import torch
|
from torch.nn import functional as F
|
from funasr.models.encoder.abs_encoder import AbsEncoder
|
from typing import Tuple, Optional
|
from funasr.models.pooling.statistic_pooling import statistic_pooling, windowed_statistic_pooling
|
from collections import OrderedDict
|
import logging
|
import numpy as np
|
|
|
class BasicLayer(torch.nn.Module):
|
|
def __init__(self, in_filters: int, filters: int, stride: int, bn_momentum: float = 0.5):
|
|
super().__init__()
|
self.stride = stride
|
self.in_filters = in_filters
|
self.filters = filters
|
|
self.bn1 = torch.nn.BatchNorm2d(in_filters, eps=1e-3, momentum=bn_momentum, affine=True)
|
self.relu1 = torch.nn.ReLU()
|
self.conv1 = torch.nn.Conv2d(in_filters, filters, 3, stride, bias=False)
|
|
self.bn2 = torch.nn.BatchNorm2d(filters, eps=1e-3, momentum=bn_momentum, affine=True)
|
self.relu2 = torch.nn.ReLU()
|
self.conv2 = torch.nn.Conv2d(filters, filters, 3, 1, bias=False)
|
|
if in_filters != filters or stride > 1:
|
self.conv_sc = torch.nn.Conv2d(in_filters, filters, 1, stride, bias=False)
|
self.bn_sc = torch.nn.BatchNorm2d(filters, eps=1e-3, momentum=bn_momentum, affine=True)
|
|
def proper_padding(self, x, stride):
|
# align padding mode to tf.layers.conv2d with padding_mod="same"
|
if stride == 1:
|
return F.pad(x, (1, 1, 1, 1), "constant", 0)
|
elif stride == 2:
|
h, w = x.size(2), x.size(3)
|
# (left, right, top, bottom)
|
return F.pad(x, (w % 2, 1, h % 2, 1), "constant", 0)
|
|
def forward(self, xs_pad, ilens):
|
identity = xs_pad
|
if self.in_filters != self.filters or self.stride > 1:
|
identity = self.conv_sc(identity)
|
identity = self.bn_sc(identity)
|
|
xs_pad = self.relu1(self.bn1(xs_pad))
|
xs_pad = self.proper_padding(xs_pad, self.stride)
|
xs_pad = self.conv1(xs_pad)
|
|
xs_pad = self.relu2(self.bn2(xs_pad))
|
xs_pad = self.proper_padding(xs_pad, 1)
|
xs_pad = self.conv2(xs_pad)
|
|
if self.stride == 2:
|
ilens = (ilens + 1) // self.stride
|
|
return xs_pad + identity, ilens
|
|
|
class BasicBlock(torch.nn.Module):
|
def __init__(self, in_filters, filters, num_layer, stride, bn_momentum=0.5):
|
super().__init__()
|
self.num_layer = num_layer
|
|
for i in range(num_layer):
|
layer = BasicLayer(in_filters if i == 0 else filters, filters,
|
stride if i == 0 else 1, bn_momentum)
|
self.add_module("layer_{}".format(i), layer)
|
|
def forward(self, xs_pad, ilens):
|
|
for i in range(self.num_layer):
|
xs_pad, ilens = self._modules["layer_{}".format(i)](xs_pad, ilens)
|
|
return xs_pad, ilens
|
|
|
class ResNet34(AbsEncoder):
|
def __init__(
|
self,
|
input_size,
|
use_head_conv=True,
|
batchnorm_momentum=0.5,
|
use_head_maxpool=False,
|
num_nodes_pooling_layer=256,
|
layers_in_block=(3, 4, 6, 3),
|
filters_in_block=(32, 64, 128, 256),
|
):
|
super(ResNet34, self).__init__()
|
|
self.use_head_conv = use_head_conv
|
self.use_head_maxpool = use_head_maxpool
|
self.num_nodes_pooling_layer = num_nodes_pooling_layer
|
self.layers_in_block = layers_in_block
|
self.filters_in_block = filters_in_block
|
self.input_size = input_size
|
|
pre_filters = filters_in_block[0]
|
if use_head_conv:
|
self.pre_conv = torch.nn.Conv2d(1, pre_filters, 3, 1, 1, bias=False, padding_mode="zeros")
|
self.pre_conv_bn = torch.nn.BatchNorm2d(pre_filters, eps=1e-3, momentum=batchnorm_momentum)
|
|
if use_head_maxpool:
|
self.head_maxpool = torch.nn.MaxPool2d(3, 1, padding=1)
|
|
for i in range(len(layers_in_block)):
|
if i == 0:
|
in_filters = pre_filters if self.use_head_conv else 1
|
else:
|
in_filters = filters_in_block[i-1]
|
|
