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| | | |
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| | | import numpy as np |
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| | | import torch |
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| | | from typeguard import check_argument_types |
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| | | |
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| | | from funasr.modules.nets_utils import make_pad_mask |
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| | | from funasr.modules.rnn.encoders import RNN |
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| | |
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| | | dropout: float = 0.0, |
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| | | subsample: Optional[Sequence[int]] = (2, 2, 1, 1), |
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| | | ): |
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| | | assert check_argument_types() |
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| | | super().__init__() |
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| | | self._output_size = output_size |
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| | | self.rnn_type = rnn_type |
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| | |
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| | | raise ValueError(f"Not supported rnn_type={rnn_type}") |
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| | | |
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| | | if subsample is None: |
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| | | subsample = np.ones(num_layers + 1, dtype=np.int) |
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| | | subsample = np.ones(num_layers + 1, dtype=np.int32) |
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| | | else: |
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| | | subsample = subsample[:num_layers] |
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| | | # Append 1 at the beginning because the second or later is used |
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| | | subsample = np.pad( |
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| | | np.array(subsample, dtype=np.int), |
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| | | np.array(subsample, dtype=np.int32), |
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| | | [1, num_layers - len(subsample)], |
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| | | mode="constant", |
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| | | constant_values=1, |
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