def get_datasets(
meta_dir: str,
batch_size: int,
num_workers: int,
fix_len: int = 0,
skip_audio: bool = False) -> Tuple[SpeechDataLoader, SpeechDataLoader]:
# TODO: update this function in general
assert os.path.isdir(meta_dir), '{} is not valid directory path!'
train_file, valid_file = MaestroMeta.frame_file_names[1:]
# load meta file
train_meta = MaestroMeta(os.path.join(meta_dir, train_file))
valid_meta = MaestroMeta(os.path.join(meta_dir, valid_file))
# create dataset
train_dataset = SpeechDataset(train_meta,
fix_len=fix_len,
skip_audio=skip_audio)
valid_dataset = SpeechDataset(valid_meta,
fix_len=fix_len,
skip_audio=skip_audio)
# create data loader
train_loader = SpeechDataLoader(train_dataset,
batch_size=batch_size,
num_workers=num_workers)
valid_loader = SpeechDataLoader(valid_dataset,
batch_size=batch_size,
num_workers=num_workers)
return train_loader, valid_loader
def get_datasets(
meta_dir: str,
batch_size: int,
num_workers: int,
fix_len: int = 0,
audio_mask: bool = False) -> Tuple[SpeechDataLoader, SpeechDataLoader]:
assert os.path.isdir(meta_dir), '{} is not valid directory path!'
train_file, valid_file = MedleyDBMeta.frame_file_names[1:]
# load meta file
train_meta = MedleyDBMeta(os.path.join(meta_dir, train_file))
valid_meta = MedleyDBMeta(os.path.join(meta_dir, valid_file))
# create dataset
train_dataset = SpeechDataset(train_meta,
fix_len=fix_len,
audio_mask=audio_mask)
valid_dataset = SpeechDataset(valid_meta,
fix_len=fix_len,
audio_mask=audio_mask)
# create data loader
train_loader = SpeechDataLoader(train_dataset,
batch_size=batch_size,
num_workers=num_workers,
is_bucket=False)
valid_loader = SpeechDataLoader(valid_dataset,
batch_size=batch_size,
num_workers=num_workers,
is_bucket=False)
return train_loader, valid_loader
def get_datasets(
meta_dir: str,
batch_size: int,
num_workers: int,
fix_len: int = 0,
skip_audio: bool = False,
audio_mask: bool = False,
skip_last_bucket: bool = True,
n_buckets: int = 10,
extra_features: List[Tuple[str, Callable]] = None
) -> Tuple[SpeechDataLoader, SpeechDataLoader]:
assert os.path.isdir(meta_dir), '{} is not valid directory path!'
train_file, valid_file = LibriTTSMeta.frame_file_names[1:]
# load meta file
train_meta = LibriTTSMeta(os.path.join(meta_dir, train_file))
valid_meta = LibriTTSMeta(os.path.join(meta_dir, valid_file))
# create dataset
train_dataset = SpeechDataset(train_meta,
fix_len=fix_len,
skip_audio=skip_audio,
audio_mask=audio_mask,
extra_features=extra_features)
valid_dataset = SpeechDataset(valid_meta,
fix_len=fix_len,
skip_audio=skip_audio,
audio_mask=audio_mask,
extra_features=extra_features)
# create data loader
train_loader = SpeechDataLoader(train_dataset,
batch_size=batch_size,
n_buckets=n_buckets,
num_workers=num_workers,
skip_last_bucket=skip_last_bucket)
valid_loader = SpeechDataLoader(valid_dataset,
batch_size=batch_size,
is_bucket=False,
num_workers=num_workers)
return train_loader, valid_loader