DataAPI.MktBonddGet(secID=u"",ticker=u"122560",tradeDate=u"",beginDate=u"20180301",endDate=u"20180315",field=u"",pandas="1")

 tickerBond  closePriceBond  bondPremRatio secShortNameBond tickerEqu  \

secID                                                                         

110066     110066          199.94        -1.2442             盛屯转债    600711   

110067     110067          119.53        25.9204             华安转债    600909   

113021     113021          105.81        45.0989             中信转债    601998   

113024     113024          101.94        36.6668             核建转债    601611   

113025     113025          129.16         0.0409             明泰转债    601677

        ROE tickerEqu

0  2.642931    600711

1  4.425438    600909

2  6.259092    601998

3  4.432315    601611

4  6.454054    601677

先 df1 = df1.reset_index()，合并之后再把 secID 那一列设为 index 。

def initialize(context):

    # 初始化此策略

    # 设置我们要操作的股票池, 这里我们只操作一支股票

    g.security = '600570.SS'

    set_universe(g.security)

    #每天9:23分运行集合竞价处理函数

    run_daily(context, aggregate_auction_func, time='9:23')  



def aggregate_auction_func(context):

    stock = g.security

    #最新价

    snapshot = get_snapshot(stock)

    price = snapshot[stock]['last_px']

    #涨停价

    up_limit = snapshot[stock]['up_px']

    #如果最新价不小于涨停价，买入

    if float(price) >= float(up_limit):

        order(g.security, 100, limit_price=up_limit)

    

def handle_data(context, data):

    pass

def initialize(context):

    # 初始化此策略

    # 设置我们要操作的股票池, 这里我们只操作一支股票

    g.security = '600570.SS'

    set_universe(g.security)

    #每3秒运行一次主函数

    run_interval(context, func, seconds=3)

      

#盘前准备历史数据

def before_trading_start(context, data):

    history = get_history(10, '1d', 'close', g.security, fq='pre', include=False)

    g.close_array = history['close'].values

    

#当五日均线高于十日均线时买入，当五日均线低于十日均线时卖出

def func(context):

    

    stock = g.security

    

    #获取最新价

    snapshot = get_snapshot(stock)

    price = snapshot[stock]['last_px']

    

    # 得到五日均线价格

    days = 5

    ma5 = get_MA_day(stock, days, g.close_array[-4:], price)   

    # 得到十日均线价格

    days = 10

    ma10 = get_MA_day(stock, days, g.close_array[-9:], price)



    # 得到当前资金余额

    cash = context.portfolio.cash

    

    # 如果当前有余额，并且五日均线大于十日均线

    if ma5 > ma10:

        # 用所有 cash 买入股票

        order_value(stock, cash)

        # 记录这次买入

        log.info("Buying %s" % (stock))

        

    # 如果五日均线小于十日均线，并且目前有头寸

    elif ma5 < ma10 and get_position(stock).amount > 0:

        # 全部卖出

        order_target(stock, 0)

        # 记录这次卖出

        log.info("Selling %s" % (stock))    



#计算实时均线函数

def get_MA_day(stock,days,close_array,current_price):

    close_sum = close_array[-(days-1):].sum()

    MA = (current_price + close_sum)/days

    return MA



def handle_data(context, data):

    pass

def initialize(context):

    # 初始化此策略

    # 设置我们要操作的股票池, 这里我们只操作一支股票

    g.security = '600570.SS'

    set_universe(g.security)

    pass

    

#当五日均线高于十日均线时买入，当五日均线低于十日均线时卖出

def handle_data(context, data):

    security = g.security



    #得到十日历史价格

    df = get_history(10, '1d', 'close', security, fq=None, include=False)

    

    # 得到五日均线价格

    ma5 = round(df['close'][-5:].mean(), 3)

    

    # 得到十日均线价格

    ma10 = round(df['close'][-10:].mean(), 3)



    # 取得昨天收盘价

    price = data[security]['close']



    # 得到当前资金余额

    cash = context.portfolio.cash

    

    # 如果当前有余额，并且五日均线大于十日均线

    if ma5 > ma10:

        # 用所有 cash 买入股票

        order_value(security, cash)

        # 记录这次买入

        log.info("Buying %s" % (security))

        

