|
|
|
# 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
|
|
|
|
|
|
|
|
|
|
|
|
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()), )
|
|
|
|
|
|
|
|
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
|