audio-transcriber

Transform audio recordings into professional Markdown documentation with intelligent atas/summaries using LLM integration (Claude/Copilot CLI) and automatic prompt engineering.

🆕 What's New in v1.1.0

• 🧠 LLM Integration - Claude CLI (primary) or GitHub Copilot CLI (fallback) for intelligent processing
• ✨ Smart Prompts - Automatic integration with prompt-engineer skill
  • User-provided prompts → automatically improved → user chooses version
  • No prompt → analyzes transcript → suggests format → generates structured prompt
• 📊 Progress Indicators - Visual progress bars (tqdm) and spinners (rich)
• 📁 Timestamp Filenames - transcript-YYYYMMDD-HHMMSS.md + ata-YYYYMMDD-HHMMSS.md
• 🧹 Auto-Cleanup - Removes temporary metadata.json and transcription.json
• 🎨 Rich Terminal UI - Beautiful formatted output with panels and colors

See CHANGELOG.md for complete v1.1.0 details.

🎯 Core Features

• 📝 Rich Markdown Output - Structured reports with metadata tables, timestamps, and formatting
• 🎙️ Speaker Diarization - Automatically identifies and labels different speakers
• 📊 Technical Metadata - Extracts file size, duration, language, processing time
• 📋 Intelligent Atas/Summaries - Generated via LLM (Claude/Copilot) with customizable prompts
• 💡 Executive Summaries - AI-generated structured summaries with topics, decisions, action items
• 🌍 Multi-language - Supports 99 languages with auto-detection
• ⚡ Zero Configuration - Auto-discovers Faster-Whisper/Whisper installation
• 🔒 Privacy-First - 100% local Whisper processing, no cloud uploads
• 🚀 Flexible Modes - Transcript-only or intelligent processing with LLM

📦 Installation

Quick Install (NPX)

npx cli-ai-skills@latest install audio-transcriber

This automatically:

• Downloads the skill
• Installs Python dependencies (faster-whisper, tqdm, rich)
• Installs ffmpeg (macOS via Homebrew)
• Sets up the skill globally

Manual Installation

1. Install Transcription Engine

Recommended (fastest):

pip install faster-whisper tqdm rich

Alternative (original Whisper):

pip install openai-whisper tqdm rich

2. Install Audio Tools (Optional)

For format conversion support:

# macOS
brew install ffmpeg

# Linux
apt install ffmpeg

3. Install LLM CLI (Optional - for intelligent summaries)

Claude CLI (recommended):

# Follow: https://docs.anthropic.com/en/docs/claude-cli

GitHub Copilot CLI (alternative):

gh extension install github/gh-copilot

4. Install Skill

Global installation (auto-updates with git pull):

cd /path/to/cli-ai-skills
./scripts/install-skills.sh $(pwd)

Repository only:

# Skill is already available if you cloned the repo

🚀 Usage

Basic Transcription

copilot> transcribe audio to markdown: meeting.mp3

Output:

• meeting.md - Full Markdown report with metadata, transcription, minutes, summary

With Subtitles

copilot> convert audio file to text with subtitles: interview.wav

Generates:

• interview.md - Markdown report
• interview.srt - Subtitle file

Batch Processing

copilot> transcreva estes áudios: recordings/*.mp3

Processes all MP3 files in the directory.

Trigger Phrases

Activate the skill with any of these phrases:

• "transcribe audio to markdown"
• "transcreva este áudio"
• "convert audio file to text"
• "extract speech from audio"
• "áudio para texto com metadados"

📋 Use Cases

1. Team Meetings

Record standups, planning sessions, or retrospectives and automatically generate:

• Participant list
• Discussion topics with timestamps
• Decisions made
• Action items assigned

2. Client Calls

Transcribe client conversations with:

• Speaker identification
• Key agreements documented
• Follow-up tasks extracted

3. Interviews

Convert interviews to text with:

• Question/answer attribution
• Subtitle generation for video
• Searchable transcript

4. Lectures & Training

Document educational content with:

• Timestamped notes
• Topic breakdown
• Key concepts summary

5. Content Creation

Analyze podcasts, videos, YouTube content:

• Full transcription
• Chapter markers (timestamps)
• Summary for show notes

📊 Output Example

# Audio Transcription Report

## 📊 Metadata

| Field | Value |
|-------|-------|
| **File Name** | team-standup.mp3 |
| **File Size** | 3.2 MB |
| **Duration** | 00:12:47 |
| **Language** | English (en) |
| **Processed Date** | 2026-02-02 14:35:21 |
| **Speakers Identified** | 5 |
| **Transcription Engine** | Faster-Whisper (model: base) |

