%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) 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) 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) 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 - 0.0.2-1 - Package Spec generated