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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """ | |
| Created on Wed Sep 5 20:58:34 2018 | |
| @author: harry | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| from utils.hparam import hparam as hp | |
| from utils.utils import get_centroids, get_cossim, calc_loss | |
| from utils.kan import KANLinear | |
| class SpeechEmbedder(nn.Module): | |
| def __init__(self): | |
| super(SpeechEmbedder, self).__init__() | |
| self.LSTM_stack = nn.LSTM(hp.data.nmels, hp.model.hidden, num_layers=hp.model.num_layer, batch_first=True) | |
| for name, param in self.LSTM_stack.named_parameters(): | |
| if 'bias' in name: | |
| nn.init.constant_(param, 0.0) | |
| elif 'weight' in name: | |
| nn.init.xavier_normal_(param) | |
| self.projection = nn.Linear(hp.model.hidden, hp.model.proj) | |
| def forward(self, x): | |
| x, _ = self.LSTM_stack(x.float()) #(batch, frames, n_mels) | |
| #only use last frame | |
| x = x[:,x.size(1)-1] | |
| x = self.projection(x.float()) | |
| x = x / torch.norm(x, dim=1).unsqueeze(1) | |
| return x | |
| class SpeechEmbedderGRU(nn.Module): | |
| def __init__(self): | |
| super(SpeechEmbedderGRU, self).__init__() | |
| self.GRU_stack = nn.GRU(hp.data.nmels, hp.model.hidden, num_layers=hp.model.num_layer, batch_first=True) | |
| for name, param in self.GRU_stack.named_parameters(): | |
| if 'bias' in name: | |
| nn.init.constant_(param, 0.0) | |
| elif 'weight' in name: | |
| nn.init.xavier_normal_(param) | |
| self.projection = nn.Linear(hp.model.hidden, hp.model.proj) | |
| def forward(self, x): | |
| x, _ = self.GRU_stack(x.float()) #(batch, frames, n_mels) | |
| #only use last frame | |
| x = x[:,x.size(1)-1] | |
| x = self.projection(x.float()) | |
| x = x / torch.norm(x, dim=1).unsqueeze(1) | |
| return x | |
| class SpeechEmbedderKAN(nn.Module): | |
| def __init__(self): | |
| super(SpeechEmbedderKAN, self).__init__() | |
| self.LSTM_stack = nn.LSTM(hp.data.nmels, hp.model.hidden, num_layers=hp.model.num_layer, batch_first=True) | |
| for name, param in self.LSTM_stack.named_parameters(): | |
| if 'bias' in name: | |
| nn.init.constant_(param, 0.0) | |
| elif 'weight' in name: | |
| nn.init.xavier_normal_(param) | |
| self.projection = KANLinear(hp.model.hidden, hp.model.proj) | |
| def forward(self, x): | |
| x, _ = self.LSTM_stack(x.float()) #(batch, frames, n_mels) | |
| #only use last frame | |
| x = x[:,x.size(1)-1] | |
| x = self.projection(x.float()) | |
| x = x / torch.norm(x, dim=1).unsqueeze(1) | |
| return x | |
| class SpeechEmbedderBidirectional(nn.Module): | |
| def __init__(self): | |
| super(SpeechEmbedderBidirectional, self).__init__() | |
| self.LSTM_stack = nn.LSTM(hp.data.nmels, hp.model.hidden, num_layers=hp.model.num_layer, batch_first=True, bidirectional=True) | |
| for name, param in self.LSTM_stack.named_parameters(): | |
| if 'bias' in name: | |
| nn.init.constant_(param, 0.0) | |
| elif 'weight' in name: | |
| nn.init.xavier_normal_(param) | |
| self.projection = nn.Linear(hp.model.hidden, hp.model.proj) | |
| def forward(self, x): | |
| x, _ = self.LSTM_stack(x.float()) #(batch, frames, n_mels) | |
| #only use last frame | |
| x = x[:, :, :hp.model.hidden] | |
| x = x[:,x.size(1)-1] | |
| x = self.projection(x.float()) | |
| x = x / torch.norm(x, dim=1).unsqueeze(1) | |
| return x | |
| class GE2ELoss(nn.Module): | |
| def __init__(self, device): | |
| super(GE2ELoss, self).__init__() | |
| self.w = nn.Parameter(torch.tensor(10.0).to(device), requires_grad=True) | |
| self.b = nn.Parameter(torch.tensor(-5.0).to(device), requires_grad=True) | |
| self.device = device | |
| def forward(self, embeddings): | |
| torch.clamp(self.w, 1e-6) | |
| centroids = get_centroids(embeddings) | |
| cossim = get_cossim(embeddings, centroids) | |
| sim_matrix = self.w*cossim.to(self.device) + self.b | |
| loss, _ = calc_loss(sim_matrix) | |
| return loss | |