def get_list_of_wav_paths(data_version: str, n_augmentations: [int, str] = 0) -> tuple:
"""
Retrieves the list of filepaths that belong to train, validation and test
:param n_augmentations: specify the number of augmentations to use or specify "all" to load all the available ones
(int|str)
:param data_version: specifies the version of the data to use (str {"0.01", "0.02"})
:return: list of training paths, list of validation paths and list of test paths (list of lists)
"""
folders = [get_training_data_path(data_version=data_version)]
if type(n_augmentations) == int:
folders += [get_augmented_data_folder(data_version=data_version, folder=str(f)) for f in range(n_augmentations)]
elif n_augmentations == "all":
base = get_augmented_data_path(data_version=data_version)
folders += [os.path.join(base, f) for f in os.listdir(base)]
else:
raise ValueError(f"'n_augmentations' parameter value not recognized as a valid argument ('all'|int): {n_augmentations}")
for path in folders:
if len(os.listdir(path)) == 0:
warnings.warn(f"Attempting to load files from an empty folder: {path}")
list_test = open(os.path.join(get_training_data_path(data_version=data_version), "testing_list.txt"))
list_test = list(
map(lambda x: os.path.normpath(os.path.join(get_training_data_path(data_version=data_version), x.strip())),
list_test))
list_val = open(os.path.join(get_training_data_path(data_version=data_version), "validation_list.txt"))
list_val = list(
map(lambda x: os.path.normpath(os.path.join(get_training_data_path(data_version=data_version), x.strip())),
list_val))
list_train = flatten([list(recursive_listdir(os.path.normpath(folder))) for folder in folders])
list_train = list(filter(lambda p: "background_noise" not in p and p.endswith("wav"), list_train))
list_train = np.setdiff1d(list_train, list_test + list_val).tolist()
return list_train, list_val, list_test
def decompress_dataset(data_version: str):
"""
Retrieves the downloaded data and decompresses it
:param data_version: specifies the version of the data to use (str {"0.01", "0.02"})
"""
fname = get_dataset_filepath(data_version=data_version)
assert os.path.exists(fname)
tar = tarfile.open(fname, "r:gz")
tar.extractall(path=get_training_data_path(data_version=data_version))
tar.close()
def load_random_real_noise_clip(data_version: str) -> np.array:
"""
Loads a random noise clip from the _background_noise_ folder and returns it
:param data_version: specifies the version of the data to use (str {"0.01", "0.02"})
:return: real noise clip (np.array)
"""
path = os.path.join(get_training_data_path(data_version=data_version), "_background_noise_")
filename = random.choice(list(filter(lambda x: x.endswith(".wav"), os.listdir(path))))
_, clip = read_wavfile(os.path.join(path, filename))
return clip
def load_real_noise_clips(data_version: str) -> np.array:
"""
Loads all the available real noise clips, which are located under the _background_noise_ folder
:param data_version: specifies the version of the data to use (str {"0.01", "0.02"})
:return: list of real noise clips (list of np.array)
"""
clips = []
path = os.path.join(get_training_data_path(data_version=data_version), "_background_noise_")
for filename in filter(lambda x: x.endswith(".wav"), os.listdir(path)):
_, wav = read_wavfile(os.path.join(path, filename))
clips.append(wav)
return clips
def test_download_and_decompress_data(self):
data_version = "0.02"
filepath = get_dataset_filepath(data_version=data_version)
if os.path.exists(filepath):
self.assertTrue(True) # skip
else: # this will run in Travis
download_dataset(data_version=data_version)
self.assertTrue(os.path.exists(filepath))
decompress_dataset(data_version=data_version)
self.assertLess(
10,
len(
os.listdir(
get_training_data_path(data_version=data_version))))
def test_get_training_data_path(self):
path = get_training_data_path("unit_testing")
self.assertTrue(os.path.exists(path))
os.rmdir(path)