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| # This module is from [WeNet](https://github.com/wenet-e2e/wenet). | |
| # ## Citations | |
| # ```bibtex | |
| # @inproceedings{yao2021wenet, | |
| # title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit}, | |
| # author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin}, | |
| # booktitle={Proc. Interspeech}, | |
| # year={2021}, | |
| # address={Brno, Czech Republic }, | |
| # organization={IEEE} | |
| # } | |
| # @article{zhang2022wenet, | |
| # title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit}, | |
| # author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei}, | |
| # journal={arXiv preprint arXiv:2203.15455}, | |
| # year={2022} | |
| # } | |
| # | |
| """Subsampling layer definition.""" | |
| from typing import Tuple, Union | |
| import torch | |
| class BaseSubsampling(torch.nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.right_context = 0 | |
| self.subsampling_rate = 1 | |
| def position_encoding( | |
| self, offset: Union[int, torch.Tensor], size: int | |
| ) -> torch.Tensor: | |
| return self.pos_enc.position_encoding(offset, size) | |
| class LinearNoSubsampling(BaseSubsampling): | |
| """Linear transform the input without subsampling | |
| Args: | |
| idim (int): Input dimension. | |
| odim (int): Output dimension. | |
| dropout_rate (float): Dropout rate. | |
| """ | |
| def __init__( | |
| self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module | |
| ): | |
| """Construct an linear object.""" | |
| super().__init__() | |
| self.out = torch.nn.Sequential( | |
| torch.nn.Linear(idim, odim), | |
| torch.nn.LayerNorm(odim, eps=1e-5), | |
| torch.nn.Dropout(dropout_rate), | |
| ) | |
| self.pos_enc = pos_enc_class | |
| self.right_context = 0 | |
| self.subsampling_rate = 1 | |
| def forward( | |
| self, | |
| x: torch.Tensor, | |
| x_mask: torch.Tensor, | |
| offset: Union[int, torch.Tensor] = 0, | |
| ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: | |
| """Input x. | |
| Args: | |
| x (torch.Tensor): Input tensor (#batch, time, idim). | |
| x_mask (torch.Tensor): Input mask (#batch, 1, time). | |
| Returns: | |
| torch.Tensor: linear input tensor (#batch, time', odim), | |
| where time' = time . | |
| torch.Tensor: linear input mask (#batch, 1, time'), | |
| where time' = time . | |
| """ | |
| x = self.out(x) | |
| x, pos_emb = self.pos_enc(x, offset) | |
| return x, pos_emb, x_mask | |
| class Conv2dSubsampling4(BaseSubsampling): | |
| """Convolutional 2D subsampling (to 1/4 length). | |
| Args: | |
| idim (int): Input dimension. | |
| odim (int): Output dimension. | |
| dropout_rate (float): Dropout rate. | |
| """ | |
| def __init__( | |
| self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module | |
| ): | |
| """Construct an Conv2dSubsampling4 object.""" | |
| super().__init__() | |
| self.conv = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| ) | |
| self.out = torch.nn.Sequential( | |
| torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim) | |
| ) | |
| self.pos_enc = pos_enc_class | |
| # The right context for every conv layer is computed by: | |
| # (kernel_size - 1) * frame_rate_of_this_layer | |
| self.subsampling_rate = 4 | |
| # 6 = (3 - 1) * 1 + (3 - 1) * 2 | |
| self.right_context = 6 | |
| def forward( | |
| self, | |
| x: torch.Tensor, | |
| x_mask: torch.Tensor, | |
| offset: Union[int, torch.Tensor] = 0, | |
| ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: | |
| """Subsample x. | |
| Args: | |
| x (torch.Tensor): Input tensor (#batch, time, idim). | |
| x_mask (torch.Tensor): Input mask (#batch, 1, time). | |
| Returns: | |
| torch.Tensor: Subsampled tensor (#batch, time', odim), | |
