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| | | |
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| | | class SlidingWindow(AbsFrontend): |
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| | | """Sliding Window. |
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| | | |
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| | | Provides a sliding window over a batched continuous raw audio tensor. |
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| | | Optionally, provides padding (Currently not implemented). |
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| | | Combine this module with a pre-encoder compatible with raw audio data, |
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| | | for example Sinc convolutions. |
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| | | |
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| | | Known issues: |
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| | | Output length is calculated incorrectly if audio shorter than win_length. |
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| | | WARNING: trailing values are discarded - padding not implemented yet. |
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| | |
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| | | fs=None, |
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| | | ): |
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| | | """Initialize. |
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| | | |
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| | | Args: |
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| | | win_length: Length of frame. |
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| | | hop_length: Relative starting point of next frame. |
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| | |
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| | | self, input: torch.Tensor, input_lengths: torch.Tensor |
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| | | ) -> Tuple[torch.Tensor, torch.Tensor]: |
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| | | """Apply a sliding window on the input. |
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| | | |
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| | | Args: |
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| | | input: Input (B, T, C*D) or (B, T*C*D), with D=C=1. |
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| | | input_lengths: Input lengths within batch. |
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| | | |
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| | | Returns: |
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| | | Tensor: Output with dimensions (B, T, C, D), with D=win_length. |
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| | | Tensor: Output lengths within batch. |
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| | |
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| | | |
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| | | def output_size(self) -> int: |
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| | | """Return output length of feature dimension D, i.e. the window length.""" |
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| | | return self.win_length |
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| | | return self.win_length |
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