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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.layers.abs_normalize import AbsNormalize |
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| | | |
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| | | class GlobalMVN(AbsNormalize, InversibleInterface): |
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| | | """Apply global mean and variance normalization |
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| | | |
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| | | TODO(kamo): Make this class portable somehow |
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| | | |
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| | | Args: |
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| | | stats_file: npy file |
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| | | norm_means: Apply mean normalization |
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| | |
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| | | norm_vars: bool = True, |
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| | | eps: float = 1.0e-20, |
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| | | ): |
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| | | assert check_argument_types() |
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| | | super().__init__() |
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| | | self.norm_means = norm_means |
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| | | self.norm_vars = norm_vars |
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| | |
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| | | self, x: torch.Tensor, ilens: torch.Tensor = None |
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| | | ) -> Tuple[torch.Tensor, torch.Tensor]: |
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| | | """Forward function |
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| | | |
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| | | Args: |
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| | | x: (B, L, ...) |
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| | | ilens: (B,) |
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| | |
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| | | if norm_means: |
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| | | x += self.mean |
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| | | x.masked_fill_(make_pad_mask(ilens, x, 1), 0.0) |
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| | | return x, ilens |
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| | | return x, ilens |
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