def _setup_dataloader_from_config(self, config: Optional[Dict]):
if config.get('manifest_filepath') is None:
return
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
featurizer = WaveformFeaturizer(sample_rate=config['sample_rate'],
int_values=config.get(
'int_values', False),
augmentor=augmentor)
dataset = AudioLabelDataset(
manifest_filepath=config['manifest_filepath'],
labels=config['labels'],
featurizer=featurizer,
max_duration=config.get('max_duration', None),
min_duration=config.get('min_duration', None),
trim=config.get('trim_silence', True),
load_audio=config.get('load_audio', True),
)
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=config['batch_size'],
collate_fn=dataset.collate_fn,
drop_last=config.get('drop_last', False),
shuffle=config['shuffle'],
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
def _setup_dataloader_from_config(self, cfg: DictConfig):
if 'manifest_filepath' in cfg and cfg['manifest_filepath'] is None:
logging.warning(
f"Could not load dataset as `manifest_filepath` was None. Provided config : {cfg}"
)
return None
if 'augmentor' in cfg:
augmentor = process_augmentations(cfg['augmentor'])
else:
augmentor = None
dataset = _AudioTextDataset(
manifest_filepath=cfg['manifest_filepath'],
parser=self.parser,
sample_rate=cfg['sample_rate'],
int_values=cfg.get('int_values', False),
augmentor=augmentor,
max_duration=cfg.get('max_duration', None),
min_duration=cfg.get('min_duration', None),
max_utts=cfg.get('max_utts', 0),
trim=cfg.get('trim_silence', True),
load_audio=cfg.get('load_audio', True),
add_misc=cfg.get('add_misc', False),
)
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=cfg['batch_size'],
collate_fn=dataset.collate_fn,
drop_last=cfg.get('drop_last', False),
shuffle=cfg['shuffle'],
num_workers=cfg.get('num_workers', 0),
)
def _setup_dataloader_from_config(self, config: Optional[Dict]):
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
shuffle = config['shuffle']
device = 'gpu' if torch.cuda.is_available() else 'cpu'
if config.get('use_dali', False):
device_id = self.local_rank if device == 'gpu' else None
dataset = audio_to_text_dataset.get_dali_char_dataset(
config=config,
shuffle=shuffle,
device_id=device_id,
global_rank=self.global_rank,
world_size=self.world_size,
preprocessor_cfg=self._cfg.preprocessor,
)
return dataset
# Instantiate tarred dataset loader or normal dataset loader
if config.get('is_tarred', False):
if ('tarred_audio_filepaths' in config and config['tarred_audio_filepaths'] is None) or (
'manifest_filepath' in config and config['manifest_filepath'] is None
):
logging.warning(
"Could not load dataset as `manifest_filepath` was None or "
f"`tarred_audio_filepaths` is None. Provided config : {config}"
)
return None
shuffle_n = config.get('shuffle_n', 4 * config['batch_size']) if shuffle else 0
dataset = audio_to_text_dataset.get_tarred_char_dataset(
config=config,
shuffle_n=shuffle_n,
global_rank=self.global_rank,
world_size=self.world_size,
augmentor=augmentor,
)
shuffle = False
else:
if 'manifest_filepath' in config and config['manifest_filepath'] is None:
logging.warning(f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}")
return None
dataset = audio_to_text_dataset.get_char_dataset(config=config, augmentor=augmentor)
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=config['batch_size'],
collate_fn=dataset.collate_fn,
drop_last=config.get('drop_last', False),
shuffle=shuffle,
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
def _setup_dataloader_from_config(self, config: Optional[Dict]):
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
shuffle = config['shuffle']
# Instantiate tarred dataset loader or normal dataset loader
if config.get('is_tarred', False):
if ('tarred_audio_filepaths' in config
and config['tarred_audio_filepaths'] is None) or (
'manifest_filepath' in config
and config['manifest_filepath'] is None):
logging.warning(
"Could not load dataset as `manifest_filepath` was None or "
f"`tarred_audio_filepaths` is None. Provided config : {config}"
)
return None
shuffle_n = config.get('shuffle_n', 4 *
config['batch_size']) if shuffle else 0
dataset = audio_to_text_dataset.get_tarred_bpe_dataset(
config=config,
tokenizer=self.tokenizer,
shuffle_n=shuffle_n,
global_rank=self.global_rank,
world_size=self.world_size,
augmentor=augmentor,
)
shuffle = False
else:
if 'manifest_filepath' in config and config[
'manifest_filepath'] is None:
logging.warning(
f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}"
)
return None
dataset = audio_to_text_dataset.get_bpe_dataset(
config=config, tokenizer=self.tokenizer, augmentor=augmentor)
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=config['batch_size'],
collate_fn=dataset.collate_fn,
drop_last=config.get('drop_last', False),
shuffle=shuffle,
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
def _setup_dataloader_from_config(self, config: Optional[Dict]):
if config.get('manifest_filepath') is None:
return
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
featurizer = WaveformFeaturizer(sample_rate=config['sample_rate'],
int_values=config.get(
'int_values', False),
augmentor=augmentor)
if 'vad_stream' in config and config['vad_stream']:
print("Perform streaming frame-level VAD")
dataset = AudioToSpeechLabelDataSet(
manifest_filepath=config['manifest_filepath'],
labels=config['labels'],
featurizer=featurizer,
max_duration=config.get('max_duration', None),
min_duration=config.get('min_duration', None),
trim=config.get('trim_silence', True),
load_audio=config.get('load_audio', True),
time_length=config.get('time_length', 0.31),
shift_length=config.get('shift_length', 0.01),
)
batch_size = 1
collate_func = dataset.vad_frame_seq_collate_fn
else:
dataset = AudioLabelDataset(
