From 9924acb5a6cdc29e213c80c62a1a9b6790d68504 Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Mon, 9 Jan 2023 11:17:23 +0900
Subject: [PATCH] from local

---
 .../.conformer_randomcombine.py.swp           | Bin 102400 -> 102400 bytes
 .../conformer_randomcombine.py                |  16 +++++++++-------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/.conformer_randomcombine.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/.conformer_randomcombine.py.swp
index 1423504dc30ea213d0d9865d5b9acde6ee14f30f..18599bdedbdfe260d804cabe17047ec737a500b3 100644
GIT binary patch
delta 679
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delta 625
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diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py
index defbdcb6e..83846158a 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py
@@ -211,7 +211,7 @@ class Conformer(EncoderInterface):
             )  # (T, N, C)
 
         x = x.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
-        
+        ''' 
         layer_output = [x.permute(1, 0, 2) for x in layer_output]
         
         x = self.layer_norm(1/12*(self.sigmoid(self.alpha[0])*layer_output[0] + \
@@ -228,12 +228,14 @@ class Conformer(EncoderInterface):
                                  self.sigmoid(self.alpha[11])*layer_output[11]
                                 )
                             )
-        
-        #x = 0
-        #for enum, alpha in enumerate(self.alpha):
-        #    x += self.sigmoid(alpha)*layer_output[enum]
-        
-        #x = self.layer_norm((1/self.group_size)*x)
+        '''
+        layer_outputs = [x.permute(1, 0, 2) for x in layer_outputs]
+
+        x = 0
+        for enum, alpha in enumerate(self.alpha):
+            x += self.sigmoid(alpha*layer_outputs[(enum+1)*self.group_layer_num-1])
+
+        x = self.layer_norm(x/self.group_num)
 
         return x, lengths