def test_set_as_downloaded():
dataset_path = './tests/data'
dataset = Dataset(dataset_path)
file_log = os.path.join(dataset_path, 'download.txt')
_clean(file_log)
dataset.set_as_downloaded()
assert os.path.exists(file_log)
def test_get_audio_paths():
dataset_path = './tests/data'
dataset = Dataset(dataset_path)
audio_paths = dataset.get_audio_paths()
assert audio_paths[0] == './tests/data/audio'
assert audio_paths[1] == ['./tests/data/audio/original']
audio_paths = dataset.get_audio_paths(22050)
assert audio_paths[0] == './tests/data/audio22050'
assert audio_paths[1] == ['./tests/data/audio22050/original']
def test_get_audio_paths():
dataset_path = "./tests/data"
dataset = Dataset(dataset_path)
audio_paths = dataset.get_audio_paths()
assert audio_paths[0] == "./tests/data/audio"
assert audio_paths[1] == ["./tests/data/audio/original"]
audio_paths = dataset.get_audio_paths(22050)
assert audio_paths[0] == "./tests/data/audio22050"
assert audio_paths[1] == ["./tests/data/audio22050/original"]
def test_check_if_extracted():
dataset_path = "./tests/data"
dataset = Dataset(dataset_path)
path = "./tests/data/features/FeatureExtractor/original"
mkdir_if_not_exists(path, parents=True)
feature_extractor = FeatureExtractor()
json_features = os.path.join(path, "parameters.json")
_clean(json_features)
feature_extractor.set_as_extracted(path)
assert feature_extractor.check_if_extracted(dataset)
def test_convert_to_wav():
dataset_path = './tests/data_aiff'
dataset = Dataset(dataset_path)
audio_path = dataset.get_audio_paths()[0]
aiff_files = ['40722-8-0-7.aiff', '147764-4-7-0.aiff', '176787-5-0-0.aiff']
for wavfile in audio_files:
wavpath = os.path.join(audio_path, wavfile)
_clean(wavpath)
dataset.convert_to_wav()
for wavfile, aifffile in zip(audio_files, aiff_files):
wavpath = os.path.join(audio_path, wavfile)
data, sr = sf.read(wavpath)
wavpath_orig = os.path.join('./tests/data/audio', wavfile)
data_orig, sr_orig = sf.read(wavpath_orig)
assert sr_orig == sr
assert np.allclose(data_orig, data, rtol=0.0001, atol=0.0001)
_clean(wavpath)
def test_check_sampling_rate():
dataset_path = './tests/data'
dataset = Dataset(dataset_path)
sr = 22050
audio_path_sr = dataset.get_audio_paths(sr)[1][0]
_clean(audio_path_sr)
dataset.change_sampling_rate(sr)
assert dataset.check_sampling_rate(sr)
def test_check_if_downloaded():
dataset_path = './tests/data'
dataset = Dataset(dataset_path)
file_log = os.path.join(dataset_path, 'download.txt')
_clean(file_log)
assert not dataset.check_if_downloaded()
dataset.set_as_downloaded()
assert dataset.check_if_downloaded()
def test_download():
dataset_path = "./tests/data"
dataset = Dataset(dataset_path)
dataset.set_as_downloaded()
dir_path = os.path.dirname(os.path.realpath(__file__))
url = "file:////" + os.path.join(dir_path, "resources")
files = ["remote.zip"]
unzip_file = os.path.join(dataset_path, "remote.wav")
_clean(unzip_file)
assert not dataset.download(url, files, force_download=False)
file_log = os.path.join(dataset_path, "download.txt")
_clean(file_log)
resp = dataset.download(url, files, force_download=True)
assert resp
assert os.path.exists(unzip_file)
_clean(unzip_file)
def test_download():
dataset_path = './tests/data'
dataset = Dataset(dataset_path)
dataset.set_as_downloaded()
dir_path = os.path.dirname(os.path.realpath(__file__))
url = 'file:////' + os.path.join(dir_path, 'resources')
files = ['remote.zip']
unzip_file = os.path.join(dataset_path, 'remote.wav')
_clean(unzip_file)
assert not dataset.download(url, files, force_download=False)
file_log = os.path.join(dataset_path, 'download.txt')
_clean(file_log)
resp = dataset.download(url, files, force_download=True)
assert resp
assert os.path.exists(unzip_file)
_clean(unzip_file)
def test_change_sampling_rate(sr):
dataset_path = './tests/data'
dataset = Dataset(dataset_path)
audio_path = dataset.get_audio_paths()[0]
audio_path_sr = dataset.get_audio_paths(sr)[1][0]
_clean(audio_path_sr)
dataset.change_sampling_rate(sr)
for file_audio in audio_files:
file_path = os.path.join(audio_path_sr, file_audio)
file_data, file_sr = sf.read(file_path)
length_seconds = len(file_data) / float(file_sr)
file_path_original = os.path.join(audio_path, file_audio)
file_data_original, file_sr_original = sf.read(file_path_original)
length_seconds_original = len(file_data_original) / \
float(file_sr_original)
assert np.allclose(length_seconds,
length_seconds_original,
rtol=0.0001,
atol=0.0001)
assert dataset.check_sampling_rate(sr)
from .figures import generate_figure_features
from .figures import generate_figure_metrics
import os
import numpy as np
import ast
import soundfile as sf
from tensorflow.compat.v1 import get_default_graph
from sklearn.decomposition import PCA
import base64
import dash
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output, State
dataset = Dataset("")
feature_extractor = FeatureExtractor()
data_generator_train = DataGenerator(dataset, feature_extractor, [])
data_generator_val = DataGenerator(dataset, feature_extractor, [])
data_generator_test = DataGenerator(dataset, feature_extractor, [])
X_pca = np.zeros((1, 4))
X = np.zeros((1, 128, 64))
Y = np.zeros((1, 10))
file_names = []
graph = get_default_graph()
# VIS TAB
def test_build():
dataset_path = "./tests/data"
dataset = Dataset(dataset_path)
assert dataset.audio_path == "./tests/data/audio"
assert dataset.fold_list == ["fold1", "fold2", "fold3"]
assert dataset.label_list == ["class1", "class2", "class3"]
def test_get_features_path():
dataset_path = "./tests/data"
dataset = Dataset(dataset_path)
feature_extractor = FeatureExtractor()
path = "./tests/data/features/FeatureExtractor"
assert feature_extractor.get_features_path(dataset) == path
def test_generate_file_lists():
dataset_path = "./tests/data"
dataset = Dataset(dataset_path)
dataset.generate_file_lists()
assert dataset.file_lists == {"fold1": [], "fold2": [], "fold3": []}
def test_build():
dataset_path = './tests/data'
dataset = Dataset(dataset_path)
assert dataset.audio_path == './tests/data/audio'
assert dataset.fold_list == ['fold1', 'fold2', 'fold3']
assert dataset.label_list == ['class1', 'class2', 'class3']
def test_get_annotations():
dataset_path = './tests/data'
dataset = Dataset(dataset_path)
with pytest.raises(NotImplementedError):
dataset.get_annotations("file_path", "features", "time_resolution")
def test_generate_file_lists():
dataset_path = './tests/data'
dataset = Dataset(dataset_path)
dataset.generate_file_lists()
assert dataset.file_lists == {'fold1': [], 'fold2': [], 'fold3': []}