From fe47243693447d1d2bc4e2c294f445e12ffff37c Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Mon, 9 Jan 2023 20:05:40 +0900
Subject: [PATCH] from local

---
 .../incremental_transf/.identity_train.py.swp | Bin 61440 -> 61440 bytes
 .../ASR/incremental_transf/identity_train.py  |   8 +++++---
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/egs/librispeech/ASR/incremental_transf/.identity_train.py.swp b/egs/librispeech/ASR/incremental_transf/.identity_train.py.swp
index 3f36fbd7b98ad743270a6ec94422c61636bf5368..f9a81e2eedee7191c55c961ce32e3ad02af527a7 100644
GIT binary patch
delta 441
zcmYk&Jxjw-6b9fETHD6NYKGua>~#=7D4|OR5kUvR4h~(!q7Z5lMYJEJDX0k6#j!^6
z2ShuFxX1+^3J!weB%&Q0yD5mcbd~zHQx81Dec{~0IhAIh(hR8U$>r1%o7R$q8X`Jt
z)y=ybbw&|%o>sWlcN`|F`r_QDawHNRKnUI=NVzay4M!Pcu9MA|tg>l4hJU&ECH1_y
zmvTdgANb@MEu9H^XA-JCNK}U_IEP(Wg#<LCL~l@oB8<V$01*ch(h!GXkU@eMndlTe
zSOE=U@GH?aJxN55Z~_L5!zeJ2pw*9oP=gDw!Gbu9z&Ccjg?-qABCLT9LHNURodvHW
zTnOHWeK`~JUmsMTHAFs@X@nLCXJWa6W$&<AwrRV@R^GLpje?WI(YpCW&(*qtX6H>b
RI0;tFI@w%ll6cz?ynoV)WJ>@5

delta 356
zcmX}mze@sf7{~F?VX0@=j|&un1_!4GGaM>fa?>HKC4z<vuZC!mXlY7lXo)yDTEZ|W
zsGudjZvFTtsx_R_DH?*HrQWyrffqbHJP*&)=)@YGSh`kNtt@5d^97MH>FoQ#5AM?o
zu2nQEuE`HJ6Vg>^FLvEyTRMV^_at3ol}-{9^=v=tS5Xn3mwP6t?8OgqOgn5v2a3@l
z@;NGf;1Mm<kwXT(5vh%B%;0BOy2dGdtYaBfjN{FbUeUxZHc&(gwj)n3L((O7u!T)j
zF^3F#DXEKF+`vZ-)A;4_XFQ>e3+$te5**uD15?THZf(+hm0I!e(3`cg=FhW&f7{4G
AY5)KL

diff --git a/egs/librispeech/ASR/incremental_transf/identity_train.py b/egs/librispeech/ASR/incremental_transf/identity_train.py
index 4e4d18091..b8b768d7a 100755
--- a/egs/librispeech/ASR/incremental_transf/identity_train.py
+++ b/egs/librispeech/ASR/incremental_transf/identity_train.py
@@ -975,12 +975,14 @@ def run(rank, world_size, args):
     logging.info(params)
 
     logging.info("About to create model")
-    model = get_transducer_model(params)
+    transducer_model = get_transducer_model(params)
     
     pre_trained_model = torch.load('/workspace/icefall/egs/librispeech/ASR/incremental_transf/conformer_12layers.pt')
     pre_trained_model = pre_trained_model['model']
-    model.load_state_dict(pre_trained_model, strict=True)
-    print(model) 
+    transducer_model.load_state_dict(pre_trained_model, strict=True)
+
+    model = get_interformer_model(transducer_model.encoder, params)
+    print(model)
     '''
     for n, p in model.named_parameters():
         if 'layer' not in n: