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| # Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) | |
| # 2024 Alibaba Inc (Xiang Lyu) | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # Modified from ESPnet(https://github.com/espnet/espnet) | |
| """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 EmbedinigNoSubsampling(BaseSubsampling): | |
| """Embedding input without subsampling | |
| """ | |
| def __init__(self, idim: int, odim: int, dropout_rate: float, | |
| pos_enc_class: torch.nn.Module): | |
| super().__init__() | |
| self.embed = torch.nn.Embedding(idim, odim) | |
| self.pos_enc = pos_enc_class | |
| 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.embed(x) | |
| x, pos_emb = self.pos_enc(x, offset) | |
| return x, pos_emb, x_mask | |
| 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 Conv1dSubsampling2(BaseSubsampling): | |
| """Convolutional 1D subsampling (to 1/2 length). | |
| It is designed for Whisper, ref: | |
| https://github.com/openai/whisper/blob/main/whisper/model.py | |
| 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 Conv1dSubsampling2 object.""" | |
| super().__init__() | |
| self.conv = torch.nn.Sequential( | |
| torch.nn.Conv1d(idim, odim, kernel_size=3, padding=1), | |
| torch.nn.GELU(), | |
| torch.nn.Conv1d(odim, odim, kernel_size=3, stride=2, padding=1), | |
| torch.nn.GELU(), | |
| ) | |
| 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 = 2 | |
| # 4 = (3 - 1) * 1 + (3 - 1) * 1 | |
| self.right_context = 4 | |
| 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 // 2. | |
| torch.Tensor: Subsampled mask (#batch, 1, time'), | |
| where time' = time // 2. | |
| torch.Tensor: positional encoding | |
| """ | |
| time = x.size(1) | |
| x = x.transpose(1, 2) # (b, f, t) | |
| x = self.conv(x) | |
| x = x.transpose(1, 2) # (b, t, f) | |
| x, pos_emb = self.pos_enc(x, offset) | |
| return x, pos_emb, x_mask[:, :, (time + 1) % 2::2] | |
| 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] | |
| class LegacyLinearNoSubsampling(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), | |
| torch.nn.ReLU(), | |
| ) | |
| 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 | |