82 lines
2.7 KiB
Python
Executable File
82 lines
2.7 KiB
Python
Executable File
# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
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# ## Citations
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# ```bibtex
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# @inproceedings{yao2021wenet,
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# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
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# 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},
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# booktitle={Proc. Interspeech},
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# year={2021},
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# address={Brno, Czech Republic },
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# organization={IEEE}
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# }
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# @article{zhang2022wenet,
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# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
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# 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},
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# journal={arXiv preprint arXiv:2203.15455},
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# year={2022}
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# }
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#
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"""Subsampling layer definition."""
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from typing import Tuple, Union
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import torch
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from modules.wenet_extractor.transformer.subsampling import BaseSubsampling
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class Conv2dSubsampling2(BaseSubsampling):
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"""Convolutional 2D subsampling (to 1/4 length).
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Args:
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idim (int): Input dimension.
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odim (int): Output dimension.
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dropout_rate (float): Dropout rate.
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"""
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def __init__(
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self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module
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):
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"""Construct an Conv2dSubsampling4 object."""
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super().__init__()
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self.conv = torch.nn.Sequential(torch.nn.Conv2d(1, odim, 3, 2), torch.nn.ReLU())
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self.out = torch.nn.Sequential(torch.nn.Linear(odim * ((idim - 1) // 2), odim))
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self.pos_enc = pos_enc_class
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# The right context for every conv layer is computed by:
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# (kernel_size - 1) * frame_rate_of_this_layer
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self.subsampling_rate = 2
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# 2 = (3 - 1) * 1
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self.right_context = 2
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def forward(
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self,
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x: torch.Tensor,
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x_mask: torch.Tensor,
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offset: Union[int, torch.Tensor] = 0,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Subsample x.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, idim).
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x_mask (torch.Tensor): Input mask (#batch, 1, time).
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Returns:
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torch.Tensor: Subsampled tensor (#batch, time', odim),
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where time' = time // 2.
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torch.Tensor: Subsampled mask (#batch, 1, time'),
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where time' = time // 2.
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torch.Tensor: positional encoding
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"""
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x = x.unsqueeze(1) # (b, c=1, t, f)
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x = self.conv(x)
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b, c, t, f = x.size()
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x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
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x, pos_emb = self.pos_enc(x, offset)
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return x, pos_emb, x_mask[:, :, :-2:2]
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