File size: 1,875 Bytes
b386992 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from nemo.utils import logging
class FallbackDataset(torch.utils.data.Dataset):
"""
FallbackDataset is a wrapper on an existing map-style ``torch.utils.data.Dataset``.
It's used to return the previous item (or batch, depending on Dataset) whenever
the underlying ``Dataset`` returns ``None``.
This is useful when ``Dataset`` returns a full batch (as e.g. Lhotse datasets typically do),
and wasn't able to read any of the items in that batch.
Example::
>>> dataset = AudioToTextLhotseDataset(...)
... dataset = FallbackDataset(dataset)
"""
def __init__(self, dataset):
self.dataset = dataset
self._fallback = None
def __getitem__(self, item):
ans = self.dataset[item]
if ans is None:
if self._fallback is None:
logging.warning(
f"FallbackDataset received None from {self.dataset} on the first call to __getitem__, "
f"and must return None instead of an actual batch."
f"This indicates an issue with data reading."
)
ans = self._fallback
self._fallback = ans
return ans
def __len__(self):
return len(self.dataset)
|