This article presents MidiTok, a Python package to encode MIDI files into sequences of tokens to be used with sequential Deep Learning models like Transformers or Recurrent Neural Networks. It allows researchers and developers to encode datasets with various strategies built around the idea that they share common parameters. This key idea makes it easy to :1) optimize the size of the vocabulary and the elements it can represent w.r.t. the MIDI specifications; 2) compare tokenization methods to see which performs best in which case; 3) measure the relevance of additional information like chords or tempo changes. Code and documentation of MidiTok are on Github.