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| import torch | |
| from typing import Optional | |
| from typing import Tuple | |
| from torch.nn import functional as F | |
| from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask | |
| class LabelAggregate(torch.nn.Module): | |
| def __init__( | |
| self, | |
| win_length: int = 512, | |
| hop_length: int = 128, | |
| center: bool = True, | |
| ): | |
| super().__init__() | |
| self.win_length = win_length | |
| self.hop_length = hop_length | |
| self.center = center | |
| def extra_repr(self): | |
| return ( | |
| f"win_length={self.win_length}, " | |
| f"hop_length={self.hop_length}, " | |
| f"center={self.center}, " | |
| ) | |
| def forward( | |
| self, input: torch.Tensor, ilens: torch.Tensor = None | |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: | |
| """LabelAggregate forward function. | |
| Args: | |
| input: (Batch, Nsamples, Label_dim) | |
| ilens: (Batch) | |
| Returns: | |
| output: (Batch, Frames, Label_dim) | |
| """ | |
| bs = input.size(0) | |
| max_length = input.size(1) | |
| label_dim = input.size(2) | |
| # NOTE(jiatong): | |
| # The default behaviour of label aggregation is compatible with | |
| # torch.stft about framing and padding. | |
| # Step1: center padding | |
| if self.center: | |
| pad = self.win_length // 2 | |
| max_length = max_length + 2 * pad | |
| input = torch.nn.functional.pad(input, (0, 0, pad, pad), "constant", 0) | |
| input[:, :pad, :] = input[:, pad : (2 * pad), :] | |
| input[:, (max_length - pad) : max_length, :] = input[ | |
| :, (max_length - 2 * pad) : (max_length - pad), : | |
| ] | |
| nframe = (max_length - self.win_length) // self.hop_length + 1 | |
| # Step2: framing | |
| output = input.as_strided( | |
| (bs, nframe, self.win_length, label_dim), | |
| (max_length * label_dim, self.hop_length * label_dim, label_dim, 1), | |
| ) | |
| # Step3: aggregate label | |
| output = torch.gt(output.sum(dim=2, keepdim=False), self.win_length // 2) | |
| output = output.float() | |
| # Step4: process lengths | |
| if ilens is not None: | |
| if self.center: | |
| pad = self.win_length // 2 | |
| ilens = ilens + 2 * pad | |
| olens = (ilens - self.win_length) // self.hop_length + 1 | |
| output.masked_fill_(make_pad_mask(olens, output, 1), 0.0) | |
| else: | |
| olens = None | |
| return output.to(input.dtype), olens | |
| class LabelAggregateMaxPooling(torch.nn.Module): | |
| def __init__( | |
| self, | |
| hop_length: int = 8, | |
| ): | |
| super().__init__() | |
| self.hop_length = hop_length | |
| def extra_repr(self): | |
| return f"hop_length={self.hop_length}, " | |
| def forward( | |
| self, input: torch.Tensor, ilens: torch.Tensor = None | |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: | |
| """LabelAggregate forward function. | |
| Args: | |
| input: (Batch, Nsamples, Label_dim) | |
| ilens: (Batch) | |
| Returns: | |
| output: (Batch, Frames, Label_dim) | |
| """ | |
| output = F.max_pool1d( | |
| input.transpose(1, 2), self.hop_length, self.hop_length | |
| ).transpose(1, 2) | |
| olens = ilens // self.hop_length | |
| return output.to(input.dtype), olens | |