%global _empty_manifest_terminate_build 0 Name: python-das Version: 0.30.0 Release: 1 Summary: DAS License: None URL: https://github.com/janclemenslab/das Source0: https://mirrors.nju.edu.cn/pypi/web/packages/40/4f/ea2bd7979e95bb9dc3e904f0723ef68736d7ce5304a6049f4e1a1298b0d2/das-0.30.0.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-h5py Requires: python3-scipy Requires: python3-scikit-learn Requires: python3-pyyaml Requires: python3-peakutils Requires: python3-zarr Requires: python3-flammkuchen Requires: python3-defopt Requires: python3-matplotlib Requires: python3-pandas Requires: python3-librosa Requires: python3-matplotlib Requires: python3-matplotlib_scalebar Requires: python3-colorcet Requires: python3-keras-tuner Requires: python3-kt-legacy Requires: python3-rich %description # Deep Audio Segmenter _DAS_ is a method for automatically annotating song from raw audio recordings based on a deep neural network. _DAS_ can be used with a graphical user interface, from the terminal, or from within python scripts. If you have questions, feedback, or find bugs please raise an [issue](https://github.com/janclemenslab/das/issues). Please cite _DAS_ as: Elsa Steinfath, Adrian Palacios, Julian Rottschäfer, Deniz Yuezak, Jan Clemens (2021). _Fast and accurate annotation of acoustic signals with deep neural networks._ [eLife](https://doi.org/10.7554/eLife.68837) ## Installation ### Pre-requisites __Anaconda__: _DAS_ is installed using an anaconda environment. For that, first install the [anaconda python distribution](https://docs.anaconda.com/anaconda/install/) (or [miniconda](https://docs.conda.io/en/latest/miniconda.html)). If you have conda already installed, make sure you have conda v4.8.4+. If not, update from an older version with `conda update conda`. __Libsoundfile on linux__: The graphical user interface (GUI) reads audio data using [soundfile](http://pysoundfile.readthedocs.io/), which relies on `libsndfile`. `libsndfile` will be automatically installed on Windows and macOS. On Linux, the library needs to be installed manually with: `sudo apt-get install libsndfile1`. Note that _DAS_ will work w/o `libsndfile` but will not be able to load exotic audio formats. ### Install _DAS_ Create an anaconda environment called `das` that contains all the required packages: ```shell conda install mamba -c conda-forge -n base -y mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y ``` For linux, the last line needs to be: ```shell CONDA_OVERRIDE_CUDA=11.2 mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y ``` ## Usage To start the graphical user interface: ```shell conda activate das das gui ``` The documentation at [https://janclemenslab.org/das/](https://janclemenslab.org/das/) provides information on the usage of _DAS_: - A [quick start tutorial](https://janclemenslab.org/das/quickstart.html) walks through all steps from manually annotating song, over training a network, to generating new annotations. - How to use the [graphical user interface](https://janclemenslab.org/das/tutorials_gui/tutorials_gui.html). - How to use _DAS_ [from the terminal or from python scripts](https://janclemenslab.org/das/tutorials/tutorials.html). ## Acknowledgements The following packages were modified and integrated into das: - Keras implementation of TCN models modified from [keras-tcn](https://github.com/philipperemy/keras-tcn) (in `das.tcn`) - Trainable STFT layer implementation modified from [kapre](https://github.com/keunwoochoi/kapre) (in `das.kapre`) See the sub-module directories for the original READMEs. %package -n python3-das Summary: DAS Provides: python-das BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-das # Deep Audio Segmenter _DAS_ is a method for automatically annotating song from raw audio recordings based on a deep neural network. _DAS_ can be used with a graphical user interface, from the terminal, or from within python scripts. If you have questions, feedback, or find bugs please raise an [issue](https://github.com/janclemenslab/das/issues). Please cite _DAS_ as: Elsa Steinfath, Adrian Palacios, Julian Rottschäfer, Deniz Yuezak, Jan Clemens (2021). _Fast and accurate annotation of acoustic signals with deep neural networks._ [eLife](https://doi.org/10.7554/eLife.68837) ## Installation ### Pre-requisites __Anaconda__: _DAS_ is installed using an anaconda environment. For that, first install the [anaconda python distribution](https://docs.anaconda.com/anaconda/install/) (or [miniconda](https://docs.conda.io/en/latest/miniconda.html)). If you have conda already installed, make sure you have conda v4.8.4+. If not, update from an older version with `conda update conda`. __Libsoundfile on linux__: The graphical user interface (GUI) reads audio data using [soundfile](http://pysoundfile.readthedocs.io/), which relies on `libsndfile`. `libsndfile` will be automatically installed on Windows and macOS. On Linux, the library needs to be installed manually with: `sudo apt-get install libsndfile1`. Note that _DAS_ will work w/o `libsndfile` but will not be able to load exotic audio formats. ### Install _DAS_ Create an anaconda environment called `das` that contains all the required packages: ```shell conda install mamba -c conda-forge -n base -y mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y ``` For linux, the last line needs to be: ```shell CONDA_OVERRIDE_CUDA=11.2 mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y ``` ## Usage To start the graphical user interface: ```shell conda activate das das gui ``` The documentation at [https://janclemenslab.org/das/](https://janclemenslab.org/das/) provides information on the usage of _DAS_: - A [quick start tutorial](https://janclemenslab.org/das/quickstart.html) walks through all steps from manually annotating song, over training a network, to generating new annotations. - How to use the [graphical user interface](https://janclemenslab.org/das/tutorials_gui/tutorials_gui.html). - How to use _DAS_ [from the terminal or from python scripts](https://janclemenslab.org/das/tutorials/tutorials.html). ## Acknowledgements The following packages were modified and integrated into das: - Keras implementation of TCN models modified from [keras-tcn](https://github.com/philipperemy/keras-tcn) (in `das.tcn`) - Trainable STFT layer implementation modified from [kapre](https://github.com/keunwoochoi/kapre) (in `das.kapre`) See the sub-module directories for the original READMEs. %package help Summary: Development documents and examples for das Provides: python3-das-doc %description help # Deep Audio Segmenter _DAS_ is a method for automatically annotating song from raw audio recordings based on a deep neural network. _DAS_ can be used with a graphical user interface, from the terminal, or from within python scripts. If you have questions, feedback, or find bugs please raise an [issue](https://github.com/janclemenslab/das/issues). Please cite _DAS_ as: Elsa Steinfath, Adrian Palacios, Julian Rottschäfer, Deniz Yuezak, Jan Clemens (2021). _Fast and accurate annotation of acoustic signals with deep neural networks._ [eLife](https://doi.org/10.7554/eLife.68837) ## Installation ### Pre-requisites __Anaconda__: _DAS_ is installed using an anaconda environment. For that, first install the [anaconda python distribution](https://docs.anaconda.com/anaconda/install/) (or [miniconda](https://docs.conda.io/en/latest/miniconda.html)). If you have conda already installed, make sure you have conda v4.8.4+. If not, update from an older version with `conda update conda`. __Libsoundfile on linux__: The graphical user interface (GUI) reads audio data using [soundfile](http://pysoundfile.readthedocs.io/), which relies on `libsndfile`. `libsndfile` will be automatically installed on Windows and macOS. On Linux, the library needs to be installed manually with: `sudo apt-get install libsndfile1`. Note that _DAS_ will work w/o `libsndfile` but will not be able to load exotic audio formats. ### Install _DAS_ Create an anaconda environment called `das` that contains all the required packages: ```shell conda install mamba -c conda-forge -n base -y mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y ``` For linux, the last line needs to be: ```shell CONDA_OVERRIDE_CUDA=11.2 mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y ``` ## Usage To start the graphical user interface: ```shell conda activate das das gui ``` The documentation at [https://janclemenslab.org/das/](https://janclemenslab.org/das/) provides information on the usage of _DAS_: - A [quick start tutorial](https://janclemenslab.org/das/quickstart.html) walks through all steps from manually annotating song, over training a network, to generating new annotations. - How to use the [graphical user interface](https://janclemenslab.org/das/tutorials_gui/tutorials_gui.html). - How to use _DAS_ [from the terminal or from python scripts](https://janclemenslab.org/das/tutorials/tutorials.html). ## Acknowledgements The following packages were modified and integrated into das: - Keras implementation of TCN models modified from [keras-tcn](https://github.com/philipperemy/keras-tcn) (in `das.tcn`) - Trainable STFT layer implementation modified from [kapre](https://github.com/keunwoochoi/kapre) (in `das.kapre`) See the sub-module directories for the original READMEs. %prep %autosetup -n das-0.30.0 %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-das -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 15 2023 Python_Bot - 0.30.0-1 - Package Spec generated