diff options
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-das.spec | 285 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 287 insertions, 0 deletions
@@ -0,0 +1 @@ +/das-0.30.0.tar.gz diff --git a/python-das.spec b/python-das.spec new file mode 100644 index 0000000..d8485cb --- /dev/null +++ b/python-das.spec @@ -0,0 +1,285 @@ +%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 +<!-- [](https://github.com/janclemenslab/das/actions/workflows/main.yml) --> + +# 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`. + +<!-- ```shell +curl https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o miniconda.sh +sh miniconda.sh -b -p $HOME/miniconda +export PATH="$HOME/miniconda/bin:$PATH" +``` --> +<!-- +__CUDA libraries for using the GPU__: While _DAS_ works well for annotating song using the CPU, a GPU will greatly improve annotation speed and is recommended for training a _DAS_ network. The network is implemented in the deep-learning framework Tensorflow. To make sure that Tensorflow can use your GPU, the required CUDA libraries need to be installed. See the [tensorflow docs](https://www.tensorflow.org/install/gpu) for details. --> + +__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 +<!-- [](https://github.com/janclemenslab/das/actions/workflows/main.yml) --> + +# 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`. + +<!-- ```shell +curl https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o miniconda.sh +sh miniconda.sh -b -p $HOME/miniconda +export PATH="$HOME/miniconda/bin:$PATH" +``` --> +<!-- +__CUDA libraries for using the GPU__: While _DAS_ works well for annotating song using the CPU, a GPU will greatly improve annotation speed and is recommended for training a _DAS_ network. The network is implemented in the deep-learning framework Tensorflow. To make sure that Tensorflow can use your GPU, the required CUDA libraries need to be installed. See the [tensorflow docs](https://www.tensorflow.org/install/gpu) for details. --> + +__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 +<!-- [](https://github.com/janclemenslab/das/actions/workflows/main.yml) --> + +# 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`. + +<!-- ```shell +curl https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o miniconda.sh +sh miniconda.sh -b -p $HOME/miniconda +export PATH="$HOME/miniconda/bin:$PATH" +``` --> +<!-- +__CUDA libraries for using the GPU__: While _DAS_ works well for annotating song using the CPU, a GPU will greatly improve annotation speed and is recommended for training a _DAS_ network. The network is implemented in the deep-learning framework Tensorflow. To make sure that Tensorflow can use your GPU, the required CUDA libraries need to be installed. See the [tensorflow docs](https://www.tensorflow.org/install/gpu) for details. --> + +__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 <Python_Bot@openeuler.org> - 0.30.0-1 +- Package Spec generated @@ -0,0 +1 @@ +11a4bc0153f5b0fb77d4998dedf18544 das-0.30.0.tar.gz |