File size: 3,996 Bytes
96b9702 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import torch
from torch import nn
from .layers import CustomDiagonalLinear, CustomLinear
class FDDT(nn.Module):
def __init__(self, d_model, non_target_rate=0.01, fddt_init=None, is_diagonal=False,
bias_only=False, use_silence=True, use_target=True, use_overlap=True, use_non_target=True):
super().__init__()
if use_target:
self.target_linear = nn.Parameter(torch.zeros(d_model)) if bias_only else (
CustomDiagonalLinear(d_model, bias=True, fddt_init=fddt_init,
init_eye_val=1.0) if is_diagonal else CustomLinear(d_model,
d_model,
bias=True, fddt_init=fddt_init,
init_eye_val=1.0))
if use_non_target:
self.non_target_linear = nn.Parameter(torch.zeros(d_model)) if bias_only else (
CustomDiagonalLinear(d_model, bias=True, fddt_init=fddt_init,
init_eye_val=non_target_rate) if is_diagonal else CustomLinear(
d_model, d_model, bias=True, fddt_init=fddt_init, init_eye_val=non_target_rate))
if use_overlap:
self.overlap_linear = nn.Parameter(torch.zeros(d_model)) if bias_only else (
CustomDiagonalLinear(d_model, bias=True, fddt_init=fddt_init,
init_eye_val=1.0) if is_diagonal else CustomLinear(d_model,
d_model,
bias=True, fddt_init=fddt_init,
init_eye_val=1.0))
if use_silence:
self.silence_linear = nn.Parameter(torch.zeros(d_model)) if bias_only else (
CustomDiagonalLinear(d_model, bias=True, fddt_init=fddt_init,
init_eye_val=non_target_rate) if is_diagonal else CustomLinear(
d_model, d_model, bias=True, fddt_init=fddt_init, init_eye_val=non_target_rate))
self.use_silence = use_silence
self.use_target = use_target
self.use_overlap = use_overlap
self.use_non_target = use_non_target
self.bias_only = bias_only
def forward(self, hidden_states, stno_mask):
stno_mask = stno_mask.to(hidden_states.device)[..., None]
if self.bias_only:
if self.use_silence:
hidden_states += stno_mask[:, 0, ...] * self.silence_linear
if self.use_target:
hidden_states += stno_mask[:, 1, ...] * self.target_linear
if self.use_non_target:
hidden_states += stno_mask[:, 2, ...] * self.non_target_linear
if self.use_overlap:
hidden_states += stno_mask[:, 3, ...] * self.overlap_linear
else:
orig_hidden_states = hidden_states
hidden_states = (self.silence_linear(
orig_hidden_states) if self.use_silence else orig_hidden_states) * stno_mask[:, 0, :] + \
(self.target_linear(
orig_hidden_states) if self.use_target else orig_hidden_states) * stno_mask[:, 1, :] + \
(self.non_target_linear(
orig_hidden_states) if self.use_non_target else orig_hidden_states) * stno_mask[:, 2,
:] + \
(self.overlap_linear(
orig_hidden_states) if self.use_overlap else orig_hidden_states) * stno_mask[:, 3, :]
return hidden_states
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