Package: miditok
MidiTokPython package to tokenize MIDI music files, presented at the ISMIR 2021 LBDs.MidiTok can tokenize MIDI files, i.e. convert them into sequences of 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 usesSymusicto read and write MIDI files, and BPE is backed byHugging Face 🤗tokenizersfor super-fast encoding.Usage exampleBelow is a complete yet concise example of how you can use MidiTok. Andhereis a simple notebook example showing how to use Hugging Face models to generate music, with MidiTok taking care of tokenizing MIDIs.frommiditokimportREMI,TokenizerConfigfrommiditok.pytorch_dataimportDatasetTok,DataCollatorfrompathlibimportPathfromsymusicimportScore# Creating a multitrack tokenizer configuration, read the doc to explore other parametersconfig=TokenizerConfig(num_velocities=16,use_chords=True,use_programs=True)tokenizer=REMI(config)# Loads a midi, converts to tokens, and back to a MIDImidi=Score("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# Trains the tokenizer with BPE, and save it to load it back latermidi_paths=list(Path("path","to","midis").glob("**/*.mid"))tokenizer.learn_bpe(vocab_size=30000,files_paths=midi_paths)tokenizer.save_params(Path("path","to","save","tokenizer.json"))# And pushing it to the Hugging Face hub (you can download it back with .from_pretrained)tokenizer.push_to_hub("username/model-name",private=True,token="your_hf_token")# Creates a Dataset and a collator to be used with a PyTorch DataLoader to train a modeldataset=DatasetTok(files_paths=midi_paths,min_seq_len=100,max_seq_len=1024,tokenizer=tokenizer,)collator=DataCollator(tokenizer["PAD_None"],tokenizer["BOS_None"],tokenizer["EOS_None"])fromtorch.utils.dataimportDataLoaderdata_loader=DataLoader(dataset=dataset,collate_fn=collator)forbatchindata_loader:print("Train your model on this batch...")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.Todosno_duration_drumsoption, discarding duration tokens for drum notes;Extend unimplemented additional tokens to all compatible tokenizations;Control Change messages;Speeding up the MIDI preprocess + global/track events parsing with Rust or C++ binding.CitationIf you use MidiTok for your research, a citation in your manuscript would be gladly appreciated. ❤️[MidiTok paper][MidiTok original ISMIR publication]@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.All Thanks To Our Contributors