inbatamilan18/identification-of-tamil-dialects-using-wav2vec-2.0-

This repository is a small student-style project that tries to tell apart different dialects of Tamil from audio recordings. Tamil is a major South Indian language with regional variations in how words are pronounced. The author trains a machine learning model that listens to a clip and predicts which dialect the speaker is using. The approach uses Wav2Vec 2.0, a speech model originally released by Facebook AI Research that turns raw audio into numerical representations a downstream classifier can work with. In this project, those representations are fed into a classifier that assigns a dialect label. The work is presented as a Jupyter notebook, which is a document that mixes code, results, and explanation in one file. The README is sparse. It does not list the specific dialects covered, the size or source of the audio dataset, the exact classifier on top of Wav2Vec, the training setup, or the accuracy that was achieved. It mentions confusion matrix and PCA cluster visualizations, which are standard ways to inspect how well a classifier separates categories and how the underlying audio embeddings cluster in a reduced space. The tools listed are Python, Jupyter Notebook, Wav2Vec 2.0, Pandas for handling tabular data, Scikit-learn for the machine learning parts, and Matplotlib for the plots. The files included are the main notebook, two PNG images for the confusion matrix and PCA cluster plot, and an Excel file with dialect predictions. The author is credited as Inbatamilan. No license, installation instructions, or run instructions are given in the README.