Voice Transcribe

Effortless speech-to-text transcription for Ubuntu desktop.

Overview

Voice Transcribe is a simple, keyboard-driven solution for transcribing spoken words to text. Powered by OpenAI's Whisper model for accurate speech recognition, this tool allows users to trigger transcription with a global hotkey, automatically inserting the transcribed text into any active application.

Key Features

• Global hotkey: Trigger transcription with a customizable hotkey combination (Shift + Windows key by default).
• Accurate speech recognition: Powered by the Whisper model for high-quality transcription.
• Automatic text insertion: Transcribed text is automatically inserted into the active application at the current cursor position.
• Ubuntu desktop integration: Seamlessly integrates with the Ubuntu desktop environment.

Getting Started

Prerequisites

• Ubuntu Linux (Tested on Ubuntu Desktop)
• Python 3.9+
• Conda (for virtual environment management)
• Docker (optional, for containerized deployment)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/voice-transcribe.git
   cd voice-transcribe

2. Set up Conda environment:

   Install dependencies using the provided Conda environment:

   conda env create -f environment.yml
   conda activate voice_transcribe

3. Install system dependencies:

   Ensure xdotool is installed to allow text insertion:

   sudo apt-get install xdotool

4. Run the application:

   python hotkey_listener.py

   This will start the application, allowing it to listen for the hotkey combination and perform transcription.

Docker Deployment (Optional)

You can also run the application in a Docker container:

1. Build the Docker image:

   docker build -t voice_transcribe:latest .

2. Run the Docker container:

   docker run -d \
     --name voice_transcribe \
     --device /dev/snd \
     -v /tmp/.X11-unix:/tmp/.X11-unix \
     -e DISPLAY=$DISPLAY \
     -v $XAUTHORITY:/root/.Xauthority \
     --network host \
     --privileged \
     voice_transcribe:latest

Development Environment

• Conda virtual environment: Dependencies are managed with Conda for consistent development environments (see environment.yml).
• Docker containerization: The application can be containerized and deployed using Docker (see Dockerfile).

Contributing

Contributions are welcome! Please see the CONTRIBUTING.md file for guidelines on how to contribute to this project.

License

[Insert license information, e.g., MIT License]

Acknowledgments

• Whisper model: This project uses the Whisper model for speech recognition, developed by OpenAI.

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