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espnet2.tasks.gan_codec.GANCodecTask

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espnet2.tasks.gan_codec.GANCodecTask

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class espnet2.tasks.gan_codec.GANCodecTask

Bases: AbsTask

GANCodecTask is a class for implementing a GAN-based neural codec task.

This class extends the AbsTask class and provides functionalities for training and building a GAN-based codec model. It supports configuration of various codec types and their respective parameters. The task utilizes the GANTrainer for optimization and training processes.

num_optimizers

The number of optimizers required for GAN training.

  • Type: int

class_choices_list

A list of codec class choices available for this task.

  • Type: list

trainer

The trainer class used for this task, which is GANTrainer.

  • Type: type

  • Parameters:parser (argparse.ArgumentParser) – The argument parser instance used to define the command-line arguments for the task.

  • Returns: A function that processes input data for training or inference.

  • Return type: Callable

  • Yields: None

  • Raises:ValueError – If the specified optimizer is not valid.

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

To use this class, one might create an argument parser and add task arguments as follows:

``
`

python import argparse from espnet2.tasks.gan_codec import GANCodecTask

parser = argparse.ArgumentParser(description=”GAN Codec Task”) GANCodecTask.add_task_arguments(parser) args = parser.parse_args()

``
`

After setting up the arguments, you can build a model:

python model = GANCodecTask.build_model(args)

To create optimizers for training:

python optimizers = GANCodecTask.build_optimizers(args, model)

######### NOTE This class requires the presence of specific codec implementations such as SoundStream, Encodec, DAC, and FunCodec.

classmethod add_task_arguments(parser: ArgumentParser)

Adds command-line arguments specific to the GANCodecTask.

This method defines the arguments related to the task and preprocessing settings. It also allows for adding codec-specific arguments through the class choices defined in class_choices_list.

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

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

To use this method, you can initialize an argument parser and call the add_task_arguments method:

``
`

python import argparse from gan_codec_task import GANCodecTask

parser = argparse.ArgumentParser() GANCodecTask.add_task_arguments(parser) args = parser.parse_args()

``
`

######### NOTE The –print_config mode cannot be used with required=True in the add_arguments method, so this has been handled appropriately.

  • Raises:argparse.ArgumentError – If there is an issue with adding the arguments to the parser.

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

Build a collate function for the GANCodecTask.

This method creates a callable function that collates a batch of data samples. The collate function is used to process a collection of tuples, where each tuple consists of a string identifier and a dictionary of numpy arrays. The collate function returns a tuple containing a list of string identifiers and a dictionary of PyTorch tensors.

  • Parameters:
    • args (argparse.Namespace) – Command line arguments containing configuration options for the task.
    • train (bool) – A flag indicating whether the function is being called during training or evaluation.
  • Returns: Callable[[Collection[Tuple[str, Dict[str, np.ndarray]]], : > Tuple[List[str], Dict[str, torch.Tensor]]]]: <br/> A function that collates data into a format suitable for model input.

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

>>> args = argparse.Namespace()
>>> args.some_arg = 'value'
>>> collate_fn = GANCodecTask.build_collate_fn(args, train=True)
>>> batch = [('sample1', {'data': np.array([1, 2, 3])}),
...          ('sample2', {'data': np.array([4, 5, 6])})]
>>> identifiers, tensors = collate_fn(batch)
>>> print(identifiers)  # Output: ['sample1', 'sample2']
>>> print(tensors)      # Output: {'data': tensor([[1, 2, 3], [4, 5, 6]])}

######### NOTE This method relies on the CommonCollateFn class for its implementation, which handles the specifics of padding and converting numpy arrays to PyTorch tensors.

classmethod build_model(args: Namespace) → ESPnetGANCodecModel

Builds and returns an ESPnetGANCodecModel instance.

This method constructs the model by first selecting the appropriate codec class based on the codec argument and then initializing an ESPnetGANCodecModel instance using the selected codec and additional configuration provided in model_conf.

  • Parameters:args (argparse.Namespace) – The parsed command line arguments, containing model configuration and codec information.
  • Returns: An instance of the ESPnetGANCodecModel : initialized with the specified codec and model configurations.
  • Return type:ESPnetGANCodecModel
  • Raises:ValueError – If the codec class cannot be found based on the provided args.codec.

