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Whisper Web UI

Whisper Web UI is a Streamlit web application (whisper_webui.py) and a command-line interface (whisper_cli.py) for transcribing audio files using the Whisper Large v3 model via either the OpenAI or Groq API. It offers a user-friendly interface for uploading audio, processing it, and obtaining transcriptions quickly and efficiently.

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UI Showcase

Features​

  • Automatic compression for files larger than 25MB
  • Support for multiple audio formats (mp3, mp4, mpeg, mpga, m4a, wav, webm)
  • Transcription using Whisper Large v3 model through OpenAI, Groq, or Fal API
  • Display of transcription time and results
  • Option to copy transcript to clipboard
  • Ability to save transcript to a file
  • Both web-based and command-line interfaces

Installation​

  1. Clone this repository:

    git clone https://github.com/yourusername/audio-transcription-app.git
    cd audio-transcription-app

  2. Install the required dependencies:

    pip install streamlit groq openai pydub pyperclip

  3. Set up your API keys as environment variables:

    export OPENAI_API_KEY='your_openai_api_key_here'
    export GROQ_API_KEY='your_groq_api_key_here'

Usage​

Streamlit Web Application (whisper_webui.py)​

  1. Run the Streamlit app:

    streamlit run whisper_webui.py

  2. Open your web browser and navigate to the provided local URL (typically http://localhost:8501).

  3. Use the interface to upload an audio file, process it, and view the transcription results.

Command-Line Interface (whisper_cli.py)​

The CLI version offers more flexibility and options for transcription. Here's how to use it:

python whisper_cli.py [-h] -i INPUT [-o OUTPUT] [--compress-only] [-c] [--api {openai,groq}]

Options:

  • -i INPUT, --input INPUT: Input audio file (required)
  • -o OUTPUT, --output OUTPUT: Output filename for the transcript
  • --compress-only: Compress the audio file only (no transcription)
  • -c, --clipboard: Copy the transcription text to the system clipboard
  • --api {openai,groq}: Choose API for transcription (default: openai)

Examples:

  1. Transcribe an audio file using OpenAI API:

    python whisper_cli.py -i input.mp3 -o transcript.txt

  2. Transcribe using Groq API and copy to clipboard:

    python whisper_cli.py -i input.wav --api groq -c

  3. Compress an audio file without transcribing:

    python whisper_cli.py -i large_file.mp3 --compress-only

Note on Fal API Usage​

If using the Fal.ai API for transcription, the application uploads your audio file to tmpfiles.org. This step is necessary because the Fal API requires input files to be accessible via a public URL.

Please ensure that you have the necessary rights to upload and make your audio public. Do not use this method for sensitive recordings.

Contributing​

Contributions are welcome! Please feel free to submit a Pull Request.

License​

This project is released under the MIT License.