From 0979652201dbf6f43d2c1eb25c1f054cbff2672c Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Tue, 11 Apr 2023 15:02:37 +0900
Subject: [PATCH] from local

---
 .../.data2vec_audio.py.swp                    | Bin 40960 -> 40960 bytes
 .../data2vec_audio.py                         |  15 ++++++++-------
 2 files changed, 8 insertions(+), 7 deletions(-)

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 5571c93cb2f371a6832149e2005ad2e8eb2b0d87..fdca8c5acef67c905d9e33c0d3384acbce448d8d 100644
GIT binary patch
delta 553
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C<X*D?

delta 529
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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 71a2fee14..8cae787ab 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
@@ -467,7 +467,8 @@ class Data2VecAudioModel(BaseFairseqModel):
 
         ## for prompt tuning
         if prompt is not None:
-            features = torch.cat([features, prompt])
+            #features = torch.cat([features, prompt])
+            features = torch.cat([prompt, features])
 
         features = self.layer_norm(features)
 
@@ -494,12 +495,12 @@ class Data2VecAudioModel(BaseFairseqModel):
         else:
             padding_mask = None
         
-        print(padding_mask.size())
-        print((padding_mask[0] == True).nonzero(as_tuple=True)[0])
-        print((padding_mask[1] == True).nonzero(as_tuple=True)[0][1])
-        print((padding_mask[2] == True).nonzero(as_tuple=True)[0][2])
-        print((padding_mask[3] == True).nonzero(as_tuple=True)[0][3])
-        exit()
+        #print(padding_mask.size())
+        #print((padding_mask[0] == True).nonzero(as_tuple=True)[0])
+        #print((padding_mask[1] == True).nonzero(as_tuple=True)[0][1])
+        #print((padding_mask[2] == True).nonzero(as_tuple=True)[0][2])
+        #print((padding_mask[3] == True).nonzero(as_tuple=True)[0][3])
+        #exit()
 
         if self.post_extract_proj is not None:
             features = self.post_extract_proj(features)