| where time' = time // 4. | |
| torch.Tensor: Subsampled mask (#batch, 1, time'), | |
| where time' = time // 4. | |
| torch.Tensor: positional encoding | |
| """ | |
| x = x.unsqueeze(1) # (b, c=1, t, f) | |
| x = self.conv(x) | |
| b, c, t, f = x.size() | |
| x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f)) | |
| x, pos_emb = self.pos_enc(x, offset) | |
| return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2] | |
| class Conv2dSubsampling6(BaseSubsampling): | |
| """Convolutional 2D subsampling (to 1/6 length). | |
| Args: | |
| idim (int): Input dimension. | |
| odim (int): Output dimension. | |
| dropout_rate (float): Dropout rate. | |
| pos_enc (torch.nn.Module): Custom position encoding layer. | |
| """ | |
| def __init__( | |
| self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module | |
| ): | |
| """Construct an Conv2dSubsampling6 object.""" | |
| super().__init__() | |
| self.conv = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, 5, 3), | |
| torch.nn.ReLU(), | |
| ) | |
| self.linear = torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim) | |
| self.pos_enc = pos_enc_class | |
| # 10 = (3 - 1) * 1 + (5 - 1) * 2 | |
| self.subsampling_rate = 6 | |
| self.right_context = 10 | |
| def forward( | |
| self, | |
| x: torch.Tensor, | |
| x_mask: torch.Tensor, | |
| offset: Union[int, torch.Tensor] = 0, | |
| ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: | |
| """Subsample x. | |
| Args: | |
| x (torch.Tensor): Input tensor (#batch, time, idim). | |
| x_mask (torch.Tensor): Input mask (#batch, 1, time). | |
| Returns: | |
| torch.Tensor: Subsampled tensor (#batch, time', odim), | |
| where time' = time // 6. | |
| torch.Tensor: Subsampled mask (#batch, 1, time'), | |
| where time' = time // 6. | |
| torch.Tensor: positional encoding | |
| """ | |
| x = x.unsqueeze(1) # (b, c, t, f) | |
| x = self.conv(x) | |
| b, c, t, f = x.size() | |
| x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f)) | |
| x, pos_emb = self.pos_enc(x, offset) | |
| return x, pos_emb, x_mask[:, :, 2::2][:, :, 4::3] | |
| class Conv2dSubsampling8(BaseSubsampling): | |
| """Convolutional 2D subsampling (to 1/8 length). | |
| Args: | |
| idim (int): Input dimension. | |
| odim (int): Output dimension. | |
| dropout_rate (float): Dropout rate. | |
| """ | |
| def __init__( | |
| self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module | |
| ): | |
| """Construct an Conv2dSubsampling8 object.""" | |
| super().__init__() | |
| self.conv = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| ) | |
| self.linear = torch.nn.Linear( | |
| odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim | |
| ) | |
| self.pos_enc = pos_enc_class | |
| self.subsampling_rate = 8 | |
| # 14 = (3 - 1) * 1 + (3 - 1) * 2 + (3 - 1) * 4 | |
| self.right_context = 14 | |
| def forward( | |
| self, | |
| x: torch.Tensor, | |
| x_mask: torch.Tensor, | |
| offset: Union[int, torch.Tensor] = 0, | |
| ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: | |
| """Subsample x. | |
| Args: | |
| x (torch.Tensor): Input tensor (#batch, time, idim). | |
| x_mask (torch.Tensor): Input mask (#batch, 1, time). | |
| Returns: | |
| torch.Tensor: Subsampled tensor (#batch, time', odim), | |
| where time' = time // 8. | |
| torch.Tensor: Subsampled mask (#batch, 1, time'), | |
| where time' = time // 8. | |
| torch.Tensor: positional encoding | |
| """ | |
| x = x.unsqueeze(1) # (b, c, t, f) | |
| x = self.conv(x) | |
| b, c, t, f = x.size() | |
| x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f)) | |
| x, pos_emb = self.pos_enc(x, offset) | |
| return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2][:, :, 2::2] | |