From bca5f83e19efa4b83457ab3930a388febf3ca095 Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Mon, 26 Dec 2022 15:25:06 +0900
Subject: [PATCH] from local

---
 egs/librispeech/ASR/.run_v3.sh.swp            | Bin 12288 -> 12288 bytes
 .../.train.py.swp                             | Bin 102400 -> 102400 bytes
 .../train.py                                  |   9 ---------
 3 files changed, 9 deletions(-)

diff --git a/egs/librispeech/ASR/.run_v3.sh.swp b/egs/librispeech/ASR/.run_v3.sh.swp
index 4d23f357fc505e934fc8f3847c5da02193014331..f888c0ddc66016142a61e10ef221164ffde0e17c 100644
GIT binary patch
delta 31
lcmZojXh;xCG6?hZRj|}EU;qLE28LU<E0Z68*(ml(9{`Gl3HAU0

delta 31
lcmZojXh;xCG6?hZRj|}EU;qLE28JNpmC53tH;TQ|2LOO*2%!J~

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train.py.swp
index 5bb01ea7302a5bcf02b7cf16e71d593054e42a92..e650fc3ad3cfe2c65d21931b94579ff1ba2f433e 100644
GIT binary patch
delta 286
zcmY+-u}eZx9L4eTv$XEh8(j#R^p)Tk2GQhT=pKSPHMO?%2Nd<7Nr=cUai_qgwUj&5
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ZiCZ$4a7$aF-1@O(eK;5-th&z$z5(GKMlb*X

delta 353
zcmZ9`F-XHu5XSL)jV(=T(uTT-#uNu1Vh1HwT?B~*K`0J_;H--dD&lS_PD0i4(5!XP
zF;w25i(nxoli((}h`70^=%R!8C2k)4hCkfFRhy}5Gu1jWSy?npn-%4Sx#;VcNuuWz
z5#@-=(!9wqHX^O|Thg1Zv$Sx+LGkmUD}0R0x$yc*H&mhri1v$~S!K?yuM=%T1#<A(
zO>_(Aum`K)LO*<Gh`!(nPT>#^U<qcyg%a5CsS&l{3~I0fE?AHS1DHk%{*kP!M-`9n
zX|aB+d87L*D_`2#gwR7HF~!9~r|+cBtT-pRXaspt2<$i~{&FT5in6!FhpF&ywzk(c
dcEo(J93PBF2NmJpn+kWhybJOPp>_rs{Q_LzR=fZJ

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
index d9581816a..086c1f25d 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
@@ -1020,9 +1020,7 @@ def train_one_epoch(
 
             # NOTE: We use reduction==sum and loss is computed over utterances
             # in the batch and there is no normalization to it so far.
-            logging.info('1')
             scaler.scale(loss).backward()
-            logging.info('2')
 
             if params.multi_optim and (batch_idx+1) % params.accum_grads == 0:
                 set_batch_count(model, params.batch_idx_train)
@@ -1034,18 +1032,11 @@ def train_one_epoch(
                 optimizer_enc.zero_grad()
                 optimizer_dec.zero_grad()
             elif not params.multi_optim and (batch_idx+1) % params.accum_grads == 0:
-                logging.info('3')
                 set_batch_count(model, params.batch_idx_train)
-                logging.info('4')
                 scheduler.step_batch(params.batch_idx_train)
-                print(optimizer)
-                logging.info('5')
                 scaler.step(optimizer)
-                logging.info('6')
                 scaler.update()
-                logging.info('7')
                 optimizer.zero_grad()
-                logging.info('8')
 
         except:  # noqa
             display_and_save_batch(batch, params=params, sp=sp)