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Data Engineering: text-to-speech data collection with Kafka, Airflow, and Spark

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text-to-speech-data-collection

Data Engineering: text-to-speech data collection with Kafka, Airflow, and Spark

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Data Engineering: text-to-speech data collection with Kafka, Airflow, and Spark

An application of Data Engineering tools to collect a stream of audio-text pair of data.


Table of Contents

Project Structure

images:

  • images/ the folder where all snapshot for the project are stored.

notebooks:

  • notebooks/ the folder which contains code snippets for algorand sdk

scripts:

  • .scripts/: the folder where the python implementation can be found.

frontend:

  • frontend/: the folder where the frontend implementation of the application can be found.

backend:

  • backend/: the folder where the backend implementation of the application can be found.

.github:

  • .github/: the folder where github actions and CML workflow is integrated.

root folder

  • requirements.txt: a text file lsiting the projet's dependancies.
  • setup.py: a configuration file for installing the scripts as a package.
  • README.md: Markdown text with a brief explanation of the project and the repository structure.

Installation guide

git clone https://github.com/10AcademyB6Week7/text-to-speech-data-collection.git
cd text-to-speech-data-collection
sudo python3 setup.py install

Getting Started

Articles

Prerequisites

Make sure you have the following components installed on your local machine.

  • Docker
  • DockerCompose

Installation

  1. Clone the repo
git clone https://github.com/10AcademyB6Week7/text-to-speech-data-collection.git

Run

 sudo python3 setup.py install

License

Distributed under the MIT License. See LICENSE for more information.

Participants

👤 Natnael Melese

👤 Yishak Tadele

👤 Henok Desalegn

👤 Gedion Abebe

👤 Bekalu Tadele

👤 Adijat Ojutomori

👤 Birtukan Kuma

Acknowledgements

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Give US a ⭐ if you like this project!

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