    # 如果五日均线小于十日均线，并且目前有头寸

    elif ma5 < ma10 and get_position(security).amount > 0:

        # 全部卖出

        order_target(security, 0)

        # 记录这次卖出

        log.info("Selling %s" % (security))

def f_expma(N,m,EXPMA1,price):

    a = m/(N+1)

    EXPMA2 = a * price + (1 - a)*EXPMA1

    return EXPMA2 #2为后一天值



#定义macd函数，输入平滑系数参数、前一日值，输出当日值

def macd(N1,N2,N3,m,EXPMA12_1,EXPMA26_1,DEA1,price):

    EXPMA12_2 = f_expma(N1,m,EXPMA12_1,price)

    EXPMA26_2 = f_expma(N2,m,EXPMA26_1,price)

    DIF2 = EXPMA12_2 - EXPMA26_2

    a = m/(N3+1)

    DEA2 = a * DIF2 + (1 - a)*DEA1

    BAR2=2*(DIF2-DEA2)

    return EXPMA12_2,EXPMA26_2,DIF2,DEA2,BAR2



def initialize(context):

    global init_price

    init_price = None

    # 获取沪深300股票

    g.security = get_index_stocks('000300.SS')

    #g.security = ['600570.SS']

    # 设置我们要操作的股票池, 这里我们只操作一支股票

    set_universe(g.security)



def handle_data(context, data):

    # 获取历史数据，这里只获取了2天的数据，如果希望最终MACD指标结果更准确最好是获取

    # 从股票上市至今的所有历史数据，即增加获取的天数

    close_price = get_history(2, '1d', field='close', security_list=g.security)

    #如果是停牌不进行计算

    for security in g.security:

        if data[security].is_open >0:

            global init_price,EXPMA12_1,EXPMA26_1,EXPMA12_2,EXPMA26_2,DIF1,DIF2,DEA1,DEA2

            if init_price is None:

                init_price = close_price[security].mean()#nan和N-1个数，mean为N-1个数的均值

                EXPMA12_1 = init_price

                EXPMA26_1 = init_price

                DIF1 = init_price

                DEA1 = init_price

            # m用于计算平滑系数a=m/(N+1)

            m = 2.0

            #设定指数平滑基期数

            N1 = 12

            N2 = 26

            N3 = 9

            EXPMA12_2,EXPMA26_2,DIF2,DEA2,BAR2 = macd(N1,N2,N3,m,EXPMA12_1,EXPMA26_1,DEA1,close_price[security][-1])

            # 取得当前价格

            current_price = data[security].price

            # 取得当前的现金

            cash = context.portfolio.cash

            # DIF、DEA均为正，DIF向上突破DEA，买入信号参考

            if DIF2 > 0 and DEA2 > 0 and DIF1 < DEA1 and DIF2 > DEA2:

                # 计算可以买多少只股票

                number_of_shares = int(cash/current_price)

                # 购买量大于0时，下单

                if number_of_shares > 0:

                    # 以市单价买入股票，日回测时即是开盘价

                    order(security, +number_of_shares)

                    # 记录这次买入

                    log.info("Buying %s" % (security))

            # DIF、DEA均为负，DIF向下突破DEA，卖出信号参考

            elif DIF2 < 0 and DEA2 < 0 and DIF1 > DEA1 and DIF2 < DEA2 and get_position(security).amount > 0:

                # 卖出所有股票,使这只股票的最终持有量为0

                order_target(security, 0)

                # 记录这次卖出

                log.info("Selling %s" % (security))

            # 将今日的值赋给全局变量作为下一次前一日的值

            DEA1 = DEA2

            DIF1 = DIF2

            EXPMA12_1 = EXPMA12_2

            EXPMA26_1 = EXPMA26_2

    browser = await pyppeteer.launch(

        {'headless': False,

         'userDataDir': UserDataDir,

         'defaultViewport': {'width': 1800, 'height': 1000},

         # 'enable-automation':False,

         # 'ignoreDefaultArgs':['--enable-automation'],

         'ignoreDefaultArgs':True,

         }

    ERROR: Command errored out with exit status 1:

     command: 'C:\anaconda\python.exe' -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\xda\\AppData\

\Local\\Temp\\pip-install-ftotbzih\\peewee\\setup.py'"'"'; __file__='"'"'C:\\Users\\xda\\AppData\\Local\\Temp\\pip-insta

ll-ftotbzih\\peewee\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"

