Language Detection Using Natural Language Processing

This research project presents a comprehensive approach to Language Detection utilizing Natural Language Processing (NLP) techniques. The system begins by ingesting audio files, converting them into textual data through audio-to-text conversion processes. Subsequently, this textual data is processed and analyzed by a machine learning model that has been trained on the Kaggle LANGUAGE DETECTION dataset. The model is designed to predict the language of the input, with a range of supported languages including but not limited to English, Arabic, French, Hindi, Urdu, Portuguese, Persian, Pushto, Spanish, Korean, Tamil, Turkish, Estonian, Russian, Romanian, Chinese, Swedish, Latin, Indonesian, Dutch, Japanese, and Thai. The project encompasses key phases such as data collection, data preprocessing, dataset partitioning for training and testing, model implementation, and the generation of language predictions. The implementation of this system holds considerable technical significance, serving as a valuable tool in applications like automated transcription services, multilingual content analysis, and language-based data processing. The combination of NLP and machine learning techniques empowers the system to accurately detect and categorize diverse languages, contributing to its robustness and practicality in real-world scenarios.

Keywords: ML evaluation, ML techniques, NLP, etc..