mnemofy 0.6.0

Extract audio from media files, transcribe speech, and produce documented meeting notes

mnemofy

mnemofy extracts audio from media files, transcribes speech using faster-whisper, and produces structured meeting notes with topics, decisions, action items, and mentions with timestamps.

Features

• 🎵 Audio Extraction: Automatically extracts audio from video files using ffmpeg
• 🎤 Speech Transcription: Fast local transcription using faster-whisper (no API keys needed)
• 📝 Structured Notes: Generates Markdown notes with:
  • Topics discussed with timestamps
  • Decisions made with timestamps
  • Action items with timestamps and @mentions
  • Full transcript with timestamps
• 🎯 Supported Formats: aac, mp3, wav, mkv, mp4
• 🚀 Production Ready: Clean modular architecture, type hints, error handling

Installation

Prerequisites

1. Python 3.9+ is required
2. ffmpeg must be installed:

   # Ubuntu/Debian
   sudo apt install ffmpeg
   
   # macOS
   brew install ffmpeg
   
   # Windows
   # Download from https://ffmpeg.org/download.html
   

Install mnemofy

# Clone the repository
git clone https://github.com/tiroq/mnemofy.git
cd mnemofy

# Install the package
pip install -e .

# Or install with development dependencies
pip install -e ".[dev]"

Usage

Basic Usage

Transcribe an audio or video file:

mnemofy transcribe meeting.mp4

This will create meeting_notes.md in the same directory.

Advanced Options

# Specify output file
mnemofy transcribe meeting.mp4 -o notes/meeting_summary.md

# Use a different Whisper model (tiny, base, small, medium, large)
# Larger models are more accurate but slower
mnemofy transcribe meeting.mp4 -m small

# Set a custom title for the notes
mnemofy transcribe meeting.mp4 -t "Team Sprint Planning"

# Keep the extracted audio file
mnemofy transcribe video.mkv --keep-audio

Get Help

# Show all options
mnemofy transcribe --help

# Show version
mnemofy version

Example Output

Given an audio file with meeting content, mnemofy generates structured Markdown like:

# Meeting Notes

## Topics Discussed

- **[00:32]** Let's talk about the new feature requirements
- **[05:12]** Now let's discuss the timeline

## Decisions Made

- **[03:45]** We've decided to use Python for the backend
- **[08:20]** The consensus is to launch in Q2

## Action Items

- **[10:15]** John needs to create the API documentation (@john)
- **[12:30]** Sarah will follow up with the design team (@sarah)

## Mentions

- @john: 10:15
- @sarah: 12:30

## Full Transcript

**[00:00]** Welcome everyone to today's meeting...

Architecture

mnemofy follows a clean, modular architecture:

mnemofy/
├── src/mnemofy/
│   ├── __init__.py       # Package initialization
│   ├── audio.py          # Audio extraction using ffmpeg
│   ├── transcriber.py    # Speech transcription using Whisper
│   ├── notes.py          # Structured note generation
│   └── cli.py            # Command-line interface with Typer
├── tests/                # Test suite
├── pyproject.toml        # Modern Python packaging (PEP 621)
└── README.md

Development

Running Tests

pytest

Linting and Type Checking

# Run ruff for linting
ruff check src/

# Run mypy for type checking
mypy src/

Whisper Models

mnemofy supports all Whisper model sizes:

Model	Parameters	Speed	Accuracy
tiny	39M	Fastest	Good
base	74M	Fast	Better
small	244M	Medium	Great
medium	769M	Slow	Excellent
large	1550M	Slowest	Best

The default base model offers a good balance of speed and accuracy. Use tiny for quick tests or medium/large for maximum accuracy.

Requirements

• Python 3.9-3.13 (Python 3.14+ not yet supported by dependencies)
• ffmpeg
• Dependencies (automatically installed):
  • typer
  • openai-whisper
  • rich
  • pydantic

License

MIT License - see LICENSE file for details.