From 946b41848eff661a375ebd4ee012627a49349070 Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Tue, 3 Jan 2023 17:00:05 +0900
Subject: [PATCH] from local

---
 egs/librispeech/ASR/.run_adapter.sh.swp       | Bin 12288 -> 12288 bytes
 .../.data2vec_audio.py.swp                    | Bin 45056 -> 45056 bytes
 .../data2vec_audio.py                         |   2 +-
 3 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/egs/librispeech/ASR/.run_adapter.sh.swp b/egs/librispeech/ASR/.run_adapter.sh.swp
index 574ba06baa60165c4112c5b4d273a30a1e013a61..8b0ab2e50220a7583bb464817d94b12378be5e91 100644
GIT binary patch
delta 32
mcmZojXh;xGG6?hZRj|}EU;qLE1_qG_o0Ie9*f)y5)CT~F;Ru@m

delta 32
mcmZojXh;xGG6?hZRj|}EU;qLE28N_5>ylpyGj0@rsSf~*6A1zU

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.data2vec_audio.py.swp
index 6dab72e99d3048626d7f98e18e5fe92d0dbf0e95..4c8e8957699e857680511c94b1899b28cafdc857 100644
GIT binary patch
delta 349
zcmWN@KT84u0EgjUXKJUNC@!rY!MO-VgPXWS&DGG5C3FNWf#@jQ4T7uTF8FU~O1LQ-
zbclwi2y$t2Z0QRqN$>E$<1Y+<VT?E1;fIjqbj<d?S58RhNMmkEW7JWCjSPObrB^&-
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zJh(W)F>HKBr6wv!VjYp*@rHZUQNt;Uk%E*Z4lJy~(h}cz!UTP^;Nl7;I6?L9B@`s>
N8`Id!2mS6v>L2>oHb(#e

delta 350
zcmWN@F-rmg0EXd@GtINS5L{Xn;aXf8+)Nj#SsfaT1P{juatyB_a5U7lK&_!A<+g07
zL6@iqba8TZ=?@gzJ3R2X3&UL)gN>$t;FIi@+1#(6SW*d5%)-(THDnP-3_sh_JI1(2
zAD1{o895}eiyc^4Zb>6Nqk&T#;Se#bL((dwy1vK*syIdhQ&Z}q4F^#ecneCexWg4(
zI4B^GIKDTfYm^beIv{=G1COZT0!17IQqmr=Vd2j&{bG(6jL<^^4l2mP_Reo6zE<Wb
N=%qUq)9c-qBL8=II9dPz

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py
index b46d8550a..dd0507e05 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/data2vec_audio.py
@@ -43,7 +43,7 @@ class TransformerEncoderAdapter(TransformerEncoder):
 
         for p in self.adapters.parameters():
         #    p.data = nn.Parameter(torch.zeros(p.size()).to('cuda'))
-            p.data = nn.Parameter(torch.randn(p.size()).to('cuda')/10.)
+            p.data = nn.Parameter(torch.randn(p.size()).to('cuda')/100.)
 
     def forward(self, x, padding_mask=None, layer=None, tgt_layer=None):
         x, layer_results = self.extract_features_with_adapter(