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Support using different musan augmentations for the same audio.

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In addition, it returns the original audio without augmentation.
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Fangjun Kuang 2025-07-01 16:58:31 +08:00
parent 075e74bcb5
commit 85f6deb8d1
1 changed files with 9 additions and 7 deletions
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16
egs/librispeech/ASR/zipformer/speech_recognition.py
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@ -103,13 +103,15 @@ class K2SpeechRecognitionDataset(torch.utils.data.Dataset):
# 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)
if self.cut_transforms:
orig_cuts = cuts
# Sort the cuts again after transforms
cuts = cuts.sort_by_duration(ascending=False)
cuts = cuts.repeat(times=2)
for tnfm in self.cut_transforms:
cuts = tnfm(cuts)
cuts = orig_cuts + cuts
# Get a tensor with batched feature matrices, shape (B, T, F)
# Collation performs auto-padding, if necessary.
@ -117,7 +119,7 @@ class K2SpeechRecognitionDataset(torch.utils.data.Dataset):
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.
# that only has cuts for which we successfully read the audio.
inputs, _, cuts = input_tpl
else:
inputs, _ = input_tpl