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Question about training process #1
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@yangdongchao Thanks for fast reply. So, T5-embedding is from padded whole utterance's transcript or caption, and quantized latent is from random crop? By the way, I really like this approach, injecting subword, word-level info directly into codec. |
Yes, You are right. I am sorry for the late reply. I donot notice this message in the pass days. |
it seems T5 embedding from FrozenT5 has shape (B, max_length, D)
LLM-Codec/codec/MSCodec.py
Lines 73 to 78 in e21c1bf
is text_feature used for semantic loss in quantizer mean-pooled T5 embedding from FrozenT5??
LLM-Codec/codec/vq.py
Line 113 in e21c1bf
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