From d2afe0ed221f41790e4ebf208806fcf89346b291 Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Tue, 10 Jan 2023 01:10:05 +0900
Subject: [PATCH] from local

---
 .../ASR/incremental_transf/.train.py.swp      | Bin 61440 -> 61440 bytes
 .../ASR/incremental_transf/train.py           |  17 +++++++++++++++++
 2 files changed, 17 insertions(+)

diff --git a/egs/librispeech/ASR/incremental_transf/.train.py.swp b/egs/librispeech/ASR/incremental_transf/.train.py.swp
index b5a1e3ce72ed604a271ecca0451ff7e5151cb46a..3cebdc975c43af3c43182d38bf46afc6ab7609a9 100644
GIT binary patch
delta 936
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Yr62}-7iZ{lK@y=+bYAS{gUjtb01l`GRsaA1

delta 198
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z-~xU|uPHz-NT>~n7Xoo65GMn15)k_Wu^AAH0P!=R)~7(c9Eg_zaUu{W0I@C*|6pZc
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diff --git a/egs/librispeech/ASR/incremental_transf/train.py b/egs/librispeech/ASR/incremental_transf/train.py
index 10b3ad142..c852b9951 100755
--- a/egs/librispeech/ASR/incremental_transf/train.py
+++ b/egs/librispeech/ASR/incremental_transf/train.py
@@ -976,7 +976,24 @@ def run(rank, world_size, args):
 
     for n, p in model.named_parameters():
         if 'layer' not in n:
+            try: p.data = pre_trained_model2[n]
+            except: print(f'pre-trained model has no parameter named {n}.')
+        else:
             layer_name_splited = n.split('.')
+            if int(layer_name_splited[3]) % 2 == 0:
+                layer_name_splited[0] = 'pt_encoder'
+                layer_name_splited[3] = str(int(layer_name_splited[3])//2)
+                old_name = '.'.join(layer_name_splited)
+                try: p.data = pre_trained_model[old_name]
+                except: print(f'pre-trained model has no parameter named {n}.')
+            else:
+                layer_name_splited[0] = 'inter_encoder'
+                layer_name_splited[3] = str(int(layer_name_splited[3])//2)
+                old_name = '.'.join(layer_name_splited)
+                try: p.data = pre_trained_model[old_name]
+                except: print(f'pre-trained model has no parameter named {n}.')
+
+
             
 
     num_param = sum([p.numel() for p in model.parameters()])