Choosing Epochs For ASR (automatic Speech Recognition) Fine Tuning Whisper, Wav2vec2, And Similar Models

This project investigates the impact of training epochs on the performance of Automatic Speech Recognition (ASR) models, specifically Whisper, Wav2Vec2, and other similar architectures. Its primary goal is to identify the most effective number of training epochs to enhance model accuracy and reduce overfitting during the fine-tuning process.

Key technologies employed in this project include Python for scripting, PyTorch for model training, and various datasets for ASR tasks. By analyzing performance metrics across different epochs, the project aims to provide insights that can inform best practices for researchers and developers working in the field of speech recognition. This project may be particularly useful for machine learning practitioners, data scientists, and organizations looking to implement or improve ASR systems.