  In this paper, we provide a large audio-visual speaker recognition dataset,
VoxBlink2, which includes approximately 10M utterances with videos from 110K+
speakers in the wild. This dataset represents a significant expansion over the
VoxBlink dataset, encompassing a broader diversity of speakers and scenarios by
the grace of an optimized data collection pipeline. Afterward, we explore the
impact of training strategies, data scale, and model complexity on speaker
verification and finally establish a new single-model state-of-the-art EER at
0.170% and minDCF at 0.006% on the VoxCeleb1-O test set. Such remarkable
results motivate us to explore speaker recognition from a new challenging
perspective. We raise the Open-Set Speaker-Identification task, which is
designed to either match a probe utterance with a known gallery speaker or
categorize it as an unknown query. Associated with this task, we design
concrete benchmark and evaluation protocols. The data and model resources can
be found in http://voxblink2.github.io.
