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|
%global _empty_manifest_terminate_build 0
Name: python-mirdata
Version: 0.3.7
Release: 1
Summary: Common loaders for MIR datasets.
License: BSD-3-Clause
URL: https://github.com/mir-dataset-loaders/mirdata
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/bd/57/00c6ce24c1a5f8db1ae0fe9256d05a2c77978b1b05585beb4db36b15389d/mirdata-0.3.7.tar.gz
BuildArch: noarch
Requires: python3-tqdm
Requires: python3-librosa
Requires: python3-numpy
Requires: python3-jams
Requires: python3-requests
Requires: python3-pretty-midi
Requires: python3-chardet
Requires: python3-pyyaml
Requires: python3-scipy
Requires: python3-h5py
Requires: python3-smart-open
Requires: python3-Deprecated
Requires: python3-pandas
Requires: python3-openpyxl
Requires: python3-openpyxl
Requires: python3-dali-dataset
Requires: python3-numpydoc
Requires: python3-recommonmark
Requires: python3-sphinx
Requires: python3-sphinxcontrib-napoleon
Requires: python3-sphinx-rtd-theme
Requires: python3-smart-open[gcs]
Requires: python3-music21
Requires: python3-smart-open[http]
Requires: python3-smart-open[s3]
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-pytest-pep8
Requires: python3-pytest-mock
Requires: python3-pytest-localserver
Requires: python3-testcontainers
Requires: python3-future
Requires: python3-coveralls
Requires: python3-types-PyYAML
Requires: python3-types-chardet
Requires: python3-smart-open[all]
%description
# mirdata
common loaders for Music Information Retrieval (MIR) datasets. Find the API documentation [here](https://mirdata.readthedocs.io/).
[](https://circleci.com/gh/mir-dataset-loaders/mirdata)
[](https://codecov.io/gh/mir-dataset-loaders/mirdata)
[](https://mirdata.readthedocs.io/en/latest/?badge=latest)
This library provides tools for working with common MIR datasets, including tools for:
* downloading datasets to a common location and format
* validating that the files for a dataset are all present
* loading annotation files to a common format, consistent with the format required by [mir_eval](https://github.com/craffel/mir_eval)
* parsing track level metadata for detailed evaluations
### Installation
To install, simply run:
```python
pip install mirdata
```
### Quick example
```python
import mirdata
orchset = mirdata.initialize('orchset')
orchset.download() # download the dataset
orchset.validate() # validate that all the expected files are there
example_track = orchset.choice_track() # choose a random example track
print(example_track) # see the available data
```
See the [documentation](https://mirdata.readthedocs.io/) for more examples and the API reference.
### Currently supported datasets
Supported datasets include [AcousticBrainz](https://zenodo.org/record/2553414#.X8jTgulKhhE), [DALI](https://github.com/gabolsgabs/DALI), [Guitarset](http://github.com/marl/guitarset/), [MAESTRO](https://magenta.tensorflow.org/datasets/maestro), [TinySOL](https://www.orch-idea.org/), among many others.
For the **complete list** of supported datasets, see the [documentation](https://mirdata.readthedocs.io/en/stable/source/quick_reference.html)
### Citing
There are two ways of citing mirdata:
If you are using the library for your work, please cite the version you used as indexed at Zenodo:
[](https://doi.org/10.5281/zenodo.4355859)
If you refer to mirdata's design principles, motivation etc., please cite the following [paper](https://zenodo.org/record/3527750#.X-Inp5NKhUI):
[](https://doi.org/10.5281/zenodo.3527750)
```
"mirdata: Software for Reproducible Usage of Datasets"
Rachel M. Bittner, Magdalena Fuentes, David Rubinstein, Andreas Jansson, Keunwoo Choi, and Thor Kell
in International Society for Music Information Retrieval (ISMIR) Conference, 2019
```
```
@inproceedings{
bittner_fuentes_2019,
title={mirdata: Software for Reproducible Usage of Datasets},
author={Bittner, Rachel M and Fuentes, Magdalena and Rubinstein, David and Jansson, Andreas and Choi, Keunwoo and Kell, Thor},
booktitle={International Society for Music Information Retrieval (ISMIR) Conference},
year={2019}
}
```
When working with datasets, please cite the version of `mirdata` that you are using (given by the `DOI` above) **AND** include the reference of the dataset,
which can be found in the respective dataset loader using the `cite()` method.
### Contributing a new dataset loader
We welcome contributions to this library, especially new datasets. Please see [contributing](https://mirdata.readthedocs.io/en/latest/source/contributing.html) for guidelines.
%package -n python3-mirdata
Summary: Common loaders for MIR datasets.
Provides: python-mirdata
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-mirdata
# mirdata
common loaders for Music Information Retrieval (MIR) datasets. Find the API documentation [here](https://mirdata.readthedocs.io/).
[](https://circleci.com/gh/mir-dataset-loaders/mirdata)
[](https://codecov.io/gh/mir-dataset-loaders/mirdata)
[](https://mirdata.readthedocs.io/en/latest/?badge=latest)
This library provides tools for working with common MIR datasets, including tools for:
* downloading datasets to a common location and format
* validating that the files for a dataset are all present
* loading annotation files to a common format, consistent with the format required by [mir_eval](https://github.com/craffel/mir_eval)
* parsing track level metadata for detailed evaluations
### Installation
To install, simply run:
```python
pip install mirdata
```
### Quick example
```python
import mirdata
orchset = mirdata.initialize('orchset')
orchset.download() # download the dataset
orchset.validate() # validate that all the expected files are there
example_track = orchset.choice_track() # choose a random example track
print(example_track) # see the available data
```
See the [documentation](https://mirdata.readthedocs.io/) for more examples and the API reference.
