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@ -490,6 +490,7 @@ class Data2VecAudioModel(BaseFairseqModel):
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## for prompt tuning
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## for prompt tuning
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if prompt is not None:
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if prompt is not None:
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if 1:
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conv_feat_all = torch.tensor([]).to(features.device)
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conv_feat_all = torch.tensor([]).to(features.device)
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length = 0
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length = 0
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for i in range(padding_mask.size()[0]):
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for i in range(padding_mask.size()[0]):
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@ -503,7 +504,6 @@ class Data2VecAudioModel(BaseFairseqModel):
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randint = np.random.randint(10000)
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randint = np.random.randint(10000)
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np.save(f'/home/work/workspace/icefall/egs/librispeech/ASR/conv_feat/{randint}.npy', conv_feat_all.cpu().numpy())
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np.save(f'/home/work/workspace/icefall/egs/librispeech/ASR/conv_feat/{randint}.npy', conv_feat_all.cpu().numpy())
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exit()
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prompt = prompt.expand((features.size()[0], prompt.size()[0], prompt.size()[1]))
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prompt = prompt.expand((features.size()[0], prompt.size()[0], prompt.size()[1]))
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features = torch.cat([prompt, features], dim=1)
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features = torch.cat([prompt, features], dim=1)
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