def _extract(data_file_name, settings_features, settings_data, dir_output_dev,
dir_output_eva):
# Load the data file.
data_file = load_numpy_object(data_file_name)
# Extract the features.
features = feature_extraction(data_file['audio_data'].item(),
**settings_features['process'])
# Populate the recarray data and dtypes.
array_data = (data_file['file_name'].item(), )
dtypes = [('file_name', data_file['file_name'].dtype)]
# Check if we keeping the raw audio data.
if settings_features['keep_raw_audio_data']:
# And add them to the recarray data and dtypes.
array_data += (data_file['audio_data'].item(), )
dtypes.append(('audio_data', data_file['audio_data'].dtype))
# Add the rest to the recarray.
array_data += (features, data_file['caption'].item(),
data_file['caption_ind'].item(),
data_file['words_ind'].item(),
data_file['chars_ind'].item())
dtypes.extend([('features', np.dtype(object)),
('caption', data_file['caption'].dtype),
('caption_ind', data_file['caption_ind'].dtype),
('words_ind', data_file['words_ind'].dtype),
('chars_ind', data_file['chars_ind'].dtype)])
# Make the recarray
np_rec_array = np.rec.array([array_data], dtype=dtypes)
# Make the path for serializing the recarray.
parent_path = dir_output_dev \
if data_file_name.parent.name == settings_data['audio_dirs']['development'] \
else dir_output_eva
file_path = parent_path.joinpath(data_file_name.name)
# Dump it.
dump_numpy_object(np_rec_array, file_path)
def extract_features_test(root_dir: str,
settings_data: MutableMapping[str, Any],
settings_features: MutableMapping[str, Any],
settings_audio: MutableMapping[str, Any]) \
-> None:
"""Extracts test features from the audio data of Clotho.
:param root_dir: Root dir for the data.
:type root_dir: str
:param settings_data: Settings for creating data files.
:type settings_data: dict[str, T]
:param settings_features: Settings for feature extraction.
:type settings_features: dict[str, T]
:param settings_audio: Settings for the audio.
:type settings_audio: dict
"""
# Get the root directory.
dir_root = Path(root_dir)
# Get the directories of files.
dir_test = dir_root.joinpath(settings_data['audio_dirs']['downloaded'],
settings_data['audio_dirs']['test'])
audio_exists = False
if dir_test.exists() and len(list(dir_test.iterdir())) != 0:
audio_exists = True
if not audio_exists:
raise AttributeError(
'Testing workflow selected, but could not find the test set audio files. '
'Please download the test set audio before making test predictions.'
)
# Get the directories for output.
dir_output_test = dir_root.joinpath(
settings_data['features_dirs']['output'],
settings_data['features_dirs']['test'])
words_list = load_pickle_file(
dir_root.joinpath(settings_data['pickle_files_dir'],
settings_data['files']['words_list_file_name']))
# Create the directories.
dir_output_test.mkdir(parents=True, exist_ok=True)
# Apply the function to each file and save the result.
for data_file_name in filter(lambda _x: _x.is_file(), dir_test.iterdir()):
# Load the audio
audio = load_audio_file(audio_file=str(data_file_name),
sr=int(settings_audio['sr']),
mono=settings_audio['to_mono'])
# Extract the features.
features = feature_extraction(audio, **settings_features['process'])
# Populate the recarray data and dtypes.
array_data = (data_file_name.name, )
dtypes = [('file_name', f'U{len(data_file_name.name)}')]
# Check if we keeping the raw audio data.
if settings_features['keep_raw_audio_data']:
# And add them to the recarray data and dtypes.
array_data += (audio, )
dtypes.append(('audio_data', audio.dtype))
# Add the rest to the recarray.
# Word indices are required for the dataloader to work
array_data += (features,
np.array([
words_list.index('<sos>'),
words_list.index('<eos>')
]))
dtypes.extend([('features', np.dtype(object)),
('words_ind', np.dtype(object))])
# Make the recarray
np_rec_array = np.rec.array([array_data], dtype=dtypes)
# Make the path for serializing the recarray.
parent_path = dir_output_test
file_template = settings_data['files'][
'np_file_name_template'].replace('_{caption_index}', '')
file_path = parent_path.joinpath(
file_template.format(audio_file_name=data_file_name.name))
# Dump it.
dump_numpy_object(np_rec_array, file_path)
def extract_features(root_dir: str,
settings_data: MutableMapping[str, Any],
settings_features: MutableMapping[str, Any]) \
-> None:
"""Extracts features from the audio data of Clotho.
:param root_dir: Root dir for the data.
:type root_dir: str
:param settings_data: Settings for creating data files.
:type settings_data: dict[str, T]
:param settings_features: Settings for feature extraction.
:type settings_features: dict[str, T]
"""
# Get the root directory.
dir_root = Path(root_dir)
# Get the directories of files.
dir_output = dir_root.joinpath(settings_data['audio_dirs']['output'])
dir_dev = dir_output.joinpath(settings_data['audio_dirs']['development'])
dir_eva = dir_output.joinpath(settings_data['audio_dirs']['evaluation'])
# Get the directories for output.
dir_output_dev = dir_root.joinpath(
settings_data['features_dirs']['output'],
settings_data['features_dirs']['development'])
dir_output_eva = dir_root.joinpath(
settings_data['features_dirs']['output'],
settings_data['features_dirs']['evaluation'])
# Create the directories.
dir_output_dev.mkdir(parents=True, exist_ok=True)
dir_output_eva.mkdir(parents=True, exist_ok=True)
# Apply the function to each file and save the result.
for data_file_name in filter(lambda _x: _x.suffix == '.npy',
chain(dir_dev.iterdir(), dir_eva.iterdir())):
# Load the data file.
data_file = load_numpy_object(data_file_name)
# Extract the features.
features = feature_extraction(data_file['audio_data'].item(),
**settings_features['process'])
# Populate the recarray data and dtypes.
array_data = (data_file['file_name'].item(), )
dtypes = [('file_name', data_file['file_name'].dtype)]
# Check if we keeping the raw audio data.
if settings_features['keep_raw_audio_data']:
# And add them to the recarray data and dtypes.
array_data += (data_file['audio_data'].item(), )
dtypes.append(('audio_data', data_file['audio_data'].dtype))
# Add the rest to the recarray.
array_data += (features, data_file['caption'].item(),
data_file['caption_ind'].item(),
data_file['words_ind'].item(),
data_file['chars_ind'].item())
dtypes.extend([('features', np.dtype(object)),
('caption', data_file['caption'].dtype),
('caption_ind', data_file['caption_ind'].dtype),
('words_ind', data_file['words_ind'].dtype),
('chars_ind', data_file['chars_ind'].dtype)])
# Make the recarray
np_rec_array = np.rec.array([array_data], dtype=dtypes)
# Make the path for serializing the recarray.
parent_path = dir_output_dev \
if data_file_name.parent.name == settings_data['audio_dirs']['development'] \
else dir_output_eva
file_path = parent_path.joinpath(data_file_name.name)
# Dump it.
dump_numpy_object(np_rec_array, file_path)