Rethinking Length-Based Training: Batch Composition and Loss Normalization in Speech Token Language Models
This work provides a systematic analysis protocol for studying length-based training in variable-length speech models through matched comparisons, and results are consistent with a link between chunk-length variation and token weighting.
Hong-Jin Song, Run-Wu Shi, Weiqiao Shan et al.
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