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from typing import Tuple, Dict
import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from funasr_detach.register import tables
class LinearTransform(nn.Module):
def __init__(self, input_dim, output_dim):
super(LinearTransform, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.linear = nn.Linear(input_dim, output_dim, bias=False)
def forward(self, input):
output = self.linear(input)
return output
class AffineTransform(nn.Module):
def __init__(self, input_dim, output_dim):
super(AffineTransform, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.linear = nn.Linear(input_dim, output_dim)
def forward(self, input):
output = self.linear(input)
return output
class RectifiedLinear(nn.Module):
def __init__(self, input_dim, output_dim):
super(RectifiedLinear, self).__init__()
self.dim = input_dim
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.1)
def forward(self, input):
out = self.relu(input)
return out
class FSMNBlock(nn.Module):
def __init__(
self,
input_dim: int,
output_dim: int,
lorder=None,
rorder=None,
lstride=1,
rstride=1,
):
super(FSMNBlock, self).__init__()
self.dim = input_dim
if lorder is None:
return
self.lorder = lorder
self.rorder = rorder
self.lstride = lstride
self.rstride = rstride
self.conv_left = nn.Conv2d(
self.dim,
self.dim,
[lorder, 1],
dilation=[lstride, 1],
groups=self.dim,
bias=False,
)
if self.rorder > 0:
self.conv_right = nn.Conv2d(
self.dim,
self.dim,
[rorder, 1],
dilation=[rstride, 1],
groups=self.dim,
bias=False,
)
else:
self.conv_right = None
def forward(self, input: torch.Tensor, cache: torch.Tensor):
x = torch.unsqueeze(input, 1)
x_per = x.permute(0, 3, 2, 1) # B D T C
cache = cache.to(x_per.device)
y_left = torch.cat((cache, x_per), dim=2)
cache = y_left[:, :, -(self.lorder - 1) * self.lstride :, :]
y_left = self.conv_left(y_left)
out = x_per + y_left
if self.conv_right is not None:
# maybe need to check
y_right = F.pad(x_per, [0, 0, 0, self.rorder * self.rstride])
y_right = y_right[:, :, self.rstride :, :]
y_right = self.conv_right(y_right)
out += y_right
out_per = out.permute(0, 3, 2, 1)
output = out_per.squeeze(1)
return output, cache
class BasicBlock(nn.Module):
def __init__(
self,
linear_dim: int,
proj_dim: int,
lorder: int,
rorder: int,
lstride: int,
rstride: int,
stack_layer: int,
):
super(BasicBlock, self).__init__()
self.lorder = lorder
self.rorder = rorder
self.lstride = lstride
self.rstride = rstride
self.stack_layer = stack_layer
self.linear = LinearTransform(linear_dim, proj_dim)
self.fsmn_block = FSMNBlock(
proj_dim, proj_dim, lorder, rorder, lstride, rstride
)
self.affine = AffineTransform(proj_dim, linear_dim)
self.relu = RectifiedLinear(linear_dim, linear_dim)
def forward(self, input: torch.Tensor, cache: Dict[str, torch.Tensor]):
x1 = self.linear(input) # B T D
cache_layer_name = "cache_layer_{}".format(self.stack_layer)
if cache_layer_name not in cache:
cache[cache_layer_name] = torch.zeros(
x1.shape[0], x1.shape[-1], (self.lorder - 1) * self.lstride, 1
)
x2, cache[cache_layer_name] = self.fsmn_block(x1, cache[cache_layer_name])
x3 = self.affine(x2)
x4 = self.relu(x3)
return x4
class FsmnStack(nn.Sequential):
def __init__(self, *args):
super(FsmnStack, self).__init__(*args)
def forward(self, input: torch.Tensor, cache: Dict[str, torch.Tensor]):
x = input
for module in self._modules.values():
x = module(x, cache)
return x
"""
FSMN net for keyword spotting
input_dim: input dimension
linear_dim: fsmn input dimensionll
proj_dim: fsmn projection dimension
lorder: fsmn left order
rorder: fsmn right order
num_syn: output dimension
fsmn_layers: no. of sequential fsmn layers
"""
@tables.register("encoder_classes", "FSMN")
class FSMN(nn.Module):
