semvec/pypi-packages huggingface dataset:
transcribe 0.0.7
Convert images or audio files to plain text on the command line
License: MIT
Keywords: ocr transcription speech-to-text speech-recognition command-line-tool

Transcribe
==========

1. *verb* - To convert a representation of language to another
   representation.
2. *noun* - Command line application to extract plain text from images
   and audio files, written in Python.

OCR
---

To extract the text from an image:

::

   $ transcribe ocr /path/to/image.jpg

Speech
------

To extract the text from an audio file:

::

   $ transcribe speech /path/to/audio.mp3

Installation
------------

Google Cloud setup
~~~~~~~~~~~~~~~~~~

Transcribe uses Google’s cloud services to perform the text extraction,
that means you have to setup a Google cloud project. Below is roughly
how I did that…

I went to `The console <https://console.cloud.google.com>`__

Selected *Project* in the top left corner (to the right of *Google Cloud
Platform*) and then create a new project, wait a bit for it to create
the project and switch to it (this took like 20 seconds while I was
trying to figure out what the heck was going on). Then activate the
vision and speech apis (I think storage is automatically activated) and
then select Credentials, and create an api key. `More auth info
here <https://cloud.google.com/vision/docs/common/auth>`__.

Turns out I also needed to `create a service json
file <https://cloud.google.com/storage/docs/authentication#generating-a-private-key>`__.
To do this I needed to go to `the dashboard for the
project <https://console.cloud.google.com/apis/credentials?project=vision-157908>`__
and then click **Create credentials** and choose **Service account
key**.

Then add this to your ``.bash_profile`` or something similar:

::

   export GOOGLE_APPLICATION_CREDENTIALS="/path/to/keyfile.json"

ffmpeg
~~~~~~

If you’re on a Mac and you use Homebrew, you can install ffmpeg like:

::

   $ brew install --with-opus ffmpeg

If you’re not on a Mac or don’t use Homebrew you’re on your own.

.. _transcribe-1:

Transcribe
~~~~~~~~~~

Install using pip:

::

   $ pip install transcribe

or the latest and greatest:

::

   $ pip install "git+https://github.com/Jaymon/transcribe#egg=transcribe"
strava-flyby 0.0.6
Python client for Strava Flyby
License: MIT


# Python client for Strava Flyby

[![PyPI version](https://badge.fury.io/py/strava-flyby.svg)](https://badge.fury.io/py/strava-flyby)
[![Build Status](https://travis-ci.org/sladkovm/strava-flyby.svg?branch=master)](https://travis-ci.org/sladkovm/strava-flyby)


## Install

$ pip install strava-flyby


## Usage

*strava-flyby* provides a convenience object wrapping of the *Strava Labs Flyby API* response. Next to providing a direct access to the unmodified content of the *Flyby API* response, the object will expose number of convenience attributes and methods to access the list of *activity*, *ids*, *matches* and *athletes*.  


Jupyter notebook example with real data is [here](https://github.com/sladkovm/strava-flyby/blob/master/examples/Flyby%20quickstart.ipynb)


Create a *flyby* object using the Strava activity ID with a help of the `flyby()` factory function:
```python
from flyby import flyby
fb = flyby(activity_id=12345)
fb
>>> Flyby object with 17 matches. 
        Attributes: ids, activity, matches, athletes
        Methods: matches_to_list(), matches_to_json(), get_ids()
```

Filter by distance in km and print all matched ids:
```python
print(fb.get_ids(distance=(110, 130)))
```

Dump filtered by distance flattened matches into a list:
```python
print(fb.matches_to_list(distance=120, tol=0.1))
```
azuredlock 0.0.16
azure + redlock
License: MIT
Requires Python: ~=3.5

# azuredlock

azure + redlock onboarding script.

This is mainly just a wrapper around the `az cli` which is a dependency.

The quickest path is to do a `pip install azuredlock` and then
run `azuredlock onboard` from the Azure Cloud Shell which is in the 
Azure Portal since it already has the cli and uses a form of 
Managed Identities so you don't even have to login for it to work.

## Install in Azure Cloud Shell

1.  Use the `bash` shell
2.  Run `pip install --user azuredlock`
3.  Put the newly installed command in your path: `export PATH=$PATH:~/.local/bin`
4.  Run `azuredlock` to see help commands or `azuredlock onboard --help`