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Add Whiten module, with whitening_limit=10.0, at output of ModifiedSEModule

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Daniel Povey 2022-11-03 13:02:54 +08:00
parent a27670d097
commit a2dbce2a9a
1 changed files with 10 additions and 3 deletions
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13
egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
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@ -1463,12 +1463,19 @@ class ModifiedSEModule(nn.Module):
max_factor=0.01, max_factor=0.01,
min_prob=0.2, min_prob=0.2,
) )
#self.bottleneck_norm = BasicNorm(bottleneck_dim)
self.from_bottleneck_proj = ScaledLinear(bottleneck_dim, d_model) self.from_bottleneck_proj = ScaledLinear(bottleneck_dim, d_model)
self.sigmoid = nn.Sigmoid() # make it a submodule for diagnostics purposes.
self.out_proj = ScaledLinear(d_model, d_model, self.out_proj = ScaledLinear(d_model, d_model,
bias=False, initial_scale=0.1) bias=False, initial_scale=0.1)
self.out_whiten = Whiten(num_groups=1,
whitening_limit=10.0,
prob=(0.025, 0.25),
grad_scale=0.01)
def forward(self, def forward(self,
@ -1497,16 +1504,16 @@ class ModifiedSEModule(nn.Module):
squeezed = self.activation(squeezed) squeezed = self.activation(squeezed)
squeezed = self.to_bottleneck_proj(squeezed) squeezed = self.to_bottleneck_proj(squeezed)
squeezed = self.bottleneck_balancer(squeezed) squeezed = self.bottleneck_balancer(squeezed)
#squeezed = self.bottleneck_norm(squeezed)
squeezed = self.from_bottleneck_proj(squeezed) squeezed = self.from_bottleneck_proj(squeezed)
if random.random() < 0.05: if random.random() < 0.05:
# to stop a hopefully-unlikely failure mode where the inputs to the sigmoid # to stop a hopefully-unlikely failure mode where the inputs to the sigmoid
# get too large and the grads get mostly too small. # get too large and the grads get mostly too small.
squeezed = penalize_abs_values_gt(squeezed, limit=10.0, penalty=1.0e-04) squeezed = penalize_abs_values_gt(squeezed, limit=10.0, penalty=1.0e-04)
scales = self.sigmoid(squeezed)
x = self.in_proj(x) x = self.in_proj(x)
x = x * squeezed x = x * squeezed
return self.out_proj(x) return self.out_whiten(self.out_proj(x))
class FeedforwardModule(nn.Module): class FeedforwardModule(nn.Module):