In this work, we propose a flexible method for generating variations of
discrete sequences in which tokens can be grouped into basic units, like
sentences in a text or bars in music. More precisely, given a template
sequence, we aim at producing novel sequences sharing perceptible similarities
with the original template without relying on any annotation; so our problem of
generating variations is intimately linked to the problem of learning relevant
high-level representations without supervision. Our contribution is two-fold:
First, we propose a self-supervised encoding technique, named Vector Quantized
Contrastive Predictive Coding which allows to learn a meaningful assignment of
the basic units over a discrete set of codes, together with mechanisms allowing
to control the information content of these learnt discrete representations.
Secondly, we show how these compressed representations can be used to generate
variations of a template sequence by using an appropriate attention pattern in
the Transformer architecture. We illustrate our approach on the corpus of J.S.
Bach chorales where we discuss the musical meaning of the learnt discrete codes
and show that our proposed method allows to generate coherent and high-quality
variations of a given template.

本文提出了一种灵活的方法，用于在离散序列中生成具有基本单元（例如文本中的句子或音乐中的小节）分组的变化。我们的方法依赖自监督编码技术和 Transformer 架构，通过学习有意义的分配和压缩表示，在不需要任何注释的情况下生成新的序列。实验展示了在巴赫合唱曲集中使用该方法能够生成连贯而高质量的曲目变化。