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GRASS: Unified Generation Model for Speech-to-Semantic Tasks
This paper explores the instruction fine-tuning technique for speech-to-semantic tasks by introducing a unified end-to-end (E2E) framework that generates target text conditioned on a task-related prompt for audio data.
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