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|
%global _empty_manifest_terminate_build 0
Name: python-miditok
Version: 2.0.6
Release: 1
Summary: A convenient MIDI tokenizer for Deep Learning networks, with multiple encoding strategies
License: MIT
URL: https://github.com/Natooz/MidiTok
Source0: https://mirrors.aliyun.com/pypi/web/packages/3c/58/587f75bd26a9717872bc5d1276ddcc10cedbf9344037fa56e062b1f0cbac/miditok-2.0.6.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-miditoolkit
Requires: python3-tqdm
%description
# MidiTok
Python package to tokenize MIDI music files, presented at the ISMIR 2021 LBD.
[](https://pypi.python.org/pypi/miditok/)
[](https://www.python.org/downloads/release/)
[](https://miditok.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/Natooz/MidiTok/actions/workflows/pytest.yml)
[](https://codecov.io/gh/Natooz/MidiTok)
[](https://github.com/Natooz/MidiTok/blob/main/LICENSE)
[](https://pepy.tech/project/MidiTok)
[](https://github.com/psf/black)
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 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.
**Documentation:** [miditok.readthedocs.com](https://miditok.readthedocs.io/en/latest/index.html)
## Install
```shell
pip install miditok
```
MidiTok uses [MIDIToolkit](https://github.com/YatingMusic/miditoolkit), which itself uses [Mido](https://github.com/mido/mido) 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
The most basic and useful methods are summarized here. 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
from miditok.utils import get_midi_programs
from miditoolkit import MidiFile
from pathlib import Path
# Creates the tokenizer and loads a MIDI
tokenizer = REMI() # using the default parameters, read the documentation to customize your tokenizer
midi = MidiFile('path/to/your_midi.mid')
# Converts MIDI to tokens, and back to a MIDI
tokens = tokenizer(midi) # calling it will automatically detect MIDIs, paths and tokens before the conversion
converted_back_midi = tokenizer(tokens, get_midi_programs(midi)) # PyTorch / Tensorflow / Numpy tensors supported
# Converts MIDI files to tokens saved as JSON files
midi_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 values
tokenizer.tokenize_midi_dataset(midi_paths, Path("path", "to", "tokens_noBPE"),
data_augment_offsets=data_augmentation_offsets)
# Constructs the vocabulary with BPE, from the tokenized files
tokenizer.learn_bpe(
vocab_size=500,
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 method
tokenizer.save_params(Path("path", "to", "save", "tokenizer"))
# Converts the tokenized musics into tokens with BPE
tokenizer.apply_bpe_to_dataset(Path('path', 'to', 'tokens_noBPE'), Path('path', 'to', 'tokens_BPE'))
```
## 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).
## Limitations
Tokenizations using Bar tokens (REMI, Compound Word and MuMIDI) **only considers a 4/x time signature** for now. This means that each bar is considered covering 4 beats.
REMI+ and Octuple support it.
## 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
* Extend Time Signature to all tokenizations
* Control Change messages
* Option to represent pitch values as pitch intervals, as [it seems to improve performances](https://ismir2022program.ismir.net/lbd_369.html).
* Speeding up MIDI read / load (Rust / C++ binding)
* Data augmentation on duration values at the MIDI level
## Citation
If you use MidiTok for your research, a citation in your manuscript would be gladly appreciated. ❤️
[**MidiTok paper**](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.
%package -n python3-miditok
Summary: A convenient MIDI tokenizer for Deep Learning networks, with multiple encoding strategies
Provides: python-miditok
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-miditok
# MidiTok
Python package to tokenize MIDI music files, presented at the ISMIR 2021 LBD.
[](https://pypi.python.org/pypi/miditok/)
[](https://www.python.org/downloads/release/)
[](https://miditok.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/Natooz/MidiTok/actions/workflows/pytest.yml)
[](https://codecov.io/gh/Natooz/MidiTok)
[](https://github.com/Natooz/MidiTok/blob/main/LICENSE)
[](https://pepy.tech/project/MidiTok)
[](https://github.com/psf/black)
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 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.
