Whisper with Streamlit UI

This project is a tool that was developed as part of a Master's thesis in cooperation with the University Clinic of Psychoanalysis and Psychotherapy of Vienna. It is based on the Whisper automatic speech recogniton system and is embedded into a Streamlit Web App.

Features

• Streamlit UI: The tool includes a user-friendly interface that allows you to upload multiple audio files and get a nicely formated transcript.
• Pause detection: The tool can detect pauses in the audio.
• Confidence scores: The tool can color the words according to its probability and display the average score.
• Translation to english
• Speaker detection: not available

Data Privacy

• Whisper is used locally as well as offline (no internet connection needed)
• Nothing is being uploaded to the cloud
• Therefore safe clinical use

User Interface

Start Screen

Results

Getting Started

To use this tool, you will need to install the required dependencies and run the Streamlit app. You can do this by following these steps:

1. Clone the repository: git clone https://github.com/jojojaeger/whisper-streamlit
2. Install prerequisites: Python, Pip, Git, PyTorch (pip install torch torchvision torchaudio)
3. Install dependencies: pip install -r requirements.txt
4. Run the Streamlit app: streamlit run Transcribe.py (you can also launch it from a desktop shortcut following these instructions: https://discuss.streamlit.io/t/launching-streamlit-webapp-from-desktop-shortcut/26297)

How to Use

1. Upload one or multiple audio files
2. Select a model (large for the best result) and set additional parameters
3. Download the resulting transcript (also saved to local transcripts-folder)

Fixes to common errors

• Error: Request failed with status code 403 -> run the app with "--server.enableWebsocketCompression=false"

Contact

If you have any questions or feedback about this project, please feel free to contact us by email at johanna.jaeger89@icloud.com.

Sources

This project includes code from multiple different sources, each licensed under the MIT License:

• [Source A] (https://github.com/openai/whisper)
• [Source A] (https://github.com/pyannote/pyannote-audio)
• [Source C] (https://github.com/hayabhay/whisper-ui)

See the LICENSE file for the full text of the licenses.