SingNet: Towards a Large-Scale, Diverse, and In-the-Wild Singing Voice Dataset

Yicheng Gu, Chaoren Wang, Junan Zhang, Xueyao Zhang, Zihao Fang, Haorui He, Zhizheng Wu

Keywords: Singing Voice Dataset, Singing Voice Corpus, Self-supervised Learning, Automatic Lyric Transcription, Neural Vocoder, Singing Voice Conversion

TL;DR: We propose SingNet, an Extensive, Multilingual, and Diverse Large-Scale Singing Voice Corpus for singing voice applications.

Abstract: The lack of a publicly-available large-scale and diverse dataset has long been a significant bottleneck for singing voice applications like Singing Voice Synthesis (SVS) and Singing Voice Conversion (SVC). To tackle this problem, we present SingNet, an extensive, diverse, and in-the-wild singing voice dataset. Specifically, we propose a data processing pipeline to extract ready-to-use training data from sample packs and songs on the internet, forming 3000 hours of singing voices in various languages and styles. Furthermore, to facilitate the use and demonstrate the effectiveness of SingNet, we pre-train and open-source various state-of-the-art (SOTA) models on Wav2vec2, BigVGAN, and NSF-HiFiGAN based on our collected singing voice data. We also conduct benchmark experiments on Automatic Lyric Transcription (ALT), Neural Vocoder, and Singing Voice Conversion (SVC). Audio demos are available at: https://singnet-dataset.github.io/.

Primary Area: datasets and benchmarks

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Submission Number: 7397