  This paper explores the modeling method of polyphonic music sequence. Due to
the great potential of Transformer models in music generation, controllable
music generation is receiving more attention. In the task of polyphonic music,
current controllable generation research focuses on controlling the generation
of chords, but lacks precise adjustment for the controllable generation of
choral music textures. This paper proposed Condition Choir Transformer
(CoCoFormer) which controls the output of the model by controlling the chord
and rhythm inputs at a fine-grained level. In this paper, the self-supervised
method improves the loss function and performs joint training through
conditional control input and unconditional input training. In order to
alleviate the lack of diversity on generated samples caused by the teacher
forcing training, this paper added an adversarial training method. CoCoFormer
enhances model performance with explicit and implicit inputs to chords and
rhythms. In this paper, the experiments proves that CoCoFormer has reached the
current better level than current models. On the premise of specifying the
polyphonic music texture, the same melody can also be generated in a variety of
ways.
