semvec/pypi-packages huggingface dataset:
file-and-string-encryption 0.0.7
Simple encryption functions. Do not simply trust this code! Pay attention to how you handle sensitive information!
Keywords: python


# file_and_string_encryption
[GitHub](https://github.com/ICreedenI/file_and_string_encryption) | [PyPI](https://pypi.org/project/file-and-string-encryption)  

Simple encryption functions. Do not simply trust this code! Pay attention to how you handle sensitive information!


Included functions:
- encrypt_directory
- encrypt_file
- encrypt_file_with_password
- decrypt_directory
- decrypt_file
- decrypt_file_with_password
- encrypt_string_with_password
- decrypt_string_with_password
- generate_password
- get_random_key
OpenRAI 1.0.0
Responsible AI framework.

.. image:: https://raw.githubusercontent.com/cisco-open/ResponsibleAI/main/docs/images/rai_logo_blue3.png

RAI is a python library that is designed to help AI developers in
various aspects of responsible AI development. It consists of a core API
and a corresponding web-based dashboard application. RAI can easily be
integrated into AI development projects and measures various metrics for
an AI project during each phase of AI development, from data quality
assessment to model selection based on performance, fairness and
robustness criteria. In addition, it provides interactive tools and
visualizations to understand and explain AI models and provides a
generic framework to perform various types of analysis including
adversarial robustness.

Note: - The dashboard GUI is currently designed around 1920x1080
displays

Documentation
=============

Documentation is available online:
https://responsibleai.readthedocs.io/en/latest/

To Install
==========

1) The project uses sqlite as storage and requires no additional
   installation for this.

    ``pip install py-rai``

2) If you need to use the dashboard as well you must  install the additional requirements

    ``pip install py-rai[dashboard]``

3) To start using the library you must configure the folder where the results will be stored and be used by the dashboard

    ``export DATABASE_FOLDER=YOUR_LOCATION``

4) To start the dashboard server use the following command:

    ``python3 -m RAIDashboard``


Demos:
======

::

   We have added a few demo projects to showcase some of the capabilities of RAI.
   to run any of the demos please use 'python demo_filename'. For instance : 
   python ./demos/adult_demo_grid_search.py

   below is a short description of the provided demos:

   File: adult_demo_grid_search.py 
   Description: This demo uses the Adults dataset (https://archive.ics.uci.edu/ml/datasets/adult) to show how RAI can be used in model selection

   File: image_class_analysis.py 
   Description: this demo uses Cifar10 dataset and shows how RAI can be used to evaluate image classification tasks

   File: image_class_training.py 
   Description: this demo uses Cifar10 dataset and shows how RAI can be used monitor image processing tasks during training


   File: tabular_class_console.py 
   Description: this demo shows how RAI can be used without the dashboard to calculate and report on the metrics for a machine learning task

   File: text.py 
   File: text_output.py 
   Description: these demos show how RAI and its dashboard can be used for evaluating the natural language modeling tasks

    

Cisco Research, Emerging Tech and Incubations,

Cisco Systems Inc.
Audiopy-ML 0.1
This is a python package useful for the automated audio preprocessing and feature extraction.
Requires Python: >=3.6

PyAudioFX

This is a python module for automated audio pre-processing and feature extraction. 

# Fetures
Automated auto-preprocessing effects like trim, time strech and remix

Automated calculation of statistical descriptors for audio data

Automated feature extraction for both single audio file and multiple audio files in a directory

Works on all formats of audio data



# basic_audio_processor(path,effect,hop_length=512,rate=1)

example- increase the speed of the audio by 1.5x times.

basic_audio_processor(r"C:\Users\sairam\Downloads\audio preprocessing\aah shit.mp3",'time-strech',512,1.5)

# statistical_analyser(path)

example- get statistical descriptors for audio data

statistical_analyser(r"C:\Users\sairam\Downloads\audio preprocessing\aah shit.mp3")

# AutomatedExtractor_single(path)

example- perform feature extraction on a single data

AutomatedExtractor_single(r"C:\Users\sairam\Downloads\audio preprocessing\aah shit.mp3")

# Automated Extractor_multiple(path)

example- perform feature extraction on multiple data given using glob function

path=glob.glob(r"\archive (2)\set_a\*.wav")
AutomatedExtractor_multiple(path)