semvec/pypi-packages huggingface dataset:
lrcup 0.10.2
Python module and CLI for the LRCLIB API
Requires Python: >=3.10

# LRCUP

Python CLI and library for interacting with the [LRCLIB.net API](https://lrclib.net/).  
Also includes tools for embedding lyrics, along with general LRC tools.

## Installation

Install via PyPI:
```sh
pip install lrcup
```

## CLI Usage

```sh
# Upload a synced/unsynced LRC file:
lrcup upload example.lrc

# Upload lyrics from an already embedded track:
lrcup upload file.flac

# Embed lyrics into a file:
lrcup embed lyrics.lrc track.flac

# Search for lyrics and download them:
lrcup search never gonna give you up

# Search and embed lyrics for a given folder:
lrcup autosearch --embed /mnt/music/

# Search and embed lyrics for a given folder, also save lrc files:
lrcup autosearch --embed --download /mnt/music/
```

## Module Usage

The class method names are based off of the LRCLIB API endpoints.  
Please refer to them for more information.

```py
from lrcup import LRCLib

lrclib = LRCLib()

# Fetch synced lyrics via search
results = lrclib.search(
    track = "Never Gonna Give You Up",
    artist = "Rick Astley"
)
print(results[0].syncedLyrics)

# Fetch synced lyrics directly
track = lrclib.get(
    track = "Never Gonna Give You Up",
    artist = "Rick Astley",
    album = "Whenever You Need Somebody",
    duration = 215
)
if track is not None:
    print(track.syncedLyrics)

# Publish synced lyrics
lrclib.publish(
    token = lrclib.request_challenge(),
    track = "Never Gonna Give You Up",
    artist = "Rick Astley",
    album = "Whenever You Need Somebody",
    duration = 215,
    plain_lyrics = "*Rickrolling*",
    synced_lyrics = "[00:00.00] *Rickrolling*"
)
```
mezmorize 0.31.0
Adds function memoization support
Requires Python: >=3.10.14
Keywords: memoize, memoization, Flask-Cache, cache

mezmorize
=========

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A python function memoization library heavily inspired by Flask-Cache.

This is a fork of the [Flask-Cache](https://github.com/thadeusb/flask-cache) extension.

Setup
-----

mezmorize is available on PyPI and can be installed with:

    pip install mezmorize

Usage
-----

```python
from random import randrange

from mezmorize import Cache

cache = Cache(CACHE_TYPE='simple')


@cache.memoize(60)
def add(a, b):
    return a + b + randrange(0, 1000)

# Initial
add(2, 5)

# Memoized
add(2, 5)
add(2, 5)

# Delete cache
cache.delete_memoized(add)

# Initial
add(2, 5)
```

For more configuration options, check out the the [examples](https://github.com/reubano/mezmorize/blob/master/examples/hello.py) or [Flask-Caching documentation](https://flask-caching.readthedocs.io).

Compatibility with Flask-Cache and Flask-Caching
-----
There are no known incompatibilities or breaking changes between either the latest [Flask-Cache v0.13](https://github.com/thadeusb/flask-cache)
or [Flask-Caching v1.8.0](https://github.com/sh4nks/flask-caching) and the current version of mezmorize.

Python versions
-----

Starting with version 0.26.0, mezmorize dropped Python 2 support. The library is tested against Python 3.11, 3.12, 3.13, and PyPy 3.10.

Environment Variables
---------------------
- *CACHE_DIR*: the directory your cache will be stored in. The default is the `cache` dir in the current folder.

Links
=====

* [Flask-Caching Documentation](https://flask-caching.readthedocs.io)
* [Source Code](https://github.com/reubano/mezmorize)
* [Issues](https://github.com/reubano/mezmorize/issues)
* [Current Flask-Caching Extension](https://github.com/sh4nks/flask-caching)
* [Original Flask-Cache Extension](https://github.com/thadeusb/flask-cache)
sonusai 1.1.0
Framework for building deep neural network models for sound, speech, and voice AI
Requires Python: <3.12,>=3.11

SonusAI: Framework for simplified creation of deep NN models for sound, speech, and voice AI

SonusAI includes functions for pre-processing training and validation data and
creating performance metrics reports for key types of Keras models:
- recurrent, convolutional, or a combination (i.e. RCNNs)
- binary, multiclass single-label, multiclass multi-label, and regression
- training with data augmentations:  noise mixing, pitch and time stretch, etc.

SonusAI python functions are used by:
 - Aaware Inc. sonusai framework:   Easily create train/validation data, run prediction, evaluate model performance
 - Keras model scripts:             User python scripts for Keras model creation, training, and prediction. These can use sonusai-specific data but also some general useful utilities for training rnn-based models like CRNN's, DSCRNN's, etc. in Keras.
bloxapi 0.1.9
Advanced roblox wrapper for the Roblox API.
Requires Python: >=3.7

# BloxAPI

**Powerful and versatile Python3 wrapper for ROBLOX Web API**

## Installation

```bash
pip install bloxapi
```

## Information

Welcome, and thank you for using BloxAPI!
BloxAPI is an object oriented, asynchronous wrapper for the Roblox Web API (and other Roblox-related APIs) with many new and interesting features.
BloxAPI allows you to automate much of what you would do on the Roblox website and on other Roblox-related websites.

## Get Started

To begin, first import and setup the client, which is the most essential part of BloxAPI, and initialize it like so:

```python
from bloxapi import Client

# Create client instance
client = Client()
```

# Disclaimer
We are not responsible for any malicious use of this library.
If you use this library in a way that violates the Roblox Terms of Use your account may be punished.
If you use code from BloxAPI in your own library, please credit us! We're working our hardest to deliver this library, and crediting us is the best way to help support the project.

# Documentation
You can view documentation for BloxAPI at bloxapi.jmksite.dev.
If something's missing from docs, feel free to dive into the code and read the docstrings as most things are documented there. The docs are generated from docstrings in the code using pdoc3.

# Installation
You can install BloxAPI from pip
```
pip3 install ro.py
```