Whisper, an open-source transcription model by OpenAI, is optimized for real-time speech transcription through WebSockets, particularly effective for applications requiring seamless, continuous data exchange such as AI transcription and live communications. Unlike traditional HTTP request-based methods, WebSockets maintain a persistent bidirectional connection, reducing latency and connection overhead, thus enhancing the performance of streaming applications. The article details an implementation example on Baseten using Whisper V3, providing a step-by-step guide to deploy the model, configure WebSockets, and stream audio from a microphone for real-time transcription. By leveraging Python's asyncio for asynchronous operations, users can simultaneously send audio data and receive transcribed text in real time. The approach allows for scalability in production, ensuring the system can handle multiple concurrent users by setting appropriate autoscaling settings. This method underscores the utility of WebSockets in creating efficient, production-grade speech-to-text transcription systems.