In a detailed exploration of model selection, adaptation, and tuning for enterprise speech data, the text emphasizes the importance of choosing the right speech-to-text (STT) model by considering factors such as weight access, customization potential, and processing type (streaming versus batch). Proprietary models like those from Deepgram, Google Cloud, Azure, and AWS are closed-weight but offer adaptation via API parameters, whereas open-weight models like Whisper allow for full fine-tuning. The article highlights the differences between streaming and batch processing, noting that batch processing provides better context for disambiguating terms but is less suited for real-time needs. Additionally, it discusses the availability of domain-specific models, including those tailored for the medical, telephony, finance, and legal sectors, which can enhance transcription accuracy by being trained on relevant terminology and use cases. The text suggests testing domain-specific models on enterprise audio before opting for further customization or fine-tuning.