A small trained 'seed' speech encoder can be unfolded to several logical depths by repeating shared layers, matching independently trained models with up to 35% parameter reduction.
2-bit conformer quantization for automatic speech recognition,
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Unfolding A Few Structures for The Many: Memory-Efficient Compression of Conformer and Speech Foundation Models
A small trained 'seed' speech encoder can be unfolded to several logical depths by repeating shared layers, matching independently trained models with up to 35% parameter reduction.