You work for a media company. You are developing a solution that requires transcription and diarizing (speaker identification) of live audio streams in near real-time. You are using an optimized WhisperX model built on the PyTorch framework that uses several libraries and weights sourced from the Hugging Face Hub. The deployment must include proprietary Python post-processing logic including speaker diarization and alignment correction that executes immediately after model inference. You need your solution to be efficient, scalable, handle high throughput, and minimize serving latency. How should you deploy this model and its required logic?
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