From bb2d62ca85ef5d983470a4ed8fc876771fc9ed2b Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Sat, 7 Jan 2023 00:30:09 +0900
Subject: [PATCH] from local

---
 .../.conformer.py.swp                         | Bin 90112 -> 94208 bytes
 .../conformer.py                              |  14 ++++++++++++--
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/.conformer.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/.conformer.py.swp
index 0c34adc04fb048fe35a042e44705de93c14c7115..86ff639e67528345dba5b73cc1bf9bf338f9c961 100644
GIT binary patch
delta 1221
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delta 644
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diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer.py b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer.py
index ba95433a9..d22ed5962 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer.py
@@ -135,7 +135,7 @@ class Conformer(EncoderInterface):
         )
         self._init_state: List[torch.Tensor] = [torch.empty(0)]
 
-        self.alpha = nn.Parameter(torch.rand(6))
+        self.alpha = nn.Parameter(torch.rand(12))
         self.sigmoid = nn.Sigmoid()
         self.layer_norm = nn.LayerNorm(512)
 
@@ -205,11 +205,21 @@ class Conformer(EncoderInterface):
         
         layer_output = [x.permute(1, 0, 2) for x in layer_output]
         
-        x = self.layer_norm(1/3*(self.sigmoid(self.alpha[0])*layer_output[3] + \
+        '''
+        x = self.layer_norm(1/12*(self.sigmoid(self.alpha[0])*layer_output[3] + \
+                                 self.sigmoid(self.alpha[1])*layer_output[7] + \
+                                 self.sigmoid(self.alpha[1])*layer_output[7] + \
+                                 self.sigmoid(self.alpha[1])*layer_output[7] + \
                                  self.sigmoid(self.alpha[1])*layer_output[7] + \
                                  self.sigmoid(self.alpha[2])*layer_output[11]
                                 )
                             )
+        '''
+        x = 0
+        for enum, alpha in enumerate(self.alpha):
+            x += self.sigmoid(alpha)*layer_output[self.group_size*(enum+1)]
+        
+        x = self.layer_norm((1/self.group_size)*x)
 
         return x, lengths