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Reduce min of bypass_scale from 0.5 to 0.3, and make it not applied in test mode.

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This commit is contained in:
Daniel Povey 2022-10-29 15:40:47 +08:00
parent 435d0dec71
commit f995426324
1 changed files with 7 additions and 6 deletions
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13
egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
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@ -363,12 +363,13 @@ class ZipformerEncoderLayer(nn.Module):
delta = src - src_orig delta = src - src_orig
bypass_scale = self.bypass_scale bypass_scale = self.bypass_scale
if torch.jit.is_scripting() or (not self.training) or random.random() > 0.1: if self.training and random.random() > 0.1:
# with probability 0.9, in training mode, or always, in testing # with probability 0.9, in training mode clamp bypass_scale to [
# mode, clamp bypass_scale to [ 0.1, 1.0 ]; this will encourage it # 0.3, 1.0 ]; this will encourage it to learn parameters within this
# to learn parameters within this range by making parameters that # range by making parameters that are outside that range range
# are outside that range range noisy. # noisy. For testing don't bother, as it will anyway end up
bypass_scale = bypass_scale.clamp(min=0.5, max=1.0) # learning values within this range or very close to it.
bypass_scale = bypass_scale.clamp(min=0.3, max=1.0)
src = src_orig + delta * bypass_scale src = src_orig + delta * bypass_scale
return self.whiten(src) return self.whiten(src)