# Visualization.plot_cepstrals(
# attr, fig_name=f'teste.png')
# Visualization.plot_audio(
# sample, rate, fig_name='./teste.png')
# Audio.write(
# f'portuguese/processed/psf/{dir}_{i}_{sample_index}.wav', sample, rate)
m['attrs'].append(attr.tolist())
del attr
del signal
return m
if __name__ == '__main__':
filename = Directory.processed_filename(language, library, sampling_rate,
n_audios, n_segments, augment)
# m = []
# for j, i in enumerate(f):
# if j < 1:
# m.append(process_directory(i, j, library))
m = Parallel(n_jobs=-1,
verbose=len(f))(delayed(process_directory)(i, j, library)
for j, i in enumerate(f)
if n_audios == None or j < n_audios)
Process.object_to_json(
filename,
m,
f,
language = args['language']
method = args['method']
library = args['representation']
people = args['people']
segments = args['segments']
normalization = args['normalization']
flat = args['flat']
augment = args['augmentation']
sampling_rate = 24000
random_state = 42
epochs = 2000
batch_size = 128
# %%
file_path = Directory.processed_filename(language, library, sampling_rate,
people, segments, augment)
# %%
X_train, X_valid, X_test, y_train, y_valid, y_test = Process.selection(
file_path, flat=flat)
param_grid = {}
# %%
if normalization == 'minmax':
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train.reshape(
-1, X_train.shape[-1])).reshape(X_train.shape)
X_test = scaler.transform(X_test.reshape(-1, X_test.shape[-1])).reshape(
X_test.shape)
# %%
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
DATASET_PATH = Directory.processed_filename(language, library, n_rate,
n_people, n_segments)
# %%
# SPLIT DOS DADOS
X_train, X_valid, X_test, y_train, y_valid, y_test = Process.selection(
DATASET_PATH)
mapping = set(y_train)
# %%
def build_model(learning_rate=0.0001):
# build the network architecture
input_shape = [X_train.shape[1]] if library == 'mixed' else [
X_train.shape[1], X_train.shape[2]
library = args['representation']
people = args['people']
segments = args['segments']
sampling_rate = 24000
random_state = 42
# language = 'mixed'
# library = 'psf'
# people = None
# segments = None
# sampling_rate = 24000
# random_state = 42
# %%
global X_train, X_valid, X_test, y_train, y_valid, y_test
file_path = Directory.processed_filename(language, library, sampling_rate,
people, segments)
# %%
if language == 'mixed' and library == 'mixed':
first_folder = Directory.processed_filename('portuguese', 'psf',
sampling_rate, None, None)
second_folder = Directory.processed_filename('portuguese', 'melbanks',
sampling_rate, None, None)
third_folder = Directory.processed_filename('english', 'psf',
sampling_rate, people,
segments)
fourth_folder = Directory.processed_filename('english', 'melbanks',
sampling_rate, people,
segments)
X_train, X_valid, X_test, y_train, y_valid, y_test = Process.mixed_selection(
first_folder,