### Currently supported datasets
Supported datasets include [AcousticBrainz](https://zenodo.org/record/2553414#.X8jTgulKhhE), [DALI](https://github.com/gabolsgabs/DALI), [Guitarset](http://github.com/marl/guitarset/), [MAESTRO](https://magenta.tensorflow.org/datasets/maestro), [TinySOL](https://www.orch-idea.org/), among many others.
For the **complete list** of supported datasets, see the [documentation](https://mirdata.readthedocs.io/en/stable/source/quick_reference.html)
### Citing
There are two ways of citing mirdata:
If you are using the library for your work, please cite the version you used as indexed at Zenodo:
[](https://doi.org/10.5281/zenodo.4355859)
If you refer to mirdata's design principles, motivation etc., please cite the following [paper](https://zenodo.org/record/3527750#.X-Inp5NKhUI):
[](https://doi.org/10.5281/zenodo.3527750)
```
"mirdata: Software for Reproducible Usage of Datasets"
Rachel M. Bittner, Magdalena Fuentes, David Rubinstein, Andreas Jansson, Keunwoo Choi, and Thor Kell
in International Society for Music Information Retrieval (ISMIR) Conference, 2019
```
```
@inproceedings{
bittner_fuentes_2019,
title={mirdata: Software for Reproducible Usage of Datasets},
author={Bittner, Rachel M and Fuentes, Magdalena and Rubinstein, David and Jansson, Andreas and Choi, Keunwoo and Kell, Thor},
booktitle={International Society for Music Information Retrieval (ISMIR) Conference},
year={2019}
}
```
When working with datasets, please cite the version of `mirdata` that you are using (given by the `DOI` above) **AND** include the reference of the dataset,
which can be found in the respective dataset loader using the `cite()` method.
### Contributing a new dataset loader
We welcome contributions to this library, especially new datasets. Please see [contributing](https://mirdata.readthedocs.io/en/latest/source/contributing.html) for guidelines.
%package help
Summary: Development documents and examples for mirdata
Provides: python3-mirdata-doc
%description help
# mirdata
common loaders for Music Information Retrieval (MIR) datasets. Find the API documentation [here](https://mirdata.readthedocs.io/).
[](https://circleci.com/gh/mir-dataset-loaders/mirdata)
[](https://codecov.io/gh/mir-dataset-loaders/mirdata)
[](https://mirdata.readthedocs.io/en/latest/?badge=latest)
This library provides tools for working with common MIR datasets, including tools for:
* downloading datasets to a common location and format
* validating that the files for a dataset are all present
* loading annotation files to a common format, consistent with the format required by [mir_eval](https://github.com/craffel/mir_eval)
* parsing track level metadata for detailed evaluations
### Installation
To install, simply run:
```python
pip install mirdata
```
### Quick example
```python
import mirdata
orchset = mirdata.initialize('orchset')
orchset.download() # download the dataset
orchset.validate() # validate that all the expected files are there
example_track = orchset.choice_track() # choose a random example track
print(example_track) # see the available data
```
See the [documentation](https://mirdata.readthedocs.io/) for more examples and the API reference.
### Currently supported datasets
Supported datasets include [AcousticBrainz](https://zenodo.org/record/2553414#.X8jTgulKhhE), [DALI](https://github.com/gabolsgabs/DALI), [Guitarset](http://github.com/marl/guitarset/), [MAESTRO](https://magenta.tensorflow.org/datasets/maestro), [TinySOL](https://www.orch-idea.org/), among many others.
For the **complete list** of supported datasets, see the [documentation](https://mirdata.readthedocs.io/en/stable/source/quick_reference.html)
### Citing
There are two ways of citing mirdata:
If you are using the library for your work, please cite the version you used as indexed at Zenodo:
[](https://doi.org/10.5281/zenodo.4355859)
If you refer to mirdata's design principles, motivation etc., please cite the following [paper](https://zenodo.org/record/3527750#.X-Inp5NKhUI):
[](https://doi.org/10.5281/zenodo.3527750)
```
"mirdata: Software for Reproducible Usage of Datasets"
Rachel M. Bittner, Magdalena Fuentes, David Rubinstein, Andreas Jansson, Keunwoo Choi, and Thor Kell
in International Society for Music Information Retrieval (ISMIR) Conference, 2019
```
```
@inproceedings{
bittner_fuentes_2019,
title={mirdata: Software for Reproducible Usage of Datasets},
author={Bittner, Rachel M and Fuentes, Magdalena and Rubinstein, David and Jansson, Andreas and Choi, Keunwoo and Kell, Thor},
booktitle={International Society for Music Information Retrieval (ISMIR) Conference},
year={2019}
}
```
When working with datasets, please cite the version of `mirdata` that you are using (given by the `DOI` above) **AND** include the reference of the dataset,
which can be found in the respective dataset loader using the `cite()` method.
### Contributing a new dataset loader
We welcome contributions to this library, especially new datasets. Please see [contributing](https://mirdata.readthedocs.io/en/latest/source/contributing.html) for guidelines.
%prep
%autosetup -n mirdata-0.3.7
%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-mirdata -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.7-1
- Package Spec generated
|