block = BasicBlock(in_filters,
|
filters=filters_in_block[i],
|
num_layer=layers_in_block[i],
|
stride=1 if i == 0 else 2,
|
bn_momentum=batchnorm_momentum)
|
self.add_module("block_{}".format(i), block)
|
|
self.resnet0_dense = torch.nn.Conv2d(filters_in_block[-1], num_nodes_pooling_layer, 1)
|
self.resnet0_bn = torch.nn.BatchNorm2d(num_nodes_pooling_layer, eps=1e-3, momentum=batchnorm_momentum)
|
|
self.time_ds_ratio = 8
|
|
def output_size(self) -> int:
|
return self.num_nodes_pooling_layer
|
|
def forward(
|
self,
|
xs_pad: torch.Tensor,
|
ilens: torch.Tensor,
|
prev_states: torch.Tensor = None
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
features = xs_pad
|
assert features.size(-1) == self.input_size, \
|
"Dimension of features {} doesn't match the input_size {}.".format(features.size(-1), self.input_size)
|
features = torch.unsqueeze(features, dim=1)
|
if self.use_head_conv:
|
features = self.pre_conv(features)
|
features = self.pre_conv_bn(features)
|
features = F.relu(features)
|
|
if self.use_head_maxpool:
|
features = self.head_maxpool(features)
|
|
resnet_outs, resnet_out_lens = features, ilens
|
for i in range(len(self.layers_in_block)):
|
block = self._modules["block_{}".format(i)]
|
resnet_outs, resnet_out_lens = block(resnet_outs, resnet_out_lens)
|
|
features = self.resnet0_dense(resnet_outs)
|
features = F.relu(features)
|
features = self.resnet0_bn(features)
|
|
return features, resnet_out_lens
|
|
# Note: For training, this implement is not equivalent to tf because of the kernel_regularizer in tf.layers.
|
# TODO: implement kernel_regularizer in torch with munal loss addition or weigth_decay in the optimizer
|
class ResNet34_SP_L2Reg(AbsEncoder):
|
def __init__(
|
self,
|
input_size,
|
use_head_conv=True,
|
batchnorm_momentum=0.5,
|
use_head_maxpool=False,
|
num_nodes_pooling_layer=256,
|
layers_in_block=(3, 4, 6, 3),
|
filters_in_block=(32, 64, 128, 256),
|
tf2torch_tensor_name_prefix_torch="encoder",
|
tf2torch_tensor_name_prefix_tf="EAND/speech_encoder",
|
tf_train_steps=720000,
|
):
|
super(ResNet34_SP_L2Reg, self).__init__()
|
|
self.use_head_conv = use_head_conv
|
self.use_head_maxpool = use_head_maxpool
|
self.num_nodes_pooling_layer = num_nodes_pooling_layer
|
self.layers_in_block = layers_in_block
|
self.filters_in_block = filters_in_block
|
self.input_size = input_size
|
self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
|
self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
|
self.tf_train_steps = tf_train_steps
|
|
pre_filters = filters_in_block[0]
|
if use_head_conv:
|
self.pre_conv = torch.nn.Conv2d(1, pre_filters, 3, 1, 1, bias=False, padding_mode="zeros")
|
self.pre_conv_bn = torch.nn.BatchNorm2d(pre_filters, eps=1e-3, momentum=batchnorm_momentum)
|
|
if use_head_maxpool:
|
self.head_maxpool = torch.nn.MaxPool2d(3, 1, padding=1)
|
|
for i in range(len(layers_in_block)):
|
if i == 0:
|
in_filters = pre_filters if self.use_head_conv else 1
|
else:
|
in_filters = filters_in_block[i-1]
|
|
block = BasicBlock(in_filters,
|
filters=filters_in_block[i],
|
num_layer=layers_in_block[i],
|
stride=1 if i == 0 else 2,
|
bn_momentum=batchnorm_momentum)
|
self.add_module("block_{}".format(i), block)
|
|
self.resnet0_dense = torch.nn.Conv1d(filters_in_block[-1] * input_size // 8, num_nodes_pooling_layer, 1)
|
self.resnet0_bn = torch.nn.BatchNorm1d(num_nodes_pooling_layer, eps=1e-3, momentum=batchnorm_momentum)
|
|
self.time_ds_ratio = 8
|
|
def output_size(self) -> int:
|
return self.num_nodes_pooling_layer
|
|
def forward(
|
self,
|
xs_pad: torch.Tensor,
