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|
%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
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.30.0-1
- Package Spec generated
|
