FastSVC: Fast Cross-Domain Singing Voice Conversion with Feature-wise Linear Modulation

Songxiang Liu, Yuewen Cao, Na Hu, Dan Su, Helen Meng
Human-Computer Communications Laboratory, The Chinese University of Hong Kong
arXiv:2011.05731 [eess.AS], (11 Nov 2020)


   title={FastSVC: Fast Cross-Domain Singing Voice Conversion with Feature-wise Linear Modulation},

   author={Songxiang Liu and Yuewen Cao and Na Hu and Dan Su and Helen Meng},






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This paper presents FastSVC, a light-weight cross-domain sing voice conversion (SVC) system, which is able to achieve high conversion performance, with inference speed 4x faster than real time on CPUs. FastSVC uses Conformer based phoneme recognizer to extract singer-agnostic linguistic features from singing signals. A feature-wise linear modulation based generator is used to synthesize waveform directly from linguistic features, leveraging information from sine-excitation signals and loudness features. The waveform generator can be trained conveniently using a multi-resolution spectral loss and an adversarial loss. Experimental results show that the proposed FastSVC system, compared with a computationally heavy baseline system, can achieve comparable conversion performance in some scenarios and significantly better conversion performance in other scenarios. Moreover, the proposed FastSVC system achieves desirable cross-lingual singing conversion performance. Inference speed of the FastSVC system is 3x and 70x faster than the baseline system on GPUs and CPUs, respectively.
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