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This paper presents ByteSing, a Chinese singing voice synthesis (SVS) systembased on duration allocated Tacotron-like acoustic models and WaveRNN neuralvocoders. Different from the conventional SVS models, the proposed ByteSingemploys Tacotron-like encoder-decoder structures as the acoustic models, inwhich the CBHG models and recurrent neural networks (RNNs) are explored asencoders and decoders respectively. Meanwhile an auxiliary phoneme durationprediction model is utilized to expand the input sequence, which can enhancethe model controllable capacity, model stability and tempo prediction accuracy.WaveRNN neural vocoders are also adopted as neural vocoders to further improvethe voice quality of synthesized songs. Both objective and subjectiveexperimental results prove that the SVS method proposed in this paper canproduce quite natural, expressive and high-fidelity songs by improving thepitch and spectrogram prediction accuracy and the models using attentionmechanism can achieve best performance.

ByteSing: A Chinese Singing Voice Synthesis System Using Duration Allocated Encoder-Decoder Acoustic Models and WaveRNN Vocoders