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Systems without an NVidia GPU

The Whisper Transcriber Service runs on Windows, macOS, and Linux systems without an NVidia GPU, it'll just run slower as the Whisper model run on the CPU.

From limited testing, the multilingual and the English-only OpenAI Whisper models for tiny(.en), small(.en), and medium(.en) models ran with acceptable performance on Windows 11 with a modern CPU and on a MacBook M2 Air with 16 GB of RAM.

Install system dependencies​

Follow the instructions for your operating system.

Install Windows 11 dependencies​

  1. Install FFmpeg.
    1. You can download the latest release from FFmpeg-Builds.
    2. Unzip the downloaded FFmpeg file and move to your preferred app folder.
    3. From System Properties, select Environment Variables, and add the path to the FFmpeg bin folder to the path.
    4. Test FFmpeg. From a new terminal window, run ffmpeg -version.

Install macOS dependencies​

  1. Install FFmpeg

    1. Open a terminal window.

    2. Install Homebrew.

    3. Install FFmpeg. Run

      brew install ffmpeg

Install Ubuntu 20.04 dependencies​

  1. Install FFmpeg and pip3
    1. Open a terminal window.
    2. Run:
      sudo apt install ffmpeg python3-pip python3-venv

Start the Whisper Transcriber Service​

  1. From a terminal window.

  2. Clone the Whisper Transcriber Sample to your preferred repo folder.

    git clone https://github.com/gloveboxes/OpenAI-Whisper-Transcriber-Sample.git

  3. Navigate to the server folder.

    cd OpenAI-Whisper-Transcriber-Sample/server

  4. Create a Python virtual environment.

    danger

    At the time of writing (June 2023), the Whisper Python library is supported on Python 3.8 to 3.10. The Whisper library worked on Python 3.11.3, but not Python 3.11.4. Be sure to check the version of Python you are using python3 --version.

    python3 -m venv .whisper-venv

  5. Activate the Python virtual environment.

    on Windows

    .\.whisper-venv\Scripts\activate

    on macOS and Linux

    source .whisper-venv/bin/activate

  6. Install the required Python libraries.

    pip3 install -r requirements.txt

  7. Review the following chart is taken from the OpenAI Whisper Project Description page and select the model that will fit in the RAM of your computer. At the time of writing, Whisper multilingual models include tiny, small, medium, and large, and English-only models include tiny.en, small.en, and medium.en.

  8. Update the server/config.json file to set your desired Whisper model. For example, to use the medium model, set the model property to medium.

    { "model": "medium" }

  9. Start the Whisper Transcriber Service. From the command line, run:

    uvicorn main:app --port 5500 --host 0.0.0.0

    Once the Whisper Transcriber Service starts, you should see output similar to the following.

    [2023-06-04 18:53:46.194411] Whisper API Key: 17ce01e9-ac65-49c8-9cc9-18d8deb78197
    [2023-06-04 18:53:50.375244] Model: medium loaded.
    [2023-06-04 18:53:50.375565] Ready to transcribe audio files.

  10. The Whisper API Key will be also be displayed. Save the Whisper API Key somewhere safe, you'll need the key to configure the Whisper client.

    Whisper API Key: <key>

  11. To stop the Whisper Transcriber Service, press CTRL+C in the terminal.

  12. To deactivate the Python virtual environment, run deactivate.