114 lines
3.3 KiB
Python
114 lines
3.3 KiB
Python
import torch
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from typing import Optional
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from typing import Tuple
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from torch.nn import functional as F
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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class LabelAggregate(torch.nn.Module):
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def __init__(
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self,
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win_length: int = 512,
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hop_length: int = 128,
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center: bool = True,
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):
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super().__init__()
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self.win_length = win_length
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self.hop_length = hop_length
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self.center = center
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def extra_repr(self):
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return (
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f"win_length={self.win_length}, "
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f"hop_length={self.hop_length}, "
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f"center={self.center}, "
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)
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def forward(
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self, input: torch.Tensor, ilens: torch.Tensor = None
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""LabelAggregate forward function.
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Args:
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input: (Batch, Nsamples, Label_dim)
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ilens: (Batch)
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Returns:
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output: (Batch, Frames, Label_dim)
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"""
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bs = input.size(0)
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max_length = input.size(1)
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label_dim = input.size(2)
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# NOTE(jiatong):
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# The default behaviour of label aggregation is compatible with
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# torch.stft about framing and padding.
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# Step1: center padding
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if self.center:
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pad = self.win_length // 2
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max_length = max_length + 2 * pad
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input = torch.nn.functional.pad(input, (0, 0, pad, pad), "constant", 0)
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input[:, :pad, :] = input[:, pad : (2 * pad), :]
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input[:, (max_length - pad) : max_length, :] = input[
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:, (max_length - 2 * pad) : (max_length - pad), :
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]
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nframe = (max_length - self.win_length) // self.hop_length + 1
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# Step2: framing
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output = input.as_strided(
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(bs, nframe, self.win_length, label_dim),
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(max_length * label_dim, self.hop_length * label_dim, label_dim, 1),
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)
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# Step3: aggregate label
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output = torch.gt(output.sum(dim=2, keepdim=False), self.win_length // 2)
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output = output.float()
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# Step4: process lengths
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if ilens is not None:
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if self.center:
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pad = self.win_length // 2
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ilens = ilens + 2 * pad
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olens = (ilens - self.win_length) // self.hop_length + 1
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output.masked_fill_(make_pad_mask(olens, output, 1), 0.0)
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else:
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olens = None
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return output.to(input.dtype), olens
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class LabelAggregateMaxPooling(torch.nn.Module):
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def __init__(
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self,
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hop_length: int = 8,
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):
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super().__init__()
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self.hop_length = hop_length
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def extra_repr(self):
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return f"hop_length={self.hop_length}, "
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def forward(
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self, input: torch.Tensor, ilens: torch.Tensor = None
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""LabelAggregate forward function.
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Args:
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input: (Batch, Nsamples, Label_dim)
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ilens: (Batch)
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Returns:
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output: (Batch, Frames, Label_dim)
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"""
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output = F.max_pool1d(input.transpose(1, 2), self.hop_length, self.hop_length).transpose(
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1, 2
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)
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olens = ilens // self.hop_length
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return output.to(input.dtype), olens
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