Fine-tuning Automatic Speech Recognition (ASR) models, like OpenAI's Whisper, involves further training a pre-existing model on domain-specific data to enhance its performance in particular areas, such as recognizing new languages, dialects, or industry-specific terms. This process uses the model's pre-trained weights as a starting point and can involve various strategies, including freezing certain layers or adding new ones to retain or extend the model's knowledge. Fine-tuning offers advantages such as saving time and resources and improving performance in specific domains, making it a cost-effective solution for small and medium enterprises (SMEs) looking to leverage AI in their workflows. However, it requires technical expertise, high-quality data, and adequate hardware, and it can pose challenges like overfitting. Different types of fine-tuning techniques and adaptations, including custom vocabulary and specialized models, are utilized based on project needs, with Whisper's fine-tuning process being detailed as an example, highlighting steps such as loading datasets, processing data, and executing training. Fine-tuning is a valuable method for expanding ASR models' capabilities without developing new models from scratch, enabling businesses to integrate AI efficiently.