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| from speaker_encoder.params_model import * | |
| from speaker_encoder.params_data import * | |
| from scipy.interpolate import interp1d | |
| from sklearn.metrics import roc_curve | |
| from torch.nn.utils import clip_grad_norm_ | |
| from scipy.optimize import brentq | |
| from torch import nn | |
| import numpy as np | |
| import torch | |
| 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, # 40 | |
| hidden_size=model_hidden_size, # 256 | |
| num_layers=model_num_layers, # 3 | |
| 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) | |
| 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, | |
| utterances_per_speaker, speakers_per_batch) | |
| """ | |
| speakers_per_batch, utterances_per_speaker = embeds.shape[:2] | |
| # Inclusive centroids (1 per speaker). Cloning is needed for reverse differentiation | |
| centroids_incl = torch.mean(embeds, dim=1, keepdim=True) | |
| centroids_incl = centroids_incl.clone() / torch.norm(centroids_incl, dim=2, keepdim=True) | |
| # Exclusive centroids (1 per utterance) | |
| centroids_excl = (torch.sum(embeds, dim=1, keepdim=True) - embeds) | |
| centroids_excl /= (utterances_per_speaker - 1) | |
| centroids_excl = centroids_excl.clone() / torch.norm(centroids_excl, dim=2, keepdim=True) | |
| # Similarity matrix. The cosine similarity of already 2-normed vectors is simply the dot | |
| # product of these vectors (which is just an element-wise multiplication reduced by a sum). | |
| # We vectorize the computation for efficiency. | |
| sim_matrix = torch.zeros(speakers_per_batch, utterances_per_speaker, | |
| speakers_per_batch).to(self.loss_device) | |
| mask_matrix = 1 - np.eye(speakers_per_batch, dtype=np.int) | |
| for j in range(speakers_per_batch): | |
| mask = np.where(mask_matrix[j])[0] | |
| sim_matrix[mask, :, j] = (embeds[mask] * centroids_incl[j]).sum(dim=2) | |
| sim_matrix[j, :, j] = (embeds[j] * centroids_excl[j]).sum(dim=1) | |
| ## Even more vectorized version (slower maybe because of transpose) | |
| # sim_matrix2 = torch.zeros(speakers_per_batch, speakers_per_batch, utterances_per_speaker | |
| # ).to(self.loss_device) | |
| # eye = np.eye(speakers_per_batch, dtype=np.int) | |
| # mask = np.where(1 - eye) | |
| # sim_matrix2[mask] = (embeds[mask[0]] * centroids_incl[mask[1]]).sum(dim=2) | |
| # mask = np.where(eye) | |
| # sim_matrix2[mask] = (embeds * centroids_excl).sum(dim=2) | |
| # sim_matrix2 = sim_matrix2.transpose(1, 2) | |
| sim_matrix = sim_matrix * self.similarity_weight + self.similarity_bias | |
| return sim_matrix | |
| def loss(self, embeds): | |
| """ | |
| Computes the softmax loss 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 loss and the EER for this batch of embeddings. | |
| """ | |
| speakers_per_batch, utterances_per_speaker = embeds.shape[:2] | |
| # Loss | |
| sim_matrix = self.similarity_matrix(embeds) | |
| sim_matrix = sim_matrix.reshape((speakers_per_batch * utterances_per_speaker, | |
| speakers_per_batch)) | |
| ground_truth = np.repeat(np.arange(speakers_per_batch), utterances_per_speaker) | |
| target = torch.from_numpy(ground_truth).long().to(self.loss_device) | |
| loss = self.loss_fn(sim_matrix, target) | |
| # EER (not backpropagated) | |
| with torch.no_grad(): | |
| inv_argmax = lambda i: np.eye(1, speakers_per_batch, i, dtype=np.int)[0] | |
| labels = np.array([inv_argmax(i) for i in ground_truth]) | |
| preds = sim_matrix.detach().cpu().numpy() | |
| # Snippet from https://yangcha.github.io/EER-ROC/ | |
| fpr, tpr, thresholds = roc_curve(labels.flatten(), preds.flatten()) | |
| eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.) | |
| return loss, eer |