CIF Related #1738
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CIF Related #1738
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robin1001
reviewed
Mar 13, 2023
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| # Copyright (c) 2023 ASLP@NWPU (authors: He Wang, Fan Yu) | |||
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The embedding.py shares almost the same code with transformer/embedding.py, I think we can reuse it.
robin1001
reviewed
Mar 13, 2023
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| # Copyright (c) 2023 ASLP@NWPU (authors: He Wang, Fan Yu) | |||
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same as embedding.py, attention shares almost the same code as transformer/attention.py, and transformer/attention.py takes streamming into account.
robin1001
reviewed
Mar 13, 2023
| import numpy as np | ||
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| def make_pad_mask(lengths: torch.Tensor, length_dim: int = -1, |
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this function is defined in utils/mask.py
robin1001
reviewed
Mar 13, 2023
| return mask | ||
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| def sequence_mask(lengths, maxlen: Optional[int] = None, |
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this function is almost the same as subsequent_mask in utils/mask.py
robin1001
approved these changes
Mar 13, 2023
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Complete the implementation of CIF-related code, including two types of Predictor and two types of CIF-Decoder. Meanwhile, four baseline experiments were conducted on the AISHELL dataset.