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alexlyzhov / whisper_with_vad.py
Created January 24, 2023 04:04
Whisper to json
# based on https://github.com/ANonEntity/WhisperWithVAD
import torch
import whisper
import os
import ffmpeg
import srt
from tqdm import tqdm
import datetime
import urllib.request
@alexlyzhov
alexlyzhov / create_subs.py
Created January 24, 2023 04:00
Whisper json processing
# %%
import os
import sys
import json
import datetime
import numpy as np
from tqdm import tqdm
from glob import glob
import argparse
import shutil
If Biden wins
538 Brier 0.1525, the Econ comment Brier 0.1502, the Econ csv brier 0.1502, (comment Brier - csv Brier) -0.000002 538-comment_Economist 0.0023 538-csv_Economist 0.0023
If Biden wins PA
538 Brier 0.1233, the Econ comment Brier 0.1164, the Econ csv brier 0.1164, (comment Brier - csv Brier) -0.000002 538-comment_Economist 0.0069 538-csv_Economist 0.0069
If Biden wins NV
538 Brier 0.1233, the Econ comment Brier 0.1162, the Econ csv brier 0.1162, (comment Brier - csv Brier) -0.000005 538-comment_Economist 0.0071 538-csv_Economist 0.0071
If Biden wins NV PA
538 Brier 0.0940, the Econ comment Brier 0.0824, the Econ csv brier 0.0824, (comment Brier - csv Brier) -0.000005 538-comment_Economist 0.0117 538-csv_Economist 0.0117
If Biden wins NC
538 Brier 0.1411, the Econ comment Brier 0.1386, the Econ csv brier 0.1386, (comment Brier - csv Brier) 0.000002 538-comment_Economist 0.0025 538-csv_Economist 0.0025
If Biden wins
538 Brier 0.1525, the Econ comment Brier 0.1502, the Econ csv brier 0.1502, (comment-spreadsheet) -0.000002 538-comment_Economist 0.0023 538-csv_Economist 0.0023
If Biden wins PA
538 Brier 0.1233, the Econ comment Brier 0.1164, the Econ csv brier 0.1164, (comment-spreadsheet) -0.000002 538-comment_Economist 0.0069 538-csv_Economist 0.0069
If Biden wins NV
538 Brier 0.1233, the Econ comment Brier 0.1162, the Econ csv brier 0.1162, (comment-spreadsheet) -0.000005 538-comment_Economist 0.0071 538-csv_Economist 0.0071
If Biden wins NV PA
538 Brier 0.0940, the Econ comment Brier 0.0824, the Econ csv brier 0.0824, (comment-spreadsheet) -0.000005 538-comment_Economist 0.0117 538-csv_Economist 0.0117
If Biden wins NC
538 Brier 0.1411, the Econ comment Brier 0.1386, the Econ csv brier 0.1386, (comment-spreadsheet) 0.000002 538-comment_Economist 0.0025 538-csv_Economist 0.0025
If Biden wins
538 Brier 0.1525, the Econ comment Brier 0.1502, the Econ spreadsheet brier 0.1502, (comment-spreadsheet) -0.000002 538-Economist 0.0023
If Biden wins PA
538 Brier 0.1233, the Econ comment Brier 0.1164, the Econ spreadsheet brier 0.1164, (comment-spreadsheet) -0.000002 538-Economist 0.0069
If Biden wins NV
538 Brier 0.1233, the Econ comment Brier 0.1162, the Econ spreadsheet brier 0.1162, (comment-spreadsheet) -0.000005 538-Economist 0.0071
If Biden wins NV PA
538 Brier 0.0940, the Econ comment Brier 0.0824, the Econ spreadsheet brier 0.0824, (comment-spreadsheet) -0.000005 538-Economist 0.0117
If Biden wins NC
538 Brier 0.1411, the Econ comment Brier 0.1386, the Econ spreadsheet brier 0.1386, (comment-spreadsheet) 0.000002 538-Economist 0.0025
