From a4375ac7d44c7dbc30a3d21210d78af1d84f947e Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Mon, 9 Jan 2023 11:28:46 +0900
Subject: [PATCH] from local

---
 .../.conformer_randomcombine.py.swp           | Bin 102400 -> 102400 bytes
 .../conformer_randomcombine.py                |  10 +++++-----
 2 files changed, 5 insertions(+), 5 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 7eccdd3c488c6ed0452bf9c527f92bc9aaf79f3c..05be579f8c43ef17fee197ed1cb992729e11dc5e 100644
GIT binary patch
delta 213
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z{FR%5VKESg0I?AeivY1O5VHgERxSpH6d)D`;zOJa423|f0L14x7#Qk+SQm)zumerp
zeA3|n+w_h3j8>B$xQcIn;C`5ywK&?)Fn01ocPl}oSWT^*#LCp7`25n6g3^-W$sfG~
pIf|oAV->U&Vx|Y=G0IL(^U(#07(qlt@)>=bpZaWn>cc4Q3;=d}H)8+*

delta 196
zcmZozz}B#UO)SYE%+puFQqO<^2m}}yDynxUU#{6G_RgMlKN|x>|71>wV&B<33=CdC
z{F9r3VFeIJ0kH)TO98PY5c2}@ZY~CfEFhKy;uD+<4CO$q2E<o67#LcB*cgZ(u>(!q
zeA3|n8<RooWJXt&$rpmyCJVTyZ5H+DV4ghB+i!B2hvQ^DAA!l4o&uBoeH<spcnR?2
dBvz&t#pjon6qJ@sUgx9JeA{RHZ68KeX8<AUGQ$7>

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 32ce2204e..0d80f918e 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py
@@ -212,11 +212,11 @@ class Conformer(EncoderInterface):
 
         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(1/4*(self.sigmoid(self.alpha[0])*layer_output[2] + \
-                                 self.sigmoid(self.alpha[1])*layer_output[5] + \
-                                 self.sigmoid(self.alpha[2])*layer_output[8] + \
-                                 self.sigmoid(self.alpha[3])*layer_output[11]
+        layer_outputs = [x.permute(1, 0, 2) for x in layer_outputs]
+        x = self.layer_norm(1/4*(self.sigmoid(self.alpha[0])*layer_outputs[2] + \
+                                 self.sigmoid(self.alpha[1])*layer_outputs[5] + \
+                                 self.sigmoid(self.alpha[2])*layer_outputs[8] + \
+                                 self.sigmoid(self.alpha[3])*layer_outputs[11]
                                 )
                             )