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Double the threshold in brelu; slightly increase max_factor.

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This commit is contained in:
Daniel Povey 2022-03-05 00:07:14 +08:00
parent 74f2b163de
commit 65b09dd5f2
2 changed files with 8 additions and 8 deletions
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8
egs/librispeech/ASR/conformer_ctc/subsampling.py
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@ -47,15 +47,15 @@ class Conv2dSubsampling(nn.Module):
nn.Conv2d( nn.Conv2d(
in_channels=1, out_channels=odim, kernel_size=3, stride=2 in_channels=1, out_channels=odim, kernel_size=3, stride=2
), ),
DerivBalancer(channel_dim=1, threshold=0.02, DerivBalancer(channel_dim=1, threshold=0.05,
max_factor=0.02), max_factor=0.025),
nn.ReLU(), nn.ReLU(),
ExpScale(odim, 1, 1, speed=20.0), ExpScale(odim, 1, 1, speed=20.0),
nn.Conv2d( nn.Conv2d(
in_channels=odim, out_channels=odim, kernel_size=3, stride=2 in_channels=odim, out_channels=odim, kernel_size=3, stride=2
), ),
DerivBalancer(channel_dim=1, threshold=0.02, DerivBalancer(channel_dim=1, threshold=0.05,
max_factor=0.02), max_factor=0.025),
nn.ReLU(), nn.ReLU(),
ExpScale(odim, 1, 1, speed=20.0), ExpScale(odim, 1, 1, speed=20.0),
) )

8
egs/librispeech/ASR/transducer_stateless/conformer.py
View File

@ -156,8 +156,8 @@ class ConformerEncoderLayer(nn.Module):
self.feed_forward = nn.Sequential( self.feed_forward = nn.Sequential(
nn.Linear(d_model, dim_feedforward), nn.Linear(d_model, dim_feedforward),
DerivBalancer(channel_dim=-1, threshold=0.02, DerivBalancer(channel_dim=-1, threshold=0.05,
max_factor=0.02), max_factor=0.025),
ExpScaleSwish(dim_feedforward, speed=20.0), ExpScaleSwish(dim_feedforward, speed=20.0),
nn.Dropout(dropout), nn.Dropout(dropout),
nn.Linear(dim_feedforward, d_model), nn.Linear(dim_feedforward, d_model),
@ -165,8 +165,8 @@ class ConformerEncoderLayer(nn.Module):
self.feed_forward_macaron = nn.Sequential( self.feed_forward_macaron = nn.Sequential(
nn.Linear(d_model, dim_feedforward), nn.Linear(d_model, dim_feedforward),
DerivBalancer(channel_dim=-1, threshold=0.02, DerivBalancer(channel_dim=-1, threshold=0.05,
max_factor=0.02), max_factor=0.025),
ExpScaleSwish(dim_feedforward, speed=20.0), ExpScaleSwish(dim_feedforward, speed=20.0),
nn.Dropout(dropout), nn.Dropout(dropout),
nn.Linear(dim_feedforward, d_model), nn.Linear(dim_feedforward, d_model),