"""ConvInput block for Transducer encoder."""
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from typing import Optional, Tuple, Union
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import torch
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import math
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from funasr.modules.nets_utils import sub_factor_to_params, pad_to_len
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class ConvInput(torch.nn.Module):
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"""ConvInput module definition.
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Args:
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input_size: Input size.
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conv_size: Convolution size.
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subsampling_factor: Subsampling factor.
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vgg_like: Whether to use a VGG-like network.
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output_size: Block output dimension.
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"""
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def __init__(
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self,
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input_size: int,
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conv_size: Union[int, Tuple],
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subsampling_factor: int = 4,
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vgg_like: bool = True,
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output_size: Optional[int] = None,
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) -> None:
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"""Construct a ConvInput object."""
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super().__init__()
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if vgg_like:
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if subsampling_factor == 1:
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conv_size1, conv_size2 = conv_size
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, conv_size1, 3, stride=1, padding=1),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size1, conv_size1, 3, stride=1, padding=1),
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torch.nn.ReLU(),
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torch.nn.MaxPool2d((1, 2)),
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torch.nn.Conv2d(conv_size1, conv_size2, 3, stride=1, padding=1),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size2, conv_size2, 3, stride=1, padding=1),
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torch.nn.ReLU(),
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torch.nn.MaxPool2d((1, 2)),
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)
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output_proj = conv_size2 * ((input_size // 2) // 2)
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self.subsampling_factor = 1
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self.stride_1 = 1
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self.create_new_mask = self.create_new_vgg_mask
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else:
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conv_size1, conv_size2 = conv_size
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kernel_1 = int(subsampling_factor / 2)
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, conv_size1, 3, stride=1, padding=1),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size1, conv_size1, 3, stride=1, padding=1),
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torch.nn.ReLU(),
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torch.nn.MaxPool2d((kernel_1, 2)),
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torch.nn.Conv2d(conv_size1, conv_size2, 3, stride=1, padding=1),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size2, conv_size2, 3, stride=1, padding=1),
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torch.nn.ReLU(),
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torch.nn.MaxPool2d((2, 2)),
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)
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output_proj = conv_size2 * ((input_size // 2) // 2)
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self.subsampling_factor = subsampling_factor
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self.create_new_mask = self.create_new_vgg_mask
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self.stride_1 = kernel_1
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else:
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if subsampling_factor == 1:
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, conv_size, 3, [1,2], [1,0]),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size, conv_size, 3, [1,2], [1,0]),
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torch.nn.ReLU(),
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)
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output_proj = conv_size * (((input_size - 1) // 2 - 1) // 2)
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self.subsampling_factor = subsampling_factor
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self.kernel_2 = 3
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self.stride_2 = 1
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self.create_new_mask = self.create_new_conv2d_mask
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else:
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kernel_2, stride_2, conv_2_output_size = sub_factor_to_params(
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subsampling_factor,
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input_size,
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)
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, conv_size, 3, 2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size, conv_size, kernel_2, stride_2),
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torch.nn.ReLU(),
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)
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output_proj = conv_size * conv_2_output_size
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self.subsampling_factor = subsampling_factor
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self.kernel_2 = kernel_2
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self.stride_2 = stride_2
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self.create_new_mask = self.create_new_conv2d_mask
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self.vgg_like = vgg_like
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self.min_frame_length = 7
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if output_size is not None:
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self.output = torch.nn.Linear(output_proj, output_size)
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self.output_size = output_size
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else:
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self.output = None
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self.output_size = output_proj
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def forward(
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self, x: torch.Tensor, mask: Optional[torch.Tensor], chunk_size: Optional[torch.Tensor]
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Encode input sequences.
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Args:
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x: ConvInput input sequences. (B, T, D_feats)
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mask: Mask of input sequences. (B, 1, T)
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Returns:
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x: ConvInput output sequences. (B, sub(T), D_out)
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mask: Mask of output sequences. (B, 1, sub(T))
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"""
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if mask is not None:
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mask = self.create_new_mask(mask)
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olens = max(mask.eq(0).sum(1))
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b, t, f = x.size()
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x = x.unsqueeze(1) # (b. 1. t. f)
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if chunk_size is not None:
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max_input_length = int(
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chunk_size * self.subsampling_factor * (math.ceil(float(t) / (chunk_size * self.subsampling_factor) ))
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)
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x = map(lambda inputs: pad_to_len(inputs, max_input_length, 1), x)
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x = list(x)
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x = torch.stack(x, dim=0)
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N_chunks = max_input_length // ( chunk_size * self.subsampling_factor)
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x = x.view(b * N_chunks, 1, chunk_size * self.subsampling_factor, f)
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x = self.conv(x)
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_, c, _, f = x.size()
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if chunk_size is not None:
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x = x.transpose(1, 2).contiguous().view(b, -1, c * f)[:,:olens,:]
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else:
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x = x.transpose(1, 2).contiguous().view(b, -1, c * f)
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if self.output is not None:
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x = self.output(x)
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return x, mask[:,:olens][:,:x.size(1)]
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def create_new_vgg_mask(self, mask: torch.Tensor) -> torch.Tensor:
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"""Create a new mask for VGG output sequences.
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Args:
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mask: Mask of input sequences. (B, T)
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Returns:
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mask: Mask of output sequences. (B, sub(T))
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"""
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if self.subsampling_factor > 1:
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vgg1_t_len = mask.size(1) - (mask.size(1) % (self.subsampling_factor // 2 ))
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mask = mask[:, :vgg1_t_len][:, ::self.subsampling_factor // 2]
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vgg2_t_len = mask.size(1) - (mask.size(1) % 2)
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mask = mask[:, :vgg2_t_len][:, ::2]
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else:
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mask = mask
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return mask
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def create_new_conv2d_mask(self, mask: torch.Tensor) -> torch.Tensor:
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"""Create new conformer mask for Conv2d output sequences.
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Args:
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mask: Mask of input sequences. (B, T)
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Returns:
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mask: Mask of output sequences. (B, sub(T))
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"""
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if self.subsampling_factor > 1:
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return mask[:, :-2:2][:, : -(self.kernel_2 - 1) : self.stride_2]
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else:
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return mask
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def get_size_before_subsampling(self, size: int) -> int:
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"""Return the original size before subsampling for a given size.
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Args:
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size: Number of frames after subsampling.
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Returns:
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: Number of frames before subsampling.
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"""
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return size * self.subsampling_factor
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