The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious manip ulation of content. This has led to an increase in studies aimed at detecting so-called “deepfake audio”. However, existing works focus on the binary detection of real audio and fake audio. In real-world scenarios such as model copyright protection and digital evidence forensics, it is needed to know what tool or model generated the deepfake audio to explain the decision. This motivates us to ask: ‘Can we recognize the system fingerprints of deepfake audio?’ In this paper, we present the first deepfake audio dataset for System Fingerprint Recognition (SFR) and conduct an initial investigation. We collected the dataset from the speech synthesis systems of seven Chinese vendors that use the latest state-of-the-art deep learning technologies, including both clean and compressed sets. In addition, we provide extensive benchmarks and research findings to facilitate the further development of system fingerprint recognition methods. The dataset is publicly available. The subsets 01, 02, and 03 represent the training set, development set, and test set, respectively. This data set is licensed with a CC BY-NC-ND 4.0 license.