From d2704cc01c13b509b0bc3c9823779a1c829c4404 Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Tue, 11 Apr 2023 15:38:45 +0900
Subject: [PATCH] from local

---
 .../.data2vec_audio.py.swp                    | Bin 40960 -> 40960 bytes
 .../.model.py.swp                             | Bin 28672 -> 28672 bytes
 .../data2vec_audio.py                         |   3 +++
 3 files changed, 3 insertions(+)

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 b0e6eaa258030dc63147ed541e444dc5462b290c..dc7325cf1513db7eb156bc19384c84315db39cf4 100644
GIT binary patch
delta 186
zcmZoTz|?SnX~P5oMz_rq1&Z}~C$lgx9A{-<U=UznaGNaX@Z2n!gMmR0h`E7y2|EKr
z6c8H&u@Mln0x=5^KVV~Em;=P2K+Ffk$5}V)InHL8e8^3Dv%gymqhwKLUWrCQVoFM8
zUV40PVsW-!ab{JjhNk9ZLp90C+uWCm7J!wcr6!h?7Nr(Lr6>D(xC(-`7Uky_lt4u{
JH+j^B0|3u#GM@kd

delta 124
zcmZoTz|?SnX~P5oM(52F1&Z}~xmXz(jMx|$7z7v?oF@x9JU8>_U|@K`&cHAai01-v
z0TAZ{u{RL21MvZ%>PjF6=?Vby{ef7XeY2k9Y?jG&a|<VnxruIm;u^y^*<Ou%a*_Me
O$yOfbn<shXg#!Rsz#NhQ

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.model.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.model.py.swp
index a7fc329d1e660d924c7838f0d7148f8339ad6e36..60ab5b44d29a7a225b320bd7b160e5139aa54b5b 100644
GIT binary patch
delta 32
mcmZp8z}WDBQ8dXQ%+puFQqO<^2m}}yyctYW@-~XT&IbUJ!3d84

delta 32
mcmZp8z}WDBQ8dXQ%+puFQqO<^2m}}yY#2;a6gG;!&IbUHv<NZ)

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 25d1c8699..3a9228396 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
@@ -492,9 +492,12 @@ class Data2VecAudioModel(BaseFairseqModel):
         if prompt is not None:
             #features = torch.cat([features, prompt])
             prompt = prompt.expand((features.size()[0], prompt.size()[0], prompt.size()[1]))
+            print(prompt.size())
             features = torch.cat([prompt, features])
+            print(features.size())
             prompt_padding_mask = torch.zeros(promt.size()).type(torch.BoolTensor).to(features.device)
             padding_mask = torch.cat([prompt_padding_mask, padding_mask])
+            print(padding_mask.size())
 
         features = self.layer_norm(features)