Haftom2323/Amharic_Speech_to_Text_Using_Deep_Learning

# African language Speech Recognition - Speech-to-Text 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. This project attempts to create a web app that does just that. It will allow users to register the list of items they bought using just their voice. This project utilizes deep learning models, Which are capable of transcribing a speech to text and deliver speech-to-text technology for the choosen two African languages: Amharic and Swahili. ## Project Structure ### Data - Dataset for Amharic ALFFA_PUBLIC ### Data Features - Input features (X): audio clips of spoken words - Target labels (y): a text transcript of what was spoken ### Requirements - Pytorch/Tensorflow , - librosa, scikit-learn, Python, ### Model Architecture - CNN (Convolutional Neural Network) plus RNN-based (Recurrent Neural - Network) architecture - RNN-based sequence-to-sequence network #### Tasks: - [x] Setting up DVC and MLflow - [x] Exploring the data and Extracting useful information - [ ] Preprocessing and Augmenting the data - [ ] Extracting features - [ ] Modelling and Deployment using MLOps - [ ] Serving predictions on a web interface #### Current Status * Integrating Preprocessing and Augmentation to the code base #### Coming Changes * Modelling and Deployment using MLOps ### Reference towardsdatascience.com kaggle.com