'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base 'C:\Users\xda\AppData\Lo

cal\Temp\pip-pip-egg-info-8ou7yi3i'

         cwd: C:\Users\xda\AppData\Local\Temp\pip-install-ftotbzih\peewee\

    Complete output (15 lines):

    Traceback (most recent call last):

      File "<string>", line 1, in <module>

      File "C:\Users\xda\AppData\Local\Temp\pip-install-ftotbzih\peewee\setup.py", line 99, in <module>

        elif not _have_sqlite_extension_support():

      File "C:\Users\xda\AppData\Local\Temp\pip-install-ftotbzih\peewee\setup.py", line 76, in _have_sqlite_extension_su

pport

        compiler.compile([src_file], output_dir=tmp_dir),

      File "C:\anaconda\lib\distutils\_msvccompiler.py", line 327, in compile

        self.initialize()

      File "C:\anaconda\lib\distutils\_msvccompiler.py", line 224, in initialize

        vc_env = _get_vc_env(plat_spec)

      File "C:\anaconda\lib\site-packages\setuptools\msvc.py", line 314, in msvc14_get_vc_env

        return _msvc14_get_vc_env(plat_spec)

      File "C:\anaconda\lib\site-packages\setuptools\msvc.py", line 273, in _msvc14_get_vc_env

        out = subprocess.check_output(

    AttributeError: 'str' object has no attribute 'decode'

    ----------------------------------------

ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.

        out = subprocess.check_output(

            'cmd /u /c "{}" {} && set'.format(vcvarsall, plat_spec),

            stderr=subprocess.STDOUT,

        )

        # ).decode('utf-16le', errors='replace')

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

# @Time : 2021/4/24 20:50

# @File : baidu_voice_service.py

# @Author : Rocky C@www.30daydo.com



import os

import time



import requests

import sys

import pickle

sys.path.append('..')

from config import API_KEY,SECRET_KEY

from base64 import b64encode

from pathlib import PurePath

import subprocess



BASE = PurePath(__file__).parent



# 需要识别的文件

# 文件格式

# 文件后缀只支持 pcm/wav/amr 格式，极速版额外支持m4a 格式



CUID = '24057753' # 随意

# 采样率

RATE = 16000  # 固定值





ASR_URL = 'http://vop.baidu.com/server_api'



#测试自训练平台需要打开以下信息， 自训练平台模型上线后，您会看见 第二步：“”获取专属模型参数pid:8001，modelid:1234”，按照这个信息获取 dev_pid=8001，lm_id=1234

'''

http://vop.baidu.com/server_api

1537	普通话(纯中文识别)	输入法模型	有标点	支持自定义词库

1737	英语	英语模型	无标点	不支持自定义词库

1637	粤语	粤语模型	有标点	不支持自定义词库

1837	四川话	四川话模型	有标点	不支持自定义词库

1936	普通话远场

'''

DEV_PID = 1737



SCOPE = 'brain_enhanced_asr'  # 有此scope表示有asr能力，没有请在网页里开通极速版





class DemoError(Exception):

    pass





TOKEN_URL = 'http://openapi.baidu.com/oauth/2.0/token'



def fetch_token():



    params = {'grant_type': 'client_credentials',

              'client_id': API_KEY,

              'client_secret': SECRET_KEY}

    r = requests.post(

        url=TOKEN_URL,

        data=params

    )



    result = r.json()

    if ('access_token' in result.keys() and 'scope' in result.keys()):

        if SCOPE and (not SCOPE in result['scope'].split(' ')):  # SCOPE = False 忽略检查

            raise DemoError('scope is not correct')



        return result['access_token']



    else:

        raise DemoError('MAYBE API_KEY or SECRET_KEY not correct: access_token or scope not found in token response')





"""  TOKEN end """



def dump_token(token):

    with open(os.path.join(BASE,'token.pkl'),'wb') as fp:

        pickle.dump({'token':token},fp)



def load_token(filename):



    if not os.path.exists(filename):

        token=fetch_token()

        dump_token(token)

        return token

    else:

        with open(filename,'rb') as fp:

            token = pickle.load(fp)

            return token['token']



def recognize_service(token,filename):

