espnet2.tasks.enh.EnhancementTask

source

class espnet2.tasks.enh.EnhancementTask

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_iter_factory(args: Namespace, distributed_option: DistributedOption, mode: str, kwargs: dict | None = None) → AbsIterFactory

Build a factory object of mini-batch iterator.

This object is invoked at every epochs to build the iterator for each epoch as following:

>>> iter_factory = cls.build_iter_factory(...)
>>> for epoch in range(1, max_epoch):
...     for keys, batch in iter_fatory.build_iter(epoch):
...         model(**batch)

The mini-batches for each epochs are fully controlled by this class. Note that the random seed used for shuffling is decided as “seed + epoch” and the generated mini-batches can be reproduces when resuming.

Note that the definition of “epoch” doesn’t always indicate to run out of the whole training corpus. “–num_iters_per_epoch” option restricts the number of iterations for each epoch and the rest of samples for the originally epoch are left for the next epoch. e.g. If The number of mini-batches equals to 4, the following two are same:

• 1 epoch without “–num_iters_per_epoch”
• 4 epoch with “–num_iters_per_epoch” == 1

classmethod build_model(args: Namespace) → ESPnetEnhancementModel

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

class_choices_list : List[[ClassChoices](../train/ClassChoices.md#espnet2.train.class_choices.ClassChoices)] = [<espnet2.train.class_choices.ClassChoices object>, <espnet2.train.class_choices.ClassChoices object>, <espnet2.train.class_choices.ClassChoices object>, <espnet2.train.class_choices.ClassChoices object>, <espnet2.train.class_choices.ClassChoices object>, <espnet2.train.class_choices.ClassChoices object>]

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')

trainer

alias of Trainer