theeluwin

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python

theeluwin

\documentclass[12pt,a4paper]{article}
%\input proof
\usepackage{mathpazo}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{graphicx}
\usepackage{color}
\usepackage{epstopdf}
\usepackage{tabularx,ragged2e,booktabs,caption}

theeluwin

image: docker:stable

before_script:
  - docker login -u gitlab-ci-token -p $CI_JOB_TOKEN $CI_REGISTRY
  - apk update && apk add -y openssh-client
  - eval $(ssh-agent -s)
  - echo "$SSH_PRIVATE_KEY" | tr -d '\r' | ssh-add - > /dev/null
  - mkdir -p ~/.ssh
  - chmod 700 ~/.ssh
  - ssh-keyscan $DEVELOP_SERVER >> ~/.ssh/known_hosts

theeluwin

# https://twitter.com/CppDDoLgi/status/1118731208790138880
# a = 6 b = 3 일 때 2 반환하고
# a = 7 b = 3 일 때 3 반환하는거 구현하고 싶은데

import multiprocessing
import torch.nn as nn

from tqdm import tqdm
from torch import Tensor
from torch.utils.data import Dataset

theeluwin

# -*- coding: utf-8 -*-
# ported from: https://gist.github.com/sclark39/9daf13eea9c0b381667b61e3d2e7bc11


class Shortcoder(object):

    LOWER = 'abcdefghijklmnopqrstuvwxyz'
    UPPER = LOWER.upper()
    NUMBERS = '0123456789'

theeluwin

version: "3"
services:
  registry:
    image: registry:2
    ports:
     - 5000:5000
    volumes:
     - registry-data-local:/var/lib/registry
    restart: always
  portainer:

theeluwin

# origin: https://github.com/fabric/fabric/blob/1.14/fabric/colors.py

import os

def _wrap_with(code):

    def inner(text, bold=False):
        c = code

        if os.environ.get('FABRIC_DISABLE_COLORS'):

theeluwin

# -*- coding: utf-8 -*-

import os
import json
import torch
import pickle
import requests
import numpy as np
import torch as t
import torch.nn as nn

theeluwin

from model import *


class HighwaySpeech(DeepSpeech):

    def __init__(self, rnn_type=nn.LSTM, labels="abc", rnn_hidden_size=1024, nb_layers=3, audio_conf=None, bidirectional=True):
        super(HighwaySpeech, self).__init__()
        # fixed-values
        rnn_type = nn.LSTM
        nb_layers = 3

theeluwin

for cdn