  We present an end-to-end method for transforming audio from one style to
another. For the case of speech, by conditioning on speaker identities, we can
train a single model to transform words spoken by multiple people into multiple
target voices. For the case of music, we can specify musical instruments and
achieve the same result. Architecturally, our method is a fully-differentiable
sequence-to-sequence model based on convolutional and hierarchical recurrent
neural networks. It is designed to capture long-term acoustic dependencies,
requires minimal post-processing, and produces realistic audio transforms.
Ablation studies confirm that our model can separate speaker and instrument
properties from acoustic content at different receptive fields. Empirically,
our method achieves competitive performance on community-standard datasets.
