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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| __all__ = [ | |
| "MeanPooling", | |
| "AttentiveStatisticsPooling" | |
| ] | |
| class Pooling(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| def compute_length_from_mask(self, mask): | |
| """ | |
| mask: (batch_size, T) | |
| Assuming that the sampling rate is 16kHz, the frame shift is 20ms | |
| """ | |
| wav_lens = torch.sum(mask, dim=1) # (batch_size, ) | |
| feat_lens = torch.div(wav_lens-1, 16000*0.02, rounding_mode="floor") + 1 | |
| feat_lens = feat_lens.int().tolist() | |
| return feat_lens | |
| def forward(self, x, mask): | |
| raise NotImplementedError | |
| class AttentiveStatisticsPooling(Pooling): | |
| """ | |
| AttentiveStatisticsPooling | |
| Paper: Attentive Statistics Pooling for Deep Speaker Embedding | |
| Link: https://arxiv.org/pdf/1803.10963.pdf | |
| """ | |
| def __init__(self, input_size): | |
| super().__init__() | |
| self._indim = input_size | |
| self.sap_linear = nn.Linear(input_size, input_size) | |
| self.attention = nn.Parameter(torch.FloatTensor(input_size, 1)) | |
| torch.nn.init.normal_(self.attention, mean=0, std=1) | |
| def forward(self, xs, mask): | |
| """ | |
| xs: (batch_size, T, feat_dim) | |
| mask: (batch_size, T) | |
| => output: (batch_size, feat_dim*2) | |
| """ | |
| feat_lens = self.compute_length_from_mask(mask) | |
| pooled_list = [] | |
| for x, feat_len in zip(xs, feat_lens): | |
| x = x[:feat_len].unsqueeze(0) | |
| h = torch.tanh(self.sap_linear(x)) | |
| w = torch.matmul(h, self.attention).squeeze(dim=2) | |
| w = F.softmax(w, dim=1).view(x.size(0), x.size(1), 1) | |
| mu = torch.sum(x * w, dim=1) | |
| rh = torch.sqrt((torch.sum((x**2) * w, dim=1) - mu**2).clamp(min=1e-5)) | |
| x = torch.cat((mu, rh), 1).squeeze(0) | |
| pooled_list.append(x) | |
| return torch.stack(pooled_list) | |