automatic import of python-ci-sdropeneuler20.03

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+/ci_sdr-0.0.2.tar.gz
diff --git a/python-ci-sdr.spec b/python-ci-sdr.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-ci-sdr
+Version:	0.0.2
+Release:	1
+Summary:	A sample Python project
+License:	MIT License
+URL:		https://github.com/fgnt/ci_sdr
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/db/c0/1f9a358e31f8d329b1fe32dc05d9b1761472443e4f759771c21f60285198/ci_sdr-0.0.2.tar.gz
+BuildArch:	noarch
+
+
+%description
+# Convolutive Transfer Function Invariant SDR
+
+![Run python tests](https://github.com/fgnt/ci_sdr/workflows/Run%20python%20tests/badge.svg)
+[![PyPI](https://img.shields.io/pypi/v/ci_sdr)](https://pypi.org/project/ci-sdr)
+[![codecov.io](https://codecov.io/github/fgnt/ci_sdr/coverage.svg?branch=main)](https://codecov.io/github/fgnt/ci_sdr?branch=main)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/ci_sdr)](https://pypi.org/project/ci-sdr)
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/fgnt/ci_sdr/blob/master/LICENSE)
+
+<!-- ![Run python dependency test](https://github.com/fgnt/ci_sdr/workflows/Run%20python%20dependency%20test/badge.svg) -->
+
+
+This repository contains an implementation for the `Convolutive transfer function Invariant Signal-to-Distortion Ratio` objective for PyTorch as described in the publication `Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation` ([link arXiv][arXiv]).
+
+Here, a small example, how you can use this CI-SDR objective in your own source code:
+
+```python
+import torch
+import ci_sdr
+
+reference: torch.tensor = ...
+# reference.shape: [speakers, samples]
+
+estimation: torch.tensor = ...
+# estimation shape: [speakers, samples]
+
+sdr = ci_sdr.pt.ci_sdr_loss(estimation, reference)
+# sdr shape: [speakers]
+```
+
+The idea of this objective function is based in the theory from `E. Vincent, R. Gribonval and C. Févotte, Performance measurement in blind audio source separation, IEEE Trans. Audio, Speech and Language Processing`, known as
+`BSSEval`.
+The original author provided MATLAB source code ([link](http://bass-db.gforge.inria.fr/bss_eval/)) and the package `mir_eval` ([link](http://craffel.github.io/mir_eval/#module-mir_eval.separation)) contains a python port. Some peoble refer to these implementations as `BSSEval v3` ([link](https://github.com/sigsep/bsseval)).
+
+The PyTorch code in this package is tested to yield the same `SDR` values as `mir_eval` with the default parameters.
+
+> **NOTE:** If you want to use `BSSEval v3 SDR` as metric, I recomment to use `mir_eval.separation.bss_eval_sources` and use as reference the clean/unreverberated source signals. The implementation in this repository has minor difference that makes it problematic to compare SDR values accorss different publications (e.g. here the permutation is calculated on the SDR, while `mir_eval` computes it based on the `SIR`.).
+
+
+
+# Installation
+
+Install it directly with Pip, if you just want to use it:
+
+```bash
+pip install ci-sdr
+```
+
+or to get the recent version:
+
+```bash
+pip install git+https://github.com/fgnt/ci_sdr.git
+```
+
+If you want to install it with `all` dependencies (test and doctest dependencies), run:
+
+```bash
+pip install git+https://github.com/fgnt/ci_sdr.git#egg=ci_sdr[all]
+```
+
+When you want to change the code, clone this repository and install it as `editable`:
+
+```bash
+git clone https://github.com/fgnt/ci_sdr.git
+cd ci_sdr
+pip install --editable .
