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

theeluwin

import logging

# setting
logger = logging.getLogger('mylog')
log_handler = logging.FileHandler('path/to/file/mylog.log')
log_formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
log_handler.setFormatter(log_formatter)
logger.addHandler(log_handler)
logger.setLevel(logging.DEBUG)

theeluwin

from collections import Counter


def F1(precision, recall):
    denominator = precision + recall
    if not denominator:
        return 0
    return 2 * precision * recall / denominator

theeluwin

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

import numpy as np
import tensorflow as tf

from yellowfin import YFOptimizer
from tensorflow.examples.tutorials.mnist import input_data


def linear(X, fanout, scope=None):

theeluwin

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import division, print_function, unicode_literals

__author__ = "theeluwin"
__email__ = "[email protected]"

import numpy as np

theeluwin

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

from __future__ import unicode_literals

from korean import hangul


def pull(text):
    exploded = [list(hangul.split_char(c)) if hangul.is_hangul(c) else c for c in text]
    for i in range(len(exploded)):

theeluwin

pip freeze --local | grep -v "^\-e" | cut -d = -f 1 | xargs pip install -U