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Fix duplicate Swish; replace norm+swish with swish+exp-scale in convolution module
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7e88999641
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@ -163,7 +163,6 @@ 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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Swish(),
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ExpScaleSwish(dim_feedforward, speed=50.0),
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nn.Dropout(dropout),
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nn.Linear(dim_feedforward, d_model),
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@ -874,7 +873,9 @@ class ConvolutionModule(nn.Module):
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groups=channels,
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bias=bias,
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)
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self.norm = nn.LayerNorm(channels)
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# shape: (channels, 1), broadcasts with (batch, channel, time).
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self.activation = ExpScaleSwish(channels, 1, speed=50.0)
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self.pointwise_conv2 = nn.Conv1d(
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channels,
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channels,
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@ -883,7 +884,6 @@ class ConvolutionModule(nn.Module):
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padding=0,
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bias=bias,
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)
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self.activation = Swish()
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def forward(self, x: Tensor) -> Tensor:
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"""Compute convolution module.
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@ -905,9 +905,6 @@ class ConvolutionModule(nn.Module):
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# 1D Depthwise Conv
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x = self.depthwise_conv(x)
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# x is (batch, channels, time)
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x = x.permute(0, 2, 1)
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x = self.norm(x)
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x = x.permute(0, 2, 1)
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x = self.activation(x)
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@ -110,7 +110,7 @@ def get_parser():
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parser.add_argument(
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"--exp-dir",
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type=str,
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default="transducer_stateless/specaugmod_baseline_randcombine1_expscale4",
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default="transducer_stateless/specaugmod_baseline_randcombine1_expscale5",
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help="""The experiment dir.
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It specifies the directory where all training related
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files, e.g., checkpoints, log, etc, are saved
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