Metadata-Version: 2.3
Name: miditok
Version: 3.0.2
Summary: MIDI / symbolic music tokenizers for Deep Learning models.
Project-URL: Homepage, https://github.com/Natooz/MidiTok
Project-URL: Documentation, https://miditok.readthedocs.io
Author: Nathan Fradet
License-Expression: MIT
License-File: LICENSE
Keywords: artificial intelligence,deep learning,midi,mir,music,tokenization,transformer
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Multimedia :: Sound/Audio :: MIDI
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8.0
Requires-Dist: huggingface-hub>=0.16.4
Requires-Dist: numpy>=1.19
Requires-Dist: symusic>=0.3.2
Requires-Dist: tokenizers>=0.13.0
Requires-Dist: tqdm
Provides-Extra: docs
Requires-Dist: furo; extra == 'docs'
Requires-Dist: sphinx-copybutton; extra == 'docs'
Requires-Dist: torch; extra == 'docs'
Provides-Extra: tests
Requires-Dist: miditoolkit; extra == 'tests'
Requires-Dist: pytest-cov; extra == 'tests'
Requires-Dist: pytest-xdist[psutil]; extra == 'tests'
Requires-Dist: tensorflow; extra == 'tests'
Requires-Dist: torch; extra == 'tests'
Description-Content-Type: text/markdown

# MidiTok

Python package to tokenize MIDI music files, presented at the ISMIR 2021 LBDs.

![MidiTok Logo](docs/assets/logo.png?raw=true "")

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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 known [MIDI tokenizations](https://miditok.readthedocs.io/en/latest/tokenizations.html) (e.g. [REMI](https://arxiv.org/abs/2002.00212), [Compound Word](https://arxiv.org/abs/2101.02402)...), and is built around the idea that they all share common parameters and methods. It supports [Byte Pair Encoding (BPE)](https://arxiv.org/abs/2301.11975) and data augmentation.

MidiTok is integrated with the Hugging Face Hub 🤗! Don't hesitate to share your models to the community!

**Documentation:** [miditok.readthedocs.com](https://miditok.readthedocs.io/en/latest/index.html)

## Install

```shell
pip install miditok
```
MidiTok uses [Symusic](https://github.com/Yikai-Liao/symusic) to read and write MIDI files, and BPE is backed by [Hugging Face 🤗tokenizers](https://github.com/huggingface/tokenizers) for super-fast encoding.

## Usage example

Tokenizing and detokenzing can be done by calling the tokenizer:

```python
from miditok import REMI, TokenizerConfig
from symusic import Score

# Creating a multitrack tokenizer, read the doc to explore all the parameters
config = TokenizerConfig(num_velocities=16, use_chords=True, use_programs=True)
tokenizer = REMI(config)

# Loads a midi, converts to tokens, and back to a MIDI
midi = Score("path/to/your_midi.mid")
tokens = tokenizer(midi)  # calling the tokenizer will automatically detect MIDIs, paths and tokens
converted_back_midi = tokenizer(tokens)  # PyTorch / Tensorflow / Numpy tensors supported
```

Here is a complete yet concise example of how you can use MidiTok to train any PyTorch model. And [here](colab-notebooks/Full_Example_HuggingFace_GPT2_Transformer.ipynb) is a simple notebook example showing how to use Hugging Face models to generate music, with MidiTok taking care of tokenizing MIDIs.

```python
from miditok import REMI, TokenizerConfig
from miditok.pytorch_data import DatasetMIDI, DataCollator, split_midis_for_training
from torch.utils.data import DataLoader
from pathlib import Path

# Creating a multitrack tokenizer, read the doc to explore all the parameters
config = TokenizerConfig(num_velocities=16, use_chords=True, use_programs=True)
tokenizer = REMI(config)

# Train the tokenizer with Byte Pair Encoding (BPE)
midi_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")

# Split MIDIs into smaller chunks for training
dataset_chunks_dir = Path("path", "to", "midi_chunks")
split_midis_for_training(
    files_paths=midi_paths,
    tokenizer=tokenizer,
    save_dir=dataset_chunks_dir,
    max_seq_len=1024,
)

# Create a Dataset, a DataLoader and a collator to train a model
dataset = DatasetMIDI(
    files_paths=list(dataset_chunks_dir.glob("**/*.mid")),
    tokenizer=tokenizer,
    max_seq_len=1024,
    bos_token_id=tokenizer["BOS_None"],
    eos_token_id=tokenizer["EOS_None"],
)
collator = DataCollator(tokenizer["PAD_None"])
dataloader = DataLoader(dataset, batch_size=64, collate_fn=collator)

# Iterate over the dataloader to train a model
for batch in dataloader:
    print("Train your model on this batch...")
```

## Tokenizations

MidiTok implements the tokenizations: (links to original papers)
* [REMI](https://dl.acm.org/doi/10.1145/3394171.3413671)
* [REMI+](https://openreview.net/forum?id=NyR8OZFHw6i)
* [MIDI-Like](https://link.springer.com/article/10.1007/s00521-018-3758-9)
* [TSD](https://arxiv.org/abs/2301.11975)
* [Structured](https://arxiv.org/abs/2107.05944)
* [CPWord](https://ojs.aaai.org/index.php/AAAI/article/view/16091)
* [Octuple](https://aclanthology.org/2021.findings-acl.70)
* [MuMIDI](https://dl.acm.org/doi/10.1145/3394171.3413721)
* [MMM](https://arxiv.org/abs/2008.06048)

You can find short presentations in the [documentation](https://miditok.readthedocs.io/en/latest/tokenizations.html).

## Contributions

Contributions 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 the [contribution guide](CONTRIBUTING.md) for details.

### Todos

* `no_duration_drums` option, 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.

## Citation

If you use MidiTok for your research, a citation in your manuscript would be gladly appreciated. ❤️

[**[MidiTok paper]**](https://arxiv.org/abs/2310.17202)
[**[MidiTok original ISMIR publication]**](https://archives.ismir.net/ismir2021/latebreaking/000005.pdf)
```bibtex
@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 found [in the documentation](https://miditok.readthedocs.io/en/latest/citations.html)


## Acknowledgments

Special thanks to all the contributors.
We acknowledge [Aubay](https://blog.aubay.com/index.php/language/en/home/?lang=en), the [LIP6](https://www.lip6.fr/?LANG=en), [LERIA](http://blog.univ-angers.fr/leria/n) and [ESEO](https://eseo.fr/en) for the initial financing and support.

## All Thanks To Our Contributors

<a href="https://github.com/Natooz/MidiTok/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=Natooz/MidiTok" />
</a>
