From f66f6c7d9dc9c6abea3f14b144efdb8b8aa9802b Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Tue, 13 Dec 2022 17:07:59 +0900
Subject: [PATCH] from local

---
 egs/librispeech/ASR/.run_v3.sh.swp            | Bin 12288 -> 12288 bytes
 .../.train.py.swp                             | Bin 73728 -> 73728 bytes
 .../train.py                                  |   2 +-
 3 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/egs/librispeech/ASR/.run_v3.sh.swp b/egs/librispeech/ASR/.run_v3.sh.swp
index c769b208bdc33e2a5a911543926b482a42cbd99a..0db17211818d9cd853f015bb082dff8cd43d48d2 100644
GIT binary patch
delta 31
lcmZojXh;xCG6?hZRj|}EU;qLE1_m9Y8Of(*H;TQ|2LOHx2t5D*

delta 31
lcmZojXh;xCG6?hZRj|}EU;qLE28N3gGm={vH;TQ|2LORx2xR~O

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 c9557d85a2269fc0e72e04dc0215c10b3ef18fcf..483165d63e6dc28f02a9706d3ec726690fdd85d3 100644
GIT binary patch
delta 314
zcmZ9`F-yZx6b0b(Fi}a2=9N}52yv+xH;d8^PF*`n!NH}7odO+7=_D4~-Qj^n!Lfr=
zg(tXaMkh52w!c7esYAiVAw3yfJn(Tj+~M9%WOO3q#+9P1r1V8zZ_BrP=hp=M1pqEk
z*7C&`PFmx6;DOF`5Z6sr`|$ctzVI^Z{}ML-b_Lb@bo<GgShS96;qhkJXf^{i-1BsG
zG%TI^7Vu2zojgaHWCggT7VXe<8F-{CI-z|EXzTzNbV$3DqwghPLhp1=J?c`0N>rpR
p%F>sO4Ge7HlG=1YMaqytbM%uwcz5lJf~BHlRVA;Ep2Wfwm;u5}Ludd1

delta 326
zcmZoTz|wGlWrKhqqvK>jL3u{C&4z-P_wj17F)(<tGcYg+Ffcf77QDdC$SVotG6FFV
z5HH!Rc`=Q3`h0oDgvpXud31`C6LV6F6wp9&eqKptUTG?_aAw-%1<%B|?Q9hc^$b#V
z4NR>zdtBdRQGb<_fuWNNVxbz40)bup3=BJgI1h;Rf%qLC1H)P%ZUW+PAT|eLULbzV
z%fK)Rh=YMx42a+JFfg14;*~(W0*EI7aR3n80kJI*vjXuaZU%;rK>P%V&v7%TF)Ri$
z;(<5>hz)^Q2#7xd9drhW_W|)<Al|Tjr2wN5*W~^0w72_6Gj3s-e92v8^2=*R0LiLD
A`v3p{

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 be454c04d..013d7c60b 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train.py
@@ -1009,7 +1009,7 @@ 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.warning(f"Grad scale is small: {cur_grad_scale}")
+            scaler.scale(loss).backward()
 
             if params.multi_optim and batch_idx % params.accum_grads == 0:
                 set_batch_count(model, params.batch_idx_train)