def __init__(
self,
input_dim: int,
input_affine_dim: int,
fsmn_layers: int,
linear_dim: int,
proj_dim: int,
lorder: int,
rorder: int,
lstride: int,
rstride: int,
output_affine_dim: int,
output_dim: int,
):
super(FSMN, self).__init__()
self.input_dim = input_dim
self.input_affine_dim = input_affine_dim
self.fsmn_layers = fsmn_layers
self.linear_dim = linear_dim
self.proj_dim = proj_dim
self.output_affine_dim = output_affine_dim
self.output_dim = output_dim
self.in_linear1 = AffineTransform(input_dim, input_affine_dim)
self.in_linear2 = AffineTransform(input_affine_dim, linear_dim)
self.relu = RectifiedLinear(linear_dim, linear_dim)
self.fsmn = FsmnStack(
*[
BasicBlock(linear_dim, proj_dim, lorder, rorder, lstride, rstride, i)
for i in range(fsmn_layers)
]
)
self.out_linear1 = AffineTransform(linear_dim, output_affine_dim)
self.out_linear2 = AffineTransform(output_affine_dim, output_dim)
self.softmax = nn.Softmax(dim=-1)
def fuse_modules(self):
pass
def forward(
self, input: torch.Tensor, cache: Dict[str, torch.Tensor]
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Args:
input (torch.Tensor): Input tensor (B, T, D)
cache: when cache is not None, the forward is in streaming. The type of cache is a dict, egs,
{'cache_layer_1': torch.Tensor(B, T1, D)}, T1 is equal to self.lorder. It is {} for the 1st frame
"""
x1 = self.in_linear1(input)
x2 = self.in_linear2(x1)
x3 = self.relu(x2)
x4 = self.fsmn(x3, cache) # self.cache will update automatically in self.fsmn
x5 = self.out_linear1(x4)
x6 = self.out_linear2(x5)
x7 = self.softmax(x6)
return x7
"""
one deep fsmn layer
dimproj: projection dimension, input and output dimension of memory blocks
dimlinear: dimension of mapping layer
lorder: left order
rorder: right order
lstride: left stride
rstride: right stride
"""
@tables.register("encoder_classes", "DFSMN")
class DFSMN(nn.Module):
def __init__(
self, dimproj=64, dimlinear=128, lorder=20, rorder=1, lstride=1, rstride=1
):
super(DFSMN, self).__init__()
self.lorder = lorder
self.rorder = rorder
self.lstride = lstride
self.rstride = rstride
self.expand = AffineTransform(dimproj, dimlinear)
self.shrink = LinearTransform(dimlinear, dimproj)
self.conv_left = nn.Conv2d(
dimproj,
dimproj,
[lorder, 1],
dilation=[lstride, 1],
groups=dimproj,
bias=False,
)
if rorder > 0:
self.conv_right = nn.Conv2d(
dimproj,
dimproj,
[rorder, 1],
dilation=[rstride, 1],
groups=dimproj,
bias=False,
)
else:
self.conv_right = None
def forward(self, input):
f1 = F.relu(self.expand(input))
p1 = self.shrink(f1)
x = torch.unsqueeze(p1, 1)
x_per = x.permute(0, 3, 2, 1)
y_left = F.pad(x_per, [0, 0, (self.lorder - 1) * self.lstride, 0])
if self.conv_right is not None:
y_right = F.pad(x_per, [0, 0, 0, (self.rorder) * self.rstride])
y_right = y_right[:, :, self.rstride :, :]
out = x_per + self.conv_left(y_left) + self.conv_right(y_right)
else:
out = x_per + self.conv_left(y_left)
out1 = out.permute(0, 3, 2, 1)
output = input + out1.squeeze(1)
return output
"""
build stacked dfsmn layers
"""
def buildDFSMNRepeats(linear_dim=128, proj_dim=64, lorder=20, rorder=1, fsmn_layers=6):
repeats = [
nn.Sequential(DFSMN(proj_dim, linear_dim, lorder, rorder, 1, 1))
for i in range(fsmn_layers)
]
return nn.Sequential(*repeats)
if __name__ == "__main__":
fsmn = FSMN(400, 140, 4, 250, 128, 10, 2, 1, 1, 140, 2599)
print(fsmn)
num_params = sum(p.numel() for p in fsmn.parameters())
print("the number of model params: {}".format(num_params))
x = torch.zeros(128, 200, 400) # batch-size * time * dim
y, _ = fsmn(x) # batch-size * time * dim
print("input shape: {}".format(x.shape))
print("output shape: {}".format(y.shape))
print(fsmn.to_kaldi_net())
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