WhisperClip simplifies your life by automatically transcribing audio recordings and saving the text directly to your clipboard. With just a click of a button, you can effortlessly convert spoken words into written text, ready to be pasted wherever you need it. This application harnesses the power of OpenAI's Whisper for free, making transcription more accessible and convenient. This is a fork from the original repo that:

• Adds support to Linux as an operating system (tested on Fedora 41).
• Refactors the transcription engine from Whisper to use Faster-Whisper, which uses CTranslate2 to speed up CPU inference.

• Features
• Installation
  • Prerequisites
  • Setting Up the Environment
  • Choosing the Right Model
• Usage
• Configuration
• Feedback
• Acknowledgments

• Record audio with a simple click.
• Automatically transcribe audio using Whisper (free).
• Option to save transcriptions directly to the clipboard.

You can install the application on Debian-based systems using:

• sudo apt install <PACKAGE_NAME.deb>
• The .deb file can be found under releasesin the repository.
• The .deb file was generated using AI assistance.

• Python 3.8 or higher
• CUDA is highly recommended for better performance but not necessary. WhisperClip can also run on a CPU. This was patched to use a lightweight implementation of Whisper that use a CPU as the main device.

1. Clone the repository:

   git clone https://github.com/Pedrohgv/whisper-clip-linux.git
   cd whisper-clip
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

For English-only applications, .en models (e.g., tiny.en, base.en) tend to perform better.

To change the model, modify the model_name variable in config.json to the desired model name.

Run the application:

python main.py

• Click the microphone button to start and stop recording.
• If "Save to Clipboard" is checked, the transcription will be copied to your clipboard automatically.

• The default shortcut for toggling recording is Alt+Shift+R. You can modify this in the config.json file.
• You can also change the Whisper model used for transcription in the config.json file.

If there's interest in a more user-friendly, executable version of WhisperClip, I'd be happy to consider creating one. Your feedback and suggestions are welcome! Just let me know through the GitHub issues.

This project uses OpenAI's Whisper for audio transcription. This project is a fork from the original whisper-clip.