  This paper aims to introduce a robust singing voice synthesis (SVS) system to
produce very natural and realistic singing voices efficiently by leveraging the
adversarial training strategy. On one hand, we designed simple but generic
random area conditional discriminators to help supervise the acoustic model,
which can effectively avoid the over-smoothed spectrogram prediction and
improve the expressiveness of SVS. On the other hand, we subtly combined the
spectrogram with the frame-level linearly-interpolated F0 sequence as the input
for the neural vocoder, which is then optimized with the help of multiple
adversarial conditional discriminators in the waveform domain and multi-scale
distance functions in the frequency domain. The experimental results and
ablation studies concluded that, compared with our previous auto-regressive
work, our new system can produce high-quality singing voices efficiently by
fine-tuning different singing datasets covering from several minutes to a few
hours. A large number of synthesized songs with different timbres are available
online https://zzw922cn.github.io/wesinger2 and we highly recommend readers to
listen to them.
