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.../v1.1/global_conf/config_fbank80_stmn_res2net50w26s8_arcs30m0.3_adam_lr0.05_amp_swa.v1.sh
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# Res2Net50 w26s8 x-vector with mixed precision training | ||
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# acoustic features | ||
feat_config=conf/fbank80_stmn_16k.yaml | ||
feat_type=fbank80_stmn | ||
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#vad | ||
vad_config=conf/vad_16k.yaml | ||
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# x-vector training | ||
nnet_data=voxceleb2cat_train | ||
nnet_num_augs=6 | ||
aug_opt="--train-aug-cfg conf/reverb_noise_aug.yaml --val-aug-cfg conf/reverb_noise_aug.yaml" | ||
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batch_size_1gpu=24 | ||
eff_batch_size=512 # effective batch size | ||
ipe=$nnet_num_augs | ||
min_chunk=4 | ||
max_chunk=4 | ||
lr=0.05 | ||
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nnet_type=res2net50 | ||
dropout=0 | ||
embed_dim=256 | ||
width_factor=3.25 | ||
scale=8 | ||
ws_tag=w26s8 | ||
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s=30 | ||
margin_warmup=20 | ||
margin=0.3 | ||
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nnet_opt="--resnet-type $nnet_type --in-feats 80 --in-channels 1 --in-kernel-size 3 --in-stride 1 --no-maxpool --res2net-width-factor $width_factor --res2net-scale $scale" | ||
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opt_opt="--optim.opt-type adam --optim.lr $lr --optim.beta1 0.9 --optim.beta2 0.95 --optim.weight-decay 1e-5 --optim.amsgrad --use-amp --swa-start 70 --swa-lr 1e-3 --swa-anneal-epochs 5" | ||
lrs_opt="--lrsched.lrsch-type exp_lr --lrsched.decay-rate 0.5 --lrsched.decay-steps 8000 --lrsched.hold-steps 40000 --lrsched.min-lr 1e-5 --lrsched.warmup-steps 1000 --lrsched.update-lr-on-opt-step" | ||
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nnet_name=${feat_type}_${nnet_type}${ws_tag}_e${embed_dim}_arcs${s}m${margin}_do${dropout}_adam_lr${lr}_b${eff_batch_size}_amp_swa.v1 | ||
nnet_num_epochs=90 | ||
nnet_dir=exp/xvector_nnets/$nnet_name | ||
nnet=$nnet_dir/swa_model_ep0091.pth | ||
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# back-end | ||
plda_aug_config=conf/reverb_noise_aug.yaml | ||
plda_num_augs=6 | ||
if [ $plda_num_augs -eq 0 ]; then | ||
plda_data=voxceleb2cat_train | ||
else | ||
plda_data=voxceleb2cat_train_augx${plda_num_augs} | ||
fi | ||
plda_type=splda | ||
lda_dim=200 | ||
plda_y_dim=150 | ||
plda_z_dim=200 | ||
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