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#!/bin/bash
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. ./path.sh
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set -e
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#######################################################################
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# stage 1: train the speaker identification model
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# stage 2: test speaker identification
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# stage 3: extract the training embeding to train the LDA and PLDA
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######################################################################
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# you can set the variable PPAUDIO_HOME to specifiy the downloaded the vox1 and vox2 dataset
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# default the dataset is the ~/.paddleaudio/
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# export PPAUDIO_HOME=
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stage=2
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dir=data/ # data directory
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exp_dir=exp/ecapa-tdnn/ # experiment directory
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mkdir -p ${dir}
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if [ $stage -le 1 ]; then
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# stage 1: train the speaker identification model
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python3 \
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-m paddle.distributed.launch --gpus=0,1,2,3 \
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local/train.py --device "gpu" --checkpoint-dir ${exp_dir} \
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--save-freq 10 --data-dir ${dir} --batch-size 256 --epochs 60
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fi
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if [ $stage -le 2 ]; then
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# stage 1: train the speaker identification model
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python3 \
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local/speaker_verification_cosine.py \
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--load-checkpoint ${exp_dir}/epoch_40/
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fi
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