espnet3.components.data.data_organizer.DataOrganizer

source

DatasetConfig | Dict[str, Any]] | None = None, valid: List[DatasetConfig | Dict[str, Any]] | None = None, test: List[DatasetConfig | Dict[str, Any]] | None = None, preprocessor: Callable[[dict], dict] | None = None)

Bases: object

Organizes training, validation, and test datasets into a unified interface.

This class constructs combined datasets for training and validation, and individual named datasets for testing, optionally applying a transform and preprocessor per dataset.

• Parameters:
  • train (Optional *[*List *[*Union [DatasetConfig , Dict *[*str , Any ] ] ] ]) – A list of training dataset configuration objects.
  • valid (Optional *[*List *[*Union [DatasetConfig , Dict *[*str , Any ] ] ] ]) – A list of validation dataset configuration objects.
  • test (Optional *[*List *[*Union [DatasetConfig , Dict *[*str , Any ] ] ] ]) – A list of test dataset configurations, each with a name and corresponding dataset and optional transform.
  • preprocessor (Optional *[*Callable ]) – A global preprocessor function applied after each dataset’s transform. If it’s an instance of AbsPreprocessor, (uid, sample) is passed.

train

Combined dataset built from training configurations, or None if not provided.

• Type:CombinedDataset

valid

Combined dataset built from validation configurations, or None if not provided.

• Type:CombinedDataset

test_sets

Dictionary mapping test set names to DatasetWithTransform instances.

• Type: Dict[str, DatasetWithTransform]

• Raises:

  • RuntimeError – If only one of train or valid is provided.
  • RuntimeError – If train and valid are of mismatched types (e.g., one is CombinedDataset, the other is None).
  • AssertionError – If preprocessor is not callable.

NOTE

The DataOrganizer is designed to support both training and testing workflows:

• For training: provide both train and valid.
• For testing only: provide test and omit train / valid.
• All three (train, valid, test) can also be provided simultaneously.

If any of the train, valid, or test are omitted, the corresponding : attributes will be set to None or empty.

Example (training + validation): : ```python

organizer = DataOrganizer( ... train=train_configs, ... valid=valid_configs, ... preprocessor=MyPreprocessor() ... ) sample = organizer.train[0] test_sample = organizer.test["test_clean"][0]

Example (testing only):
: ```python
>>> organizer = DataOrganizer(
...     test=test_configs,
...     preprocessor=MyPreprocessor()
... )
>>> test_sample = organizer.test["test_clean"][0]

Initialize DataOrganizer object.

property test

Get the dictionary of test datasets.