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import torch
from torchaudio.datasets import LIBRISPEECH
class MapMemoryCache(torch.utils.data.Dataset):
"""
Wrap a dataset so that, whenever a new item is returned, it is saved to memory.
"""
def __init__(self, dataset):
self.dataset = dataset
self._cache = [None] * len(dataset)
def __getitem__(self, n):
if self._cache[n] is not None:
return self._cache[n]
item = self.dataset[n]
self._cache[n] = item
return item
def __len__(self):
return len(self.dataset)
class Processed(torch.utils.data.Dataset):
def __init__(self, dataset, transforms, encode):
self.dataset = dataset
self.transforms = transforms
self.encode = encode
def __getitem__(self, key):
item = self.dataset[key]
return self.process_datapoint(item)
def __len__(self):
return len(self.dataset)
def process_datapoint(self, item):
transformed = item[0]
target = item[2].lower()
transformed = self.transforms(transformed)
transformed = transformed[0, ...].transpose(0, -1)
target = self.encode(target)
target = torch.tensor(target, dtype=torch.long, device=transformed.device)
return transformed, target
def split_process_librispeech(
datasets,
transforms,
language_model,
root,
folder_in_archive,
):
def create(tags, cache=True):
if isinstance(tags, str):
tags = [tags]
if isinstance(transforms, list):
transform_list = transforms
else:
transform_list = [transforms]
data = torch.utils.data.ConcatDataset(
[
Processed(
LIBRISPEECH(
root,
tag,
folder_in_archive=folder_in_archive,
download=False,
),
transform,
language_model.encode,
)
for tag, transform in zip(tags, transform_list)
]
)
data = MapMemoryCache(data)
return data
# For performance, we cache all datasets
return tuple(create(dataset) for dataset in datasets)
def collate_factory(model_length_function, transforms=None):
if transforms is None:
transforms = torch.nn.Sequential()
def collate_fn(batch):
tensors = [transforms(b[0]) for b in batch if b]
tensors_lengths = torch.tensor(
[model_length_function(t) for t in tensors],
dtype=torch.long,
device=tensors[0].device,
)
tensors = torch.nn.utils.rnn.pad_sequence(tensors, batch_first=True)
tensors = tensors.transpose(1, -1)
targets = [b[1] for b in batch if b]
target_lengths = torch.tensor(
[target.shape[0] for target in targets],
dtype=torch.long,
device=tensors.device,
)
targets = torch.nn.utils.rnn.pad_sequence(targets, batch_first=True)
return tensors, targets, tensors_lengths, target_lengths
return collate_fn
|
