# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
[docs]class SpeakerIdetification(nn.Layer):
def __init__(
self,
backbone,
num_class,
lin_blocks=0,
lin_neurons=192,
dropout=0.1, ):
"""The speaker identification model, which includes the speaker backbone network
and the a linear transform to speaker class num in training
Args:
backbone (Paddle.nn.Layer class): the speaker identification backbone network model
num_class (_type_): the speaker class num in the training dataset
lin_blocks (int, optional): the linear layer transform between the embedding and the final linear layer. Defaults to 0.
lin_neurons (int, optional): the output dimension of final linear layer. Defaults to 192.
dropout (float, optional): the dropout factor on the embedding. Defaults to 0.1.
"""
super(SpeakerIdetification, self).__init__()
# speaker idenfication backbone network model
# the output of the backbond network is the target embedding
self.backbone = backbone
if dropout > 0:
self.dropout = nn.Dropout(dropout)
else:
self.dropout = None
# construct the speaker classifer
input_size = self.backbone.emb_size
self.blocks = nn.LayerList()
for i in range(lin_blocks):
self.blocks.extend([
nn.BatchNorm1D(input_size),
nn.Linear(in_features=input_size, out_features=lin_neurons),
])
input_size = lin_neurons
# the final layer
self.weight = paddle.create_parameter(
shape=(input_size, num_class),
dtype='float32',
attr=paddle.ParamAttr(initializer=nn.initializer.XavierUniform()), )
[docs] def forward(self, x, lengths=None):
"""Do the speaker identification model forwrd,
including the speaker embedding model and the classifier model network
Args:
x (paddle.Tensor): input audio feats,
shape=[batch, dimension, times]
lengths (paddle.Tensor, optional): input audio length.
shape=[batch, times]
Defaults to None.
Returns:
paddle.Tensor: return the logits of the feats
"""
# x.shape: (N, C, L)
x = self.backbone(x, lengths).squeeze(
-1) # (N, emb_size, 1) -> (N, emb_size)
if self.dropout is not None:
x = self.dropout(x)
for fc in self.blocks:
x = fc(x)
logits = F.linear(F.normalize(x), F.normalize(self.weight, axis=0))
return logits