AITUS - An atomatic notr maker for CYTUS

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Deep Learning AITUS
Overview

AITUS

an automatic note maker for CYTUS.

利用AI根据指定乐曲生成CYTUS游戏谱面。

效果展示:https://www.bilibili.com/video/BV1Lf4y1F7aq

这只是作者的一个初次尝试,欢迎感兴趣的小伙伴进行优化或提出新方法!

共享链接

下面的3、4部分介绍了AITUS的使用方法,比较繁琐,且需要安装许多软件。鉴于此,作者在这里设置了一个共享链接,用于分享AITUS创作的游戏谱面。

链接:https://pan.baidu.com/s/1dGaLOuBKdeXBRZt1NuP9WA?pwd=aicy 提取码:aicy

您可以私信给作者您想要创作谱面的乐曲,作者生成谱面后会上传到这个链接里。

前置准备

使用AITUS一共需要以下软件作为辅助:

  • 格式工厂(或其他音频文件格式转化软件)
  • MixMeister BPM Analyzer,用于获取乐曲bpm
  • Cylheim,CYTUS游戏谱面制作器
  • Python3
  • PyTorch2

使用说明

【step 1】

将乐曲转为wav格式。

【step 2】

使用软件【MixMeister BPM Analyzer】测量乐曲的bpm。

【step 3】

使用【Cylheim】创建空谱面,创建空谱面时需要导入乐曲、bpm等信息。创建好的谱面是一个json文件。该json文件的命名应与乐曲文件的命名相同。

【step 4】

将创建好的谱面json文件、乐曲wav文件、model下的四个pt文件、code下的【NoteMake.py】放在同一目录下,并修改【NoteMake.py】中如下图所示的乐曲信息:

image-20220119105537401

然后运行NoteMake.py,约5-10分钟后运行结束,得到生成的json谱面文件(命名与乐曲命名相同)。

【step 5】

用生成的json去替换原【Cylheim】项目下的json文件,然后打开【Cylheim】项目即可看见和演示生成的谱面。

原理简介

训练数据来自CYTUS

训练所用的乐曲和谱面信息来自CYTUS。

从音乐到图像

为了利用CNN,将读入的一段乐曲信号按顺序转化为若干80×80的图片,并根据谱面文件的信息给每张图打tag。

分工训练

为了生成游戏谱面,一共训练了四个模型:

ExistModel:判断一张图是否有key。

PosModel:如果一张图中有key,判断这个key的横坐标。

TypeModel:如果一张图中有key,判断这个key的类型(由于CYTUS1代只有click、hold、chain三种类型的key,因此AITUS目前也只考虑了这三种类型)。

TimeModel:如果一张图中对应的key是hold,判断这个hold的持续的时间。

一些调整

生成的谱面谱面并不那么如意,因此在【NoteMake.py】中还对模型的输出结果做了调整(详情请见代码)。

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