schihei
c47089aa0d
- Add support for WAV files with automatic conversion to MP3 - Save converted MP3 files in the same directory as WAV files - Reuse existing MP3 files if already converted - Update documentation and requirements
80 lines
2.7 KiB
Markdown
80 lines
2.7 KiB
Markdown
# mknotes
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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.
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## Features
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- Batch transcribes all `.mp3`, `.m4a`, and `.wav` files in a specified directory
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- Automatically converts WAV files to MP3 format before processing
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- Converted MP3 files are saved in the same directory as the original WAV files
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- Reuses existing MP3 files if they've already been converted
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- Saves transcriptions as `.txt` files
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- Enhances notes using GPT-4.1 with a custom prompt
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- Outputs enhanced notes in markdown format
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- Configurable input and output directories
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## Installation
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```bash
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# Clone the repository
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git clone https://github.com/yourusername/mknotes.git
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cd mknotes
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# Create a virtual environment
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python -m venv venv
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source venv/bin/activate # On Windows: venv\Scripts\activate
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# Install dependencies
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pip install -r requirements.txt
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# Install ffmpeg (required for WAV to MP3 conversion)
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# On Ubuntu/Debian:
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# sudo apt-get install ffmpeg
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# On macOS with Homebrew:
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# brew install ffmpeg
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# On Windows:
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# Download from https://ffmpeg.org/download.html and add to PATH
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```
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## Usage
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```bash
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export OPENAI_API_KEY="your-api-key-here"
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python main.py --input-dir /path/to/audio/files --output-dir /path/to/output [--turbo]
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```
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- `--input-dir`: Directory containing audio files (.mp3, .m4a, .wav) (required)
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- `--output-dir`: Directory for output files (default: "output")
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- `--turbo`: Enable turbo mode for faster inference (uses int8_float16 compute type)
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- `--force`: Force re-processing of files even if output files already exist
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### Turbo Mode Hardware Requirements
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The `--turbo` flag enables faster inference using the `int8_float16` compute type, which can significantly speed up transcription. However, this requires:
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- CUDA-compatible GPU with Tensor Cores (NVIDIA Ampere, Turing, or newer architecture)
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- Or CPU with AVX2 support
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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.
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#### Compute Type Fallback
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The program will attempt to use the most efficient compute type supported by your hardware, in the following order:
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- `int8_float16` (if `--turbo` is enabled)
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- `float16`
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- `int8`
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- `float32` (most compatible, works on virtually all hardware)
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If a compute type is not supported, the program will try the next one in the list until successful.
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## Requirements
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- Python 3.8+
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- [Faster Whisper](https://github.com/SYSTRAN/faster-whisper)
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- [OpenAI Python SDK](https://github.com/openai/openai-python)
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- [pydub](https://github.com/jiaaro/pydub) (for WAV to MP3 conversion)
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- [ffmpeg](https://ffmpeg.org/) (required by pydub for audio conversion)
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