There has been a persistent lack of publicly accessible data in singing voice research, particularly concerning the diversity of languages and performance styles. In this paper, we introduce SingStyle111, a large studio-quality singing dataset with multiple languages and different singing styles, and present singing style transfer examples. The dataset features 111 songs performed by eight professional singers, spanning 12.8 hours and covering English, Chinese, and Italian. SingStyle111 incorporates different singing styles, such as bel canto opera, Chinese folk singing, pop, jazz, and children. Specifically, 80 songs include at least two distinct singing styles performed by the same singer. All recordings were conducted in professional studios, yielding clean, dry vocal tracks in mono format with a 44.1 kHz sample rate. We have segmented the singing voices into phrases, providing lyrics, performance MIDI, and scores with phoneme-level alignment. We also extracted acoustic features such as Mel-Spectrogram, F0 contour, and loudness curves. This dataset applies to various MIR tasks such as Singing Voice Synthesis, Singing Voice Conversion, Singing Transcription, Score Following, and Lyrics Detection. It is also designed for Singing Style Transfer, including both performance and voice timbre style. We make the dataset freely available for research purposes. Examples and download information can be found at https://shuqid.net/singstyle111.

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