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import copy
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from funasr.register import tables
def toKaldiMatrix(np_mat):
np.set_printoptions(threshold=np.inf, linewidth=np.nan)
out_str = str(np_mat)
out_str = out_str.replace('[', '')
out_str = out_str.replace(']', '')
return '[ %s ]\n' % out_str
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
def to_kaldi_net(self):
re_str = ''
re_str += '<LinearTransform> %d %d\n' % (self.output_dim,
self.input_dim)
re_str += '<LearnRateCoef> 1\n'
linear_weights = self.state_dict()['linear.weight']
x = linear_weights.squeeze().numpy()
re_str += toKaldiMatrix(x)
return re_str
def to_pytorch_net(self, fread):
linear_line = fread.readline()
linear_split = linear_line.strip().split()
assert len(linear_split) == 3
assert linear_split[0] == '<LinearTransform>'
self.output_dim = int(linear_split[1])
self.input_dim = int(linear_split[2])
learn_rate_line = fread.readline()
assert learn_rate_line.find('LearnRateCoef') != -1
self.linear.reset_parameters()
linear_weights = self.state_dict()['linear.weight']
#print(linear_weights.shape)
new_weights = torch.zeros((self.output_dim, self.input_dim),
dtype=torch.float32)
for i in range(self.output_dim):
line = fread.readline()
splits = line.strip().strip('\[\]').strip().split()
assert len(splits) == self.input_dim
cols = torch.tensor([float(item) for item in splits],
dtype=torch.float32)
new_weights[i, :] = cols
self.linear.weight.data = new_weights
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
def to_kaldi_net(self):
re_str = ''
re_str += '<AffineTransform> %d %d\n' % (self.output_dim,
self.input_dim)
re_str += '<LearnRateCoef> 1 <BiasLearnRateCoef> 1 <MaxNorm> 0\n'
linear_weights = self.state_dict()['linear.weight']
x = linear_weights.squeeze().numpy()
re_str += toKaldiMatrix(x)
linear_bias = self.state_dict()['linear.bias']
x = linear_bias.squeeze().numpy()
re_str += toKaldiMatrix(x)
return re_str
def to_pytorch_net(self, fread):
affine_line = fread.readline()
affine_split = affine_line.strip().split()
assert len(affine_split) == 3
assert affine_split[0] == '<AffineTransform>'
self.output_dim = int(affine_split[1])
self.input_dim = int(affine_split[2])
print('AffineTransform output/input dim: %d %d' %
(self.output_dim, self.input_dim))
learn_rate_line = fread.readline()
assert learn_rate_line.find('LearnRateCoef') != -1
#linear_weights = self.state_dict()['linear.weight']
#print(linear_weights.shape)
self.linear.reset_parameters()
new_weights = torch.zeros((self.output_dim, self.input_dim),
dtype=torch.float32)
for i in range(self.output_dim):
line = fread.readline()
splits = line.strip().strip('\[\]').strip().split()
assert len(splits) == self.input_dim
cols = torch.tensor([float(item) for item in splits],
dtype=torch.float32)
new_weights[i, :] = cols
self.linear.weight.data = new_weights
linear_bias = self.state_dict()['linear.bias']
#print(linear_bias.shape)
bias_line = fread.readline()
splits = bias_line.strip().strip('\[\]').strip().split()
assert len(splits) == self.output_dim
new_bias = torch.tensor([float(item) for item in splits],
dtype=torch.float32)
self.linear.bias.data = new_bias
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
def to_kaldi_net(self):
re_str = ''
re_str += '<RectifiedLinear> %d %d\n' % (self.dim, self.dim)
return re_str
def to_pytorch_net(self, fread):
line = fread.readline()
splits = line.strip().split()
assert len(splits) == 3
assert splits[0] == '<RectifiedLinear>'
assert int(splits[1]) == int(splits[2])
assert int(splits[1]) == self.dim
self.dim = int(splits[1])
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 = None):
x = torch.unsqueeze(input, 1)
x_per = x.permute(0, 3, 2, 1) # B D T C
if cache is not None:
cache = cache.to(x_per.device)
y_left = torch.cat((cache, x_per), dim=2)
cache = y_left[:, :, -(self.lorder - 1) * self.lstride :, :]
else:
y_left = F.pad(x_per, [0, 0, (self.lorder - 1) * self.lstride, 0])
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
def to_kaldi_net(self):
re_str = ''
re_str += '<Fsmn> %d %d\n' % (self.dim, self.dim)
re_str += '<LearnRateCoef> %d <LOrder> %d <ROrder> %d <LStride> %d <RStride> %d <MaxNorm> 0\n' % (
1, self.lorder, self.rorder, self.lstride, self.rstride)
#print(self.conv_left.weight,self.conv_right.weight)
lfiters = self.state_dict()['conv_left.weight']
x = np.flipud(lfiters.squeeze().numpy().T)
re_str += toKaldiMatrix(x)
if self.conv_right is not None:
rfiters = self.state_dict()['conv_right.weight']
x = (rfiters.squeeze().numpy().T)
re_str += toKaldiMatrix(x)
return re_str
def to_pytorch_net(self, fread):
fsmn_line = fread.readline()
fsmn_split = fsmn_line.strip().split()
assert len(fsmn_split) == 3
assert fsmn_split[0] == '<Fsmn>'
self.dim = int(fsmn_split[1])
params_line = fread.readline()
params_split = params_line.strip().strip('\[\]').strip().split()
assert len(params_split) == 12
assert params_split[0] == '<LearnRateCoef>'
assert params_split[2] == '<LOrder>'
