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Merge branch 'perseus' into master
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‎egs/sre20-cts/v1/conf/linfbank64_stmn_8k.pyconf‎

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--feats-audio-feat
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logfb
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--feats-sample-frequency
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8000
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--feats-frame-length
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25
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--feats-fb-type
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linear
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--feats-low-freq
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20
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--feats-high-freq
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3700
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--feats-num-filters
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64
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--feats-snip-edges
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false
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--feats-use-energy
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false
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--mvn-context
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‎egs/sre20-cts/v1/config_fbank64_stmn_effnetb4_v2_arcs30m0.3_trn_alllangs_nocv_nocnceleb_adam_lr0.01.amp.v1.sh‎

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@@ -40,18 +40,18 @@ nnet=$nnet_dir/model_ep0060.pth
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ft_batch_size_1gpu=4
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ft_eff_batch_size=128 # effective batch size
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ft_min_chunk=10
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ft_max_chunk=60
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ft_max_chunk=10
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ft_ipe=1
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ft_lr=0.05
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ft_nnet_num_epochs=21
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ft_nnet_num_epochs=30
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ft_margin=0.3
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ft_margin_warmup=3
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ft_opt_opt="--opt-optimizer sgd --opt-lr $ft_lr --opt-momentum 0.9 --opt-weight-decay 1e-5 --use-amp --var-batch-size"
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ft_lrs_opt="--lrsch-lrsch-type cos_lr --lrsch-t 2500 --lrsch-t-mul 2 --lrsch-warm-restarts --lrsch-gamma 0.75 --lrsch-min-lr 1e-4 --lrsch-warmup-steps 100 --lrsch-update-lr-on-opt-step"
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ft_nnet_name=${nnet_name}.ft_${ft_min_chunk}_${ft_max_chunk}_arcm${ft_margin}_sgdcos_lr${ft_lr}_b${ft_eff_batch_size}_amp.v2
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ft_nnet_dir=exp/xvector_nnets/$ft_nnet_name
54-
ft_nnet=$ft_nnet_dir/model_ep0020.pth
54+
ft_nnet=$ft_nnet_dir/model_ep0030.pth
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# xvector last-layer finetuning alllangs

‎egs/sre20-cts/v1/config_fbank64_stmn_res2net50w26s4_eina_hln_arcs30m0.3_trn_alllangs_nocv_nocnceleb_adam_lr0.01.amp.v1.sh‎