+# pip install --editable .[all]
+```
+
+# Citation
+
+To cite this implementation, you can cite the following paper ([link][arXiv]):
+```
+@article{boeddeker2020convolutive,
+  title   = {Convolutive Transfer Function Invariant {SDR} training criteria for Multi-Channel Reverberant Speech Separation},
+  author  = {Boeddeker, Christoph and Zhang, Wangyou and Nakatani, Tomohiro and Kinoshita, Keisuke and Ochiai, Tsubasa and Delcroix, Marc and Kamo, Naoyuki and Qian, Yanmin and Haeb-Umbach, Reinhold},
+  journal = {arXiv preprint arXiv:2011.15003},
+  year    = {2020}
+}
+```
+
+
+
+[arXiv]: https://arxiv.org/abs/2011.15003
+
+
+
+%package -n python3-ci-sdr
+Summary:	A sample Python project
+Provides:	python-ci-sdr
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-ci-sdr
+# Convolutive Transfer Function Invariant SDR
+
+![Run python tests](https://github.com/fgnt/ci_sdr/workflows/Run%20python%20tests/badge.svg)
+[![PyPI](https://img.shields.io/pypi/v/ci_sdr)](https://pypi.org/project/ci-sdr)
+[![codecov.io](https://codecov.io/github/fgnt/ci_sdr/coverage.svg?branch=main)](https://codecov.io/github/fgnt/ci_sdr?branch=main)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/ci_sdr)](https://pypi.org/project/ci-sdr)
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/fgnt/ci_sdr/blob/master/LICENSE)
+
+<!-- ![Run python dependency test](https://github.com/fgnt/ci_sdr/workflows/Run%20python%20dependency%20test/badge.svg) -->
+
+
+This repository contains an implementation for the `Convolutive transfer function Invariant Signal-to-Distortion Ratio` objective for PyTorch as described in the publication `Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation` ([link arXiv][arXiv]).
+
+Here, a small example, how you can use this CI-SDR objective in your own source code:
+
+```python
+import torch
+import ci_sdr
+
+reference: torch.tensor = ...
+# reference.shape: [speakers, samples]
+
+estimation: torch.tensor = ...
+# estimation shape: [speakers, samples]
+
+sdr = ci_sdr.pt.ci_sdr_loss(estimation, reference)
+# sdr shape: [speakers]
+```
+
+The idea of this objective function is based in the theory from `E. Vincent, R. Gribonval and C. Févotte, Performance measurement in blind audio source separation, IEEE Trans. Audio, Speech and Language Processing`, known as
+`BSSEval`.
+The original author provided MATLAB source code ([link](http://bass-db.gforge.inria.fr/bss_eval/)) and the package `mir_eval` ([link](http://craffel.github.io/mir_eval/#module-mir_eval.separation)) contains a python port. Some peoble refer to these implementations as `BSSEval v3` ([link](https://github.com/sigsep/bsseval)).
+
+The PyTorch code in this package is tested to yield the same `SDR` values as `mir_eval` with the default parameters.
+
+> **NOTE:** If you want to use `BSSEval v3 SDR` as metric, I recomment to use `mir_eval.separation.bss_eval_sources` and use as reference the clean/unreverberated source signals. The implementation in this repository has minor difference that makes it problematic to compare SDR values accorss different publications (e.g. here the permutation is calculated on the SDR, while `mir_eval` computes it based on the `SIR`.).
+
+
+
+# Installation
+
+Install it directly with Pip, if you just want to use it:
+
+```bash
+pip install ci-sdr
+```
+
+or to get the recent version:
+
+```bash
+pip install git+https://github.com/fgnt/ci_sdr.git
+```
+
+If you want to install it with `all` dependencies (test and doctest dependencies), run:
+
+```bash
+pip install git+https://github.com/fgnt/ci_sdr.git#egg=ci_sdr[all]
+```
+
+When you want to change the code, clone this repository and install it as `editable`:
+
+```bash
+git clone https://github.com/fgnt/ci_sdr.git
+cd ci_sdr
+pip install --editable .