manifest_filepath=config['manifest_filepath'],
labels=config['labels'],
featurizer=featurizer,
max_duration=config.get('max_duration', None),
min_duration=config.get('min_duration', None),
trim=config.get('trim_silence', True),
load_audio=config.get('load_audio', True),
)
batch_size = config['batch_size']
collate_func = dataset.collate_fn
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=batch_size,
collate_fn=collate_func,
drop_last=config.get('drop_last', False),
shuffle=config['shuffle'],
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
def __setup_dataloader_from_config(self, config: Optional[Dict]):
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
featurizer = WaveformFeaturizer(sample_rate=config['sample_rate'],
int_values=config.get(
'int_values', False),
augmentor=augmentor)
self.dataset = AudioToSpeechLabelDataset(
manifest_filepath=config['manifest_filepath'],
labels=config['labels'],
featurizer=featurizer,
max_duration=config.get('max_duration', None),
min_duration=config.get('min_duration', None),
trim=False,
load_audio=config.get('load_audio', True),
time_length=config.get('time_length', 8),
shift_length=config.get('shift_length', 0.75),
)
if self.task == 'diarization':
logging.info("Setting up diarization parameters")
_collate_func = self.dataset.sliced_seq_collate_fn
batch_size = 1
shuffle = False
else:
logging.info("Setting up identification parameters")
_collate_func = self.dataset.fixed_seq_collate_fn
batch_size = config['batch_size']
shuffle = config.get('shuffle', False)
return torch.utils.data.DataLoader(
dataset=self.dataset,
batch_size=batch_size,
collate_fn=_collate_func,
drop_last=config.get('drop_last', False),
shuffle=shuffle,
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
def _setup_dataloader_from_config(self, config: Optional[Dict]):
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
shuffle = config['shuffle']
# Instantiate tarred dataset loader or normal dataset loader
if config.get('is_tarred', False):
if ('tarred_audio_filepaths' in config
and config['tarred_audio_filepaths'] is None) or (
'manifest_filepath' in config
and config['manifest_filepath'] is None):
logging.warning(
"Could not load dataset as `manifest_filepath` was None or "
f"`tarred_audio_filepaths` is None. Provided config : {config}"
)
return None
shuffle_n = config.get('shuffle_n', 4 * config['batch_size'])
dataset = TarredAudioToBPEDataset(
audio_tar_filepaths=config['tarred_audio_filepaths'],
manifest_filepath=config['manifest_filepath'],
tokenizer=self.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', True),
add_misc=config.get('add_misc', False),
global_rank=self.global_rank,
world_size=self.world_size,
)
shuffle = False
else:
if 'manifest_filepath' in config and config[
'manifest_filepath'] is None:
logging.warning(
f"Could not load dataset as `manifest_filepath` was None. Provided config : {config}"
)
return None
dataset = AudioToBPEDataset(
manifest_filepath=config['manifest_filepath'],
tokenizer=self.tokenizer,
sample_rate=config['sample_rate'],
int_values=config.get('int_values', False),
augmentor=augmentor,
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', True),
load_audio=config.get('load_audio', True),
add_misc=config.get('add_misc', False),
)
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=config['batch_size'],
collate_fn=dataset.collate_fn,
drop_last=config.get('drop_last', False),
shuffle=shuffle,
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
def _setup_dataloader_from_config(self, config: DictConfig):
OmegaConf.set_struct(config, False)
config.is_regression_task = self.is_regression_task
OmegaConf.set_struct(config, True)
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
featurizer = WaveformFeaturizer(sample_rate=config['sample_rate'],
int_values=config.get(
'int_values', False),
augmentor=augmentor)
shuffle = config['shuffle']
# Instantiate tarred dataset loader or normal dataset loader
if config.get('is_tarred', False):
if ('tarred_audio_filepaths' in config
and config['tarred_audio_filepaths'] is None) or (
'manifest_filepath' in config
and config['manifest_filepath'] is None):
logging.warning(
"Could not load dataset as `manifest_filepath` is None or "
f"`tarred_audio_filepaths` is None. Provided config : {config}"
)
return None
if 'vad_stream' in config and config['vad_stream']:
logging.warning(
"VAD inference does not support tarred dataset now")
return None
shuffle_n = config.get('shuffle_n', 4 *
config['batch_size']) if shuffle else 0
dataset = audio_to_label_dataset.get_tarred_classification_label_dataset(
featurizer=featurizer,
config=OmegaConf.to_container(config),
shuffle_n=shuffle_n,
global_rank=self.global_rank,
world_size=self.world_size,
)
shuffle = False
batch_size = config['batch_size']
collate_func = dataset.collate_fn
else:
if 'manifest_filepath' in config and config[
'manifest_filepath'] is None:
logging.warning(
f"Could not load dataset as `manifest_filepath` is None. Provided config : {config}"
)
return None
if 'vad_stream' in config and config['vad_stream']:
logging.info("Perform streaming frame-level VAD")
dataset = audio_to_label_dataset.get_speech_label_dataset(
featurizer=featurizer,
config=OmegaConf.to_container(config))
batch_size = 1
collate_func = dataset.vad_frame_seq_collate_fn
else:
dataset = audio_to_label_dataset.get_classification_label_dataset(
featurizer=featurizer,
config=OmegaConf.to_container(config))
batch_size = config['batch_size']
collate_func = dataset.collate_fn
return torch.utils.data.DataLoader(
dataset=dataset,
batch_size=batch_size,
collate_fn=collate_func,
drop_last=config.get('drop_last', False),
shuffle=shuffle,
num_workers=config.get('num_workers', 0),
pin_memory=config.get('pin_memory', False),
)
