  We present a wav-to-wav generative model for the task of singing voice
conversion from any identity. Our method utilizes both an acoustic model,
trained for the task of automatic speech recognition, together with melody
extracted features to drive a waveform-based generator. The proposed generative
architecture is invariant to the speaker's identity and can be trained to
generate target singers from unlabeled training data, using either speech or
singing sources. The model is optimized in an end-to-end fashion without any
manual supervision, such as lyrics, musical notes or parallel samples. The
proposed approach is fully-convolutional and can generate audio in real-time.
Experiments show that our method significantly outperforms the baseline methods
while generating convincingly better audio samples than alternative attempts.
