Amharic-STT/STT-engine

Speech-to-Text implementation for the Amharic language # Amharic Speech-to-Text engine ## 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. Key Topics the following topics will be covered in this unit: 1.Data pre-processing .Load audio file .Load transcriptions .Convert into channels .Standardize sampling rate .Resize to the same length .Data argumentation .Feature extraction: .Acoustic modeling: 2.Modelling and Deployment using MLOps .Modeling: Build a Deep learning model that converts speech to text. .Choose one of deep learning architecture for speech recognition Use Connectionist Temporal Classification Algorithm for training and inference CTC takes the character probabilities output of the last hidden layer and derives the correct sequence of characters .Evaluate your model. .Effect of data augmentation: apply diffe …