Reflector

Reflector Audio Management and Analysis is a cutting-edge web application under development by Monadical. It utilizes AI to record meetings, providing a permanent record with transcripts, translations, and automated summaries.

The project architecture consists of three primary components:

• Front-End: NextJS React project hosted on Vercel, located in www/.
• Back-End: Python server that offers an API and data persistence, found in server/.
• GPU implementation: Providing services such as speech-to-text transcription, topic generation, automated summaries, and translations. Most reliable option is Modal deployment

It also uses https://github.com/fief-dev for authentication, and Vercel for deployment and configuration of the front-end.

Table of Contents

• Reflector
  • Table of Contents
  • Miscellaneous
    • Contribution Guidelines
    • How to Install Blackhole (Mac Only)
  • Front-End
    • Installation
    • Run the Application
    • OpenAPI Code Generation
  • Back-End
    • Installation
    • Start the API/Backend
    • Redis (Mac)
    • Redis (Windows)
  • Update the database schema (run on first install, and after each pull containing a migration)
  • Main Server
    • Crontab (optional)
      • Using docker
    • Using local GPT4All
    • Using local files
  • AI Models

Miscellaneous

Contribution Guidelines

All new contributions should be made in a separate branch. Before any code is merged into main, it requires a code review.

Usage instructions

To record both your voice and the meeting you're taking part in, you need :

• For an in-person meeting, make sure your microphone is in range of all participants.
• If using several microphones, make sure to merge the audio feeds into one with an external tool.
• For an online meeting, if you do not use headphones, your microphone should be able to pick up both your voice and the audio feed of the meeting.
• If you want to use headphones, you need to merge the audio feeds with an external tool.

Permissions:

You may have to add permission for browser's microphone access to record audio in System Preferences -> Privacy & Security -> Microphone System Preferences -> Privacy & Security -> Accessibility. You will be prompted to provide these when you try to connect.

How to Install Blackhole (Mac Only)

This is an external tool for merging the audio feeds as explained in the previous section of this document. Note: We currently do not have instructions for Windows users.

• Install Blackhole-2ch (2 ch is enough) by 1 of 2 options listed.
• Setup "Aggregate device" to route web audio and local microphone input.
• Setup Multi-Output device
• Then goto System Preferences -> Sound and choose the devices created from the Output and Input tabs.
• The input from your local microphone, the browser run meeting should be aggregated into one virtual stream to listen to and the output should be fed back to your specified output devices if everything is configured properly.

Front-End

Start with cd www.

Installation

To install the application, run:

yarn install
cp .env_template .env
cp config-template.ts config.ts

Then, fill in the environment variables in .env and the configuration in config.ts as needed. If you are unsure on how to proceed, ask in Zulip.

Run the Application

To run the application in development mode, run:

yarn dev

Then (after completing server setup and starting it) open http://localhost:3000 to view it in the browser.

OpenAPI Code Generation

To generate the TypeScript files from the openapi.json file, make sure the python server is running, then run:

yarn openapi

Back-End

Start with cd server.

Quick-run instructions (only if you installed everything already)

redis-server # Mac
docker compose up -d redis # Windows
poetry run celery -A reflector.worker.app worker --loglevel=info
poetry run python -m reflector.app

Installation

Download Python 3.11 from the official website and ensure you have version 3.11 by running python --version.

Run:

python --version # It should say 3.11
pip install poetry
poetry install --no-root
cp .env_template .env

Then fill .env with the omitted values (ask in Zulip). At the moment of this writing, the only value omitted is AUTH_FIEF_CLIENT_SECRET.

Start the API/Backend

Start the background worker:

poetry run celery -A reflector.worker.app worker --loglevel=info

Redis (Mac)

yarn add redis
poetry run celery -A reflector.worker.app worker --loglevel=info
redis-server

Redis (Windows)

Option 1

docker compose up -d redis

Option 2

Install:

• Git for Windows
• Windows Subsystem for Linux (WSL)
• Install your preferred Linux distribution via the Microsoft Store (e.g., Ubuntu).

Open your Linux distribution and update the package list:

sudo apt update
sudo apt install redis-server
redis-server

Update the database schema (run on first install, and after each pull containing a migration)

poetry run alembic heads

Main Server

poetry run python -m reflector.app

Crontab (optional)

For crontab (only healthcheck for now), start the celery beat (you don't need it on your local dev environment):

poetry run celery -A reflector.worker.app beat

Using docker

Use:

docker-compose up server

Using local GPT4All

• Start GPT4All with any model you want
• Ensure the API server is activated in GPT4all
• Run with: LLM_BACKEND=openai LLM_URL=http://localhost:4891/v1/completions LLM_OPENAI_MODEL="GPT4All Falcon" python -m reflector.app

Using local files

poetry run python -m reflector.tools.process path/to/audio.wav

AI Models

Modal

To deploy llm changes to modal, you need.

• a modal account
• set up the required secret in your modal account (REFLECTOR_GPU_APIKEY)
• install the modal cli
• connect your modal cli to your account if not done previously
• modal run path/to/required/llm

(Documentation for this section is pending.)