mknotes

A command-line tool to transcribe all MP3, M4A, and WAV audio files in a directory using Faster Whisper, then enhance the transcriptions into comprehensive notes using OpenAI's GPT-4.1 model.

Features

• Batch transcribes all .mp3, .m4a, and .wav files in a specified directory
• Automatically converts WAV files to MP3 format before processing
  • Converted MP3 files are saved in the same directory as the original WAV files
  • Reuses existing MP3 files if they've already been converted
• Saves transcriptions as .txt files
• Enhances notes using GPT-4.1 with a custom prompt
• Outputs enhanced notes in markdown format
• Configurable input and output directories

Installation

# Clone the repository
git clone https://github.com/yourusername/mknotes.git
cd mknotes

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install ffmpeg (required for WAV to MP3 conversion)
# On Ubuntu/Debian:
# sudo apt-get install ffmpeg

# On macOS with Homebrew:
# brew install ffmpeg

# On Windows:
# Download from https://ffmpeg.org/download.html and add to PATH

Usage

export OPENAI_API_KEY="your-api-key-here"
python main.py --input-dir /path/to/audio/files --output-dir /path/to/output [--turbo]

• --input-dir: Directory containing audio files (.mp3, .m4a, .wav) (required)
• --output-dir: Directory for output files (default: "output")
• --turbo: Enable turbo mode for faster inference (uses int8_float16 compute type)
• --force: Force re-processing of files even if output files already exist
• --prompt-type: Type of content to enhance (choices: "lecture", "meeting", "interview", default: "lecture")

Turbo Mode Hardware Requirements

The --turbo flag enables faster inference using the int8_float16 compute type, which can significantly speed up transcription. However, this requires:

• CUDA-compatible GPU with Tensor Cores (NVIDIA Ampere, Turing, or newer architecture)
• Or CPU with AVX2 support

If your hardware does not support this optimization, the program will automatically fall back to the next most compatible compute type and print a warning.

Compute Type Fallback

The program will attempt to use the most efficient compute type supported by your hardware, in the following order:

• int8_float16 (if --turbo is enabled)
• float16
• int8
• float32 (most compatible, works on virtually all hardware)

If a compute type is not supported, the program will try the next one in the list until successful.

Requirements

• Python 3.8+
• Faster Whisper
• OpenAI Python SDK
• pydub (for WAV to MP3 conversion)
• ffmpeg (required by pydub for audio conversion)