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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import paddle
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from paddle import nn
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from paddle.nn import functional as F
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from paddle.nn import initializer as I
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from scipy.interpolate import interp1d
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from scipy.optimize import brentq
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from sklearn.metrics import roc_curve
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class LSTMSpeakerEncoder(nn.Layer):
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def __init__(self, n_mels, num_layers, hidden_size, output_size):
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super().__init__()
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self.lstm = nn.LSTM(n_mels, hidden_size, num_layers)
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self.linear = nn.Linear(hidden_size, output_size)
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self.similarity_weight = self.create_parameter(
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[1], default_initializer=I.Constant(10.))
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self.similarity_bias = self.create_parameter(
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[1], default_initializer=I.Constant(-5.))
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def forward(self, utterances, num_speakers, initial_states=None):
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normalized_embeds = self.embed_sequences(utterances, initial_states)
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embeds = normalized_embeds.reshape([num_speakers, -1, num_speakers])
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loss, eer = self.loss(embeds)
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return loss, eer
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def embed_sequences(self, utterances, initial_states=None, reduce=False):
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out, (h, c) = self.lstm(utterances, initial_states)
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embeds = F.relu(self.linear(h[-1]))
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normalized_embeds = F.normalize(embeds)
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if reduce:
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embed = paddle.mean(normalized_embeds, 0)
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embed = F.normalize(embed, axis=0)
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return embed
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return normalized_embeds
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def embed_utterance(self, utterances, initial_states=None):
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# utterances: [B, T, C] -> embed [C']
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embed = self.embed_sequences(utterances, initial_states, reduce=True)
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return embed
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def similarity_matrix(self, embeds):
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# (N, M, C)
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speakers_per_batch, utterances_per_speaker, embed_dim = embeds.shape
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# Inclusive centroids (1 per speaker). Cloning is needed for reverse differentiation
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centroids_incl = paddle.mean(embeds, axis=1)
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centroids_incl_norm = paddle.norm(
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centroids_incl, p=2, axis=1, keepdim=True)
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normalized_centroids_incl = centroids_incl / centroids_incl_norm
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# Exclusive centroids (1 per utterance)
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centroids_excl = paddle.broadcast_to(
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paddle.sum(embeds, axis=1, keepdim=True), embeds.shape) - embeds
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centroids_excl /= (utterances_per_speaker - 1)
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centroids_excl_norm = paddle.norm(
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centroids_excl, p=2, axis=2, keepdim=True)
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normalized_centroids_excl = centroids_excl / centroids_excl_norm
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p1 = paddle.matmul(
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embeds.reshape([-1, embed_dim]),
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normalized_centroids_incl,
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transpose_y=True) # (NMN)
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p1 = p1.reshape([-1])
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# print("p1: ", p1.shape)
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p2 = paddle.bmm(
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embeds.reshape([-1, 1, embed_dim]),
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normalized_centroids_excl.reshape([-1, embed_dim, 1])) # (NM, 1, 1)
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p2 = p2.reshape([-1]) # （NM)
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# begin: alternative implementation for scatter
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with paddle.no_grad():
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index = paddle.arange(
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0, speakers_per_batch * utterances_per_speaker,
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dtype="int64").reshape(
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[speakers_per_batch, utterances_per_speaker])
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index = index * speakers_per_batch + paddle.arange(
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0, speakers_per_batch, dtype="int64").unsqueeze(-1)
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index = paddle.reshape(index, [-1])
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ones = paddle.ones(
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[speakers_per_batch * utterances_per_speaker * speakers_per_batch])
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zeros = paddle.zeros_like(index, dtype=ones.dtype)
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mask_p1 = paddle.scatter(ones, index, zeros)
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p = p1 * mask_p1 + (1 - mask_p1) * paddle.scatter(ones, index, p2)
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# end: alternative implementation for scatter
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# p = paddle.scatter(p1, index, p2)
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p = p * self.similarity_weight + self.similarity_bias # neg
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p = p.reshape(
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[speakers_per_batch * utterances_per_speaker, speakers_per_batch])
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return p, p1, p2
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def do_gradient_ops(self):
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for p in [self.similarity_weight, self.similarity_bias]:
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g = p._grad_ivar()
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g = g * 0.01
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def inv_argmax(self, i, num):
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return np.eye(1, num, i, dtype=int)[0]
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def loss(self, embeds):
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"""
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Computes the softmax loss according the section 2.1 of GE2E.
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:param embeds: the embeddings as a tensor of shape (speakers_per_batch,
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utterances_per_speaker, embedding_size)
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:return: the loss and the EER for this batch of embeddings.
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"""
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speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
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# Loss
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sim_matrix, *_ = self.similarity_matrix(embeds)
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sim_matrix = sim_matrix.reshape(
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[speakers_per_batch * utterances_per_speaker, speakers_per_batch])
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target = paddle.arange(
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0, speakers_per_batch, dtype="int64").unsqueeze(-1)
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target = paddle.expand(target,
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[speakers_per_batch, utterances_per_speaker])
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target = paddle.reshape(target, [-1])
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loss = nn.CrossEntropyLoss()(sim_matrix, target)
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# EER (not backpropagated)
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with paddle.no_grad():
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ground_truth = target.numpy()
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labels = np.array(
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[self.inv_argmax(i, speakers_per_batch) for i in ground_truth])
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preds = sim_matrix.numpy()
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# Snippet from https://yangcha.github.io/EER-ROC/
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fpr, tpr, thresholds = roc_curve(labels.flatten(), preds.flatten())
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eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
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return loss, eer
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