MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

racerxdl

#!/usr/bin/env python

'''
 _______     ______  
|_   _\ \   / / ___| 
  | |  \ \ / /\___ \ 
  | |   \ V /  ___) |
  |_|    \_/  |____/ 
Teske Virtual System  

Bonno

ffmpeg -i <infile> -ac 2 -f wav <outfile>

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

shagunsodhani

Pixel Recurrent Neural Network

Introduction

• Problem: Building an expressive, tractable and scalable image model which can be used in downstream tasks like image generation, reconstruction, compression etc.
• Link to the paper

Model

• Scan the image, one row at a time and one pixel at a time (within each row).

hadware

from scipy.io.wavfile import read, write
import io

## This may look a bit intricate/useless, considering the fact that scipy's read() and write() function already return a
## numpy ndarray, but the BytesIO "hack" may be useful in case you get the wav not through a file, but trough some websocket or
## HTTP Post request. This should obviously work with any other sound format, as long as you have the proper decoding function

with open("input_wav.wav", "rb") as wavfile:
    input_wav = wavfile.read()

yasufumy

def argmax1(array):
  return array.index(max(array))

def argmax2(array):
  return max(range(len(array)), key=lambda x: array[x])

tilfin

import cv2
import sys
import os


class FaceCropper(object):
    CASCADE_PATH = "data/haarcascades/haarcascade_frontalface_default.xml"

    def __init__(self):
        self.face_cascade = cv2.CascadeClassifier(self.CASCADE_PATH)

wangg12

from graphviz import Digraph
from torch.autograd import Variable
import torch


def make_dot(var, params=None):
    if params is not None:
        assert isinstance(params.values()[0], Variable)
        param_map = {id(v): k for k, v in params.items()}

awni

"""
Author: Awni Hannun

This is an example CTC decoder written in Python. The code is
intended to be a simple example and is not designed to be
especially efficient.

The algorithm is a prefix beam search for a model trained
with the CTC loss function.