    FORMAT = filename[-3:]

    with open(filename, 'rb') as speech_file:

        speech_data = speech_file.read()



    length = len(speech_data)

    if length == 0:

        raise DemoError('file %s length read 0 bytes' % filename)



    b64_data = b64encode(speech_data)

    params = {'cuid': CUID, 'token': token, 'dev_pid': DEV_PID,'speech':b64_data,'len':length,'format':FORMAT,'rate':RATE,'channel':1}



    headers = {

        'Content-Type':'application/json',

    }

    r = requests.post(url=ASR_URL,json=params,headers=headers)

    return r.json()



def rate_convertor(filename):

    filename = filename.split('.')[0]

    CMD=f'ffmpeg.exe -y -i {filename}.mp3 -ac 1 -ar 16000 {filename}.wav'

    try:

        p=subprocess.Popen(CMD, stdin=subprocess.PIPE)

        p.communicate()

        time.sleep(1)

    except Exception as e:

        print(e)

        return False,None

    else:

        return True,f'{filename}.wav'



def clear(file):

    try:

        os.remove(file)

    except Exception as e:

        print(e)



def get_voice_text(audio_file):



    filename = 'token.pkl'

    token = load_token(filename)

    convert_status,file = rate_convertor(audio_file)

    clear(file)

    if not convert_status:

        return None



    result = recognize_service(token,file)



    return result['result'][0]



if __name__ == '__main__':

    get_voice_text('1.mp3')

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

# @Time : 2021/4/25 15:16

# @File : download_mp3.py

# @Author : Rocky C@www.30daydo.com







#!/usr/bin/env python3

import os

import subprocess

import time

import re

import requests

import urllib.request

import zipfile

import io

from google.cloud import speech_v1

from random import randint, uniform

from bs4 import BeautifulSoup

from selenium import webdriver

from selenium.common.exceptions import NoSuchElementException

from selenium.webdriver.common.by import By

from selenium.webdriver.common.action_chains import ActionChains

from selenium.webdriver.support import expected_conditions as EC

from selenium.webdriver.support.ui import WebDriverWait

from google_recaptcha.baidu_voice_service import get_voice_text,clear





class Gcaptcha:



    def __init__(self, url):

        self.response = None



        self.attempts = 0

        self.successful = 0

        self.failed = 0

        self.solved = 0



        self.mp3 = 'audio.mp3'

        # self.wav='audio.wav'



        # Set Chrome to run in headless mode and mute the audio

        opts = webdriver.ChromeOptions()

        # opts.headless = True

        opts.add_argument("--mute-audio")

        # opts.add_argument("--headless",)



      

        CHROME_PATH = r'C:\git\EZProject\bin\chromedriver.exe'

        self.driver = webdriver.Chrome(executable_path=CHROME_PATH,options=opts)

        self.driver.maximize_window()

        self.driver.get(url)

        self.__bypass_webdriver_check()



        # Initialize gcaptcha solver

        self.__initialize()



        while True:

            # Download MP3 file

            mp3_file = self.__download_mp3()



            # Transcribe MP3 file

            result = get_voice_text(self.mp3)



            # audio_transcription = transcribe(mp3_file)

            # self.transcription.attempts += 1



            # If the MP3 file is properly transcribed

            if result is not None:

                # self.transcription.successful += 1



                # Verify transcription

                verify = self.__submit_transcription(result)



                # Transcription successful with confidence >60%

                if verify:

                    gcaptcha_response = self.__get_response()

                    self.response = gcaptcha_response

                    # self.recaptcha.solved += 1



                    # Delete MP3 file



                    self.driver.close()

                    self.driver.quit()

                    break

                # Multiple correct solutions required. Solving again.

                else:

                    self.solved += 1

                    clear(self.mp3)



            # If the MP3 file could not be transcribed

            else:

                self.failed += 1

                clear(self.mp3)



                # Click on the "Get a new challenge" button to use a new MP3 file

                self.__refresh_mp3()

                # time.sleep(uniform(2, 4))







    def __initialize(self):

        # Access initial gcaptcha iframe

        self.driver.switch_to.frame(self.driver.find_element(By.CSS_SELECTOR, 'iframe[name^=a]'))

        self.__bypass_webdriver_check()



