DAS
Project 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.
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
Installation
Pre-requisites
Anaconda: DAS is installed using an anaconda environment. For that, first install the anaconda python distribution (or miniconda).
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, 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.
On windows:
conda install mamba -c conda-forge -n base -y
mamba create python=3.9 das=0.32.2 "numpy<1.24" -c conda-forge -c ncb -c anaconda -c nvidia -n das -y
On Linux or MacOS (intel and arm):
conda install mamba -c conda-forge -n base -y
mamba create python=3.10 das=0.32.2 -c conda-forge -c ncb -c anaconda -c nvidia -c apple -n das -y
Usage
To start the graphical user interface:
conda activate das
das gui
The documentation at https://janclemenslab.org/das/ provides information on the usage of DAS:
- A quick start tutorial walks through all steps from manually annotating song, over training a network, to generating new annotations.
- How to use the graphical user interface.
- How to use DAS from the terminal or from python scripts.
Acknowledgements
The following packages were modified and integrated into das:
- Keras implementation of TCN models modified from keras-tcn (in
das.tcn
) - Trainable STFT layer implementation modified from kapre (in
das.kapre
)
See the sub-module directories for the original READMEs.
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