|
ilens: torch.Tensor,
|
prev_states: torch.Tensor = None
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
features = xs_pad
|
assert features.size(-1) == self.input_size, \
|
"Dimension of features {} doesn't match the input_size {}.".format(features.size(-1), self.input_size)
|
features = torch.unsqueeze(features, dim=1)
|
if self.use_head_conv:
|
features = self.pre_conv(features)
|
features = self.pre_conv_bn(features)
|
features = F.relu(features)
|
|
if self.use_head_maxpool:
|
features = self.head_maxpool(features)
|
|
resnet_outs, resnet_out_lens = features, ilens
|
for i in range(len(self.layers_in_block)):
|
block = self._modules["block_{}".format(i)]
|
resnet_outs, resnet_out_lens = block(resnet_outs, resnet_out_lens)
|
|
# B, C, T, F
|
bb, cc, tt, ff = resnet_outs.shape
|
resnet_outs = torch.reshape(resnet_outs.permute(0, 3, 1, 2), [bb, ff*cc, tt])
|
features = self.resnet0_dense(resnet_outs)
|
features = F.relu(features)
|
features = self.resnet0_bn(features)
|
|
return features, resnet_out_lens
|
|
|
class ResNet34Diar(ResNet34):
|
def __init__(
|
self,
|
input_size,
|
embedding_node="resnet1_dense",
|
use_head_conv=True,
|
batchnorm_momentum=0.5,
|
use_head_maxpool=False,
|
num_nodes_pooling_layer=256,
|
layers_in_block=(3, 4, 6, 3),
|
filters_in_block=(32, 64, 128, 256),
|
num_nodes_resnet1=256,
|
num_nodes_last_layer=256,
|
pooling_type="window_shift",
|
pool_size=20,
|
stride=1,
|
tf2torch_tensor_name_prefix_torch="encoder",
|
tf2torch_tensor_name_prefix_tf="seq2seq/speech_encoder"
|
):
|
"""
|
Author: Speech Lab, Alibaba Group, China
|
SOND: Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis
|
https://arxiv.org/abs/2211.10243
|
"""
|
|
super(ResNet34Diar, self).__init__(
|
input_size,
|
use_head_conv=use_head_conv,
|
batchnorm_momentum=batchnorm_momentum,
|
use_head_maxpool=use_head_maxpool,
|
num_nodes_pooling_layer=num_nodes_pooling_layer,
|
layers_in_block=layers_in_block,
|
filters_in_block=filters_in_block,
|
)
|
|
self.embedding_node = embedding_node
|
self.num_nodes_resnet1 = num_nodes_resnet1
|
self.num_nodes_last_layer = num_nodes_last_layer
|
self.pooling_type = pooling_type
|
self.pool_size = pool_size
|
self.stride = stride
|
self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
|
self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
|
|
self.resnet1_dense = torch.nn.Linear(num_nodes_pooling_layer * 2, num_nodes_resnet1)
|
self.resnet1_bn = torch.nn.BatchNorm1d(num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum)
|
|
self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer)
|
self.resnet2_bn = torch.nn.BatchNorm1d(num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum)
|
|
def output_size(self) -> int:
|
if self.embedding_node.startswith("resnet1"):
|
return self.num_nodes_resnet1
|
elif self.embedding_node.startswith("resnet2"):
|
return self.num_nodes_last_layer
|
|
return self.num_nodes_pooling_layer
|
|
def forward(
|
self,
|
xs_pad: torch.Tensor,
|
ilens: torch.Tensor,
|
prev_states: torch.Tensor = None,
|
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
|
|
endpoints = OrderedDict()
|
res_out, ilens = super().forward(xs_pad, ilens)
|
endpoints["resnet0_bn"] = res_out
|
if self.pooling_type == "frame_gsp":
|
features = statistic_pooling(res_out, ilens, (3, ))
|
else:
|
features, ilens = windowed_statistic_pooling(res_out, ilens, (2, 3), self.pool_size, self.stride)
|
features = features.transpose(1, 2)
|
endpoints["pooling"] = features
|
|
features = self.resnet1_dense(features)
|
endpoints["resnet1_dense"] = features
