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88 lines
3.3 KiB
88 lines
3.3 KiB
# 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 paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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class SpeakerIdetification(nn.Layer):
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def __init__(
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self,
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backbone,
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num_class,
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lin_blocks=0,
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lin_neurons=192,
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dropout=0.1, ):
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"""The speaker identification model, which includes the speaker backbone network
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and the a linear transform to speaker class num in training
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Args:
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backbone (Paddle.nn.Layer class): the speaker identification backbone network model
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num_class (_type_): the speaker class num in the training dataset
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lin_blocks (int, optional): the linear layer transform between the embedding and the final linear layer. Defaults to 0.
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lin_neurons (int, optional): the output dimension of final linear layer. Defaults to 192.
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dropout (float, optional): the dropout factor on the embedding. Defaults to 0.1.
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"""
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super(SpeakerIdetification, self).__init__()
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# speaker idenfication backbone network model
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# the output of the backbond network is the target embedding
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self.backbone = backbone
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if dropout > 0:
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self.dropout = nn.Dropout(dropout)
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else:
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self.dropout = None
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# construct the speaker classifer
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input_size = self.backbone.emb_size
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self.blocks = nn.LayerList()
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for i in range(lin_blocks):
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self.blocks.extend([
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nn.BatchNorm1D(input_size),
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nn.Linear(in_features=input_size, out_features=lin_neurons),
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])
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input_size = lin_neurons
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# the final layer
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self.weight = paddle.create_parameter(
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shape=(input_size, num_class),
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dtype='float32',
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attr=paddle.ParamAttr(initializer=nn.initializer.XavierUniform()), )
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def forward(self, x, lengths=None):
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"""Do the speaker identification model forwrd,
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including the speaker embedding model and the classifier model network
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Args:
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x (paddle.Tensor): input audio feats,
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shape=[batch, dimension, times]
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lengths (paddle.Tensor, optional): input audio length.
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shape=[batch, times]
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Defaults to None.
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Returns:
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paddle.Tensor: return the logits of the feats
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"""
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# x.shape: (N, C, L)
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x = self.backbone(x, lengths).squeeze(
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-1) # (N, emb_size, 1) -> (N, emb_size)
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if self.dropout is not None:
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x = self.dropout(x)
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for fc in self.blocks:
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x = fc(x)
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logits = F.linear(F.normalize(x), F.normalize(self.weight, axis=0))
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return logits
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