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# Res2Net50 x-vector with mixed precision training
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# acoustic features
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feat_config=conf/fbank64_mvn_8k.pyconf
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feat_type=fbank64_stmn
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# x-vector training
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nnet_data=alllangs_nocv_nocnceleb
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nnet_num_augs=4
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aug_opt="--train-aug-cfg conf/reverb_noise_aug.yml --val-aug-cfg conf/reverb_noise_aug.yml"
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13+
batch_size_1gpu=8
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eff_batch_size=512 # effective batch size
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ipe=$nnet_num_augs
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min_chunk=4
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max_chunk=4
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lr=0.01
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nnet_type=res2net50
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dropout=0
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embed_dim=256
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width_factor=1.625
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scale=4
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ws_tag=w26s4
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s=30
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margin_warmup=20
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margin=0.3
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nnet_opt="--resnet-type $nnet_type --in-feats 64 --in-channels 1 --in-kernel-size 3 --in-stride 1 --no-maxpool --norm-layer instance-norm-affine --head-norm-layer layer-norm --no-maxpool --res2net-width-factor $width_factor --res2net-scale $scale"
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opt_opt="--opt-optimizer adam --opt-lr $lr --opt-beta1 0.9 --opt-beta2 0.95 --opt-weight-decay 1e-5 --opt-amsgrad" # --use-amp"
34+
lrs_opt="--lrsch-lrsch-type exp_lr --lrsch-decay-rate 0.5 --lrsch-decay-steps 10000 --lrsch-hold-steps 40000 --lrsch-min-lr 1e-5 --lrsch-warmup-steps 1000 --lrsch-update-lr-on-opt-step"
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nnet_name=${feat_type}_${nnet_type}${ws_tag}_eina_hln_e${embed_dim}_arcs${s}m${margin}_do${dropout}_adam_lr${lr}_b${eff_batch_size}_amp.v1.$nnet_data
37+
nnet_num_epochs=50
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nnet_dir=exp/xvector_nnets/$nnet_name
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nnet=$nnet_dir/model_ep0050.pth
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# xvector full net finetuning with out-of-domain
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ft_batch_size_1gpu=4
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ft_eff_batch_size=128 # effective batch size
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ft_min_chunk=10
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ft_max_chunk=20
47+
ft_ipe=1
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ft_lr=0.05
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ft_nnet_num_epochs=21
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ft_nnet_num_epochs=45
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ft_margin=0.3
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ft_margin_warmup=3
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ft_opt_opt="--opt-optimizer sgd --opt-lr $ft_lr --opt-momentum 0.9 --opt-weight-decay 1e-5 --use-amp --var-batch-size"
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ft_lrs_opt="--lrsch-lrsch-type cos_lr --lrsch-t 2500 --lrsch-t-mul 2 --lrsch-warm-restarts --lrsch-gamma 0.75 --lrsch-min-lr 1e-4 --lrsch-warmup-steps 100 --lrsch-update-lr-on-opt-step"
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ft_nnet_name=${nnet_name}.ft_${ft_min_chunk}_${ft_max_chunk}_arcm${ft_margin}_sgdcos_lr${ft_lr}_b${ft_eff_batch_size}_amp.v2
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ft_nnet_dir=exp/xvector_nnets/$ft_nnet_name
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ft_nnet=$ft_nnet_dir/model_ep0014.pth
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60+
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# xvector last-layer finetuning realtel
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reg_layers_classif=0
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reg_layers_enc="0 1 2 3 4"
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nnet_adapt_data=realtel
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ft2_batch_size_1gpu=16
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ft2_eff_batch_size=128 # effective batch size
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ft2_ipe=1
68+
ft2_lr=0.01
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ft2_nnet_num_epochs=35
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ft2_margin_warmup=3
71+
ft2_reg_weight_embed=0.1
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ft2_min_chunk=10
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ft2_max_chunk=60
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ft2_opt_opt="--opt-optimizer sgd --opt-lr $ft2_lr --opt-momentum 0.9 --opt-weight-decay 1e-5 --use-amp --var-batch-size"
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ft2_lrs_opt="--lrsch-lrsch-type cos_lr --lrsch-t 2500 --lrsch-t-mul 2 --lrsch-warm-restarts --lrsch-gamma 0.75 --lrsch-min-lr 1e-4 --lrsch-warmup-steps 100 --lrsch-update-lr-on-opt-step"
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ft2_nnet_name=${ft_nnet_name}.ft_eaffine_rege_w${ft2_reg_weight_embed}_${ft2_min_chunk}_${ft2_max_chunk}_sgdcos_lr${ft2_lr}_b${ft2_eff_batch_size}_amp.v2.$nnet_adapt_data
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ft2_nnet_dir=exp/xvector_nnets/$ft2_nnet_name
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ft2_nnet=$ft2_nnet_dir/model_ep0015.pth
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# xvector full nnet finetuning
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ft3_batch_size_1gpu=2
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ft3_eff_batch_size=128 # effective batch size
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ft3_ipe=1
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ft3_lr=0.01
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ft3_nnet_num_epochs=10
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ft3_margin_warmup=20
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ft3_reg_weight_embed=0.1
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ft3_reg_weight_enc=0.1
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ft3_min_chunk=10
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ft3_max_chunk=60
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ft3_opt_opt="--opt-optimizer sgd --opt-lr $ft3_lr --opt-momentum 0.9 --opt-weight-decay 1e-5 --use-amp --var-batch-size"
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ft3_lrs_opt="--lrsch-lrsch-type cos_lr --lrsch-t 2500 --lrsch-t-mul 2 --lrsch-warm-restarts --lrsch-gamma 0.75 --lrsch-min-lr 1e-4 --lrsch-warmup-steps 100 --lrsch-update-lr-on-opt-step"
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ft3_nnet_name=${ft2_nnet_name}.ft_reg_wenc${ft3_reg_weight_enc}_we${ft3_reg_weigth_embed}_${ft3_min_chunk}_${ft3_max_chunk}_sgdcos_lr${ft3_lr}_b${ft3_eff_batch_size}_amp.v2
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ft3_nnet_name=${ft2_nnet_name}.ft_${ft3_min_chunk}_${ft3_max_chunk}_sgdcos_lr${ft3_lr}_b${ft3_eff_batch_size}_amp.v2
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ft3_nnet_dir=exp/xvector_nnets/$ft3_nnet_name
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ft3_nnet=$ft3_nnet_dir/model_ep0010.pth
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# back-end
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plda_aug_config=conf/noise_aug.yml
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plda_num_augs=0
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# if [ $plda_num_augs -eq 0 ]; then
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# plda_data=sre_tel
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# plda_adapt_data=sre18_cmn2_adapt_lab
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# else
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# plda_data=sre_tel_augx${plda_num_augs}
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# plda_adapt_data=sre18_cmn2_adapt_lab_augx${plda_num_augs}
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# fi
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# plda_type=splda
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# lda_dim=200
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# plda_y_dim=150
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# plda_z_dim=200
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‎egs/sre20-cts/v1/config_fbank64_stmn_resnet34_eina_hln_bmhah64d8192_arcs30m0.3_trn_alllangs_nocv_nocnceleb_adam_lr0.01.amp.v2.sh‎

