semvec/pypi-packages huggingface dataset:
dnn-tts-torch 0.0.9
This is a library consisting of pre-trained models for the synthesis of Russian and English speech
Requires Python: >=3.12
Keywords: tts speech synthesis

# DNN TTS Torch #

DNN TTS Torch это инструмент для синтеза качественной русской и английской речи, использующий обученные модели. 

**Проект на GitHub**: https://github.com/KirillPogranichny/dnn_tts_torch

### Инструкция ###

Команда для установки библиотеки:

`pip install dnn_tts_torch`

Функция для синтеза речи:

`synthesize(x, dataset=y)`

`x` _- список, содержащий строки, которые будут преобразованы в аудиофайл формата .wav._

`y` _- аргумент для выбора языка модели. Может принимать_ `'ruspeech'` _для синтеза русской речи или_ `'ljspeech'` _для синтеза английской речи. По умолчанию_ `dataset='ruspeech'`.

### Пример работы ###

    from dnn_tts_torch.synthesize import synthesize
    
    text = ['Факультет математики и компьютерных наук сделал меня человеком.']
    synthesize(text, dataset='ruspeech')

В качестве результата пользователь получит .wav файл переданного текста. Для того, чтобы получить сразу несколько аудиофайлов - добавьте соответствующие элементы в список.
fa-models 1.1.20
A simple library of trading models.
Requires Python: >=3.9
Keywords: finance, trading, models

# fa-models
A set of models often used for trading services. The library offers merely models and is database agnostic.

## Sponsors
Freya Alpha,
The Kára System,
Spark & Hale Robotic Industries


## General
Run and compiled for Python 3.9.13.
Expected to run for Python 3+

## Development

### Testing
run tests with `pytest -s -vv` to see all the details. This is extremely important for the library. It could make multiple pods fail.

### Installation as Consuming Developer

Simply run: `pip install fa-models`

Import in modules without the dash (e.g.): `from famodels.signal import Signal`

### Setup as Contributor
Create the virtul environment: 
```
py -m venv .venv
```
Start the Environment: 
```
./.venv/Scripts/activate
```
 (or allow VS Code to start it). Use `deactivate`to stop it.

Upgrade the PIP.
```
python -m pip install --upgrade pip
```

All the required libraries must be listed in requirements.txt and installed by  
```
python -m pip install -r .\requirements.txt
```
For Dev use 
```
python -m pip install -r .\requirements-dev.txt
```

To cleanup the environment run:
```
pip3 freeze > to-uninstall.txt
```
 and then
```
pip3 uninstall -y -r to-uninstall.txt
```

or 
```
pip3 install pip-autoremove
```

### Build Library
Prerequisite: make sure that you give your Operating System user the right to modify files in the python directory. The directory where pyhton is installed.
Use `python setup.py bdist_wheel` to create the dist, build and .eggs folder.

## Reference from a different project
In order to use your own version of the project - to maybe contribute to the library - simply clone the code from github into new directory. Then add the path of that new directory to the requirements.txt file of your project. Then change in fa-models whatever you recommend to improve. Don't forget the Open-Closed Principle: extend only (unless it requires a breaking change)


## Releasing a new version

Simply commit and push into MAIN. Make sure you have tested well.

Github Actions (.github/workflows/testing_and_deployment.yml) will deploy the file to pypi.org.

### Manual and old process

Delete any old files in the /dist and build folder of your local environment.
Update your pip: 
```
python -m pip install --upgrade pip
```

Install the tools build, twine and bumpver: 
```
python -m pip install build twine bumpver
```
Upgrade the setuptools: 

```
pip install --upgrade setuptools
```

Bump the version in pyproject.toml: 
```
bumpver update --patch
```
This will commit a new version to GitHub.

Build the project: 
```
python -m build
```

Check the distribution: 
```
twine check dist/*
```

Upload to test-pypi to validate: 
```
twine upload -r testpypi dist/*
```

Login with username: (password should be known)

If the test-upload was successful, finally, upload to pypi production: 

```
twine upload dist/*
```

Done.

(P.S. Do not forget to update the library in your projects: `pip install --upgrade fa-models`)bumpver update --patch