def _setup_dataloader_from_config(self, config: Optional[Dict]):
if 'augmentor' in config:
augmentor = process_augmentations(config['augmentor'])
else:
augmentor = None
# Automatically inject args from model config to dataloader config
audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='sample_rate')
audio_to_text_dataset.inject_dataloader_value_from_model_config(self.cfg, config, key='labels')
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 test_dali_char_vs_ref_dataset(self, test_data_dir):
manifest_path = os.path.abspath(os.path.join(test_data_dir, 'asr/an4_val.json'))
num_samples = 10
batch_size = 1
device = 'gpu' if torch.cuda.is_available() else 'cpu'
texts = []
with tempfile.NamedTemporaryFile(mode='w', encoding='utf-8') as f:
with open(manifest_path, 'r') as m:
for ix, line in enumerate(m):
if ix >= num_samples:
break
line = line.replace("tests/data/", "tests/.data/").replace("\n", "")
f.write(f"{line}\n")
data = json.loads(line)
texts.append(data['text'])
f.seek(0)
preprocessor = {
'_target_': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor',
'dither': 0.0,
}
preprocessor_cfg = DictConfig(preprocessor)
dataset_cfg = {
'manifest_filepath': f.name,
'sample_rate': 16000,
'labels': self.labels,
'batch_size': batch_size,
'trim_silence': False,
'max_duration': 16.7,
'shuffle': False,
'is_tarred': False,
}
dali_dataset = audio_to_text_dataset.get_dali_char_dataset(
config=dataset_cfg,
shuffle=False,
device_id=0,
global_rank=0,
world_size=1,
preprocessor_cfg=preprocessor_cfg,
)
ref_dataset = audio_to_text_dataset.get_char_dataset(config=dataset_cfg,)
ref_dataloader = DataLoader(
dataset=ref_dataset,
batch_size=batch_size,
collate_fn=ref_dataset.collate_fn,
drop_last=False,
shuffle=False,
num_workers=0,
pin_memory=False,
)
ref_preprocessor = EncDecCTCModel.from_config_dict(preprocessor_cfg)
count = 0
for ref_data, dali_data in zip(ref_dataloader, dali_dataset):
ref_audio, ref_audio_len, _, _ = ref_data
ref_features, ref_features_len = ref_preprocessor(input_signal=ref_audio, length=ref_audio_len)
dali_features, dali_features_len, _, _ = dali_data
a = ref_features.cpu().numpy()[:, :, :ref_features_len]
b = dali_features.cpu().numpy()[:, :, :dali_features_len]
err = np.abs(a - b)
assert np.mean(err) < 0.0001
assert np.max(err) < 0.01