AISHELL

AISHELL

class openspeech.datasets.aishell.lit_data_module.LightningAIShellDataModule(*args: Any, **kwargs: Any)[source]

Lightning data module for AIShell-1. The corpus includes training set, development set and test sets. Training set contains 120,098 utterances from 340 speakers; development set contains 14,326 utterance from the 40 speakers; Test set contains 7,176 utterances from 20 speakers. For each speaker, around 360 utterances (about 26 minutes of speech) are released.

Parameters

configs (DictConfig) – configuration set.

prepare_data()[source]

Prepare AI-Shell manifest file. If there is not exist manifest file, generate manifest file.

Returns

tokenizer is in charge of preparing the inputs for a model.

Return type

tokenizer (Tokenizer)

setup(stage: Optional[str] = None, tokenizer: openspeech.tokenizers.tokenizer.Tokenizer = None)[source]

Split train and valid dataset for training.

Parameters

  • stage (str) – stage of training. train or valid

  • tokenizer (Tokenizer) – tokenizer is in charge of preparing the inputs for a model.

Returns

None

test_dataloader() → torch.utils.data.dataloader.DataLoader[source]

Implement one or multiple PyTorch DataLoaders for testing.

The dataloader you return will not be called every epoch unless you set :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to True.

For data processing use the following pattern:

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

Note

Lightning adds the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Returns

Single or multiple PyTorch DataLoaders.

Example:

def test_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,
                    download=True)
    loader = torch.utils.data.DataLoader(
        dataset=dataset,
        batch_size=self.batch_size,
        shuffle=False
    )

    return loader

# can also return multiple dataloaders
def test_dataloader(self):
    return [loader_a, loader_b, ..., loader_n]

Note

If you don’t need a test dataset and a test_step(), you don’t need to implement this method.

Note

In the case where you return multiple test dataloaders, the test_step() will have an argument dataloader_idx which matches the order here.

train_dataloader() → torch.utils.data.dataloader.DataLoader[source]

Implement one or more PyTorch DataLoaders for training.

Returns

Either a single PyTorch DataLoader or a collection of these (list, dict, nested lists and dicts). In the case of multiple dataloaders, please see this page

The dataloader you return will not be called every epoch unless you set :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to True.

For data processing use the following pattern:

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

Note

Lightning adds the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Example:

# single dataloader
def train_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,
                    download=True)
    loader = torch.utils.data.DataLoader(
        dataset=dataset,
        batch_size=self.batch_size,
        shuffle=True
    )
    return loader

# multiple dataloaders, return as list
def train_dataloader(self):
    mnist = MNIST(...)
    cifar = CIFAR(...)
    mnist_loader = torch.utils.data.DataLoader(
        dataset=mnist, batch_size=self.batch_size, shuffle=True
    )
    cifar_loader = torch.utils.data.DataLoader(
        dataset=cifar, batch_size=self.batch_size, shuffle=True
    )
    # each batch will be a list of tensors: [batch_mnist, batch_cifar]
    return [mnist_loader, cifar_loader]

# multiple dataloader, return as dict
def train_dataloader(self):
    mnist = MNIST(...)
    cifar = CIFAR(...)
    mnist_loader = torch.utils.data.DataLoader(
        dataset=mnist, batch_size=self.batch_size, shuffle=True
    )
    cifar_loader = torch.utils.data.DataLoader(
        dataset=cifar, batch_size=self.batch_size, shuffle=True
    )
    # each batch will be a dict of tensors: {'mnist': batch_mnist, 'cifar': batch_cifar}
    return {'mnist': mnist_loader, 'cifar': cifar_loader}
val_dataloader() → torch.utils.data.dataloader.DataLoader[source]

Implement one or multiple PyTorch DataLoaders for validation.

The dataloader you return will not be called every epoch unless you set :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to True.

It’s recommended that all data downloads and preparation happen in prepare_data().

Note

Lightning adds the correct sampler for distributed and arbitrary hardware There is no need to set it yourself.

Returns

Single or multiple PyTorch DataLoaders.

Examples:

def val_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=False,
                    transform=transform, download=True)
    loader = torch.utils.data.DataLoader(
        dataset=dataset,
        batch_size=self.batch_size,
        shuffle=False
    )

    return loader

# can also return multiple dataloaders
def val_dataloader(self):
    return [loader_a, loader_b, ..., loader_n]

Note

If you don’t need a validation dataset and a validation_step(), you don’t need to implement this method.

Note

In the case where you return multiple validation dataloaders, the validation_step() will have an argument dataloader_idx which matches the order here.