Telegram WebM Converter

This project started off as a shell script for my own personal needs , so please do expect some rough edges.
If you find any bugs or have suggestions, please open an issue. Thank you for your feedback!

Convert images to Telegram WebM stickers.

Features

• Convert an image to 100x100 WebM icon (max 32KB)
• Convert images to 512x512 WebM stickers (max 256KB)
• Batch processing support
• Automatic size optimization

Requirements

• Python 3.8+
• FFmpeg

This application requires FFmpeg to be installed on your system. FFmpeg is open-source software licensed under the LGPLv2.1/GPLv2 (or later). Please refer to the FFmpeg website for more details on its licensing.

Installation

pip install tg-webm-converter

Usage

# Convert all images in current directory
tg-webm-converter

# Convert specific file to icon, others to stickers
tg-webm-converter -i icon.png

# Convert only one file to sticker
tg-webm-converter -f image.jpg

# Convert only one file to icon
tg-webm-converter --icon-file icon.png

Build from Source

Prerequisites

• Python 3.8+
• Poetry: curl -sSL https://install.python-poetry.org | python3 -
• FFmpeg: As mentioned above, FFmpeg is a core dependency. Ensure it's installed and accessible in your system's PATH.

Setup

1. Clone the repository

   git clone https://github.com/7591yj/tg-webm-converter.git
   cd tg-webm-converter
   

2. Install Dependencies with Poetry

   Poetry will automatically create a virtual environment for the project and install all required dependencies.

   poetry install
   

Running from Source

Once the development environment is set up, you can run the tg-webm-converter command directly using Poetry's run command:

# Run the application (same as `tg-webm-converter` if installed via pip)
poetry run tg-webm-converter

# Example: Convert all images in current directory
poetry run tg-webm-converter

# Example: Convert specific file to icon
poetry run tg-webm-converter --icon-file icon.png

# Run tests
poetry run pytest

Future Plans

• Consider a GUI version for easier use.
• Optimize performance for large batch conversions.
• Explore options for even smaller file sizes.

License

MIT License