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Whisper Transcription Project

Overview

This project utilizes OpenAI's Whisper model to transcribe audio and video files. It provides an automated workflow for processing multiple files, organizing transcriptions, and managing the transcription history.

Features

  • Transcribe audio and video files using OpenAI's Whisper model
  • Automatic file organization for input and output
  • Maintains a history of transcriptions
  • Supports multiple audio and video formats

Prerequisites

Before you begin, ensure you have the following installed:

  • Python 3.7 or higher
  • pip (Python package manager)
  • Git (for version control)

You'll also need an OpenAI API key to use the Whisper model.

Installation

  1. Clone the repository:

    git clone https://github.com/Yan-Yu-Lin/whisper-transcription.git
cd whisper-transcription

  2. Create a virtual environment:

    python -m venv myenv

  3. Activate the virtual environment:

    • On macOS and Linux: 
      source myenv/bin/activate
    • On Windows: 
      myenv\Scripts\activate

  4. Install the required packages:

    pip install -r requirements.txt

  5. Create a .env file in the project root and add your OpenAI API key:

    OPENAI_API_KEY=your_api_key_here

Project Structure

The project creates and uses the following folder structure:

  • Processing_Video: Place your audio/video files here for transcription
  • Processed_Video: Processed files are moved here after transcription
  • Result_Text: Contains the latest transcription results
  • Result_Archive: Stores previous transcription results

Usage

  1. Place the audio or video files you want to transcribe in the Processing_Video folder.

  2. Run the transcription script:

    python transcribe.py

  3. The script will process all files in the Processing_Video folder:

    • Transcribe each file using the Whisper model
    • Move processed files to the Processed_Video folder
    • Save transcriptions in the Result_Text folder
    • Move any existing transcriptions to the Result_Archive folder

  4. Check the Result_Text folder for your transcriptions.

Supported File Formats

This project supports the following input file types:

  • Audio: mp3, wav, m4a, flac, aac, ogg, wma
  • Video: mp4, avi, mov, wmv, flv, mkv

Troubleshooting

If you encounter any issues:

  1. Ensure your OpenAI API key is correctly set in the .env file
  2. Check that you have sufficient API credits with OpenAI
  3. Verify that your input files are in a supported format
  4. Make sure you're running the script from the project root directory

Contributing

Contributions to improve the project are welcome. Please follow these steps:

  1. Fork the repository
  2. Create a new branch (git checkout -b feature/AmazingFeature)
  3. Make your changes
  4. Commit your changes (git commit -m 'Add some AmazingFeature')
  5. Push to the branch (git push origin feature/AmazingFeature)
  6. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • OpenAI for providing the Whisper model
  • All contributors who have helped to improve this project