Previous generalized biological voice models were trained on large amounts of data from multiple species. However, on average, there is very little training data on species-specific voices, while large differences between the vocalizations of species may even be a barrier to encoding vocal features. This leads to potentially large errors in using generic models for species-specific vocalization studies. We collected over 6000 hours of dog barking videos and presented the first animal-specific bioacoustic embedding model, Dog2vec.1 The results indicate that Dog2vec outperforms species-independent pre-trained models and achieves state-of-the-art results on a series of dog-related tasks, including dog bark type recognition and dog sound event detection, and obtain a relative 8.2% performance increase.