korakot

import google.auth
from google.auth.transport.requests import AuthorizedSession
# login
from google.colab import auth
auth.authenticate_user()
# use default credentials to create session
cred = google.auth.default()[0]   # for cred.token
s = AuthorizedSession(cred)
# s.get or s.post
url = 'https://storage.googleapis.com/storage/v1/b/co-lab'

korakot

def to_sec(tstr):
  """ 1:01:01 -> 3661 """
  t = 0
  for p in tstr.split(':'):
    t *= 60
    t += int(p)
  return t

korakot

from requests import get
!pip install isodate -q
import isodate

key = 'AZxxxx'  # from https://console.cloud.google.com/apis/credentials

def get_durations(id_list):
  url = 'https://www.googleapis.com/youtube/v3/videos'
  kw = {
      'id': ','.join(id_list),

korakot

d = '''
a 1
b 2
c 3
'''.strip().split('\n')
d = dict(row.split() for row in d)
# {'a': '1', 'b': '2', 'c': '3'}

korakot

import pandas as pd
from io import StringIO

data = StringIO('''
symbol,quan,v0
ONE-UGERMF,  71472.71, 21.9749
ONE-UGG-ASSF, 5593.87, 35.7534
1SG-LTF-T,   11523.57, 31.9562
'''.strip())

korakot

# manually install dependencies, then use no-deps
!pip install backtrader croniter cryptography twython yfinance
!pip install fastquant ccxt --no-deps

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!pip install -U plotly

import pandas as pd
pd.options.plotting.backend = "plotly" 

df.col.plot()

# To get log scale
fig = df.col.plot()  # can't logy=True
fig.update_layout(yaxis_type="log")

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!curl -L bit.ly/kenlm-colab | tar xz -C /

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# install
import pandas as pd
!pip install textgrid -q
from textgrid import TextGrid
url = 'https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner/releases/download/v1.1.0-beta.2/montreal-forced-aligner_linux.tar.gz'
!curl -L $url | tar xz --strip-components=2
!wget https://github.com/MontrealCorpusTools/mfa-models/raw/master/acoustic/thai.zip
!mv thai.zip pretrained_models/
!gdown --id 1-61YXBHhuXWmsRsi_QG-3nmCzzD2MlPR
!mv mfa_dict_2.txt dict.txt

korakot

import pandas as pd
!pip install textgrid -q
from textgrid import TextGrid

def read_tg(tg_file):
  tg = TextGrid.fromFile(tg_file)
  data = [(w.minTime, w.maxTime, w.mark) for w in tg[0]]
  left, right, words = zip(*data)
  index = pd.IntervalIndex.from_arrays(left, right, name='time')
  df = pd.DataFrame(words, index, ['word'])