def __getitem__(self, cuts: CutSet) -> Dict[str, torch.Tensor]:
validate(cuts)
cuts = cuts.sort_by_duration()
for tfnm in self.cut_transforms:
cuts = tfnm(cuts)
inputs, input_lens = self.input_strategy(cuts)
for tfnm in self.input_transforms:
inputs = tfnm(inputs)
return {
"inputs": inputs,
"input_lens": input_lens,
"is_voice": self.input_strategy.supervision_masks(cuts),
"cut": cuts,
}
def __getitem__(self,
cuts: CutSet) -> Dict[str, Union[torch.Tensor, List[str]]]:
"""
Return a new batch, with the batch size automatically determined using the constraints
of max_frames and max_cuts.
"""
validate_for_asr(cuts)
self.hdf5_fix.update()
# Sort the cuts by duration so that the first one determines the batch time dimensions.
cuts = cuts.sort_by_duration(ascending=False)
# Optional CutSet transforms - e.g. padding, or speed perturbation that adjusts
# the supervision boundaries.
for tnfm in self.cut_transforms:
cuts = tnfm(cuts)
# Get a tensor with batched feature matrices, shape (B, T, F)
# Collation performs auto-padding, if necessary.
input_tpl = self.input_strategy(cuts)
if len(input_tpl) == 3:
# An input strategy with fault tolerant audio reading mode.
# "cuts" may be a subset of the original "cuts" variable,
# that only has cuts for which we succesfully read the audio.
inputs, _, cuts = input_tpl
else:
inputs, _ = input_tpl
# Get a dict of tensors that encode the positional information about supervisions
# in the batch of feature matrices. The tensors are named "sequence_idx",
# "start_frame/sample" and "num_frames/samples".
supervision_intervals = self.input_strategy.supervision_intervals(cuts)
# Apply all available transforms on the inputs, i.e. either audio or features.
# This could be feature extraction, global MVN, SpecAugment, etc.
segments = torch.stack(list(supervision_intervals.values()), dim=1)
for tnfm in self.input_transforms:
inputs = tnfm(inputs, supervision_segments=segments)
batch = {
"inputs":
inputs,
"supervisions":
default_collate([{
"text": supervision.text,
} for sequence_idx, cut in enumerate(cuts)
for supervision in cut.supervisions]),
}
# Update the 'supervisions' field with sequence_idx and start/num frames/samples
batch["supervisions"].update(supervision_intervals)
if self.return_cuts:
batch["supervisions"]["cut"] = [
cut for cut in cuts for sup in cut.supervisions
]
has_word_alignments = all(
s.alignment is not None and "word" in s.alignment for c in cuts
for s in c.supervisions)
if has_word_alignments:
# TODO: might need to refactor BatchIO API to move the following conditional logic
# into these objects (e.g. use like: self.input_strategy.convert_timestamp(),
# that returns either num_frames or num_samples depending on the strategy).
words, starts, ends = [], [], []
frame_shift = cuts[0].frame_shift
sampling_rate = cuts[0].sampling_rate
if frame_shift is None:
try:
frame_shift = self.input_strategy.extractor.frame_shift
except AttributeError:
raise ValueError(
"Can't determine the frame_shift -- it is not present either in cuts or the input_strategy. "
)
for c in cuts:
for s in c.supervisions:
words.append(
[aliword.symbol for aliword in s.alignment["word"]])
starts.append([
compute_num_frames(
aliword.start,
frame_shift=frame_shift,
sampling_rate=sampling_rate,
) for aliword in s.alignment["word"]
])
ends.append([
compute_num_frames(
aliword.end,
frame_shift=frame_shift,
sampling_rate=sampling_rate,
) for aliword in s.alignment["word"]
])
batch["supervisions"]["word"] = words
batch["supervisions"]["word_start"] = starts
batch["supervisions"]["word_end"] = ends
return batch