21434809/Evaluating-ASR-Transcription-Topic-Modeling-for-Setswana

This project will explore how well existing ASR systems perform for African languages ( Setswana ) and how extracted transcripts from local language podcasts can be used for topic modeling and how what NLP techniques improve topic modeling. # Evaluating ASR Transcription and Topic Modeling for Setswana ## Project Overview This project evaluates the performance of Automatic Speech Recognition (ASR) systems and applies topic modeling techniques to Setswana transcriptions. The goal is to analyze the quality of ASR transcriptions and explore how well topic modeling can extract meaningful topics from Setswana text data. ## Features - **ASR Transcription**: Scripts for processing audio files and generating transcriptions using models like Wav2Vec2. - **Topic Modeling**: Implementation of Latent Dirichlet Allocation (LDA) and BERTopic for extracting topics from Setswana text. - **Data Preprocessing**: Includes lemmatization, stopword removal, and sentence splitting. - **Visualization**: Generate visualizations such as word clouds and statistical charts. ## Repository Structure - **data/**: Contains raw and processed datasets, including Setswana stopwords and transcriptions. - **docs/**: Includes results and visualizations generated during the analysis. - **models/**: Directory for storing trained models. - **notebooks/**: Jupyter notebooks for interactive exploration and experimentation. - **src/**: Python scripts for preprocessing, transcription, and topic modeling. - **references/**: Additional reference materials. ## Requirements ### Python Packages - `numpy` - `pandas` - `matplotlib` - `seaborn` - `scikit-learn` - `gensim` - `nltk` - `torch` - `transformers` ### Additional Tools - Jupyter Notebook (optional, for interactive exploration) ## Installation 1. Clone the repository: ```bash git clone github.com cd Evaluating-ASR-Transcription-Topic-Modeling-for-Setswana ``` 2. Install the required packages: ```bash pip install -r requirements.txt ``` ## How to Run the Project ### Data Preparation 1. Place your Setswana audio files in the appropriate directory. 2. Ensure the stopword list and other preprocessing files are in t …