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Building a high-quality singing corpus for a person who is not good atsinging is non-trivial, thus making it challenging to create a singing voicesynthesizer for this person. Learn2Sing is dedicated to synthesizing thesinging voice of a speaker without his or her singing data by learning fromdata recorded by others, i.e., the singing teacher. Inspired by the fact thatpitch is the key style factor to distinguish singing from speaking voice, theproposed Learn2Sing 2.0 first generates the preliminary acoustic feature withaveraged pitch value in the phone level, which allows the training of thisprocess for different styles, i.e., speaking or singing, share same conditionsexcept for the speaker information. Then, conditioned on the specific style, adiffusion decoder, which is accelerated by a fast sampling algorithm during theinference stage, is adopted to gradually restore the final acoustic feature.During the training, to avoid the information confusion of the speakerembedding and the style embedding, mutual information is employed to restrainthe learning of speaker embedding and style embedding. Experiments show thatthe proposed approach is capable of synthesizing high-quality singing voice forthe target speaker without singing data with 10 decoding steps.

Learn2Sing 2.0: Diffusion and Mutual Information-Based Target Speaker SVS by Learning from Singing Teacher