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@ -135,6 +135,10 @@ class Conformer(EncoderInterface):
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)
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self._init_state: List[torch.Tensor] = [torch.empty(0)]
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self.alpha = nn.Parameters(torch.rand(4))
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self.sigmoid = nn.Sigmoid()
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self.layer_norm = nn.LayerNorm(512)
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def forward(
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self, x: torch.Tensor, x_lens: torch.Tensor, warmup: float = 1.0
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) -> Tuple[torch.Tensor, torch.Tensor]:
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@ -198,7 +202,12 @@ class Conformer(EncoderInterface):
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x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
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layer_output = [x.permute(1, 0, 2) for x in layer_output]
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x = self.layer_norm(1/4*(self.sigmoid(self.alpha[0])*layer_output[2] + \
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self.sigmoid(self.alpha[1])*layer_output[5] + \
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self.sigmoid(self.alpha[2])*layer_output[8] + \
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self.sigmoid(self.alpha[3])*layer_output[11]
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)
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)
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return x, lengths
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@torch.jit.export
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