def get_tarred_char_dataset(
config: dict,
shuffle_n: int,
global_rank: int,
world_size: int,
augmentor: Optional['AudioAugmentor'] = None
) -> audio_to_text.TarredAudioToCharDataset:
"""
Instantiates a Character Encoding based TarredAudioToCharDataset.
Args:
config: Config of the TarredAudioToCharDataset.
shuffle_n: How many samples to look ahead and load to be shuffled.
See WebDataset documentation for more details.
global_rank: Global rank of this device.
world_size: Global world size in the training method.
augmentor: Optional AudioAugmentor object for augmentations on audio data.
Returns:
An instance of TarredAudioToCharDataset.
"""
dataset = audio_to_text.TarredAudioToCharDataset(
audio_tar_filepaths=config['tarred_audio_filepaths'],
manifest_filepath=config['manifest_filepath'],
labels=config['labels'],
sample_rate=config['sample_rate'],
int_values=config.get('int_values', False),
augmentor=augmentor,
shuffle_n=shuffle_n,
max_duration=config.get('max_duration', None),
min_duration=config.get('min_duration', None),
max_utts=config.get('max_utts', 0),
blank_index=config.get('blank_index', -1),
unk_index=config.get('unk_index', -1),
normalize=config.get('normalize_transcripts', False),
trim=config.get('trim_silence', True),
parser=config.get('parser', 'en'),
add_misc=config.get('add_misc', False),
shard_strategy=config.get('tarred_shard_strategy', 'scatter'),
global_rank=global_rank,
world_size=world_size,
)
return dataset
def get_tarred_dataset(
config: dict,
shuffle_n: int,
global_rank: int,
world_size: int,
tokenizer: Optional['TokenizerSpec'] = None,
augmentor: Optional['AudioAugmentor'] = None,
) -> Union[audio_to_text.TarredAudioToBPEDataset,
audio_to_text.TarredAudioToCharDataset]:
"""
Instantiates a Word Piece/BPE Encoding based TarredAudioToBPEDataset or a char based TarredAudioToCharDataset.
Args:
config: Config of the TarredAudioToBPEDataset or TarredAudioToCharDataset.
tokenizer: An instance of a TokenizerSpec object if BPE dataset is needed.
Passsing None would return a char-based dataset.
shuffle_n: How many samples to look ahead and load to be shuffled.
See WebDataset documentation for more details.
global_rank: Global rank of this device.
world_size: Global world size in the training method.
augmentor: Optional AudioAugmentor object for augmentations on audio data.
Returns:
An instance of TarredAudioToBPEDataset or TarredAudioToCharDataset.
"""
tarred_audio_filepaths = config['tarred_audio_filepaths']
manifest_filepaths = config['manifest_filepath']
datasets = []
tarred_audio_filepaths = convert_to_config_list(tarred_audio_filepaths)
manifest_filepaths = convert_to_config_list(manifest_filepaths)
if len(manifest_filepaths) != len(tarred_audio_filepaths):
raise ValueError(
f"manifest_filepaths and tarred_audio_filepaths need to have the same number of buckets."
)
for dataset_idx, (tarred_audio_filepath, manifest_filepath) in enumerate(
zip(tarred_audio_filepaths, manifest_filepaths)):
if len(tarred_audio_filepath) == 1:
tarred_audio_filepath = tarred_audio_filepath[0]
if tokenizer is None:
dataset = audio_to_text.TarredAudioToCharDataset(
audio_tar_filepaths=tarred_audio_filepath,
manifest_filepath=manifest_filepath,
labels=config['labels'],
sample_rate=config['sample_rate'],
int_values=config.get('int_values', False),
augmentor=augmentor,
shuffle_n=shuffle_n,
max_duration=config.get('max_duration', None),
min_duration=config.get('min_duration', None),
max_utts=config.get('max_utts', 0),
blank_index=config.get('blank_index', -1),
unk_index=config.get('unk_index', -1),
normalize=config.get('normalize_transcripts', False),
trim=config.get('trim_silence', False),
parser=config.get('parser', 'en'),
shard_strategy=config.get('tarred_shard_strategy', 'scatter'),
global_rank=global_rank,
world_size=world_size,
)
else:
dataset = audio_to_text.TarredAudioToBPEDataset(
audio_tar_filepaths=tarred_audio_filepath,
manifest_filepath=manifest_filepath,
tokenizer=tokenizer,
sample_rate=config['sample_rate'],
int_values=config.get('int_values', False),
augmentor=augmentor,
shuffle_n=shuffle_n,
max_duration=config.get('max_duration', None),
min_duration=config.get('min_duration', None),
max_utts=config.get('max_utts', 0),
trim=config.get('trim_silence', False),
use_start_end_token=config.get('use_start_end_token', True),
shard_strategy=config.get('tarred_shard_strategy', 'scatter'),
global_rank=global_rank,
world_size=world_size,
)
datasets.append(dataset)
if len(datasets) > 1:
return ChainDataset(datasets)
else:
return datasets[0]
