From fef78e281f1a2e44c59f9038b305c7cd9fb10d8e Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Mon, 9 Jan 2023 15:08:41 +0900
Subject: [PATCH] from local

---
 .../.train_adapter.py.swp                     | Bin 77824 -> 77824 bytes
 .../train_adapter.py                          |  10 +++++-----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_adapter.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.train_adapter.py.swp
index e3e78daf132484c9d32656005dc14528fd7d5779..24cf9f3199fd055bdeee31af534fb382aa618a34 100644
GIT binary patch
delta 235
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z8HhE2SQLmwfcP9M1H)_}E(hXBAT|TyUn~p^`+ztHh_iv%7l@^S_%1U8!&D$n24X`X
zzQM%6a14l#0`X=bt_NaWAl3n5J|Mos2(%uE7cnwSKKD?3vcVU&%{xD4vWO@vK!GL$
kjHNu;<eL?v^5nd43P7^&n-;TjX4+)Ozfzm8eABT608cA3>;M1&

delta 211
zcmZp8z|!!5MJ&l6%+puFQqO<^2m}}yZmip#{BiR}v3KuTxq%{!H*<dAXRP1A#=y`9
z#A!gR1H@85ED6MySs54>0C5cv#{sc55dUXkU^ooKg+N>Y#6du;0K|`&85m{(aRv~Z
z0r6cX28Po>d<uwn0C5Wt8v(H)5DNqGLq?$WK)i^NVKQHm(&jB6Gg+o<%QMPNw*96z
aIr+QF<nHfklXv~&n(X;kX!DhCI+g(D^D(*r

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py
index efa974310..80022ed6a 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/train_adapter.py
@@ -1115,11 +1115,11 @@ def train_one_epoch(
                     f"grad_scale is too small, exiting: {cur_grad_scale}"
                 )
 
-        if params.batch_idx_train > 4000 and loss > 300 and params.wandb:
-            wb.log({"valid/loss": 10000})
-            raise RuntimeError(
-                    f"divergence... exiting: loss={loss}"
-                )
+        #if params.batch_idx_train > 4000 and loss > 300 and params.wandb:
+        #    wb.log({"valid/loss": 10000})
+        #    raise RuntimeError(
+        #            f"divergence... exiting: loss={loss}"
+        #        )
 
         if batch_idx % (params.log_interval*params.accum_grads) == 0:
             #for n, p in model.named_parameters():