espnet2.tasks.lid.LIDTask

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class espnet2.tasks.lid.LIDTask

Bases: AbsTask

classmethod add_task_arguments(parser: ArgumentParser)

classmethod build_collate_fn(args: Namespace, train: bool) → Callable[[Collection[Tuple[str, Dict[str, ndarray]]]], Tuple[List[str], Dict[str, Tensor]]]

Return “collate_fn”, which is a callable object and given to DataLoader.

>>> from torch.utils.data import DataLoader
>>> loader = DataLoader(collate_fn=cls.build_collate_fn(args, train=True), ...)

In many cases, you can use our common collate_fn.

classmethod build_preprocess_fn(args: Namespace, train: bool) → Callable[[str, Dict[str, array]], Dict[str, ndarray]] | None

num_optimizers : int = 1

classmethod optional_data_names(train: bool = True, inference: bool = False) → Tuple[str, ...]

Define the optional names by Task

This function is used by

cls.check_task_requirements() If your model is defined as follows,

>>> from espnet2.train.abs_espnet_model import AbsESPnetModel
>>> class Model(AbsESPnetModel):
...     def forward(self, input, output, opt=None):  pass

then “optional_data_names” should be as

>>> optional_data_names = ('opt',)

classmethod required_data_names(train: bool = True, inference: bool = False) → Tuple[str, ...]

Define the required names by Task

This function is used by

cls.check_task_requirements() If your model is defined as following,

>>> from espnet2.train.abs_espnet_model import AbsESPnetModel
>>> class Model(AbsESPnetModel):
...     def forward(self, input, output, opt=None):  pass

then “required_data_names” should be as

>>> required_data_names = ('input', 'output')