gedi123c/Speech-to-Text

Building Speech-To-Text models for Amharic language. It involve data pre-processing , visualization and modeling using deep learning algorithms African language Speech Recognition - Speech-to-Text ## Table of Contents * African language Speech Recognition - Introduction - speech-to-text deep learning architecture - Project Structure * data * models * notebooks * scripts * sql * tests * logs * root folder - Installation guide ## Introduction Speech recognition technology allows for hands-free control of smartphones, speakers, and even vehicles in a wide variety of languages. Companies have moved towards the goal of enabling machines to understand and respond to more and more of our verbalized commands. There are many matured speech recognition systems available, such as Google Assistant, Amazon Alexa, and Apple’s Siri. However, all of those voice assistants work for limited languages only. 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. Our responsibility was to 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 is robust against background noise. ## Installation guide ### Conda Enviroment ```bash conda create --name mlenv python==3.7.5 conda activate mlenv ``` ### Installation of dependencies ```bash git clone github.com cd Speech-to-Text sudo python3 setup.py install ``` ## Architecture ## Project Structure ### images: - `images/` the folder where all snapshot for the project are stored. ### data: - `*.dvc` t …