Coming soon.!!
Large Speech Models (LSMs) are effective in transcribing audio data while offering high computational complexity and lacking scalability for different computational budgets. To effectively leverage the capabilities of a LSMs for different computational budgets, methods like early exit, adaptive pruning, and layer dropping are used.
DLD is an end-to-end framework for Automatic Speech Recongition (ASR) that performs knowledge distillation from the teacher network to the dynamic / scalable child network, thus, minimizing the difference between models embeddings and optimizing the performance on all computational budgets.
