kenenbek

import unittest
import os
import shutil
import file_distributor

class TestFileDistributor(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        # Clean up output directory before each test run
        if os.path.exists(file_distributor.OUTPUT_DIR):

kenenbek

import os
import shutil
from math import ceil

# Configuration
SOURCE_DIR = 'my_source_files_test'  # Path to source files
OUTPUT_DIR = 'output'                # Path to output worker folders
NUM_LABELERS = 5                    # Number of workers
NUM_COPIES = 3                       # Number of workers each file should be copied to

kenenbek

kenenbek

import os
import pandas as pd
import librosa
import csv
import re

# Load the DataFrame from a CSV file
input_file = "deperson_13_files.csv"  # Replace with the actual file path
input_df = pd.read_csv(input_file)

kenenbek

import pandas as pd
import matplotlib.pyplot as plt

# Load the DataFrame from the CSV file
csv_file_real = "real_deperson_13.csv"
df_real = pd.read_csv(csv_file_real)

csv_file_test = "test_deperson_13_files_5.csv"
df_test = pd.read_csv(csv_file_test)

kenenbek

import json
import os, re, argparse, shutil
import sys
import multiprocessing
from functools import partial

from silero_vad import load_silero_vad, read_audio, get_speech_timestamps
from pydub import AudioSegment
from dotenv import load_dotenv
import google.generativeai as genai

kenenbek

kenenbek

kenenbek

from comet_ml import OfflineExperiment

import sys


if __name__ == '__main__':
    experiment = OfflineExperiment(project_name="pytorch")
    experiment.log_model("model", sys.argv[1])
    

kenenbek

from comet_ml import Experiment

import sys

if __name__ == '__main__':
    experiment = Experiment(project_name="pytorch")
    experiment.log_model("model", sys.argv[1])