Class SpeakerEncoder

encoder/model.py:12–135

class SpeakerEncoder(nn.Module):
    def __init__(self, device, loss_device):
        super().__init__()
        self.loss_device = loss_device
        
        # Network defition
        self.lstm = nn.LSTM(input_size=mel_n_channels,
                            hidden_size=model_hidden_size, 
                            num_layers=model_num_layers, 
                            batch_first=True).to(device)
        self.linear = nn.Linear(in_features=model_hidden_size, 
                                out_features=model_embedding_size).to(device)
        self.relu = torch.nn.ReLU().to(device)
        
        # Cosine similarity scaling (with fixed initial parameter values)
        self.similarity_weight = nn.Parameter(torch.tensor([10.])).to(loss_device)
        self.similarity_bias = nn.Parameter(torch.tensor([-5.])).to(loss_device)

        # Loss
        self.loss_fn = nn.CrossEntropyLoss().to(loss_device)
        
    def do_gradient_ops(self):
        # Gradient scale
        self.similarity_weight.grad *= 0.01
        self.similarity_bias.grad *= 0.01
            
        # Gradient clipping
        clip_grad_norm_(self.parameters(), 3, norm_type=2)
    
    def forward(self, utterances, hidden_init=None):
        """
        Computes the embeddings of a batch of utterance spectrograms.
        
        :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape 
        (batch_size, n_frames, n_channels) 
        :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, 
        batch_size, hidden_size). Will default to a tensor of zeros if None.
        :return: the embeddings as a tensor of shape (batch_size, embedding_size)
        """
        # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state
        # and the final cell state.
        out, (hidden, cell) = self.lstm(utterances, hidden_init)
        
        # We take only the hidden state of the last layer
        embeds_raw = self.relu(self.linear(hidden[-1]))
        
        # L2-normalize it
        embeds = embeds_raw / (torch.norm(embeds_raw, dim=1, keepdim=True) + 1e-5)        

        return embeds
    
    def similarity_matrix(self, embeds):
        """
        Computes the similarity matrix according the section 2.1 of GE2E.

        :param embeds: the embeddings as a tensor of shape (speakers_per_batch, 
        utterances_per_speaker, embedding_size)
        :return: the similarity matrix as a tensor of shape (speakers_per_batch,