|
features = F.relu(features)
|
endpoints["resnet1_relu"] = features
|
features = self.resnet1_bn(features.transpose(1, 2)).transpose(1, 2)
|
endpoints["resnet1_bn"] = features
|
|
features = self.resnet2_dense(features)
|
endpoints["resnet2_dense"] = features
|
features = F.relu(features)
|
endpoints["resnet2_relu"] = features
|
features = self.resnet2_bn(features.transpose(1, 2)).transpose(1, 2)
|
endpoints["resnet2_bn"] = features
|
|
return endpoints[self.embedding_node], ilens, None
|
|
|
class ResNet34SpL2RegDiar(ResNet34_SP_L2Reg):
|
def __init__(
|
self,
|
input_size,
|
embedding_node="resnet1_dense",
|
use_head_conv=True,
|
batchnorm_momentum=0.5,
|
use_head_maxpool=False,
|
num_nodes_pooling_layer=256,
|
layers_in_block=(3, 4, 6, 3),
|
filters_in_block=(32, 64, 128, 256),
|
num_nodes_resnet1=256,
|
num_nodes_last_layer=256,
|
pooling_type="window_shift",
|
pool_size=20,
|
stride=1,
|
tf2torch_tensor_name_prefix_torch="encoder",
|
tf2torch_tensor_name_prefix_tf="seq2seq/speech_encoder"
|
):
|
"""
|
Author: Speech Lab, Alibaba Group, China
|
TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization
|
https://arxiv.org/abs/2303.05397
|
"""
|
|
super(ResNet34SpL2RegDiar, self).__init__(
|
input_size,
|
use_head_conv=use_head_conv,
|
batchnorm_momentum=batchnorm_momentum,
|
use_head_maxpool=use_head_maxpool,
|
num_nodes_pooling_layer=num_nodes_pooling_layer,
|
layers_in_block=layers_in_block,
|
filters_in_block=filters_in_block,
|
)
|
|
self.embedding_node = embedding_node
|
self.num_nodes_resnet1 = num_nodes_resnet1
|
self.num_nodes_last_layer = num_nodes_last_layer
|
self.pooling_type = pooling_type
|
self.pool_size = pool_size
|
self.stride = stride
|
self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
|
self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
|
|
self.resnet1_dense = torch.nn.Linear(num_nodes_pooling_layer * 2, num_nodes_resnet1)
|
self.resnet1_bn = torch.nn.BatchNorm1d(num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum)
|
|
self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer)
|
self.resnet2_bn = torch.nn.BatchNorm1d(num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum)
|
|
def output_size(self) -> int:
|
if self.embedding_node.startswith("resnet1"):
|
return self.num_nodes_resnet1
|
elif self.embedding_node.startswith("resnet2"):
|
return self.num_nodes_last_layer
|
|
return self.num_nodes_pooling_layer
|
|
def forward(
|
self,
|
xs_pad: torch.Tensor,
|
ilens: torch.Tensor,
|
prev_states: torch.Tensor = None,
|
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
|
|
endpoints = OrderedDict()
|
res_out, ilens = super().forward(xs_pad, ilens)
|
endpoints["resnet0_bn"] = res_out
|
if self.pooling_type == "frame_gsp":
|
features = statistic_pooling(res_out, ilens, (2, ))
|
else:
|
features, ilens = windowed_statistic_pooling(res_out, ilens, (2, ), self.pool_size, self.stride)
|
features = features.transpose(1, 2)
|
endpoints["pooling"] = features
|
|
features = self.resnet1_dense(features)
|
endpoints["resnet1_dense"] = features
|
features = F.relu(features)
|
endpoints["resnet1_relu"] = features
|
features = self.resnet1_bn(features.transpose(1, 2)).transpose(1, 2)
|
endpoints["resnet1_bn"] = features
|
|
features = self.resnet2_dense(features)
|
endpoints["resnet2_dense"] = features
|
features = F.relu(features)
|
endpoints["resnet2_relu"] = features
|
features = self.resnet2_bn(features.transpose(1, 2)).transpose(1, 2)
|
endpoints["resnet2_bn"] = features
|
|
return endpoints[self.embedding_node], ilens, None
|