If Biden wins no additional states
538 Brier 0.1525, the Economist Brier 0.1502, 538-Economist 0.0023
If Biden wins PA
538 Brier 0.1233, the Economist Brier 0.1164, 538-Economist 0.0069
If Biden wins NV
538 Brier 0.1233, the Economist Brier 0.1162, 538-Economist 0.0071
If Biden wins NV PA
538 Brier 0.0940, the Economist Brier 0.0824, 538-Economist 0.0117
If Biden wins NC
538 Brier 0.1411, the Economist Brier 0.1386, 538-Economist 0.0025
import pandas as pd
import itertools
scores = {'AK': 0, 'AL': 0, 'AR': 0, 'AZ': 0.5, 'CA': 1, 'CO': 1, 'CT': 1, 'DC': 1, 'DE': 1, 'FL': 0, 'GA': 0.5, 'HI': 1, 'IA': 0, 'ID': 0, 'IL': 1, 'IN': 0, 'KS': 0, 'KY': 0, 'LA': 0, 'MA': 1, 'MD': 1, 'ME': 1, 'MI': 1, 'MN': 1, 'MO': 0, 'MS': 0, 'MT': 0, 'NC': 0.5, 'ND': 0, 'NE': 0, 'NH': 1, 'NJ': 1, 'NM': 1, 'NV': 0.5, 'NY': 1, 'OH': 0, 'OK': 0, 'OR': 1, 'PA': 0.5, 'RI': 1, 'SC': 0, 'SD': 0, 'TN': 0, 'TX': 0, 'UT': 0, 'VA': 1, 'VT': 1, 'WA': 1, 'WI': 1, 'WV': 0, 'WY': 0,}
state_list = ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY',]
five = [0.1514, 0.0164, 0.0095, 0.7112, 0.998, 0.9644, 0.9993, 1.0, 1.0, 0.6817, 0.5744, 0.9933, 0.3753, 0.0058, 0.9985, 0.0451, 0.0291, 0.0153, 0.027, 0.9995, 0.9995, 0.9068, 0.9506, 0
import pandas as pd
import itertools
states = ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY']
five = [0.1514, 0.0164, 0.0095, 0.7112, 0.998, 0.9644, 0.9993, 1.0, 1.0, 0.6817, 0.5744, 0.9933, 0.3753, 0.0058, 0.9985, 0.0451, 0.0291, 0.0153, 0.027, 0.9995, 0.9995, 0.9068, 0.9506, 0.9585, 0.0711, 0.0858, 0.1565, 0.6461, 0.0226, 0.0058, 0.8886, 0.994, 0.9766, 0.873, 0.9999, 0.4913, 0.0056, 0.9782, 0.8726, 0.9993, 0.0916, 0.0524, 0.0294, 0.3945, 0.0421, 0.9899, 0.9951, 0.9917, 0.939, 0.0086, 0.0016]
econ = [0.0432, 0.0, 0.0, 0.7215, 1.0, 0.9983, 1.0, 1.0, 1.0, 0.7368, 0.534, 1.0, 0.3694, 0.0, 1.0, 0.0002, 0.0015, 0.0, 0.0004, 1.0, 1.0, 0.9984, 0.9759, 0.9824, 0.0155, 0.0034, 0.0094, 0.6486, 0.0, 0.0001, 0.9756, 1.0, 0.9965, 0.934, 1.0, 0.3337, 0.0, 1.0, 0.9309
import pandas as pd
import itertools
five_csv = pd.read_csv('C:\\Users\\Alexander\\Documents\\downloads\\presidential_state_toplines_2020.csv')
states = ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL', 'GA', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND', 'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA', 'VT', 'WA', 'WI', 'WV', 'WY']
five = [0.1514, 0.0164, 0.0095, 0.7112, 0.998, 0.9644, 0.9993, 1.0, 1.0, 0.6817, 0.5744, 0.9933, 0.3753, 0.0058, 0.9985, 0.0451, 0.0291, 0.0153, 0.027, 0.9995, 0.9995, 0.9068, 0.9506, 0.9585, 0.0711, 0.0858, 0.1565, 0.6461, 0.0226, 0.0058, 0.8886, 0.994, 0.9766, 0.873, 0.9999, 0.4913, 0.0056, 0.9782, 0.8726, 0.9993, 0.0916, 0.0524, 0.0294, 0.3945, 0.0421, 0.9899, 0.9951, 0.9917, 0.939, 0.0086, 0.0016]
econ = [0.0432, 0.0, 0.0, 0.7215, 1.0, 0.9983, 1.0, 1.0, 1.0, 0.7368, 0.534, 1.0, 0.3694, 0.0, 1.0, 0.0002, 0.0015, 0.0, 0.0004, 1.0, 1.0, 0.9984, 0.9759, 0.9
import json
import os
from collections import defaultdict
import numpy as np
from PIL import Image
MODEL_REL_FILENAME = 'class.txt'
class DSModel: