Data Engineer: Take home project

Introduction

The goal of this project is to evaluate your knowledge and skills in the design and implementation of a scalable data pre-processing pipeline.

Problem statement

Reads audio data being populated by Metavoice product, Studio, into a CloudFlare R2 bucket
Runs two data transformation steps on the audio files:
- Transcription - use Whisper
- Tokenisation - use mock code here
Stores the results using the example schema below. <id - relative path of audio file>, <transcription>, <token array>

Requirements

Install ffmpeg by following instructions here
Use pipenv to install the required packages: pipenv install
Go to where the main.py file is located and run:

python main.py

Notes

For scalability, I decided to read the audio file with a given chunk_size, and so preprocess the audio file in chunks. This is to avoid memory issues when dealing with large audio files. The script is broken after a while (probably an audio file it does not like) as it shows:

pydub.exceptions.CouldntDecodeError: Decoding failed. ffmpeg returned error code: 1

I think there is a better solution, but by lack of time and not 100% sure if that feasible, that would be to:

create a HuggingFace loading-script
And so we could use the HF Dataset API to load the audio files and preprocess it.

For the Whisper model, HF provide useful functions to preprocess it:

from transformers import WhisperFeatureExtractor
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
...