thepycoach

transcript_endpoint = "https://api.assemblyai.com/v2/transcript"

response = requests.post(transcript_endpoint,
                         headers=headers,
                         json={
                             "audio_url": audio_url,
                             "sentiment_analysis": True,
                         })
transcript_id = response.json()['id']

thepycoach

# STEP 1: UPLOAD YOUR AUDIO FILE AND GET URL
import requests

filename = '/Users/frankandrade/Desktop/Steve Jobs.mp3'  # Your path goes here
api_key =  # Your API key goes here
upload_endpoint = 'https://api.assemblyai.com/v2/upload'

def read_file(filename, chunk_size=5242880):
    with open(filename, 'rb') as f:
        while True:

thepycoach

# STEP 3: SAVE THE TRANSCRIPT AND SUMMARY
import os
import sys
import time
import json

polling_endpoint =  os.path.join(transcript_endpoint, transcript_id)

status = ''
while status != 'completed':

thepycoach

transcript_endpoint = "https://api.assemblyai.com/v2/transcript"

response = requests.post(transcript_endpoint,
                         headers=headers,
                         json={
                             "audio_url": audio_url,
                             "auto_chapters": True
                         })
transcript_id = response.json()['id']

thepycoach

# STEP 1: UPLOAD YOUR AUDIO FILE AND GET URL
import requests

filename = '/Users/frankandrade/Desktop/Steve Jobs.mp3'  # Your path goes here
api_key =  # Your API key goes here
upload_endpoint = 'https://api.assemblyai.com/v2/upload'

def read_file(filename, chunk_size=5242880):
    with open(filename, 'rb') as f:
        while True:

thepycoach

import websockets
import asyncio
import base64
import json
from configure import auth_key
 
# the AssemblyAI endpoint we're going to hit
URL = "wss://api.assemblyai.com/v2/realtime/ws?sample_rate=16000"
 
async def send_receive():

thepycoach

import pyaudio
 
FRAMES_PER_BUFFER = 3200
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
p = pyaudio.PyAudio()
 
# starts recording
stream = p.open(

thepycoach

# Imported StudentsPerformance_id.csv
import pandas as pd
StudentsPerformance_id_csv = pd.read_csv(r'StudentsPerformance_id.csv')
# Imported LanguageScore.csv
import pandas as pd
LanguageScore_csv = pd.read_csv(r'LanguageScore.csv')
# Merged LanguageScore_csv and StudentsPerformance_id_csv
temp_df = StudentsPerformance_id_csv.drop_duplicates(subset='id') # Remove duplicates so lookup merge only returns first match
df3 = LanguageScore_csv.merge(temp_df, left_on=['id'], right_on=['id'], how='left', suffixes=['_LanguageScore_csv', '_StudentsPerformance_id_csv'])

thepycoach

# Imported StudentsPerformance.csv
import pandas as pd
StudentsPerformance_csv = pd.read_csv(r'StudentsPerformance.csv')
# Pivoted StudentsPerformance_csv into df2
unused_columns = StudentsPerformance_csv.columns.difference(set(['race/ethnicity']).union(set([])).union(set({'math score', 'reading score'})))
tmp_df = StudentsPerformance_csv.drop(unused_columns, axis=1)
pivot_table = tmp_df.pivot_table(
    index=['race/ethnicity'],
    values=['math score', 'reading score'],
    aggfunc={'math score': ['mean'], 'reading score': ['mean']}

thepycoach

# Filtered gender in StudentsPerformance_csv
StudentsPerformance_csv = StudentsPerformance_csv[StudentsPerformance_csv['gender'] == 'female']
# Filtered race/ethnicity in StudentsPerformance_csv
StudentsPerformance_csv = StudentsPerformance_csv[StudentsPerformance_csv['race/ethnicity'] == 'group B']