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import subprocess | |
from pathlib import Path | |
lab = Path("01.lab.txt").read_text() | |
i = 0 | |
out_labs = [] | |
for l in lab.splitlines(): | |
a, b, c = l.split() | |
if c == "pau": |
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from pathlib import Path | |
import numpy | |
from scipy.spatial.distance import cdist | |
def load_vector(path_format: Path): | |
predicted_vectors = [] | |
true_speaker_nums = [] | |
for speaker_num in range(100): |
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from pathlib import Path | |
from typing import Union, Sequence | |
import librosa | |
import numpy | |
from librosa.effects import _signal_to_frame_nonsilent | |
from matplotlib import pyplot | |
from tqdm import tqdm |
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import multiprocessing | |
import threading | |
import time | |
import timeout_decorator | |
def countup(): | |
for i in range(1, 10): | |
time.sleep(1) |
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""" | |
python chainer_log_to_csv.py [input path] [output path] | |
""" | |
import argparse | |
import csv | |
import json | |
parser = argparse.ArgumentParser() | |
parser.add_argument('input') |
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