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espnet2.tasks.spk.SpeakerTask

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espnet2.tasks.spk.SpeakerTask

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class espnet2.tasks.spk.SpeakerTask

Bases: AbsTask

SpeakerTask is a class that defines a speaker recognition task in the ESPnet2

framework. It inherits from AbsTask and encapsulates methods for managing training and inference, handling data processing, and building the model architecture.

num_optimizers

The number of optimizers used in the task.

  • Type: int

class_choices_list

A list of ClassChoices instances for various components such as frontend, specaug, normalize, encoder, pooling, projector, preprocessor, and loss.

  • Type: list

trainer

The trainer class associated with this task.

add_task_arguments(parser

argparse.ArgumentParser): Adds task-specific arguments to the provided argument parser.

build_collate_fn(args

argparse.Namespace, train: bool) -> Callable: Constructs a collate function based on the provided arguments.

build_preprocess_fn(args

argparse.Namespace, train: bool) -> Optional[Callable]: Constructs a preprocessing function based on the provided arguments.

required_data_names(train

bool = True, inference: bool = False) -> Tuple[str, …]: Returns a tuple of required data names based on the training or inference mode.

optional_data_names(train

bool = True, inference: bool = False) -> Tuple[str, …]: Returns a tuple of optional data names based on the training or inference mode.

build_model(args

argparse.Namespace) -> ESPnetSpeakerModel: Constructs and returns an ESPnetSpeakerModel based on the provided arguments.

################# Examples

To add task arguments to a parser: : import argparse parser = argparse.ArgumentParser() SpeakerTask.add_task_arguments(parser)

To build a model: : args = parser.parse_args() model = SpeakerTask.build_model(args)

########## NOTE This class requires the ESPnet2 library and is designed for speaker recognition tasks.

classmethod add_task_arguments(parser: ArgumentParser)

Adds task-related arguments to the provided argument parser.

This method is used to define various command-line arguments that are specific to the speaker task. The arguments include options for initialization methods, preprocessing settings, input size, target duration, and speaker-related configurations.

  • Parameters:parser (argparse.ArgumentParser) – The argument parser instance to which the task arguments will be added.

################# Examples

>>> import argparse
>>> parser = argparse.ArgumentParser()
>>> SpeakerTask.add_task_arguments(parser)
>>> args = parser.parse_args()

########## NOTE The method dynamically adds arguments based on the choices defined in the class-level class_choices_list.

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

Builds a collate function for processing batches of data during training or

inference.

This method constructs a collate function that will be used by a data loader to collate data samples into a batch. The collate function is responsible for ensuring that all samples in the batch are appropriately padded and organized for model input.

  • Parameters:
    • args (argparse.Namespace) – The command-line arguments containing various configurations for the task.
    • train (bool) – A flag indicating whether the function is being called in training mode. This can affect the way data is collated.
  • Returns: Callable[[Collection[Tuple[str, Dict[str, np.ndarray]]]], : > Tuple[List[str], Dict[str, torch.Tensor]]]: <br/> A callable that takes a collection of data samples and returns a tuple containing a list of keys and a dictionary of batched tensors.

################# Examples

>>> from espnet2.tasks.speaker_task import SpeakerTask
>>> args = argparse.Namespace(use_preprocessor=True, ...)
>>> collate_fn = SpeakerTask.build_collate_fn(args, train=True)
>>> batch = collate_fn(data_samples)

########## NOTE The function utilizes CommonCollateFn to handle the actual collating logic, which can be customized based on specific requirements.

classmethod build_model(args: Namespace) → ESPnetSpeakerModel

Builds the speaker model based on the provided arguments.

This method constructs an ESPnetSpeakerModel by utilizing various components such as frontend, spec augmentation, normalization, encoder, pooling, projector, and loss functions. The components are instantiated based on the specified arguments, and their configurations.

  • Parameters:args (argparse.Namespace) – The arguments containing configurations for model components including frontend, specaug, normalize, encoder, pooling, projector, and loss.
  • Returns: An instance of the constructed speaker model.
  • Return type:ESPnetSpeakerModel

################# Examples

>>> args = argparse.Namespace()
>>> args.frontend = "default"
>>> args.specaug = "specaug"
>>> args.normalize = "global_mvn"
>>> args.encoder = "ecapa_tdnn"
>>> args.pooling = "mean"
>>> args.projector = "rawnet3"
>>> args.loss = "aamsoftmax"
>>> args.spk_num = 10
>>> model = SpeakerTask.build_model(args)

########## NOTE This method assumes that all necessary configurations are provided in the args namespace. It will raise an error if required arguments are missing.

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

Builds a preprocessing function based on the provided arguments.

This function constructs a callable that processes input data according to the specified preprocessing class, which can be configured for training or inference.

  • Parameters:
    • args (argparse.Namespace) – Command-line arguments containing preprocessing configuration.
    • train (bool) – A flag indicating whether the function is being built for training or inference.
  • Returns: Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]: A preprocessing function that takes a file path and a dictionary of features, returning a processed dictionary of features. If preprocessing is not enabled, returns None.

################# Examples

>>> args = argparse.Namespace(use_preprocessor=True,
...                            preprocessor='spk',
...                            spk2utt='path/to/spk2utt',
...                            preprocessor_conf={})
>>> preprocess_fn = SpeakerTask.build_preprocess_fn(args, train=True)
>>> processed_data = preprocess_fn('path/to/audio.wav',
...                                  {'feature': np.array([...])})

########## NOTE The actual preprocessing class is selected based on the args.preprocessor attribute. Make sure the specified preprocessor is available in the choices.

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>, <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, ...]

A task for speaker recognition and related operations.

This class extends the AbsTask class and provides methods to handle various aspects of speaker recognition tasks, including model building, preprocessing, and argument parsing.

num_optimizers

The number of optimizers to use.

  • Type: int

class_choices_list

List of available class choices for various components such as frontend, encoder, and loss.

trainer

The trainer class for the task.

  • Type: Type[Trainer]

  • Parameters:parser (argparse.ArgumentParser) – The argument parser instance to add task-related arguments.

  • Returns: None

  • Yields: None

  • Raises:None –

################# Examples

To add task-related arguments to a parser:

>>> import argparse
>>> parser = argparse.ArgumentParser()
>>> SpeakerTask.add_task_arguments(parser)

To build a model based on provided arguments:

>>> args = parser.parse_args()
>>> model = SpeakerTask.build_model(args)

########## NOTE This task supports optional components for frontend, normalization, encoding, pooling, projection, and loss functions.

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

Returns the required data names for training or inference.

This method provides the necessary data names based on the task mode. For training, it typically includes the speech data and speaker labels, while for inference, it usually only includes the speech data.

  • Parameters:
    • train (bool , optional) – A flag indicating whether the mode is training. Defaults to True.
    • inference (bool , optional) – A flag indicating whether the mode is inference. Defaults to False.
  • Returns: A tuple containing the required data names.
  • Return type: Tuple[str, …]

################# Examples

>>> SpeakerTask.required_data_names(train=True)
('speech', 'spk_labels')
>>> SpeakerTask.required_data_names(train=False)
('speech',)

trainer

alias of SpkTrainer

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