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Double the threshold in brelu; slightly increase max_factor.
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@ -47,15 +47,15 @@ class Conv2dSubsampling(nn.Module):
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nn.Conv2d(
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in_channels=1, out_channels=odim, kernel_size=3, stride=2
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),
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DerivBalancer(channel_dim=1, threshold=0.02,
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max_factor=0.02),
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DerivBalancer(channel_dim=1, threshold=0.05,
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max_factor=0.025),
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nn.ReLU(),
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ExpScale(odim, 1, 1, speed=20.0),
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nn.Conv2d(
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in_channels=odim, out_channels=odim, kernel_size=3, stride=2
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),
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DerivBalancer(channel_dim=1, threshold=0.02,
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max_factor=0.02),
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DerivBalancer(channel_dim=1, threshold=0.05,
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max_factor=0.025),
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nn.ReLU(),
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ExpScale(odim, 1, 1, speed=20.0),
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)
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@ -156,8 +156,8 @@ class ConformerEncoderLayer(nn.Module):
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self.feed_forward = nn.Sequential(
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nn.Linear(d_model, dim_feedforward),
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DerivBalancer(channel_dim=-1, threshold=0.02,
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max_factor=0.02),
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DerivBalancer(channel_dim=-1, threshold=0.05,
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max_factor=0.025),
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ExpScaleSwish(dim_feedforward, speed=20.0),
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nn.Dropout(dropout),
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nn.Linear(dim_feedforward, d_model),
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@ -165,8 +165,8 @@ class ConformerEncoderLayer(nn.Module):
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self.feed_forward_macaron = nn.Sequential(
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nn.Linear(d_model, dim_feedforward),
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DerivBalancer(channel_dim=-1, threshold=0.02,
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max_factor=0.02),
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DerivBalancer(channel_dim=-1, threshold=0.05,
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max_factor=0.025),
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ExpScaleSwish(dim_feedforward, speed=20.0),
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nn.Dropout(dropout),
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nn.Linear(dim_feedforward, d_model),
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