Automatic speech recognition (ASR), or speech-to-text, has evolved significantly with advancements in open-source models, making the technology more accessible and customizable for various applications without the constraints of proprietary licenses. Leading open-source ASR models like Whisper ASR, DeepSpeech, Kaldi, Wav2vec, and SpeechBrain provide developers with tools to integrate speech recognition into applications across industries such as telecommunications, healthcare, and customer service. Whisper, developed by OpenAI, is notable for its accuracy and ability to handle diverse languages and accents, while Mozilla's DeepSpeech offers flexibility, albeit with limitations in audio duration. Meta's Wav2vec focuses on training with unlabeled data to cover underrepresented languages, and Kaldi provides a flexible toolkit for building custom ASR systems. SpeechBrain stands out for its comprehensive approach to conversational AI tasks. Despite their advantages, deploying open-source ASR models involves practical challenges such as significant hardware requirements and the need for AI expertise, prompting some organizations to consider hybrid solutions or specialized APIs for a more streamlined implementation.