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