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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| # Copyright 2019 Shigeki Karita | |
| # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) | |
| """Subsampling layer definition.""" | |
| import torch | |
| from vec2wav2.models.conformer.embedding import PositionalEncoding | |
| class TooShortUttError(Exception): | |
| """Raised when the utt is too short for subsampling. | |
| Args: | |
| message (str): Message for error catch | |
| actual_size (int): the short size that cannot pass the subsampling | |
| limit (int): the limit size for subsampling | |
| """ | |
| def __init__(self, message, actual_size, limit): | |
| """Construct a TooShortUttError for error handler.""" | |
| super().__init__(message) | |
| self.actual_size = actual_size | |
| self.limit = limit | |
| def check_short_utt(ins, size): | |
| """Check if the utterance is too short for subsampling.""" | |
| if isinstance(ins, Conv2dSubsampling2) and size < 3: | |
| return True, 3 | |
| if isinstance(ins, Conv2dSubsampling) and size < 7: | |
| return True, 7 | |
| if isinstance(ins, Conv2dSubsampling6) and size < 11: | |
| return True, 11 | |
| if isinstance(ins, Conv2dSubsampling8) and size < 15: | |
| return True, 15 | |
| return False, -1 | |
| class Conv2dSubsampling(torch.nn.Module): | |
| """Convolutional 2D subsampling (to 1/4 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, odim, dropout_rate, pos_enc=None): | |
| """Construct an Conv2dSubsampling object.""" | |
| super(Conv2dSubsampling, self).__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), | |
| pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), | |
| ) | |
| def forward(self, x, x_mask): | |
| """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. | |
| """ | |
| x = x.unsqueeze(1) # (b, c, 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)) | |
| if x_mask is None: | |
| return x, None | |
| return x, x_mask[:, :, :-2:2][:, :, :-2:2] | |
| def __getitem__(self, key): | |
| """Get item. | |
| When reset_parameters() is called, if use_scaled_pos_enc is used, | |
| return the positioning encoding. | |
| """ | |
| if key != -1: | |
| raise NotImplementedError("Support only `-1` (for `reset_parameters`).") | |
| return self.out[key] | |
| class Conv2dSubsampling2(torch.nn.Module): | |
| """Convolutional 2D subsampling (to 1/2 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, odim, dropout_rate, pos_enc=None): | |
| """Construct an Conv2dSubsampling2 object.""" | |
| super(Conv2dSubsampling2, self).__init__() | |
| self.conv = torch.nn.Sequential( | |
| torch.nn.Conv2d(1, odim, 3, 2), | |
| torch.nn.ReLU(), | |
| torch.nn.Conv2d(odim, odim, 3, 1), | |
| torch.nn.ReLU(), | |
| ) | |
| self.out = torch.nn.Sequential( | |
| torch.nn.Linear(odim * (((idim - 1) // 2 - 2)), odim), | |
| pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), | |
| ) | |
| def forward(self, x, x_mask): | |
| """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 // 2. | |
| torch.Tensor: Subsampled mask (#batch, 1, time'), | |
| where time' = time // 2. | |
| """ | |
| x = x.unsqueeze(1) # (b, c, 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)) | |
| if x_mask is None: | |
| return x, None | |
| return x, x_mask[:, :, :-2:2][:, :, :-2:1] | |
| def __getitem__(self, key): | |
| """Get item. | |
| When reset_parameters() is called, if use_scaled_pos_enc is used, | |
| return the positioning encoding. | |
| """ | |
| if key != -1: | |
| raise NotImplementedError("Support only `-1` (for `reset_parameters`).") | |
| return self.out[key] | |
| class Conv2dSubsampling6(torch.nn.Module): | |
| """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, odim, dropout_rate, pos_enc=None): | |
| """Construct an Conv2dSubsampling6 object.""" | |
| super(Conv2dSubsampling6, self).__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.out = torch.nn.Sequential( | |
| torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim), | |
| pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), | |
| ) | |
| def forward(self, x, x_mask): | |
| """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. | |
| """ | |
| x = x.unsqueeze(1) # (b, c, 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)) | |
| if x_mask is None: | |
| return x, None | |
| return x, x_mask[:, :, :-2:2][:, :, :-4:3] | |
| class Conv2dSubsampling8(torch.nn.Module): | |
| """Convolutional 2D subsampling (to 1/8 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, odim, dropout_rate, pos_enc=None): | |
| """Construct an Conv2dSubsampling8 object.""" | |
| super(Conv2dSubsampling8, self).__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.out = torch.nn.Sequential( | |
| torch.nn.Linear(odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim), | |
| pos_enc if pos_enc is not None else PositionalEncoding(odim, dropout_rate), | |
| ) | |
| def forward(self, x, x_mask): | |
| """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. | |
| """ | |
| x = x.unsqueeze(1) # (b, c, 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)) | |
| if x_mask is None: | |
| return x, None | |
| return x, x_mask[:, :, :-2:2][:, :, :-2:2][:, :, :-2:2] | |