self.lorder = int(params_split[3])
assert params_split[4] == '<ROrder>'
self.rorder = int(params_split[5])
assert params_split[6] == '<LStride>'
self.lstride = int(params_split[7])
assert params_split[8] == '<RStride>'
self.rstride = int(params_split[9])
assert params_split[10] == '<MaxNorm>'
#lfilters = self.state_dict()['conv_left.weight']
#print(lfilters.shape)
print('read conv_left weight')
new_lfilters = torch.zeros((self.lorder, 1, self.dim, 1),
dtype=torch.float32)
for i in range(self.lorder):
print('read conv_left weight -- %d' % i)
line = fread.readline()
splits = line.strip().strip('\[\]').strip().split()
assert len(splits) == self.dim
cols = torch.tensor([float(item) for item in splits],
dtype=torch.float32)
new_lfilters[self.lorder - 1 - i, 0, :, 0] = cols
new_lfilters = torch.transpose(new_lfilters, 0, 2)
#print(new_lfilters.shape)
self.conv_left.reset_parameters()
self.conv_left.weight.data = new_lfilters
#print(self.conv_left.weight.shape)
if self.rorder > 0:
#rfilters = self.state_dict()['conv_right.weight']
#print(rfilters.shape)
print('read conv_right weight')
new_rfilters = torch.zeros((self.rorder, 1, self.dim, 1),
dtype=torch.float32)
line = fread.readline()
for i in range(self.rorder):
print('read conv_right weight -- %d' % i)
line = fread.readline()
splits = line.strip().strip('\[\]').strip().split()
assert len(splits) == self.dim
cols = torch.tensor([float(item) for item in splits],
dtype=torch.float32)
new_rfilters[i, 0, :, 0] = cols
new_rfilters = torch.transpose(new_rfilters, 0, 2)
#print(new_rfilters.shape)
self.conv_right.reset_parameters()
self.conv_right.weight.data = new_rfilters
#print(self.conv_right.weight.shape)
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] = None):
x1 = self.linear(input) # B T D
if cache is not None:
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])
else:
x2, _ = self.fsmn_block(x1, None)
x3 = self.affine(x2)
x4 = self.relu(x3)
return x4
def to_kaldi_net(self):
re_str = ''
re_str += self.linear.to_kaldi_net()
re_str += self.fsmn_block.to_kaldi_net()
re_str += self.affine.to_kaldi_net()
re_str += self.relu.to_kaldi_net()
return re_str
def to_pytorch_net(self, fread):
self.linear.to_pytorch_net(fread)
self.fsmn_block.to_pytorch_net(fread)
self.affine.to_pytorch_net(fread)
self.relu.to_pytorch_net(fread)
class BasicBlock_export(nn.Module):
def __init__(
self,
model,
):
super(BasicBlock_export, self).__init__()
self.linear = model.linear
self.fsmn_block = model.fsmn_block
self.affine = model.affine
self.relu = model.relu
def forward(self, input: torch.Tensor, in_cache: torch.Tensor):
x = self.linear(input) # B T D
# cache_layer_name = 'cache_layer_{}'.format(self.stack_layer)
# if cache_layer_name not in in_cache:
# in_cache[cache_layer_name] = torch.zeros(x1.shape[0], x1.shape[-1], (self.lorder - 1) * self.lstride, 1)
x, out_cache = self.fsmn_block(x, in_cache)
x = self.affine(x)
x = self.relu(x)
return x, out_cache
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
def to_kaldi_net(self):
re_str = ''
for module in self._modules.values():
re_str += module.to_kaldi_net()
return re_str
def to_pytorch_net(self, fread):
for module in self._modules.values():
module.to_pytorch_net(fread)
"""
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", "FSMNConvert")
class FSMNConvert(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,
use_softmax: bool = True,
):
super().__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.use_softmax = use_softmax
if self.use_softmax:
self.softmax = nn.Softmax(dim=-1)
def output_size(self) -> int:
return self.output_dim
def forward(
self,
input: torch.Tensor,
cache: Dict[str, torch.Tensor] = None
) -> 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)
if self.use_softmax:
x7 = self.softmax(x6)
return x7
return x6
def to_kaldi_net(self):
re_str = ''
re_str += '<Nnet>\n'
re_str += self.in_linear1.to_kaldi_net()
re_str += self.in_linear2.to_kaldi_net()
re_str += self.relu.to_kaldi_net()
for fsmn in self.fsmn:
re_str += fsmn.to_kaldi_net()
re_str += self.out_linear1.to_kaldi_net()
re_str += self.out_linear2.to_kaldi_net()
re_str += '<Softmax> %d %d\n' % (self.output_dim, self.output_dim)
re_str += '</Nnet>\n'
return re_str
def to_pytorch_net(self, kaldi_file):
with open(kaldi_file, 'r', encoding='utf8') as fread:
fread = open(kaldi_file, 'r')
nnet_start_line = fread.readline()
assert nnet_start_line.strip() == '<Nnet>'
self.in_linear1.to_pytorch_net(fread)
self.in_linear2.to_pytorch_net(fread)
self.relu.to_pytorch_net(fread)
for fsmn in self.fsmn:
fsmn.to_pytorch_net(fread)
self.out_linear1.to_pytorch_net(fread)
self.out_linear2.to_pytorch_net(fread)
softmax_line = fread.readline()
softmax_split = softmax_line.strip().split()
assert softmax_split[0].strip() == '<Softmax>'
assert int(softmax_split[1]) == self.output_dim
assert int(softmax_split[2]) == self.output_dim
nnet_end_line = fread.readline()
assert nnet_end_line.strip() == '</Nnet>'
fread.close()
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