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340
funasr_local/layers/mask_along_axis.py
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340
funasr_local/layers/mask_along_axis.py
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import math
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import torch
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from typeguard import check_argument_types
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from typing import Sequence
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from typing import Union
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def mask_along_axis(
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spec: torch.Tensor,
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spec_lengths: torch.Tensor,
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mask_width_range: Sequence[int] = (0, 30),
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dim: int = 1,
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num_mask: int = 2,
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replace_with_zero: bool = True,
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):
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"""Apply mask along the specified direction.
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Args:
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spec: (Batch, Length, Freq)
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spec_lengths: (Length): Not using lengths in this implementation
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mask_width_range: Select the width randomly between this range
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"""
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org_size = spec.size()
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if spec.dim() == 4:
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# spec: (Batch, Channel, Length, Freq) -> (Batch * Channel, Length, Freq)
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spec = spec.view(-1, spec.size(2), spec.size(3))
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B = spec.shape[0]
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# D = Length or Freq
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D = spec.shape[dim]
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# mask_length: (B, num_mask, 1)
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mask_length = torch.randint(
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mask_width_range[0],
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mask_width_range[1],
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(B, num_mask),
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device=spec.device,
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).unsqueeze(2)
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# mask_pos: (B, num_mask, 1)
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mask_pos = torch.randint(
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0, max(1, D - mask_length.max()), (B, num_mask), device=spec.device
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).unsqueeze(2)
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# aran: (1, 1, D)
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aran = torch.arange(D, device=spec.device)[None, None, :]
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# mask: (Batch, num_mask, D)
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mask = (mask_pos <= aran) * (aran < (mask_pos + mask_length))
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# Multiply masks: (Batch, num_mask, D) -> (Batch, D)
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mask = mask.any(dim=1)
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if dim == 1:
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# mask: (Batch, Length, 1)
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mask = mask.unsqueeze(2)
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elif dim == 2:
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# mask: (Batch, 1, Freq)
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mask = mask.unsqueeze(1)
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if replace_with_zero:
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value = 0.0
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else:
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value = spec.mean()
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if spec.requires_grad:
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spec = spec.masked_fill(mask, value)
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else:
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spec = spec.masked_fill_(mask, value)
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spec = spec.view(*org_size)
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return spec, spec_lengths
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def mask_along_axis_lfr(
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spec: torch.Tensor,
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spec_lengths: torch.Tensor,
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mask_width_range: Sequence[int] = (0, 30),
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dim: int = 1,
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num_mask: int = 2,
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replace_with_zero: bool = True,
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lfr_rate: int = 1,
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):
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"""Apply mask along the specified direction.
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Args:
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spec: (Batch, Length, Freq)
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spec_lengths: (Length): Not using lengths in this implementation
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mask_width_range: Select the width randomly between this range
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lfr_rate：low frame rate
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"""
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org_size = spec.size()
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if spec.dim() == 4:
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# spec: (Batch, Channel, Length, Freq) -> (Batch * Channel, Length, Freq)
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spec = spec.view(-1, spec.size(2), spec.size(3))
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B = spec.shape[0]
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# D = Length or Freq
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D = spec.shape[dim] // lfr_rate
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# mask_length: (B, num_mask, 1)
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mask_length = torch.randint(
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mask_width_range[0],
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mask_width_range[1],
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(B, num_mask),
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device=spec.device,
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).unsqueeze(2)
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if lfr_rate > 1:
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mask_length = mask_length.repeat(1, lfr_rate, 1)
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# mask_pos: (B, num_mask, 1)
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mask_pos = torch.randint(
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0, max(1, D - mask_length.max()), (B, num_mask), device=spec.device
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).unsqueeze(2)
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if lfr_rate > 1:
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mask_pos_raw = mask_pos.clone()
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mask_pos = torch.zeros((B, 0, 1), device=spec.device, dtype=torch.int32)
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for i in range(lfr_rate):
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mask_pos_i = mask_pos_raw + D * i
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mask_pos = torch.cat((mask_pos, mask_pos_i), dim=1)
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# aran: (1, 1, D)
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D = spec.shape[dim]
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aran = torch.arange(D, device=spec.device)[None, None, :]
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# mask: (Batch, num_mask, D)
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mask = (mask_pos <= aran) * (aran < (mask_pos + mask_length))
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# Multiply masks: (Batch, num_mask, D) -> (Batch, D)
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mask = mask.any(dim=1)
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if dim == 1:
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# mask: (Batch, Length, 1)
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mask = mask.unsqueeze(2)
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elif dim == 2:
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# mask: (Batch, 1, Freq)
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mask = mask.unsqueeze(1)
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if replace_with_zero:
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value = 0.0
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else:
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value = spec.mean()
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if spec.requires_grad:
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spec = spec.masked_fill(mask, value)
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else:
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spec = spec.masked_fill_(mask, value)
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spec = spec.view(*org_size)
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return spec, spec_lengths
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class MaskAlongAxis(torch.nn.Module):
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def __init__(
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self,
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mask_width_range: Union[int, Sequence[int]] = (0, 30),
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num_mask: int = 2,
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dim: Union[int, str] = "time",
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replace_with_zero: bool = True,
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):
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assert check_argument_types()
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if isinstance(mask_width_range, int):
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mask_width_range = (0, mask_width_range)
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if len(mask_width_range) != 2:
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raise TypeError(
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f"mask_width_range must be a tuple of int and int values: "
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f"{mask_width_range}",
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)
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assert mask_width_range[1] > mask_width_range[0]
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if isinstance(dim, str):
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if dim == "time":
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dim = 1
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elif dim == "freq":
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dim = 2
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else:
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raise ValueError("dim must be int, 'time' or 'freq'")
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if dim == 1:
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self.mask_axis = "time"
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elif dim == 2:
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self.mask_axis = "freq"
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else:
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self.mask_axis = "unknown"
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super().__init__()
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self.mask_width_range = mask_width_range
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self.num_mask = num_mask
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self.dim = dim
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self.replace_with_zero = replace_with_zero
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def extra_repr(self):
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return (
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f"mask_width_range={self.mask_width_range}, "
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f"num_mask={self.num_mask}, axis={self.mask_axis}"
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)
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def forward(self, spec: torch.Tensor, spec_lengths: torch.Tensor = None):
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"""Forward function.
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Args:
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spec: (Batch, Length, Freq)
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"""
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return mask_along_axis(
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spec,
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spec_lengths,
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mask_width_range=self.mask_width_range,
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dim=self.dim,
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num_mask=self.num_mask,
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replace_with_zero=self.replace_with_zero,
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)
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class MaskAlongAxisVariableMaxWidth(torch.nn.Module):
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"""Mask input spec along a specified axis with variable maximum width.
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Formula:
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max_width = max_width_ratio * seq_len
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"""
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def __init__(
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self,
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mask_width_ratio_range: Union[float, Sequence[float]] = (0.0, 0.05),
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num_mask: int = 2,
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dim: Union[int, str] = "time",
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replace_with_zero: bool = True,
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):
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assert check_argument_types()
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if isinstance(mask_width_ratio_range, float):
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mask_width_ratio_range = (0.0, mask_width_ratio_range)
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if len(mask_width_ratio_range) != 2:
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raise TypeError(
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f"mask_width_ratio_range must be a tuple of float and float values: "
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f"{mask_width_ratio_range}",
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)
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assert mask_width_ratio_range[1] > mask_width_ratio_range[0]
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if isinstance(dim, str):
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if dim == "time":
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dim = 1
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elif dim == "freq":
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dim = 2
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else:
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raise ValueError("dim must be int, 'time' or 'freq'")
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if dim == 1:
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self.mask_axis = "time"
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elif dim == 2:
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self.mask_axis = "freq"
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else:
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self.mask_axis = "unknown"
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super().__init__()
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self.mask_width_ratio_range = mask_width_ratio_range
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self.num_mask = num_mask
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self.dim = dim
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self.replace_with_zero = replace_with_zero
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def extra_repr(self):
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return (
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f"mask_width_ratio_range={self.mask_width_ratio_range}, "
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f"num_mask={self.num_mask}, axis={self.mask_axis}"
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)
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def forward(self, spec: torch.Tensor, spec_lengths: torch.Tensor = None):
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"""Forward function.
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Args:
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spec: (Batch, Length, Freq)
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"""
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max_seq_len = spec.shape[self.dim]
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min_mask_width = math.floor(max_seq_len * self.mask_width_ratio_range[0])
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min_mask_width = max([0, min_mask_width])
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max_mask_width = math.floor(max_seq_len * self.mask_width_ratio_range[1])
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max_mask_width = min([max_seq_len, max_mask_width])
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if max_mask_width > min_mask_width:
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return mask_along_axis(
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spec,
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spec_lengths,
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mask_width_range=(min_mask_width, max_mask_width),
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dim=self.dim,
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num_mask=self.num_mask,
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replace_with_zero=self.replace_with_zero,
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)
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return spec, spec_lengths
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class MaskAlongAxisLFR(torch.nn.Module):
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def __init__(
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self,
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mask_width_range: Union[int, Sequence[int]] = (0, 30),
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num_mask: int = 2,
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dim: Union[int, str] = "time",
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replace_with_zero: bool = True,
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lfr_rate: int = 1,
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):
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assert check_argument_types()
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if isinstance(mask_width_range, int):
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mask_width_range = (0, mask_width_range)
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if len(mask_width_range) != 2:
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raise TypeError(
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f"mask_width_range must be a tuple of int and int values: "
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f"{mask_width_range}",
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)
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assert mask_width_range[1] > mask_width_range[0]
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if isinstance(dim, str):
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if dim == "time":
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dim = 1
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lfr_rate = 1
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elif dim == "freq":
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dim = 2
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else:
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raise ValueError("dim must be int, 'time' or 'freq'")
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if dim == 1:
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self.mask_axis = "time"
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lfr_rate = 1
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elif dim == 2:
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self.mask_axis = "freq"
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else:
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self.mask_axis = "unknown"
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super().__init__()
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self.mask_width_range = mask_width_range
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self.num_mask = num_mask
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self.dim = dim
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self.replace_with_zero = replace_with_zero
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self.lfr_rate = lfr_rate
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def extra_repr(self):
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return (
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f"mask_width_range={self.mask_width_range}, "
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f"num_mask={self.num_mask}, axis={self.mask_axis}"
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)
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def forward(self, spec: torch.Tensor, spec_lengths: torch.Tensor = None):
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"""Forward function.
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Args:
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spec: (Batch, Length, Freq)
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"""
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return mask_along_axis_lfr(
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spec,
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spec_lengths,
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mask_width_range=self.mask_width_range,
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dim=self.dim,
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num_mask=self.num_mask,
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replace_with_zero=self.replace_with_zero,
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lfr_rate=self.lfr_rate,
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)
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