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Abstract This thesis investigates the implementation and efficiency of Whisper AI for transcribing and translating speech to text on iOS devices. Leveraging a large-scale weakly supervised dataset, Whisper AI demonstrates robust performance across multiple languages and tasks. The study explores its architecture, implementation on iOS, and performance comparisons with existing models. Findings indicate significant potential for real-world applications, despite some computational and accuracy challenges.
Chandan Kumar Dwivedi (Wed,) studied this question.