def __init__(
self,
audio_list,
audio_length_threshold=None,
audio_load_fn=sf.read,
return_filename=False,
allow_cache=False,
):
"""Initialize dataset.
Args:
audio_list (str): Filename of the list of audio files.
audio_load_fn (func): Function to load audio file.
audio_length_threshold (int): Threshold to remove short audio files.
return_filename (bool): Whether to return the filename with arrays.
allow_cache (bool): Whether to allow cache of the loaded files.
"""
# load audio and world file list
audio_files = read_txt(audio_list)
# filter by threshold
if audio_length_threshold is not None:
audio_lengths = [audio_load_fn(f).shape[0] for f in audio_files]
idxs = [
idx for idx in range(len(audio_files))
if audio_lengths[idx] > audio_length_threshold
]
if len(audio_files) != len(idxs):
logging.warning(
f"some files are filtered by audio length threshold "
f"({len(audio_files)} -> {len(idxs)}).")
audio_files = [audio_files[idx] for idx in idxs]
# assert the number of files
assert len(
audio_files) != 0, f"Not found any audio files in ${audio_list}."
self.audio_files = audio_files
self.audio_load_fn = audio_load_fn
self.return_filename = return_filename
self.allow_cache = allow_cache
if allow_cache:
# NOTE(kan-bayashi): Manager is need to share memory in dataloader with num_workers > 0
self.manager = Manager()
self.caches = self.manager.list()
self.caches += [() for _ in range(len(audio_files))]
def __init__(
self,
stats,
audio_list,
world_list,
audio_load_fn=sf.read,
world_load_fn=lambda x: read_hdf5(x, "world"),
hop_size=110,
audio_length_threshold=None,
world_length_threshold=None,
return_filename=False,
allow_cache=False,
mean_path="/world/mean",
scale_path="/world/scale",
):
"""Initialize dataset.
Args:
stats (str): Filename of the statistic hdf5 file.
audio_list (str): Filename of the list of audio files.
world_list (str): Filename of the list of world feature files.
audio_load_fn (func): Function to load audio file.
world_load_fn (func): Function to load world feature file.
hop_size (int): Hope size of world feature
audio_length_threshold (int): Threshold to remove short audio files.
world_length_threshold (int): Threshold to remove short world feature files.
return_filename (bool): Whether to return the filename with arrays.
allow_cache (bool): Whether to allow cache of the loaded files.
mean_path (str): The data path (channel) of the mean in the statistic hdf5 file.
scale_path (str): The data path (channel) of the scale in the statistic hdf5 file.
"""
# load audio and world file list
audio_files = read_txt(audio_list)
world_files = read_txt(world_list)
# check filename
assert check_filename(audio_files, world_files)
# filter by threshold
if audio_length_threshold is not None:
audio_lengths = [audio_load_fn(f).shape[0] for f in audio_files]
idxs = [
idx for idx in range(len(audio_files))
if audio_lengths[idx] > audio_length_threshold
]
if len(audio_files) != len(idxs):
logging.warning(
f"Some files are filtered by audio length threshold "
f"({len(audio_files)} -> {len(idxs)}).")
audio_files = [audio_files[idx] for idx in idxs]
world_files = [world_files[idx] for idx in idxs]
if world_length_threshold is not None:
world_lengths = [world_load_fn(f).shape[0] for f in world_files]
idxs = [
idx for idx in range(len(world_files))
if world_lengths[idx] > world_length_threshold
]
if len(world_files) != len(idxs):
logging.warning(
f"Some files are filtered by world length threshold "
f"({len(world_files)} -> {len(idxs)}).")
audio_files = [audio_files[idx] for idx in idxs]
world_files = [world_files[idx] for idx in idxs]
# assert the number of files
assert len(
audio_files) != 0, f"Not found any audio files in ${audio_list}."
assert len(audio_files) == len(world_files), \
f"Number of audio and world files are different ({len(audio_files)} vs {len(world_files)})."
self.audio_files = audio_files
self.world_files = world_files
self.audio_load_fn = audio_load_fn
self.world_load_fn = world_load_fn
self.return_filename = return_filename
self.allow_cache = allow_cache
self.hop_size = hop_size
if allow_cache:
# NOTE(kan-bayashi): Manager is need to share memory in dataloader with num_workers > 0
self.manager = Manager()
self.caches = self.manager.list()
self.caches += [() for _ in range(len(audio_files))]
# define feature pre-processing funtion
scaler = StandardScaler()
scaler.mean_ = read_hdf5(stats, mean_path)
scaler.scale_ = read_hdf5(stats, scale_path)
# for version 0.23.0, this information is needed
scaler.n_features_in_ = scaler.mean_.shape[0]
self.feat_transform = lambda x: scaler.transform(x)