📦 ESPnet3 DataOrganizer

Masao SomekiAbout 1 min

ESPnet3 DataOrganizer

DataOrganizer is the config-driven wrapper that turns dataset entries into the objects consumed by collect_stats, train, infer, and measure.

Implementation:

espnet3.components.data.data_organizer.DataOrganizer

Dataset resolution itself is documented in:

Dataset references and builders

Role in the pipeline

training.yaml / inference.yaml
  └── dataset:
        _target_: espnet3.components.data.data_organizer.DataOrganizer
        recipe_dir: ${recipe_dir}
        train: ...
        valid: ...
        test: ...
        preprocessor: ...

DataOrganizer then:

builds train and valid as CombinedDataset
builds each named test entry as DatasetWithTransform
applies transform, then preprocessor

Current dataset entry format

Each dataset entry uses:

data_src
data_src_args
transform
name

Minimal example:

dataset:
  _target_: espnet3.components.data.data_organizer.DataOrganizer
  recipe_dir: ${recipe_dir}

  train:
    - data_src: mini_an4/asr
      data_src_args:
        split: train

  valid:
    - data_src: mini_an4/asr
      data_src_args:
        split: valid

  test:
    - name: test
      data_src: mini_an4/asr
      data_src_args:
        split: test

Important behavior

`data_src_args` only goes to `Dataset(...)`

DataOrganizer resolves the dataset module, gets its exported Dataset class, and instantiates:

Dataset(**data_src_args)

Top-level organizer keys such as:

name
transform

stay in organizer space and are not forwarded to the dataset constructor.

`recipe_dir` matters for local datasets

If a dataset entry omits data_src, DataOrganizer resolves the local recipe module:

${recipe_dir}/dataset/__init__.py

So local recipes should set:

dataset:
  _target_: espnet3.components.data.data_organizer.DataOrganizer
  recipe_dir: ${recipe_dir}

`train` and `valid` must move together

Current DataOrganizer requires:

both train and valid present, or
both omitted

Providing only one side raises an error.

`test[*].name` becomes the test-set key

Inference and measurement use the name field to choose test sets and to build per-test output directories.

Transforms and preprocessor

Each dataset entry may define transform, and the organizer itself may define a shared preprocessor.

The order is:

transform(sample)
preprocessor(sample) or preprocessor(uid, sample)

If the preprocessor is an AbsPreprocessor, ESPnet3 uses the ESPnet-style (uid, sample) call automatically.

Example: local dataset module

mini_an4 uses the local recipe mode:

dataset:
  _target_: espnet3.components.data.data_organizer.DataOrganizer
  recipe_dir: ${recipe_dir}
  train:
    - data_src_args:
        split: train
  valid:
    - data_src_args:
        split: valid

Because data_src is omitted, ESPnet3 loads:

egs3/mini_an4/asr/dataset/__init__.py

and instantiates the exported Dataset.

Example: tag-based dataset source

test:
  - name: test-clean
    data_src: librispeech_100/asr
    data_src_args:
      split: test-clean
      recipe_dir: ${recipe_dir}

This resolves to:

egs3.librispeech_100.asr.dataset