+# pip install --editable .[all]
+```
+
+# Citation
+
+To cite this implementation, you can cite the following paper ([link][arXiv]):
+```
+@article{boeddeker2020convolutive,
+  title   = {Convolutive Transfer Function Invariant {SDR} training criteria for Multi-Channel Reverberant Speech Separation},
+  author  = {Boeddeker, Christoph and Zhang, Wangyou and Nakatani, Tomohiro and Kinoshita, Keisuke and Ochiai, Tsubasa and Delcroix, Marc and Kamo, Naoyuki and Qian, Yanmin and Haeb-Umbach, Reinhold},
+  journal = {arXiv preprint arXiv:2011.15003},
+  year    = {2020}
+}
+```
+
+
+
+[arXiv]: https://arxiv.org/abs/2011.15003
+
+
+
+%package help
+Summary:	Development documents and examples for ci-sdr
+Provides:	python3-ci-sdr-doc
+%description help
+# Convolutive Transfer Function Invariant SDR
+
+![Run python tests](https://github.com/fgnt/ci_sdr/workflows/Run%20python%20tests/badge.svg)
+[![PyPI](https://img.shields.io/pypi/v/ci_sdr)](https://pypi.org/project/ci-sdr)
+[![codecov.io](https://codecov.io/github/fgnt/ci_sdr/coverage.svg?branch=main)](https://codecov.io/github/fgnt/ci_sdr?branch=main)
+[![PyPI - Downloads](https://img.shields.io/pypi/dm/ci_sdr)](https://pypi.org/project/ci-sdr)
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/fgnt/ci_sdr/blob/master/LICENSE)
+
+<!-- ![Run python dependency test](https://github.com/fgnt/ci_sdr/workflows/Run%20python%20dependency%20test/badge.svg) -->
+
+
+This repository contains an implementation for the `Convolutive transfer function Invariant Signal-to-Distortion Ratio` objective for PyTorch as described in the publication `Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation` ([link arXiv][arXiv]).
+
+Here, a small example, how you can use this CI-SDR objective in your own source code:
+
+```python
+import torch
+import ci_sdr
+
+reference: torch.tensor = ...
+# reference.shape: [speakers, samples]
+
+estimation: torch.tensor = ...
+# estimation shape: [speakers, samples]
+
+sdr = ci_sdr.pt.ci_sdr_loss(estimation, reference)
+# sdr shape: [speakers]
+```
+
+The idea of this objective function is based in the theory from `E. Vincent, R. Gribonval and C. Févotte, Performance measurement in blind audio source separation, IEEE Trans. Audio, Speech and Language Processing`, known as
+`BSSEval`.
+The original author provided MATLAB source code ([link](http://bass-db.gforge.inria.fr/bss_eval/)) and the package `mir_eval` ([link](http://craffel.github.io/mir_eval/#module-mir_eval.separation)) contains a python port. Some peoble refer to these implementations as `BSSEval v3` ([link](https://github.com/sigsep/bsseval)).
+
+The PyTorch code in this package is tested to yield the same `SDR` values as `mir_eval` with the default parameters.
+
+> **NOTE:** If you want to use `BSSEval v3 SDR` as metric, I recomment to use `mir_eval.separation.bss_eval_sources` and use as reference the clean/unreverberated source signals. The implementation in this repository has minor difference that makes it problematic to compare SDR values accorss different publications (e.g. here the permutation is calculated on the SDR, while `mir_eval` computes it based on the `SIR`.).
+
+
+
+# Installation
+
+Install it directly with Pip, if you just want to use it:
+
+```bash
+pip install ci-sdr
+```
+
+or to get the recent version:
+
+```bash
+pip install git+https://github.com/fgnt/ci_sdr.git
+```
+
+If you want to install it with `all` dependencies (test and doctest dependencies), run:
+
+```bash
+pip install git+https://github.com/fgnt/ci_sdr.git#egg=ci_sdr[all]
+```
+
+When you want to change the code, clone this repository and install it as `editable`:
+
+```bash
+git clone https://github.com/fgnt/ci_sdr.git
+cd ci_sdr
+pip install --editable .
+# pip install --editable .[all]
+```
+
+# Citation
+
+To cite this implementation, you can cite the following paper ([link][arXiv]):
+```
+@article{boeddeker2020convolutive,
+  title   = {Convolutive Transfer Function Invariant {SDR} training criteria for Multi-Channel Reverberant Speech Separation},
+  author  = {Boeddeker, Christoph and Zhang, Wangyou and Nakatani, Tomohiro and Kinoshita, Keisuke and Ochiai, Tsubasa and Delcroix, Marc and Kamo, Naoyuki and Qian, Yanmin and Haeb-Umbach, Reinhold},
+  journal = {arXiv preprint arXiv:2011.15003},
+  year    = {2020}
+}
+```
+
+
+
+[arXiv]: https://arxiv.org/abs/2011.15003
+
+
+
+%prep
+%autosetup -n ci-sdr-0.0.2
+
+%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-ci-sdr -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..6e12fcb
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+5de0ad6814667cb21fba469a5834f7b1  ci_sdr-0.0.2.tar.gz