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.

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 project
      • 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.

How to Install Blackhole (Mac Only)

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 miscrophones, 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.

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.

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.

Front-End

Start with cd www.

Installation

To install the application, run:

yarn install

Then create an .env with:

FIEF_URL=https://auth.reflector-ui.dev/reflector-local
FIEF_CLIENT_ID=s03<omitted>
FIEF_CLIENT_SECRET=<omitted>

EDGE_CONFIG=<omitted>

Run the Application

To run the application in development mode, run:

yarn dev

Then 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

You may need to run yarn global add @openapitools/openapi-generator-cli first. You also need a Java runtime installed on your machine.

Back-End

Start with cd server.

Installation

Run:

poetry install

Then create an .env with:

TRANSCRIPT_BACKEND=modal
TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-web.modal.run
TRANSCRIPT_MODAL_API_KEY=<omitted>

LLM_BACKEND=modal
LLM_URL=https://monadical-sas--reflector-llm-web.modal.run
LLM_MODAL_API_KEY=<omitted>
TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
ZEPHYR_LLM_URL=https://monadical-sas--reflector-llm-zephyr-web.modal.run
DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run

AUTH_BACKEND=fief
AUTH_FIEF_URL=https://auth.reflector.media/reflector-local
AUTH_FIEF_CLIENT_ID=KQzRsNgoY<omitted>
AUTH_FIEF_CLIENT_SECRET=<omitted>

TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
ZEPHYR_LLM_URL=https://monadical-sas--reflector-llm-zephyr-web.modal.run

Start the API/Backend

Start the API server:

poetry run python3 -m reflector.app

Start the background worker:

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

For crontab (only healthcheck for now), start the celery beat:

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

(Documentation for this section is pending.)