File size: 6,377 Bytes
8896961 | 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 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | from __future__ import annotations
from collections.abc import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
class FireRedVadStreamModule(nn.Module):
def __init__(
self,
idim: int,
odim: int,
R: int,
M: int,
H: int,
P: int,
N1: int,
S1: int,
N2: int = 0,
S2: int = 1,
dropout: float = 0.05,
) -> None:
super().__init__()
self.dfsmn = DFSMN(idim, R, M, H, P, N1, S1, N2, S2, dropout)
self.out = nn.Linear(H, odim)
@classmethod
def from_config(cls, config) -> "FireRedVadStreamModule":
return cls(
idim=config.idim,
odim=config.odim,
R=config.R,
M=config.M,
H=config.H,
P=config.P,
N1=config.N1,
S1=config.S1,
N2=config.N2,
S2=config.S2,
dropout=config.dropout,
)
def forward(
self,
input_features: torch.Tensor,
caches: Sequence[torch.Tensor] | None = None,
) -> tuple[torch.Tensor, list[torch.Tensor]]:
x, new_caches = self.dfsmn(input_features, caches=caches)
logits = self.out(x)
probs = torch.sigmoid(logits)
return probs, new_caches
class DFSMN(nn.Module):
def __init__(
self,
D: int,
R: int,
M: int,
H: int,
P: int,
N1: int,
S1: int,
N2: int = 0,
S2: int = 1,
dropout: float = 0.05,
) -> None:
super().__init__()
self.fc1 = nn.Sequential(nn.Linear(D, H), nn.ReLU(), nn.Dropout(dropout))
self.fc2 = nn.Sequential(nn.Linear(H, P), nn.ReLU(), nn.Dropout(dropout))
self.fsmn1 = FSMN(P, N1, S1, N2, S2)
self.fsmns = nn.ModuleList(
[DFSMNBlock(H, P, N1, S1, N2, S2, dropout) for _ in range(R - 1)]
)
dnn: list[nn.Module] = [nn.Linear(P, H), nn.ReLU(), nn.Dropout(dropout)]
for _ in range(M - 1):
dnn += [nn.Linear(H, H), nn.ReLU(), nn.Dropout(dropout)]
self.dnns = nn.Sequential(*dnn)
def forward(
self,
inputs: torch.Tensor,
input_lengths: torch.Tensor | None = None,
caches: Sequence[torch.Tensor] | None = None,
) -> tuple[torch.Tensor, list[torch.Tensor]]:
mask = None if input_lengths is None else get_mask_from_lengths(input_lengths)
h = self.fc1(inputs)
p = self.fc2(h)
new_caches = []
cache = None if caches is None else caches[0]
memory, new_cache = self.fsmn1(p, mask=mask, cache=cache)
new_caches.append(new_cache)
for i, fsmn in enumerate(self.fsmns, start=1):
cache = None if caches is None else caches[i]
memory, new_cache = fsmn(memory, mask=mask, cache=cache)
new_caches.append(new_cache)
output = self.dnns(memory)
return output, new_caches
def get_mask_from_lengths(lengths: torch.Tensor) -> torch.Tensor:
batch_size = lengths.size(0)
max_length = torch.max(lengths).item()
mask = torch.zeros(batch_size, max_length, device=lengths.device)
for i in range(batch_size):
mask[i, lengths[i] :] = 1
return mask.to(torch.uint8)
class DFSMNBlock(nn.Module):
def __init__(
self,
H: int,
P: int,
N1: int,
S1: int,
N2: int = 0,
S2: int = 1,
dropout: float = 0.05,
) -> None:
super().__init__()
self.fc1 = nn.Sequential(nn.Linear(P, H), nn.ReLU(), nn.Dropout(dropout))
self.fc2 = nn.Linear(H, P, bias=False)
self.fsmn = FSMN(P, N1, S1, N2, S2)
def forward(
self,
inputs: torch.Tensor,
mask: torch.Tensor | None = None,
cache: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
residual = inputs
h = self.fc1(inputs)
p = self.fc2(h)
memory, new_cache = self.fsmn(p, mask=mask, cache=cache)
output = memory + residual
return output, new_cache
class FSMN(nn.Module):
def __init__(
self,
P: int,
N1: int,
S1: int,
N2: int = 0,
S2: int = 1,
) -> None:
super().__init__()
if N1 < 1:
raise ValueError("N1 must be greater than or equal to 1")
self.N1, self.S1, self.N2, self.S2 = N1, S1, N2, S2
self.lookback_padding = (N1 - 1) * S1
self.lookback_filter = nn.Conv1d(
in_channels=P,
out_channels=P,
kernel_size=N1,
stride=1,
padding=self.lookback_padding,
dilation=S1,
groups=P,
bias=False,
)
if self.N2 > 0:
self.lookahead_filter = nn.Conv1d(
in_channels=P,
out_channels=P,
kernel_size=N2,
stride=1,
padding=(N2 - 1) * S2,
dilation=S2,
groups=P,
bias=False,
)
else:
self.lookahead_filter = nn.Identity()
def forward(
self,
inputs: torch.Tensor,
mask: torch.Tensor | None = None,
cache: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
sequence_length = inputs.size(1)
if mask is not None:
mask = mask.unsqueeze(-1)
inputs = inputs.masked_fill(mask, 0.0)
inputs = inputs.permute((0, 2, 1)).contiguous()
residual = inputs
if cache is not None:
inputs = torch.cat((cache, inputs), dim=2)
new_cache = inputs[:, :, -self.lookback_padding :]
lookback = self.lookback_filter(inputs)
if self.N1 > 1:
lookback = lookback[:, :, : -(self.N1 - 1) * self.S1]
if cache is not None:
lookback = lookback[:, :, cache.size(2) :]
memory = residual + lookback
if self.N2 > 0 and sequence_length > 1:
lookahead = self.lookahead_filter(inputs)
memory += F.pad(lookahead[:, :, self.N2 * self.S2 :], (0, self.S2))
memory = memory.permute((0, 2, 1)).contiguous()
if mask is not None:
memory = memory.masked_fill(mask, 0.0)
return memory, new_cache
|