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conda install -c conda-forge jupyter_contrib_nbextensions -y
# pip install jupyter jupyter_contrib_nbextensions
# jupyter contrib nbextension install --user


jupyter nbextension enable toc2/main
jupyter nbextension enable execute_time/ExecuteTime
jupyter nbextension enable python-markdown/main
jupyter nbextension enable scratchpad/main
jupyter nbextension enable move_selected_cells/main

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wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-5.2.0-Linux-x86_64.sh
sh Anaconda3-5.2.0-Linux-x86_64.sh

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#!/bin/sh

# Warning: This script is untested. (copy from command history)

# Install
curl -fsSL get.docker.com -o get-docker.sh | exit -1
sudo sh get-docker.sh | exit -1

# Add user to docker group (sudoless)
sudo gpasswd -a ${USER} docker | exit -1

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deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial main multiverse restricted universe
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-backports main multiverse restricted universe
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-proposed main multiverse restricted universe
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-security main multiverse restricted universe
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-updates main multiverse restricted universe
deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial main multiverse restricted universe
deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-backports main multiverse restricted universe
deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-proposed main multiverse restricted universe
deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-security main multiverse restricted universe
deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ xenial-updates main multiverse restricted universe

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from sklearn import metrics
from scipy.optimize import brentq
from scipy.interpolate import interp1d
def cal_eer(score_true, score_false):
    """ 计算EER

    Args:
    scores_true: 正样例的分数列表
    scores_false: 负样例的分数列表

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# coding=utf-8

import os
from multiprocessing.dummy import Pool


def can_ping(ip):
    ret = os.system('ping -n 1 -w 1 %s > nul' % ip)
    can_ping = True if ret == 0 else False
    return can_ping, ip

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#ifndef __PYTHON_LIKE__
#define __PYTHON_LIKE__

#include <stdio.h>
#include <string>
#include <fstream>
#include <streambuf>
#include <vector>
#include <iostream>
#include <stdarg.h>

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import wave
import os


def is_wav(f):
    res = True
    try:
        wave.open(f)
    except wave.Error as e:
        res = False

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import librosa
import wave
from pathlib import Path
import numpy as np
import os

all_in_one_wav = "@all_in_one.wav"
all_in_one_wav_info = "@all_in_one.txt"
all_in_one_label = "@all_in_one_vad.txt"
label_file = "@label.txt"

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import bob.io.audio
import bob.kaldi # https://www.idiap.ch/software/bob/docs/bob/docs/stable/bob/doc/install.html
import matplotlib.pyplot as plt

def vad(sig, sr, vad_energy_mean_scale=0.5, vad_energy_th=9, vad_frames_context=20, vad_proportion_th=0.4):
    """ Energy Based Voice Activate Detection algorithm. (based on kaldi)
        Param:
            sig: list or np.array
                the signal, list of samples
            sr: int