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# 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)