End-to-end models for raw audio generation are a challenge, specially if they
have to work with non-parallel data, which is a desirable setup in many
situations. Voice conversion, in which a model has to impersonate a speaker in
a recording, is one of those situations. In this paper, we propose Blow, a
single-scale normalizing flow using hypernetwork conditioning to perform
many-to-many voice conversion between raw audio. Blow is trained end-to-end,
with non-parallel data, on a frame-by-frame basis using a single speaker
identifier. We show that Blow compares favorably to existing flow-based
architectures and other competitive baselines, obtaining equal or better
performance in both objective and subjective evaluations. We further assess the
impact of its main components with an ablation study, and quantify a number of
properties such as the necessary amount of training data or the preference for
source or target speakers.

该研究提出了一种使用超网络条件的单尺度归一化流模型（Blow），用于以单个讲话者身份识别器为基础的一对多语音转换，可以在处理非平行数据时达到良好的目标和主观性能，为原始音频生成提供了一种新的解决方案。