        # Click the gcaptcha checkbox

        checkbox = self.driver.find_element(By.CSS_SELECTOR, '#recaptcha-anchor')

        self.__mouse_click(checkbox)



        # Go back to original content to access second gcaptcha iframe

        self.driver.switch_to.default_content()



        # Wait roughly 3 seconds for second gcaptcha iframe to load

        time.sleep(uniform(2.5, 3))



        # Find second gcaptcha iframe

        gcaptcha = WebDriverWait(self.driver, 10).until(

            EC.presence_of_element_located((By.CSS_SELECTOR, 'iframe[name^=c]'))

        )



        # Access second gcaptcha iframe

        self.driver.switch_to.frame(gcaptcha)

        self.__bypass_webdriver_check()



        # Click the audio button

        audio_button = WebDriverWait(self.driver, 10).until(

            EC.presence_of_element_located((By.CSS_SELECTOR, '.rc-button-audio'))

        )

        self.__mouse_click(audio_button)

        time.sleep(0.5)



    def __mouse_click(self, element):

        cursor = ActionChains(self.driver)

        cursor.move_to_element(element)

        cursor.pause(uniform(0.3, 0.5))

        cursor.click()

        cursor.perform()



    def __bypass_webdriver_check(self):

        self.driver.execute_script(

            'const newProto = navigator.__proto__; delete newProto.webdriver; navigator.__proto__ = newProto;')



    def __download_mp3(self):

        self.driver.switch_to.default_content()

        self.driver.switch_to.frame(self.driver.find_element(By.CSS_SELECTOR, 'iframe[name^=c]'))

        self.__bypass_webdriver_check()



        # Check if the Google servers are blocking us

        if len(self.driver.find_elements(By.CSS_SELECTOR, '.rc-doscaptcha-body-text')) == 0:

            audio_file = WebDriverWait(self.driver, 10).until(

                EC.presence_of_element_located((By.CSS_SELECTOR, '.rc-audiochallenge-tdownload-link'))

            )



            # Click the play button

            play_button = WebDriverWait(self.driver, 10).until(

                EC.presence_of_element_located((By.CSS_SELECTOR, '.rc-audiochallenge-play-button > button'))

            )

            self.__mouse_click(play_button)



            # Get URL of MP3 file

            audio_url = audio_file.get_attribute('href')



            # Predefine the MP3 file name



            # Download the MP3 file

            try:

                urllib.request.urlretrieve(audio_url, self.mp3)

            except Exception as e:

                print(e)

                return None

            else:

                return self.mp3

        else:

            Error('Too many requests have been sent to Google. You are currently being blocked by their servers.')

            exit(-1)



    def __refresh_mp3(self):

        self.driver.switch_to.default_content()

        self.driver.switch_to.frame(self.driver.find_element(By.CSS_SELECTOR, 'iframe[name^=c]'))

        self.__bypass_webdriver_check()



        # Click on the refresh button to retrieve a new mp3 file

        refresh_button = self.driver.find_element(By.CSS_SELECTOR, '#recaptcha-reload-button')

        self.__mouse_click(refresh_button)



    def __submit_transcription(self, text):

        self.driver.switch_to.default_content()

        self.driver.switch_to.frame(self.driver.find_element(By.CSS_SELECTOR, 'iframe[name^=c]'))

        self.__bypass_webdriver_check()



        # Input field for response

        input_field = self.driver.find_element(By.CSS_SELECTOR, '#audio-response')



        # Instantly type the full text without delays because Google isn't checking delays between keystrokes

        input_field.send_keys(text)



        # Click "Verify" button

        verify_button = self.driver.find_element(By.CSS_SELECTOR, '#recaptcha-verify-button')

        self.__mouse_click(verify_button)



        # Wait roughly 3 seconds for verification to complete

        time.sleep(uniform(2, 3))



        self.driver.switch_to.default_content()

        self.driver.switch_to.frame(self.driver.find_element(By.CSS_SELECTOR, 'iframe[name^=a]'))

        self.__bypass_webdriver_check()



        # Check to see if verified by recaptcha

        try:

            self.driver.find_element(By.CSS_SELECTOR, '.recaptcha-checkbox-checked')

        except NoSuchElementException:

            return False

        else:

            return True



    def __get_response(self):

