Ishita95-AIML/Kinyarwanda-Automatic-Speech-Recognition-Track-A-An-ASR-

# Kinyarwanda Automatic Speech Recognition Track A An ASR hackathon on 500-hour Kinyarwanda speech dataset covering Health, Government, Finance, Education, and Agriculture domains. Here's a README file for your project, Kinyarwanda Automatic Speech Recognition Track A, outlining its purpose, setup, and usage. ## Overview This project is part of an **ASR Hackathon** focused on building and improving **Automatic Speech Recognition (ASR) models** for Kinyarwanda. The dataset consists of **500 hours** of Kinyarwanda speech across various domains, including **Health, Government, Finance, Education, and Agriculture**. The goal is to develop models that enhance speech-to-text accuracy for Kinyarwanda speakers. ## Features - Large-scale **500-hour** dataset for training ASR models. - Covers multiple **domains** to ensure broad applicability. - Supports **fine-tuning and benchmarking** ASR models. - Open-source approach to improving **Kinyarwanda speech recognition**. ## Installation To set up the environment, clone the repository and install dependencies: ```sh git clone github.com cd Kinyarwanda-ASR-TrackA pip install -r requirements.txt  Dataset The dataset contains spoken Kinyarwanda sentences collected from various sources. It is structured as follows: - Health – Medical conversations, health education. - Government – Speeches, government notices, public communication. - Finance – Banking discussions, financial reports. - Education – Classroom lectures, educational materials. - Agriculture – Farming practices, agricultural advice. Model Training To train an ASR model on this dataset, use the following command: python train.py --data_path data/kinyarwanda/ --epochs 50 --batch_size 32 Evaluation To evaluate the trained ASR model: python evaluate.py --model_path models/asr_model.pth --test_data data/kinyarwanda/test/  Technologies Used - Python - DeepSpeech / Wav2Vec 2.0 - Pandas & NumPy - Torch / TensorFlow - S …