African language Speech Recognition - Speech-to-Text

This project demonstartes how to build speech-to-text deep learning model that process and convert African language((Amharic/Swahili) in to text.

Content

• African language Speech Recognition - Speech-to-Text
  • Content
  • Introduction
  • Objective
  • Data & Features
  • Usage
    • Docker-compose
  • Project Structure
    • .dvc
    • Images
    • data
    • notebooks
    • scripts
    • root directory
  • contributors

Introduction

The World Food Program wants to deploy an intelligent form that collects nutritional information of food bought and sold at markets in two different countries in Africa - Ethiopia and Kenya. The design of this intelligent form requires selected people to install an app on their mobile phone, and whenever they buy food, they use their voice to activate the app to register the list of items they just bought in their own language. The intelligent systems in the app are expected to live to transcribe the speech-to-text and organize the information in an easy-to-process way in a database.

Here we will build a deep learning model that is capable of transcribing a speech to text in the Amharic language. The model we produce will be accurate and robust against background noise.

Article

Objective

To build a deep learning model that is capable of transcribing a speech to text.

Data & Features

Dataset for: Amharic Swahili

Input features (X): audio clips of spoken words
Target labels (y): a text transcript of what was spoken

Usage

Docker-compose

Both the front-end and the back-end could be run on a docker container.

1. Clone the repo

git clone https://github.com/Speech-to-text-tenac/STT

2. cd into repo

cd STT

3.Start docker container:

docker-compose up -d

Project Structure

.dvc

used to track large files, models, dataset directories.

Images

a directory for images and results

data

a directory to hold versioned datasets

notebooks

a directory for notebook files.

scripts

directory for scripts files.

root directory

.dvcignore : to hide unnecesary files from dvc.
.gitignore :to hide unnecesary at the root directory.
LICENSE : for preservation of copyright and license notices.
README.md: Markdown text with a brief explanation of the project and the repository structure.
setup.py: a configuration file for installing the scripts as a package requirements.txt: a text file lsiting the projet's dependancies

contributors

clone https://github.com/Speech-to-text-tenac/STT
pip install -r requirements.txt