LibriSpeech

LibriSpeech

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

PyTorch Lightning Data Module for LibriSpeech Dataset. LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.

Parameters

configs (DictConfig) – configuraion set

prepare_data() → openspeech.tokenizers.tokenizer.Tokenizer[source]

Prepare librispeech data

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) → None[source]

Split dataset into train, valid, and test.

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.