Metadata-Version: 2.5
Name: das
Version: 0.33.2
Summary: DAS
Author-email: Jan Clemens <clemensjan@googlemail.com>
Requires-Python: >=3.12
Description-Content-Type: text/markdown
Classifier: License :: OSI Approved :: Apache Software License
License-File: LICENSE
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: pandas
Requires-Dist: matplotlib_scalebar
Requires-Dist: xarray-behave>=0.37.8,<0.38
Requires-Dist: tqdm
Requires-Dist: numpy
Requires-Dist: keras~=3.0
Requires-Dist: torch
Requires-Dist: pytest ; extra == "dev"
Requires-Dist: flit ; extra == "dev"
Requires-Dist: ipykernel ; extra == "dev"
Project-URL: documentation, https://janclemenslab.org/das/
Project-URL: source, https://github.com/janclemenslab/das
Provides-Extra: dev
Import-Name: das

# Deep Audio Segmenter (DAS)

_DAS_ automatically annotates animal vocalizations in raw audio recordings using a deep neural network. It can be used through a graphical user interface, from the terminal, or from Python scripts.

## Installation

On Windows and Linux:

```shell
conda create -n das -c conda-forge --strict-channel-priority python=3.14 pyside6 ffmpeg "numcodecs<0.16" uv -y
```

On macOS, select the OpenBLAS build that avoids an OpenMP conflict with PyTorch:

```shell
conda create -n das -c conda-forge --strict-channel-priority python=3.14 pyside6 ffmpeg "numcodecs<0.16" "libopenblas=*=*pthreads*" uv -y
```

Then, on all platforms:

```shell
conda activate das
uv pip install das --torch-backend=auto
das version
```

See the [installation guide](https://janclemenslab.org/das/installation.html) for CPU- and GPU-specific options.

Users who need the TensorFlow backend should follow the [TensorFlow installation instructions](https://janclemenslab.org/das/install_tf.html) for the final TensorFlow-backed release (0.32.13).

## Documentation

See the [DAS documentation](https://janclemenslab.org/das/) for the complete user guide:

- The quick-start tutorials for [flies](https://janclemenslab.org/das/quickstart_fly.html) and [birds](https://janclemenslab.org/das/quickstart_bird.html) cover manual annotation, network training, and generating new annotations.
- Use the [graphical user interface](https://janclemenslab.org/das/tutorials_gui/tutorials_gui.html).
- Use DAS [from the terminal or Python scripts](https://janclemenslab.org/das/tutorials/tutorials.html).

If you have questions, feedback, or find a bug, please [open an issue](https://github.com/janclemenslab/das/issues).

## Citation

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)

## 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.models.tcn`)
- Trainable STFT layer implementation modified from [kapre](https://github.com/keunwoochoi/kapre) (in `das.models.kapre`)

See the corresponding source directories for the original READMEs.

