#!/usr/bin/env python
# coding: utf-8
# %%
from joblib import Parallel, delayed
import multiprocessing
from numpy import array
from deep_audio import Audio, Directory, Terminal
import sys
#%%
args = Terminal.get_args(sys.argv[1:])
# %%
num_cores = multiprocessing.cpu_count()
language = args['language'] or 'portguese'
origin_path = f'base_{language}'
dest_path = f'{language}/audios'
s_rate = [24000]
n_audios = args['people'] or None
print(dest_path)
# %%
f = Directory.filenames_recursive(origin_path)
def process_directory(dir, n_rate):
signal = []
for j, audioname in enumerate(f[dir]):
holder_signal, sr = Audio.read(f'{origin_path}/{dir}/{audioname}',
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
from numpy.core.fromnumeric import squeeze
from sklearn.model_selection import train_test_split
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
import time
import matplotlib.pyplot as plt
from numpy import lib, max
from deep_audio import Directory, JSON, Process, Terminal
args = Terminal.get_args()
# %%
model_algo = 'perceptron'
language = args['language'] or 'portuguese'
library = args['representation'] or 'psf'
n_people = args['people'] or None
n_segments = args['segments'] or None
n_rate = 24000
random_state = 42
filename_ps = Directory.verify_people_segments(people=n_people,
segments=n_segments)
# %%
global X_train, X_valid, X_test, y_train, y_valid, y_test