semvec/pypi-packages huggingface dataset:
jtalkpy 1.0.0
Python rapper module of open_jtalk
License: MIT
Requires Python: >=3.5.0

# jtalkpy
Python rapper module of open_jtalk

## Install

```bash
pip install jtalkpy
```

In order to use this module, `open_jtalk` command must be able to use.
See [OpenJtalk](http://open-jtalk.sourceforge.net/).

#### For Ubuntu system
```bash
sudo apt-get install open-jtalk open-jtalk-mecab-naist-jdic hts-voice-nitech-jp-atr503-m001
```
`dict_dir` should be `/var/lib/mecab/dic/open-jtalk/naist-jdic`

`voice_file` should be `/usr/share/hts-voice/nitech-jp-atr503-m001/nitech_jp_atr503_m001.htsvoice`

### For other systems
Pleanse install from [souce](http://open-jtalk.sourceforge.net/).

## VOICE FILES
There are many open source HTS voice model files.

[This page](http://mahoro-ba.net/e1875.html) (Japanse) introduces many models.

## Usage

```python
from jtalkpy import Jtalk
dict_dir = "/var/lib/mecab/dic/open-jtalk/naist-jdic"
voice_file = "/usr/share/hts-voice/nitech-jp-atr503-m001/nitech_jp_atr503_m001.htsvoice"
# 
jtalk = Jtalk(dict_dir, voice_file)
jtalk.from_string("こんにちは、世界", output_file="/home/ubuntu/jtalk_wavs/hello.wav")
```

Open Jtalk options can be set at initialization of `Jtalk` object.
And when `from_string` is called, you can also overwrite these options temporary.


## Contribution
Feel free to create pull request.
1. Fork it ( http://github.com/yhay81/jtalkpy/fork )
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create new Pull Request

## Licence

[MIT](https://github.com/tcnksm/tool/blob/master/LICENCE)
ai-tools 0.3.9
basic tools for ai
License: MIT

Info
====
`ai_tool 2018-05-25`

`Author: Zhao Mingming <471106585@qq.com>`

`Copyright: This module has been placed in the public domain.`

`version:0.0.6`
`version:0.1.0`
- `add func roc`	
- `add func photo_stiching`
- `add func flv2img`
`version:0.1.1`
- `add func microsoft_demo`
`version:0.1.3`
- `add func luminance`
`version:0.1.4`
- `add func dbop ,opreate the database`
`version:0.1.5`
- `add func util, make the print has time`
`version:0.1.6`
- `add func txt2html, make the txt 2 html table`
`version:0.1.8`
- `modify file microsoft_demo, add zprint into it`
`version:0.1.9`
- `modify roc_yolo`
`version:0.2.1`
- `modify insert_image2db, can insert data into 2 database`
`version:0.2.3`
- `add class2info.py function is get_class_info`
`version:0.2.4`
- `add darkchannel.py to decreate the haze of an image`
`version:0.2.7`
- `add lumi_classfy to luminance.py`
`version:0.3.1`
- `add modify video2img`
`version:0.3.3`
- `add wjdc`
- `add video2gif`
`version:0.3.6`
- `add get_histfeature_from_one_img`


Functions:

- `draw_curve`: draw a curve in a image and return the image 
- `image2text`: translate a image to be text style
- `save2server`: save a image on the local server 
- `image2bw`:  turn a gray image to be a binary weights image

How To Use This Module
======================
.. image:: funny.gif
   :height: 100px
   :width: 100px
   :alt: funny cat picture
   :align: center

1. example code:


.. code:: python


    x=np.array([-0.2,0.3,0.4,0.5])
    y=np.array([0.2,0.4,0.1,-0.4])
    norm(x,np.array([0,500]))

    img=draw_curve(x,y)
    img=draw_curve(x,y,title='my title',xlabel='my x label',ylabel='my y lable')

    img=cv2.imread('../examples/faces/2007_007763.jpg')
    img_gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    # img2bw
    img_bw= image2bw(img_gray)


    print img.shape
    image2text(img,(80,40))
    image2text(img,(80,40))



Refresh
========
20180821
flower-oauth-azure 1.0
Azure Oauth support for Flower
License: MIT

Flower oAuth Azure
------------------


Microsoft Azure login handler for [Flower](https://flower.readthedocs.io/en/latest/)

Flower provides internal support for Google and GitHub OAuth authentication, this package enable 
similar functionality for [Microsoft Azure](https://portal.azure.com/)


## Quick start

    pip install flower-oauth-azure
    

Azure OAuth 2.0 authentication is enabled using the `--auth`, `--oauth2_key`, `--oauth2_secret` and `--oauth2_redirect_uri` options,
to set the Azure tenant `FLOWER_OAUTH2_TENANT` environment variable must be set.

`--auth` is a regular expression, for granting access only to the specified email pattern. 
`–-oauth2_key` and `–-oauth2_secret` are your credentials from your Azure Account. 
`–-oauth2_redirect_uri` is there to specify what is the redirect_uri associated to you key and secret


For instance, if you want to grant access to me@saxix.onmicrosoft.com and you@saxix.onmicrosoft.com:

    $ export FLOWER_OAUTH2_TENANT=saxix.onmicrosoft.com    
    $ celery flower --auth_provider=flower_oauth_azure.tenant.AzureTenantLoginHandler \
                    --auth="me@saxix.onmicrosoft.com|you@saxix.onmicrosoft.com" \
                    --oauth2_key=... \
                    --oauth2_secret=... 
                    --oauth2_redirect_uri=http://flower.example.com/login

Alternatively you can set environment variables instead of command line arguments:

    $ export FLOWER_OAUTH2_KEY=...
    $ export FLOWER_OAUTH2_SECRET=...
    $ export FLOWER_OAUTH2_TENANT=saxix.onmicrosoft.com
    $ export FLOWER_AUTH=.*@saxix\.onmicrosoft\.com
    $ export FLOWER_AUTH_PROVIDER=flower_oauth_azure.tenant.AzureTenantLoginHandler
    $ export FLOWER_OAUTH2_REDIRECT_URI=http://flower.example.com/login
    $ celery flower
foliantcontrib.templates.preprocessor 1.0.3
Preprocessor template for `foliant init` command.
License: MIT

# Preprocessor Template for `foliant init` Command

Template for a Foliant preprocessor. Instead of looking for an existing preprocessor, cloning it, and modifying its source, install this package and generate a preprocessor directory. As simple as:

```shell
$ foliant init -t preprocessor
```
## Installation

```shell
$ pip install --no-compile foliantcontrib.templates.preprocessor
```

## Usage

```shell
$ foliant init -t preprocessor
Enter the project name: Awesome Preprocessor
✔ Generating project
─────────────────────
Project "Awesome Preprocessor" created in awesome-preprocessor
```

Or:

```shell
$ foliant init -t preprocessor -n "Awesome Preprocessor"
✔ Generating project
─────────────────────
Project "Awesome Preprocessor" created in awesome-preprocessor
```

Result:

```shell
$ tree awesome-preprocessor
.
├── changelog.md
├── foliant
│   └── preprocessors
│       └── awesome-preprocessor.py
├── LICENSE
├── README.md
└── setup.py

2 directories, 5 files
```
steppy 0.1.16
A lightweight, open-source, Python library for fast and reproducible experimentation
License: MIT
Keywords: machine-learning


Steppy is lightweight, Python library for fast and reproducible experimentation.
The goal of this package is to provide data scientist with minimal interface
that allows her to build complex, yet elegant machine learning pipelines.

Steppy is designed for data scientists who run a lot of experiments.

Steppy is compatible with Python>=3.5
and is distributed under the MIT license.