doorbell_detector

An over engineered doorbell detector based on machine learning

Install

Install docker:

curl -fsSL https://get.docker.com -o get-docker.sh
sudo get-docker.sh

Install git, git lfs and docker compose sudo apt install git git-lfs docker-compose

This repository is structured as follows:

raw_data: folder to keep track of original, uncut recordings for reference. It's always a good idea to keep the original data around. split_data: folder to put the manually extracted bell sounds in and the automatically extracted non-bell and non-silence parts. bell: bell sounds of varying lengths noise: ambient noise sounds of varying lengths

Quickstart on your own data: Extract the bell sounds and put each audio file under split_data/bell Throw the ambient sounds file in raw_data as ambient.wav, it will be split as part of the notebook

Run the notebook to generate a model file

Add a `creds.py` file under `deploy` and add an `app_token` and `client_token` variable of your pushover api keys
Copy the deploy folder (also containing the trained model file now) to the raspberry pi

You can use `screen` to run the python script until it is deployed with docker in the next version

TODO: add retraining code for corrected windows

https://github.com/veirs/sounddevice/blob/master/Dockerfile

TODO:

• [*] Get model server running
• Get dashboard running
• Get retraining pipeline running
• [*] Update model server features
• Implement model hotswap in model server