Audiophile

A service that stores information about audio files, provides access to this information and periodically updates this data with updates from an included deep learning model.

API Reference

Get all audio files

  GET /api/files

Get audio file

  GET /api/files/{file_id}

Parameter	Type	Description
`id`	`string`	Required. The ID of file to fetch

Get audio file with predictions filtered by model

  GET /api/files/{file_id}/{model}

Parameter	Type	Description
`id`	`string`	Required. The ID of file to fetch

  POST /api/files/upload

Uploads a file to the local media directory

Body	Type	Description
`file`	`binary`	Required. The audio file to be uploaded

  POST /api/files/upload/s3

Uploads a file to an S3 bucket

Body	Type	Description
`file`	`binary`	Required. The audio file to be uploaded

Authors

@f-gozie

Demo

https://www.github.com/f-gozie

Environment Variables

To run this project, you will need to add the following environment variables to your .env file

Mandatory -ish .env Variables

This is for if you choose to run this project with POSTGRESQL as the database. You'd need to add the .env variables for postgresql. If you'd rather use a local database, you can change the SQLALCHEMY_DATABASE_URL in audiophile/config/databases.py to use SQLite instead.

DB_USER

DB_PASSWORD

DB_HOST

DB_PORT

DB_NAME

Optional .env variables (if you want to upload to s3 instead of locally)

AWS_ACCESS_KEY_ID

AWS_SECRET_ACCESS_KEY

AWS_REGION

AWS_S3_BUCKET

S3_BUCKET_URL

Run Locally (Without Docker)

Clone the project

  git clone https://github.com/f-gozie/audiophile

Go to the project directory

  cd audiophile

Activate preferred virtual environment

  conda activate audiophile

Install dependencies

  pip install -r requirements.txt

Start the server

  uvicorn audiophile.main:app --reload

Run Locally (With Docker [ideally using sqlite3])

Clone the project

  git clone https://github.com/f-gozie/audiophile

Go to the project directory

  cd audiophile

Build docker image using Dockerfile

  docker build -t audiophile .

Create a volume to persist DB

  docker volume create audiophile-db

Start the server

  docker run -p 8000:8000 --env-file=.env -v audiophile-db:/app audiophile

Run Locally (With Docker Compose [the easiest way])

Clone the project

  git clone https://github.com/f-gozie/audiophile

Go to the project directory

  cd audiophile

Build docker images

  docker compose build

Start the server

  docker compose up

Appendix

For the Prediction model, the reference field on them is basically used to retrieve the latest predictions for a particular file. Whenever new predictions are generated (every 2 minutes), we create a new reference and update the file with the latest reference, and all newly generated predictions have the same reference. So when getting a file's detail, we filter the predictions by the reference on the file whic returns the latest predictions for the file.

Audio files can be added on demand by uploading them using the /api/files/upload endpoint. There is also an option to upload to an S3 bucket, but the default is to upload locally (since the code solution should be local and easy to run)

For the ability to have multiple models to run inferences against, we have a models class where we can add our models, and we group the models together which we use when running inferences. We run each phrase/utterance against each model and store the model information with the prediction that was generated.

We have a helper method does_data_drift_exist which helps us monitor our input data and compare against existing data to check for cases of data drifting. We then log these in a list, which we could potentially write into a DB, exclude from the new data to add, notify an admin, or whatever action we would like to perform at that point