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SmartScribe

This repository contains the web application for SmartScribe, a neural speaker diarization system. Below is the directory structure and instructions for running both the frontend and backend.

Folder and File Structure

  • frontend/

    • Contains all the frontend code necessary to run the website interface.

  • backend/

    • Contains all the backend code required to run the models and expose the necessary API endpoints.
    • ASR/: Folder for the Automatic Speech Recognition (ASR) section (code to run ASR models).
    • SD/: Folder for the Speaker Diarization (SD) section (code to run SD models).
    • main.py: The Flask server that runs the backend.
    • ASRSDCombined/: Code responsible for combining the outputs of ASR and SD models.
    • Transcription/: Generates the transcriptions by merging ASR and SD outputs.
    • TranscriptionSummary/: Uses OpenAI to generate summaries of the transcriptions.

  • poc-research-env.yml

    • Conda environment file for setting up the backend environment with the necessary dependencies.

How to Run

Frontend

  1. Navigate to the frontend/ directory and run the frontend 
    cd frontend
npm install
npm run dev
  2. Open localhost

Backend

  1. Navigate to the backend/ directory and start the server
  cd backend
  conda env create -f poc-research-env.yml
  conda activate poc-research-env
  python main.py

Notes

The backend must be run by someone connected to the university's GPUs. Currently, this has been hardcoded to use Apollo, but the IP address can be changed as needed.

The frontend person must also be connected to the university’s internet, either via VPN or by being physically on campus.

Authors

  • Adi Shenoy
  • Jason Yang
  • Jeremy Ting
  • Matthew Wai
  • Steven Li

Environment Variables

To run this project, you will need to add the following environment variables to your .env file, which you can find by asking Adi Shenoy.

OPENAI_API_KEY