        # Switch back to main parent window and get gcaptcha response

        self.driver.switch_to.default_content()

        response = self.driver.find_element(By.CSS_SELECTOR, '#g-recaptcha-response').get_attribute('value')

        return response





class Error(Exception):

    def __init__(self, message):

        get_files = os.listdir()

        match_regex = re.compile(r'^audio\d+.mp3|chromedriver_\w+\d+.zip$')

        filtered_files = [f for f in get_files if match_regex.match(f)]



        for file in filtered_files:

            clear(file)



        raise Exception(f'ERROR: {message}')





if __name__=='__main__':

    gcaptcha = Gcaptcha('https://www.google.com/recaptcha/api2/demo')

ffmpeg.exe -i 3.mp3 -ac 1 -ar 16000 16k.wav

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

# @Time : 2021/4/24 20:50

# @File : baidu_voice_service.py

# @Author : Rocky C@www.30daydo.com



import os

import requests

import sys

import pickle

sys.path.append('..')

from config import API_KEY,SECRET_KEY

from base64 import b64encode

from pathlib import PurePath





BASE = PurePath(__file__).parent



# 需要识别的文件

AUDIO_FILE = r'C:\OtherGit\speech-demo\rest-api-asr\python\audio\2.m4a'  # 只支持 pcm/wav/amr 格式，极速版额外支持m4a 格式

# 文件格式

FORMAT = AUDIO_FILE[-3:]  # 文件后缀只支持 pcm/wav/amr 格式，极速版额外支持m4a 格式



CUID = '24057753'

# 采样率

RATE = 16000  # 固定值





ASR_URL = 'http://vop.baidu.com/server_api'



#测试自训练平台需要打开以下信息， 自训练平台模型上线后，您会看见 第二步：“”获取专属模型参数pid:8001，modelid:1234”，按照这个信息获取 dev_pid=8001，lm_id=1234

'''

http://vop.baidu.com/server_api

1537	普通话(纯中文识别)	输入法模型	有标点	支持自定义词库

1737	英语	英语模型	无标点	不支持自定义词库

1637	粤语	粤语模型	有标点	不支持自定义词库

1837	四川话	四川话模型	有标点	不支持自定义词库

1936	普通话远场

'''

DEV_PID = 1737



SCOPE = 'brain_enhanced_asr'  # 有此scope表示有asr能力，没有请在网页里开通极速版





class DemoError(Exception):

    pass





TOKEN_URL = 'http://openapi.baidu.com/oauth/2.0/token'



def fetch_token():



    params = {'grant_type': 'client_credentials',

              'client_id': API_KEY,

              'client_secret': SECRET_KEY}

    r = requests.post(

        url=TOKEN_URL,

        data=params

    )



    result = r.json()

    if ('access_token' in result.keys() and 'scope' in result.keys()):

        if SCOPE and (not SCOPE in result['scope'].split(' ')):  # SCOPE = False 忽略检查

            raise DemoError('scope is not correct')



        return result['access_token']



    else:

        raise DemoError('MAYBE API_KEY or SECRET_KEY not correct: access_token or scope not found in token response')





"""  TOKEN end """



def dump_token(token):

    with open(os.path.join(BASE,'token.pkl'),'wb') as fp:

        pickle.dump({'token':token},fp)



def load_token(filename):



    if not os.path.exists(filename):

        token=fetch_token()

        dump_token(token)

        return token

    else:

        with open(filename,'rb') as fp:

            token = pickle.load(fp)

            return token['token']



def recognize_service(token,filename):



    with open(filename, 'rb') as speech_file:

        speech_data = speech_file.read()



    length = len(speech_data)

    if length == 0:

        raise DemoError('file %s length read 0 bytes' % AUDIO_FILE)



    b64_data = b64encode(speech_data)

    params = {'cuid': CUID, 'token': token, 'dev_pid': DEV_PID,'speech':b64_data,'len':length,'format':FORMAT,'rate':RATE,'channel':1}



    headers = {

        'Content-Type':'application/json',

    }

    r = requests.post(url=ASR_URL,json=params,headers=headers)

    return r.json()





def main():

    filename = 'token.pkl'

    token = load_token(filename)

    result = recognize_service(token,AUDIO_FILE)

    print(result['result'])



if __name__ == '__main__':

    main()