From 19e1c1ee3a602f45401c086171953397e75e18e2 Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Tue, 21 Feb 2023 09:34:16 +0900
Subject: [PATCH] from local

---
 .../ASR/conformer_ctc2/.conformer.py.swp      | Bin 65536 -> 65536 bytes
 .../ASR/conformer_ctc2/.train.py.swp          | Bin 65536 -> 65536 bytes
 .../ASR/conformer_ctc2/conformer.py           |   9 ++++++---
 3 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/egs/librispeech/ASR/conformer_ctc2/.conformer.py.swp b/egs/librispeech/ASR/conformer_ctc2/.conformer.py.swp
index 254c0e8b42a7257701476bee579c1fc4c4336cff..9b65969a11dcc0d0807f3fdb70c8254600a47e9b 100644
GIT binary patch
delta 197
zcmZo@U}<Pz*-#+B=(@R3VDf$5Di#KYRjdpQ3<3-cu9F2n+_t;T&cLt`h@F7g0Eqvx
zF)%Cu;#wfC0b)ZSW(DH!KwWo$_%;yV0^&_Tyoz-*BTEqruL2M#DJd}|=Oh*vZ)W_)
z&N#V1pJg)N_eiC*{33<aywY5S%shpn#Ju!WjpEdtG`;kq{L+GWptz>h<adXZfy!AY
eZ*mHo%;Ib{IsLoD<Q?9;o9&#RuuuN*UjP8fL^$~X

delta 158
zcmZo@U}<Pz*-#+B=(M>|VDf$5dn^nLOl%Ac3<3-cPLl;c+_pQ#&cF~3#6N(dSAqBn
z5O)G`ED-wxu?Y|x1F;bha{)0(w*U}7VP#;r#JZW0Wd+ORIbZo$ob&V2CO?c6ne6wS
ub8>+`>*TGOQj_!2*(P6be8ifXQ=DowdGoiZ$<Mz_Y~JWp$T9iDe*pldSS@M*

diff --git a/egs/librispeech/ASR/conformer_ctc2/.train.py.swp b/egs/librispeech/ASR/conformer_ctc2/.train.py.swp
index 61a4d39fb43c21297d0148395c516303e4bdcac4..f78aaf1a82b3373b6e8c9be98fd1e56f8f193dcd 100644
GIT binary patch
delta 33
ncmZo@U}<Pz5lb=%^Ym4))H7fJ0s#hwU8_DP%d~D3d-Wdxl#>c?

delta 33
ncmZo@U}<Pz5lb=%^Ym4))H7fJ0s#gFg_WO^{aZGQz4{LTkFyE}

diff --git a/egs/librispeech/ASR/conformer_ctc2/conformer.py b/egs/librispeech/ASR/conformer_ctc2/conformer.py
index b6983f728..69d89467a 100644
--- a/egs/librispeech/ASR/conformer_ctc2/conformer.py
+++ b/egs/librispeech/ASR/conformer_ctc2/conformer.py
@@ -194,9 +194,12 @@ class Conformer(Transformer):
         
         if self.group_num > 0:
             x = 0
-            for enum, alpha in enumerate(self.alpha):
-                if self.learnable_alpha: x += self.sigmoid(alpha) * layer_outputs[(enum+1)*self.group_layer_num-1]
-                else: x += (1/self.group_num) * layer_outputs[(enum+1)*self.group_layer_num-1]
+            if self.learnable_alpha:
+                for enum, alpha in enumerate(self.alpha):
+                     x += self.sigmoid(alpha) * layer_outputs[(enum+1)*self.group_layer_num-1]
+            else:
+                for enum in range(self.group_num):
+                    x += (1/self.group_num) * layer_outputs[(enum+1)*self.group_layer_num-1]
             x = self.layer_norm(x)
 
         if self.interctc or self.interctc_condition or self.group_num > 0: