83 lines
2.1 KiB
Python
83 lines
2.1 KiB
Python
from typing import Tuple
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import torch
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from .nets_utils import make_pad_mask
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class UtteranceMVN(torch.nn.Module):
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def __init__(
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self, norm_means: bool = True, norm_vars: bool = False, eps: float = 1.0e-20,
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):
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super().__init__()
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self.norm_means = norm_means
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self.norm_vars = norm_vars
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self.eps = eps
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def extra_repr(self):
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return f"norm_means={self.norm_means}, norm_vars={self.norm_vars}"
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def forward(
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self, x: torch.Tensor, ilens: torch.Tensor = None
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Forward function
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Args:
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x: (B, L, ...)
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ilens: (B,)
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"""
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return utterance_mvn(
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x,
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ilens,
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norm_means=self.norm_means,
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norm_vars=self.norm_vars,
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eps=self.eps,
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)
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def utterance_mvn(
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x: torch.Tensor,
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ilens: torch.Tensor = None,
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norm_means: bool = True,
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norm_vars: bool = False,
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eps: float = 1.0e-20,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Apply utterance mean and variance normalization
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Args:
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x: (B, T, D), assumed zero padded
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ilens: (B,)
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norm_means:
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norm_vars:
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eps:
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"""
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if ilens is None:
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ilens = x.new_full([x.size(0)], x.size(1))
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ilens_ = ilens.to(x.device, x.dtype).view(-1, *[1 for _ in range(x.dim() - 1)])
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# Zero padding
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if x.requires_grad:
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x = x.masked_fill(make_pad_mask(ilens, x, 1), 0.0)
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else:
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x.masked_fill_(make_pad_mask(ilens, x, 1), 0.0)
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# mean: (B, 1, D)
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mean = x.sum(dim=1, keepdim=True) / ilens_
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if norm_means:
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x -= mean
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if norm_vars:
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var = x.pow(2).sum(dim=1, keepdim=True) / ilens_
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std = torch.clamp(var.sqrt(), min=eps)
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x = x / std.sqrt()
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return x, ilens
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else:
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if norm_vars:
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y = x - mean
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y.masked_fill_(make_pad_mask(ilens, y, 1), 0.0)
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var = y.pow(2).sum(dim=1, keepdim=True) / ilens_
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std = torch.clamp(var.sqrt(), min=eps)
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x /= std
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return x, ilens
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