semvec/pypi-packages huggingface dataset:
quru 0.0.2
A Python workflow framework
Requires Python: >=3.7

# Quru in one sentence
Quru is a python workflow framework to easily swarm up a bunch of workers to streamly process tasks.

# What "Quru" means?
Quru (Chinese 瞿如, pronounce keeru) is a bird-like beast with human face and three feet, initially described in Shan Hai Jing, a classic book that describes mythic geography and beasts. 

![Quru](quru.png "Quru")


# How to run demo
You will need to setup a rabbitmq and redis instances to get Quru running. A docker compose file for quick setup is provided in `demo` folder.
0. Git clone this repo and add the current path to your `PYTHONPATH` environment variables.
1. In your terminal, `cd` to the demo folder.
2. Run `make run-infra` to get rabbitmq and redis running.
3. Run `python worker.py`. This instance starts the worker that handles tasks.
4. Run `python sender.py`. This instance periodically dispatches tasks to worker.
discord-debug 1.0.1
This is can debug for discord.ext

# discord-debug
[![Downloads](https://pepy.tech/badge/discord-debug)](https://pepy.tech/project/discord-debug)
# set up
first you add 
```py 
bot.load_extension("discord_debug")
```
only that!

setting a who is useing command
```py
from discord_debug import discord_debug
discord_debug.log_set(log channel id)
```

# command
`!dd reboot` - It's can be restart

`!dd load [cog name]` - It's can load a cog.

`!dd reload [cog name]` - It's can reload a cog.

`!dd unload [cog name]` - It's can unload a cog.

`!dd status` - It's can see discord bot status

`!dd eval [eval text]` - It's can do eval.

`!dd shell [shell text]` - It's can do shell command.

`!dd update` - It's can do update the discord-debug.
cdpam 0.0.6
A pip package for an improved perceptual audio metric

# Contrastive learning-based Deep Perceptual Audio Metric (CDPAM) [[Webpage]](https://percepaudio.cs.princeton.edu/Manocha20_CDPAM/)

**Contrastive Learning For Perceptual Audio Similarity**

[Pranay Manocha](https://www.cs.princeton.edu/~pmanocha/), [Zeyu Jin](https://research.adobe.com/person/zeyu-jin/), [Richard Zhang](http://richzhang.github.io/), [Adam Finkelstein](https://www.cs.princeton.edu/~af/)   

<img src='https://richzhang.github.io/index_files/audio_teaser.jpg' width=500>

This is a Pytorch implementation of our new and improved audio perceptual metric. It contains (0) minimal code to run our perceptual metric (CDPAM).

## (0) Usage as a loss function

### Minimal basic usage as a distance metric

Running the command below takes two audio files as input and gives the perceptual distance between the files. It should return (approx)**distance = 0.1696**. Some GPU's are non-deterministic, and so the distance could vary in the lsb.

Installing the metric (CDPAM - perceptual audio similarity metric)
```bash
pip install cdpam
```

Using the metric is as simple as: 
```bash
import cdpam
loss_fn = cdpam.DPAM()
wav_ref = cdpam.load_audio('sample_audio/ref.wav')
wav_out = cdpam.load_audio('sample_audio/2.wav')

dist = loss_fn.forward(wav_ref,wav_out)
```