing may take several minutes"
fi
- Validate format (supported: MP3, WAV, M4A, OGG, FLAC, WEBM):
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
| Field | Value |
|-------|-------|
| **File Name** | {filename} |
| **File Size** | {file_size} |
| **Duration** | {duration_hms} |
| **Language** | {language} ({language_code}) |
| **Processed Date** | {process_date} |
| **Speakers Identified** | {num_speakers} |
| **Transcription Engine** | {engine} (model: {model}) |
## 📋 Meeting Minutes
### Participants
- {speaker_1}
- {speaker_2}
- ...
### Topics Discussed
1. **{topic_1}** ({timestamp})
- {key_point_1}
- {key_point_2}
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:
- Display user's prompt:
📝 Prompt fornecido pelo usuário:
┌──────────────────────────────────┐
│ [User's prompt preview] │
└──────────────────────────────────┘
- Automatically improve with prompt-engineer (if available):
🔧 Melhorando prompt com prompt-engineer...
[Invokes: gh copilot -p "melhore este prompt: {user_prompt}"]
- Show both versions:
✨ Versão melhorada:
┌──────────────────────────────────┐
│ Role: Você é um documentador... │
│ Instructions: Transforme... │
│ Steps: 1) ... 2) ... │
│ End Goal: ... │
└──────────────────────────────────┘
📝 Versão original:
┌──────────────────────────────────┐
│ [User's original prompt] │
└──────────────────────────────────┘
- Ask which to use:
💡 Usar versão melhorada? [s/n] (default: s):
- Process with selected prompt:
- If "s": use improved
- If "n": use original
LLM Processing (Both Scenarios)
Once prompt is finalized:
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
Progress output:
🤖 Processando com claude... ⠋
[After completion:]
✅ Ata gerada com sucesso!
Final Output
Success (both files):
💾 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
Transcript only (user declined LLM):
💾 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!
Step 5: Display Results Summary
Objective: Show completion status and next steps.
Output:
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.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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How to use it
Take lingxling/audio-transcriber from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip, brew.
Without those the skill loads but fails at the first command.