pelu_base->expscale, add 2xExpScale in subsampling, and in feedforward units.

egs/librispeech/ASR/conformer_ctc/subsampling.py

@@ -48,10 +48,12 @@ class Conv2dSubsampling(nn.Module):
                 in_channels=1, out_channels=odim, kernel_size=3, stride=2
             ),
             nn.ReLU(),
+            ExpScale(odim, 1, 1, speed=2.0),
             nn.Conv2d(
                 in_channels=odim, out_channels=odim, kernel_size=3, stride=2
             ),
             nn.ReLU(),
+            ExpScale(odim, 1, 1, speed=2.0),
         )
         self.out = nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim)
         self.out_norm = nn.LayerNorm(odim, elementwise_affine=False)
@@ -195,3 +197,12 @@ class PeLU(torch.nn.Module):
         self.alpha = alpha
     def forward(self, x: Tensor) -> Tensor:
         return PeLUFunction.apply(x, self.cutoff, self.alpha)
+
+class ExpScale(torch.nn.Module):
+    def __init__(self, *shape, speed: float = 1.0):
+        super(ExpScale, self).__init__()
+        self.scale = nn.Parameter(torch.zeros(*shape))
+        self.speed = speed
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x * (self.scale * self.speed).exp()

egs/librispeech/ASR/transducer_stateless/conformer.py

@@ -19,7 +19,7 @@ import copy
 import math
 import warnings
 from typing import Optional, Tuple, Sequence
-from subsampling import PeLU
+from subsampling import PeLU, ExpScale
 
 import torch
 from torch import Tensor, nn
@@ -157,6 +157,7 @@ class ConformerEncoderLayer(nn.Module):
         self.feed_forward = nn.Sequential(
             nn.Linear(d_model, dim_feedforward),
             Swish(),
+            ExpScale(dim_feedforward, speed=2.0),
             nn.Dropout(dropout),
             nn.Linear(dim_feedforward, d_model),
         )
@@ -164,6 +165,7 @@ class ConformerEncoderLayer(nn.Module):
         self.feed_forward_macaron = nn.Sequential(
             nn.Linear(d_model, dim_feedforward),
             Swish(),
+            ExpScale(dim_feedforward, speed=2.0),
             nn.Dropout(dropout),
             nn.Linear(dim_feedforward, d_model),
         )

egs/librispeech/ASR/transducer_stateless/train.py

@@ -110,7 +110,7 @@ def get_parser():
     parser.add_argument(
         "--exp-dir",
         type=str,
-        default="transducer_stateless/specaugmod_baseline_randcombine1_pelu_base",
+        default="transducer_stateless/specaugmod_baseline_randcombine1_expscale",
         help="""The experiment dir.
         It specifies the directory where all training related
         files, e.g., checkpoints, log, etc, are saved