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

>>> import argparse
>>> parser = argparse.ArgumentParser()
>>> parser.add_argument("--codec", type=str, default="soundstream")
>>> parser.add_argument("--model_conf", type=dict, default={})
>>> args = parser.parse_args()
>>> model = GANCodecTask.build_model(args)
>>> print(type(model))
<class 'espnet2.gan_codec.espnet_model.ESPnetGANCodecModel'>

classmethod build_optimizers(args: Namespace, model: ESPnetGANCodecModel) → List[Optimizer]

Builds optimizers for the generator and discriminator of the GAN model.

This method initializes two optimizers: one for the generator and one for the discriminator, based on the specified optimization algorithms. It checks if the model has the required components and raises appropriate errors if any of the optimizers are not found.

  • Parameters:
    • args (argparse.Namespace) – The command-line arguments containing optimizer configurations and flags.
    • model (ESPnetGANCodecModel) – The GAN codec model which contains generator and discriminator components.
  • Returns: A list containing the optimizers for both the generator and discriminator.
  • Return type: List[torch.optim.Optimizer]
  • Raises:
    • ValueError – If the specified optimizer class for the generator or
    • discriminator is not valid. –
    • RuntimeError – If the fairscale library is required but not installed.

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

>>> from argparse import Namespace
>>> args = Namespace(optim='Adam', optim_conf={'lr': 0.001},
...                  optim2='SGD', optim2_conf={'lr': 0.01},
...                  sharded_ddp=False)
>>> model = ESPnetGANCodecModel(...)  # Assuming model is created properly
>>> optimizers = GANCodecTask.build_optimizers(args, model)
>>> len(optimizers)  # Should return 2
2

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

Builds a preprocessing function based on the task arguments.

This method checks if preprocessing is enabled in the arguments and constructs a CommonPreprocessor if it is. If not, it returns None.

  • Parameters:
    • cls – The class type of the calling object.
    • args (argparse.Namespace) – The arguments namespace containing task configuration options.
    • train (bool) – A flag indicating whether the function is being built for training or not.
  • Returns: A preprocessing function that takes a string and a dictionary of numpy arrays, and returns a dictionary of numpy arrays, or None if preprocessing is not enabled.
  • Return type: Optional[Callable[[str, Dict[str, np.ndarray]], Dict[str, np.ndarray]]]

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

>>> from argparse import Namespace
>>> args = Namespace(use_preprocessor=True, iterator_type='chunk',
... chunk_length=16000)
>>> preprocess_fn = GANCodecTask.build_preprocess_fn(args, train=True)
>>> audio_data = {"audio": np.random.rand(16000)}
>>> processed_data = preprocess_fn("audio", audio_data)

######### NOTE The preprocessing function is designed to work with single-channel audio data only.

class_choices_list

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

num_optimizers

*: int* *= 2*

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

Returns the optional data names used in the GAN codec task.

This method can be overridden by subclasses to specify any optional data names that the task may utilize. By default, it returns an empty tuple, indicating that no optional data names are defined.

  • Parameters:
    • train (bool) – Indicates whether the task is in training mode. Default is True.
    • inference (bool) – Indicates whether the task is in inference mode. Default is False.
  • Returns: A tuple of optional data names used in the task.
  • Return type: Tuple[str, …]

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

>>> optional_data = GANCodecTask.optional_data_names()
>>> print(optional_data)
()  # This will output an empty tuple by default.

######### NOTE This method is a class method and can be called directly on the class without creating an instance.

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

Returns the required data names for the GAN codec task.

This method provides the necessary data names based on the mode of operation, which can be either training or inference. In this implementation, the required data name is “audio” for both modes.

  • Parameters:
    • train (bool) – Indicates if the data is for training. Defaults to True.
    • inference (bool) – Indicates if the data is for inference. Defaults to False.
  • Returns: A tuple containing the required data names. In this case, it returns (“audio”,) for both training and inference modes.
  • Return type: Tuple[str, …]

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

>>> GANCodecTask.required_data_names(train=True, inference=False)
('audio',)
>>> GANCodecTask.required_data_names(train=False, inference=True)
('audio',)

trainer

alias of GANTrainer

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