This paper presents VoxCeleb-ESP, a collection of pointers and timestamps to YouTube videos facilitating the creation of a novel speaker recognition dataset. VoxCeleb-ESP captures real-world scenarios, incorporating diverse speaking styles, noises, and channel distortions. It includes 160 Spanish celebrities spanning various categories, ensuring a representative distribution across age groups and geographic regions in Spain. We provide two speaker trial lists for speaker identification tasks, each of them with same-video or different-video target trials respectively, accompanied by a cross-lingual evaluation of ResNet pretrained models. Preliminary speaker identification results suggest that the complexity of the detection task in VoxCeleb-ESP is equivalent to that of the original and much larger VoxCeleb in English. VoxCeleb-ESP contributes to the expansion of speaker recognition benchmarks with a comprehensive and diverse dataset for the Spanish language.