semvec/pypi-packages huggingface dataset:
danspeech 1.0.4
Speech recognition for Danish

# DanSpeech
An open-source python package for Danish speech recognition.

You can find all relevant information in the documentation and we provide you with some extra links below. 

[![Documentation](https://img.shields.io/badge/Documentation-blue.svg?style=for-the-badge)](https://danspeech.github.io/danspeech/)

[![Installation](https://img.shields.io/badge/Installation-blue.svg?style=for-the-badge)](https://danspeech.github.io/danspeech/html/installation.html)

[![Examples](https://img.shields.io/badge/Examples-blue.svg?style=for-the-badge)](https://danspeech.github.io/danspeech/html/auto_examples/index.html)

## Demo
To experience the danspeech package, we've created a simple demo with a nice GUI. It depends on danspeech 
and django.

The demo resides at [https://github.com/danspeech/danspeechdemo](https://github.com/danspeech/danspeechdemo).

## Train models
If you wish to train your own models, or perhaps finetune a DanSpeech model to your specific use case, we then 
refer to the [DanSpeech training repository](https://github.com/danspeech/danspeech_training). 

## Support
If you require help with the software, then feel free to create issues here on github. We will continually solve issues
and answer any questions that you might have. 

## Authors and acknowledgment
Main authors: 
* Martin Carsten Nielsen  ([mcnielsen4270@gmail.com](mcnielsen4270@gmail.com))
* Rasmus Arpe Fogh Jensen ([rasmus.arpe@gmail.com](rasmus.arpe@gmail.com))

This project is supported by Innovation Foundation Denmark through the projects DABAI and ATEL

Other acknowledgements:

* We've trained the models based on the code from [https://github.com/SeanNaren/deepspeech.pytorch](https://github.com/SeanNaren/deepspeech.pytorch).
* The audio handling and recognizing flow is based on [https://github.com/Uberi/speech_recognition](https://github.com/Uberi/speech_recognition).
* Handling of the pretrained models is based on [keras](https://github.com/keras-team/keras).
* We've trained all models with the aid of DTU using data from Sprakbanken ([NST](https://www.nb.no/sprakbanken/show?serial=oai%3Anb.no%3Asbr-19&lang=en)).

## Licence
The software is in general licenced under the Apache-2 licence and may be used commercially. 

[![Licence](https://img.shields.io/badge/license-Apache--2.0-blue)](https://github.com/danspeech/LICENCE.txt)

**NOTE:**
Some code parts in DanSpeech contain links to other original sources. If this is the case, then the specific code 
part is licenced differently depending on the source and if you wish to redistribute DanSpeech code, then you must
make sure you also comply with the original code licences.  

The flac binaries are licenced under GPLv2. See more information at [Flac licence](https://github.com/Uberi/speech_recognition/blob/master/LICENSE-FLAC.txt).
pyposeidon 0.5.2
Framework for Sea Level Hydrodynamic simulations
Requires Python: >=3.7,<3.10
Keywords: Hydrodynamic,Sea,Simulation,Visualization

Framework for Sea Level Hydrodynamic simulations
================================================

[![Documentation Status](https://readthedocs.org/projects/pyposeidon/badge/?version=latest)](https://pyposeidon.readthedocs.io/en/latest/?badge=latest) ![GitHub release (latest by date)](https://img.shields.io/github/v/release/ec-jrc/pyPoseidon) ![CI](https://github.com/ec-jrc/pyPoseidon/actions/workflows/conda_and_nested_venv.yml/badge.svg) ![CI](https://github.com/ec-jrc/pyPoseidon/actions/workflows/conda.yml/badge.svg) ![CI](https://github.com/ec-jrc/pyPoseidon/actions/workflows/code_quality.yml/badge.svg) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ec-jrc/pyPoseidon/master?urlpath=%2Flab)

This is a development project utilising multiple solvers (currently DELFT3D & SCHISM) for simulating sea level height (currently only storm surge). The purpose is to create a simple, portable and transparent way of setting up, running and analysing hydrodynamic computations through python scripts and Jupyter Notebooks (http://jupyter.org). See Notebooks in Tutorial/ for relevant prototypes.

## Installation


`conda install -c gbrey pyposeidon`

Afterwards, for now, one needs to install gmsh manually with

`pip install gmsh`

**Note**: Due to an upstream issue, *pydap* needs to be installed manually. See *environment.yml* for info.

### Prerequisities

DELFT3D needs to be compiled for your system. You can download it from http://oss.deltares.nl/web/delft3d/source-code. See Wiki for more details.

SCHISM needs to be compiled for your system. You can download it from  http://columbia.vims.edu/schism/tags/. See http://ccrm.vims.edu/schismweb/ for more info.


You can also install the solvers easily with conda

`conda install -c gbrey pschism delft3d4`


## Tests

There are several sets of tests. You can run pyPoseidon unitests with

`pytest`

In order to test also the solver integration use

`pytest --runschism`

or

`python --rundelft`

if you are using a local installation of the solvers please specify the PATH to the executables in your system such as

`export D3D = '/path_to_folder_bin/lnx64/flow2d3d/'`

`export LD3D = '/path_to_folder_bin/lnx64/flow2d3d/'`

`export SCHISM = '/path_to_schism_executable'`

## docs

```
mkdocs build
mkdocs serve
```

## License
* The project is released under the EUPL v1.2 license.
pupil-analysis 0.4.1
Spatial motion analysis program
Requires Python: >=3.0

<h1>Pseudopupil Analysis suite</h1>
Specialised spatial motion analysis software for Pupil Imsoft data.

In general, can be used for data that follows hierarchy
```
data_directory
├── specimen_01
│   ├── experiment_01
│   │   ├── image_001.tif
│   │   └── image_002.tif
│   └── ...
└── ...
```


<h2>Installing</h2>

There are two supported installation ways at the moment.
On Windows, the stand-alone installer is possibly the best option unless you feel familiar with Python.
On other platforms, use pip.

<h3>Installer on Windows (easiest)</h3>

A Windows installer that bundles together the Pseudopupil Analysis suite and all its depencies,
including a complete Python runtime, is provided at
[Releases](https://github.com/jkemppainen/pupil-analysis/releases).

The installer creates a start menu shorcut called <em>Pupil Analysis</em>,
which can be used to launch the program.

To uninstall, use <em>Add or Remove programs</em> feature in Windows.


<h3>Using pip (the python standard way)</h3>

The latest version from [PyPi](https://pypi.org/) can be installed with the command

```
pip install pupil-analysis
```

This should install all the required dependencies, except when on Windows, OpenCV may require
[Visual C++ Runtime 2015](https://www.microsoft.com/download/details.aspx?id=48145) to be installed.


Afterwards, to upgrade an existing installation to the latest version

```
pip install --upgrade pupil-analysis
```

In case of regressions, a specific version of the suite (for example 0.1.2) can be installed

```
pip install pupil-analysis==0.1.2
```

Finally, to open the program

```
python -m pupilanalysis.tkgui
```

<h2>How to use</h2>

First, open a data directory (containing the folders containing the images).
Next, select the regions of interest (ROIs) and then run the motion analysis.
The ROIs and movements are saved on disk (<em>C:\Users\USER\PupilAnalysis</em> or <em>/home/USER/.pupilanalysis</em>), so these steps are needed only once per specimen.

After the initial steps you, can perform further analyses in the program or
export the data by
1) copy-pasting to your favourite spread sheet or plotting program
or 2) exporting CSV files.

<h3>Notes</h3>
This is still an early development version (expect rough edges).