semvec/pypi-packages huggingface dataset:
sapit 1.5
A Text-to-speech module

# Sapit - Python Text-to-Speech Module

Sapit is a Python module that enables users to utilize a text-to-speech function similar to the "say" command on macOS. With Sapit, you can easily convert text into speech in your Python scripts or projects.

## Usage

Using Sapit is simple. Just import the `say` function from the `sapit` module and call it with the text you want to convert into speech:

```python
from sapit import say

say("Hello, World!")
```
# Founder

Sapit was created by "ALI LODHI" He is from Pakistan, Ali lodhi is well experienced python programmer who loves writing python code. The Idea of Sapit came when he was making a Desktop Assistant for his personal use and he had to write a lot of code so that's why to ease of use he created sapit. 

# Versions

Sapit 1.0
Sapit 1.2.0
Sapit 1.4

## Sapit 1.5 (Latest)

Sapit 1.5 is the Minor update and In this Update you can adjust the volume. We also added a pitch controller but it is in `beta`

### Example

```python
from sapit import say

say ("Sapit is the best", gender = "male", volume = 0.1) # This the lowest volume 
say ("Sapit is the best", gender = "male", volume = 1.0) # This the highest volume 
```

### Features of Sapit 1.5

- You can save your Text-to-speech words into an audio file
- You can import male or female voice `from sapit import say_male` and `from sapit import say_female`
- Bug Fixes
sr2ml-ravenframework 0.1rc2
SR2ML plugin for RAVEN framework
Requires Python: >=3.9

![SR2ML Logo](./doc/logos/SR2ML.png)

# SR2ML: Safety Risk and Reliability Model Library

SR2ML is a software package which contains a set of safety and reliability models
designed to be interfaced with the INL developed RAVEN code. These models can be
employed to perform both static and dynamic system risk analysis and determine risk
importance of specific elements of the considered system. Two classes of reliability
models have been developed; the first class includes all classical reliability models
(Fault-Trees, Event-Trees, Markov models and Reliability Block Diagrams) which have
been extended to deal not only with Boolean logic values but also time dependent
values. The second class includes several components aging models. Models of these
two classes are designed to be included in a RAVEN ensemble model to perform time
dependent system reliability analysis (dynamic analysis). Similarly, these models
can be interfaced with system analysis codes to determine failure time of systems
and evaluate accident progression (static analysis).

## Available Safety Risk and Reliability Models
- Event Tree (ET) Model
- Fault Tree (FT) Model
- Markov Model
- Reliability Block Diagram (RBD) Model
- Data Classifier
- Event Tree Data Importer
- Fault Tree Data Importer
- Reliability models with time dependent failure rates

## Installation and How to Use?

Please check: https://github.com/idaholab/raven/wiki/Plugins

### Other Software
Idaho National Laboratory is a cutting edge research facility which is a constantly producing high quality research and software. Feel free to take a look at our other software and scientific offerings at:

[Primary Technology Offerings Page](https://www.inl.gov/inl-initiatives/technology-deployment)

[Supported Open Source Software](https://github.com/idaholab)

[Raw Experiment Open Source Software](https://github.com/IdahoLabResearch)

[Unsupported Open Source Software](https://github.com/IdahoLabCuttingBoard)

### Licensing
--------------
This software is licensed under the terms you may find in the file named "LICENSE" in this directory.

### Developers
---------------
By contributing to this software project, you are agreeing to the following terms and conditions for your contributions:

You agree your contributions are submitted under the Apache license. You represent you are authorized to make the contributions and grant the license. If your employer has rights to intellectual property that includes your contributions, you represent that you have received permission to make contributions and grant the required license on behalf of that employer.