Package: miditok-for-musiclang
MidiTokPython package to tokenize MIDI music files, presented at the ISMIR 2021 LBD.Using Deep Learning with symbolic music ? MidiTok can take care of converting (tokenizing) your MIDI files into tokens, ready to be fed to models such as Transformer, for any generation, transcription or MIR task.
MidiTok features most knownMIDI tokenizations(e.g.REMI,Compound Word...), and is built around the idea that they all share common parameters and methods. It supportsByte Pair Encoding (BPE)and data augmentation.Documentation:miditok.readthedocs.comInstallpipinstallmiditokMidiTok usesMIDIToolkit, which itself usesMidoto read and write MIDI files, and BPE is backed byHugging Face 🤗tokenizersfor super-fast encoding.Usage exampleThe most basic and useful methods are summarized here. Andhereis a simple notebook example showing how to use Hugging Face models to generate music, with MidiTok taking care of tokenizing MIDIs.frommiditokimportREMI,TokenizerConfigfrommiditoolkitimportMidiFilefrompathlibimportPath# Creating a multitrack tokenizer configuration, read the doc to explore other parametersconfig=TokenizerConfig(nb_velocities=16,use_chords=True,use_programs=True)tokenizer=REMI(config)# Loads a midi, converts to tokens, and back to a MIDImidi=MidiFile('path/to/your_midi.mid')tokens=tokenizer(midi)# calling the tokenizer will automatically detect MIDIs, paths and tokensconverted_back_midi=tokenizer(tokens)# PyTorch / Tensorflow / Numpy tensors supported# Tokenize a whole dataset and save it at Json filesmidi_paths=list(Path("path","to","dataset").glob("**/*.mid"))data_augmentation_offsets=[2,1,1]# data augmentation on 2 pitch octaves, 1 velocity and 1 duration valuestokenizer.tokenize_midi_dataset(midi_paths,Path("path","to","tokens_noBPE"),data_augment_offsets=data_augmentation_offsets)# Constructs the vocabulary with BPE, from the token filestokenizer.learn_bpe(vocab_size=10000,tokens_paths=list(Path("path","to","tokens_noBPE").glob("**/*.json")),start_from_empty_voc=False,)# Saving our tokenizer, to retrieve it back later with the load_params methodtokenizer.save_params(Path("path","to","save","tokenizer.json"))# Applies BPE to the previous tokenstokenizer.apply_bpe_to_dataset(Path('path','to','tokens_noBPE'),Path('path','to','tokens_BPE'))TokenizationsMidiTok implements the tokenizations: (links to original papers)REMIREMI+MIDI-LikeTSDStructuredCPWordOctupleMuMIDIMMMYou can find short presentations in thedocumentation.ContributionsContributions are gratefully welcomed, feel free to open an issue or send a PR if you want to add a tokenization or speed up the code. You can read thecontribution guidefor details.TodosExtend unimplemented additional tokens to all compatible tokenizations;Control Change messages;Option to represent pitch values as pitch intervals, asit seems to improve performances;Speeding up MIDI read / load (using a Rust / C++ io library + Python binding ?);Data augmentation on duration values at the MIDI level.CitationIf you use MidiTok for your research, a citation in your manuscript would be gladly appreciated. ❤️MidiTok paper@inproceedings{miditok2021,title={{MidiTok}: A Python package for {MIDI} file tokenization},author={Fradet, Nathan and Briot, Jean-Pierre and Chhel, Fabien and El Fallah Seghrouchni, Amal and Gutowski, Nicolas},booktitle={Extended Abstracts for the Late-Breaking Demo Session of the 22nd International Society for Music Information Retrieval Conference},year={2021},url={https://archives.ismir.net/ismir2021/latebreaking/000005.pdf},}The BibTeX citations of all tokenizations can be foundin the documentationAcknowledgmentsSpecial thanks to all the contributors.
We acknowledgeAubay, theLIP6,LERIAandESEOfor the initial financing and support.