Fine-tuning WavLM on the diarization task before structured pruning yields 80% parameter removal at nearly unchanged diarization error, with 2.6x to 4x faster GPU inference.
Datasets We follow the data setups in [3] to use the far-field single- channel data from AMI [21, 22], AISHELL-4 [23], and Al- iMeeting [24], for system evaluation
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Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization
Fine-tuning WavLM on the diarization task before structured pruning yields 80% parameter removal at nearly unchanged diarization error, with 2.6x to 4x faster GPU inference.