---

## 🎙️ Full Transcription

**[00:00:12 → 00:00:45]** *Speaker 1*  
Good morning everyone. Let's start with updates from the frontend team.

**[00:00:46 → 00:01:23]** *Speaker 2*  
We completed the dashboard redesign and deployed to staging yesterday.

---

## 📋 Meeting Minutes

### Participants
- Speaker 1 (Meeting Lead)
- Speaker 2 (Frontend Developer)
- Speaker 3 (Backend Developer)
- Speaker 4 (Designer)
- Speaker 5 (Product Manager)

### Topics Discussed
1. **Dashboard Redesign** (00:00:46)
   - Completed and deployed to staging
   - Positive feedback from QA team

2. **API Performance Issues** (00:03:12)
   - Database query optimization needed
   - Target response time < 200ms

### Decisions Made
- ✅ Approved dashboard for production deployment
- ✅ Allocated 2 sprint points for API optimization

### Action Items
- [ ] **Deploy dashboard to production** - Assigned to: Speaker 2 - Due: 2026-02-05
- [ ] **Optimize database queries** - Assigned to: Speaker 3
- [ ] **Schedule user testing session** - Assigned to: Speaker 5

---

## 📝 Executive Summary

The team standup covered progress on the dashboard redesign, which has been successfully completed and is ready for production deployment. The frontend team received positive feedback from QA and the design aligns with user requirements.

Backend performance concerns were raised regarding API response times. The team decided to prioritize query optimization in the current sprint, with a target of sub-200ms response times.

Next steps include production deployment of the dashboard by end of week and scheduling user testing sessions to validate the new design with real users.

### Key Points
- 🔹 Dashboard redesign complete and staging-approved
- 🔹 API performance optimization prioritized
- 🔹 User testing scheduled for next week

### Next Steps
1. Production deployment (Speaker 2)
2. Database optimization (Speaker 3)
3. User testing coordination (Speaker 5)

⚙️ Configuration

No configuration needed! The skill automatically:

• Detects Faster-Whisper or Whisper installation
• Chooses the fastest available engine
• Selects appropriate model based on file size
• Auto-detects language

🔧 Troubleshooting

"No transcription tool found"

Solution: Install Whisper:

pip install faster-whisper

"Unsupported format"

Solution: Install ffmpeg:

brew install ffmpeg  # macOS
apt install ffmpeg   # Linux

Slow processing

Solution: Use a smaller Whisper model:

# Edit the skill to use "tiny" or "base" model instead of "medium"

Poor speaker identification

Solution:

• Ensure clear audio with minimal background noise
• Use a better microphone for recordings
• Try the "medium" or "large" Whisper model

🛠️ Advanced Usage

Custom Model Selection

Edit SKILL.md Step 2 to change model:

model = WhisperModel("small", device="cpu")  # Change "base" to "small", "medium", etc.

Output Language Control

Force output in specific language:

# Edit Step 3 to set language explicitly

Batch Settings

Process specific file types only:

copilot> transcribe audio: recordings/*.wav  # Only WAV files

📚 FAQ

Q: Does this work offline?
A: Yes! 100% local processing, no internet required after initial model download.

Q: What's the difference between Whisper and Faster-Whisper?
A: Faster-Whisper is 4-5x faster with same quality. Always prefer it if available.

Q: Can I transcribe YouTube videos?
A: Not directly. Use a YouTube downloader first, then transcribe the audio file. Or use the youtube-summarizer skill instead.

Q: How accurate is speaker identification?
A: Accuracy depends on audio quality. Clear recordings with distinct voices work best. Currently uses simple estimation; future versions will use advanced diarization.

Q: What languages are supported?
A: 99 languages including English, Portuguese, Spanish, French, German, Chinese, Japanese, Arabic, and more.

Q: Can I edit the meeting minutes format?
A: Yes! Edit the Markdown template in SKILL.md Step 3.

🔗 Related Skills

• youtube-summarizer - Extract and summarize YouTube video transcripts
• prompt-engineer - Optimize prompts for better AI summaries

📄 License

This skill is part of the cli-ai-skills repository.
MIT License - See repository LICENSE file.

🤝 Contributing

Found a bug or have a feature request?
Open an issue in the cli-ai-skills repository.

---

Version: 1.0.0
Author: Eric Andrade
Created: 2026-02-02