91 lines
3.4 KiB
Python
91 lines
3.4 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn import Parameter
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class SpeakerIdetification(nn.Module):
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def __init__(
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self,
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backbone,
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num_class=1,
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loss_type='AAMLoss',
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lin_blocks=0,
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lin_neurons=192,
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dropout=0.1, ):
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"""
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初始化说话人识别模型，包括说话人背骨网络和在训练中针对说话人类别数的线性变换。
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参数：
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backbone (Paddle.nn.Layer class): 说话人识别背骨网络模型。
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num_class (_type_): 训练数据集中说话人的类别数。
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lin_blocks (int, 可选): 从嵌入向量到最终线性层之间的线性层变换数量。默认为0。
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lin_neurons (int, 可选): 最终线性层的输出维度。默认为192。
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dropout (float, 可选): 嵌入向量上的dropout因子。默认为0.1。
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"""
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super(SpeakerIdetification, self).__init__()
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# 初始化背骨网络模型
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# 背骨网络的输出为目标嵌入向量
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self.backbone = backbone
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self.loss_type = loss_type
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if dropout > 0:
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self.dropout = nn.Dropout(dropout)
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else:
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self.dropout = None
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# 构建说话人分类器
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input_size = self.backbone.emb_size
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self.blocks = list()
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# 添加线性层变换
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for i in range(lin_blocks):
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self.blocks.extend([
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nn.BatchNorm1d(input_size),
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nn.Linear(in_features=input_size, out_features=lin_neurons),
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])
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input_size = lin_neurons
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# 最终层初始化
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if self.loss_type == 'AAMLoss':
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self.weight = Parameter(torch.FloatTensor(num_class, input_size), requires_grad=True)
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nn.init.xavier_normal_(self.weight, gain=1)
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elif self.loss_type == 'AMLoss' or self.loss_type == 'ARMLoss':
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self.weight = Parameter(torch.FloatTensor(input_size, num_class), requires_grad=True)
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nn.init.xavier_normal_(self.weight, gain=1)
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elif self.loss_type == 'CELoss':
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self.output = nn.Linear(input_size, num_class)
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else:
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raise Exception(f'没有{self.loss_type}损失函数！')
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def forward(self, x):
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"""
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执行说话人识别模型的前向传播，
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包括说话人嵌入模型和分类器模型网络
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参数:
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x (paddle.Tensor): 输入的音频特征，
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形状=[批大小, 时间, 维度]
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返回值:
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paddle.Tensor: 返回特征的logits
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"""
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# x.shape: (N, L, C)
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x = self.backbone(x) # (N, emb_size)
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if self.dropout is not None:
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x = self.dropout(x)
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for fc in self.blocks:
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x = fc(x)
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if self.loss_type == 'AAMLoss':
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logits = F.linear(F.normalize(x), F.normalize(self.weight, dim=-1))
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elif self.loss_type == 'AMLoss' or self.loss_type == 'ARMLoss':
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x_norm = torch.norm(x, p=2, dim=1, keepdim=True).clamp(min=1e-12)
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x_norm = torch.div(x, x_norm)
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w_norm = torch.norm(self.weight, p=2, dim=0, keepdim=True).clamp(min=1e-12)
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w_norm = torch.div(self.weight, w_norm)
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logits = torch.mm(x_norm, w_norm)
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else:
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logits = self.output(x)
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return logits
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