**Documentation:** [miditok.readthedocs.com](https://miditok.readthedocs.io/en/latest/index.html)
## Install
```shell
pip install miditok
```
MidiTok uses [MIDIToolkit](https://github.com/YatingMusic/miditoolkit), which itself uses [Mido](https://github.com/mido/mido) 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
The most basic and useful methods are summarized here. 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
from miditok.utils import get_midi_programs
from miditoolkit import MidiFile
from pathlib import Path
# Creates the tokenizer and loads a MIDI
tokenizer = REMI() # using the default parameters, read the documentation to customize your tokenizer
midi = MidiFile('path/to/your_midi.mid')
# Converts MIDI to tokens, and back to a MIDI
tokens = tokenizer(midi) # calling it will automatically detect MIDIs, paths and tokens before the conversion
converted_back_midi = tokenizer(tokens, get_midi_programs(midi)) # PyTorch / Tensorflow / Numpy tensors supported
# Converts MIDI files to tokens saved as JSON files
midi_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 values
tokenizer.tokenize_midi_dataset(midi_paths, Path("path", "to", "tokens_noBPE"),
data_augment_offsets=data_augmentation_offsets)
# Constructs the vocabulary with BPE, from the tokenized files
tokenizer.learn_bpe(
vocab_size=500,
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 method
tokenizer.save_params(Path("path", "to", "save", "tokenizer"))
# Converts the tokenized musics into tokens with BPE
tokenizer.apply_bpe_to_dataset(Path('path', 'to', 'tokens_noBPE'), Path('path', 'to', 'tokens_BPE'))
```
## 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).
## Limitations
Tokenizations using Bar tokens (REMI, Compound Word and MuMIDI) **only considers a 4/x time signature** for now. This means that each bar is considered covering 4 beats.
REMI+ and Octuple support it.
## 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
* Extend Time Signature to all tokenizations
* Control Change messages
* Option to represent pitch values as pitch intervals, as [it seems to improve performances](https://ismir2022program.ismir.net/lbd_369.html).
* Speeding up MIDI read / load (Rust / C++ binding)
* Data augmentation on duration values at the MIDI level
## Citation
If you use MidiTok for your research, a citation in your manuscript would be gladly appreciated. ❤️
[**MidiTok paper**](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.
%package help
Summary: Development documents and examples for miditok
Provides: python3-miditok-doc
%description help
# MidiTok
Python package to tokenize MIDI music files, presented at the ISMIR 2021 LBD.
[](https://pypi.python.org/pypi/miditok/)
[](https://www.python.org/downloads/release/)
[](https://miditok.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/Natooz/MidiTok/actions/workflows/pytest.yml)
[](https://codecov.io/gh/Natooz/MidiTok)
[](https://github.com/Natooz/MidiTok/blob/main/LICENSE)
[](https://pepy.tech/project/MidiTok)
[](https://github.com/psf/black)
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 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.
**Documentation:** [miditok.readthedocs.com](https://miditok.readthedocs.io/en/latest/index.html)
## Install
```shell
pip install miditok
```
MidiTok uses [MIDIToolkit](https://github.com/YatingMusic/miditoolkit), which itself uses [Mido](https://github.com/mido/mido) 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
The most basic and useful methods are summarized here. 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
from miditok.utils import get_midi_programs
from miditoolkit import MidiFile
from pathlib import Path
# Creates the tokenizer and loads a MIDI
tokenizer = REMI() # using the default parameters, read the documentation to customize your tokenizer
midi = MidiFile('path/to/your_midi.mid')
# Converts MIDI to tokens, and back to a MIDI
tokens = tokenizer(midi) # calling it will automatically detect MIDIs, paths and tokens before the conversion
converted_back_midi = tokenizer(tokens, get_midi_programs(midi)) # PyTorch / Tensorflow / Numpy tensors supported
# Converts MIDI files to tokens saved as JSON files
midi_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 values
tokenizer.tokenize_midi_dataset(midi_paths, Path("path", "to", "tokens_noBPE"),
data_augment_offsets=data_augmentation_offsets)
# Constructs the vocabulary with BPE, from the tokenized files
tokenizer.learn_bpe(
vocab_size=500,
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 method
tokenizer.save_params(Path("path", "to", "save", "tokenizer"))
# Converts the tokenized musics into tokens with BPE
tokenizer.apply_bpe_to_dataset(Path('path', 'to', 'tokens_noBPE'), Path('path', 'to', 'tokens_BPE'))
```
## 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).
## Limitations
Tokenizations using Bar tokens (REMI, Compound Word and MuMIDI) **only considers a 4/x time signature** for now. This means that each bar is considered covering 4 beats.
REMI+ and Octuple support it.
## 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
* Extend Time Signature to all tokenizations
* Control Change messages
* Option to represent pitch values as pitch intervals, as [it seems to improve performances](https://ismir2022program.ismir.net/lbd_369.html).
* Speeding up MIDI read / load (Rust / C++ binding)
* Data augmentation on duration values at the MIDI level
## Citation
If you use MidiTok for your research, a citation in your manuscript would be gladly appreciated. ❤️
[**MidiTok paper**](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.
%prep
%autosetup -n miditok-2.0.6
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-miditok -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.6-1
- Package Spec generated
|