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# ResNet34 x-vector with mixed precision training
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# acoustic features
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feat_config=conf/fbank64_mvn_8k.pyconf
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feat_type=fbank64_stmn
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# x-vector training
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nnet_data=alllangs_nocv_nocnceleb
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nnet_num_augs=4
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aug_opt="--train-aug-cfg conf/reverb_noise_aug.yml --val-aug-cfg conf/reverb_noise_aug.yml"
12+
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batch_size_1gpu=32
14+
eff_batch_size=512 # effective batch size
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ipe=$nnet_num_augs
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min_chunk=4
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max_chunk=4
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lr=0.01
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nnet_type=resnet34
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dropout=0
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embed_dim=256
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s=30
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margin_warmup=20
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margin=0.3
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nnet_opt="--resnet-type $nnet_type --in-feats 64 --in-channels 1 --in-kernel-size 3 --in-stride 1 --no-maxpool --norm-layer instance-norm-affine --head-norm-layer layer-norm --pool-type scaled-dot-prod-att-v1 --pool-num-heads 64 --pool-d-k 128 --pool-d-v 128 --pool-bin-attn"
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opt_opt="--opt-optimizer adam --opt-lr $lr --opt-beta1 0.9 --opt-beta2 0.95 --opt-weight-decay 1e-5 --opt-amsgrad" # --use-amp"
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lrs_opt="--lrsch-lrsch-type exp_lr --lrsch-decay-rate 0.5 --lrsch-decay-steps 10000 --lrsch-hold-steps 40000 --lrsch-min-lr 1e-5 --lrsch-warmup-steps 1000 --lrsch-update-lr-on-opt-step"
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nnet_name=${feat_type}_${nnet_type}_eina_hln_bmhah64d8192_e${embed_dim}_arcs${s}m${margin}_do${dropout}_adam_lr${lr}_b${eff_batch_size}_amp.v1.$nnet_data
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nnet_num_epochs=50
35+
nnet_dir=exp/xvector_nnets/$nnet_name
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nnet=$nnet_dir/model_ep0050.pth
37+
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# xvector full net finetuning with out-of-domain
40+
ft_batch_size_1gpu=4
41+
ft_eff_batch_size=128 # effective batch size
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ft_min_chunk=10
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ft_max_chunk=60
44+
ft_ipe=1
45+
ft_lr=0.05
46+
ft_nnet_num_epochs=21
47+
ft_margin=0.3
48+
ft_margin_warmup=3
49+
50+
ft_opt_opt="--opt-optimizer sgd --opt-lr $ft_lr --opt-momentum 0.9 --opt-weight-decay 1e-5 --use-amp --var-batch-size"
51+
ft_lrs_opt="--lrsch-lrsch-type cos_lr --lrsch-t 2500 --lrsch-t-mul 2 --lrsch-warm-restarts --lrsch-gamma 0.75 --lrsch-min-lr 1e-4 --lrsch-warmup-steps 100 --lrsch-update-lr-on-opt-step"
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ft_nnet_name=${nnet_name}.ft_${ft_min_chunk}_${ft_max_chunk}_arcm${ft_margin}_sgdcos_lr${ft_lr}_b${ft_eff_batch_size}_amp.v2
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ft_nnet_dir=exp/xvector_nnets/$ft_nnet_name
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ft_nnet=$ft_nnet_dir/model_ep0021.pth
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# xvector last-layer finetuning realtel
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reg_layers_classif=0
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reg_layers_enc="0 1 2 3 4"
60+
nnet_adapt_data=realtel
61+
ft2_batch_size_1gpu=16
62+
ft2_eff_batch_size=128 # effective batch size
63+
ft2_ipe=1
64+
ft2_lr=0.01
65+
ft2_nnet_num_epochs=35
66+
ft2_margin_warmup=3
67+
ft2_reg_weight_embed=0.1
68+
ft2_min_chunk=10
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ft2_max_chunk=60
70+
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ft2_opt_opt="--opt-optimizer sgd --opt-lr $ft2_lr --opt-momentum 0.9 --opt-weight-decay 1e-5 --use-amp --var-batch-size"
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ft2_lrs_opt="--lrsch-lrsch-type cos_lr --lrsch-t 2500 --lrsch-t-mul 2 --lrsch-warm-restarts --lrsch-gamma 0.75 --lrsch-min-lr 1e-4 --lrsch-warmup-steps 100 --lrsch-update-lr-on-opt-step"
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ft2_nnet_name=${ft_nnet_name}.ft_eaffine_rege_w${ft2_reg_weight_embed}_${ft2_min_chunk}_${ft2_max_chunk}_sgdcos_lr${ft2_lr}_b${ft2_eff_batch_size}_amp.v2.$nnet_adapt_data
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ft2_nnet_dir=exp/xvector_nnets/$ft2_nnet_name
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ft2_nnet=$ft2_nnet_dir/model_ep0015.pth
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# xvector full nnet finetuning
79+
ft3_batch_size_1gpu=2
80+
ft3_eff_batch_size=128 # effective batch size
81+
ft3_ipe=1
82+
ft3_lr=0.01
83+
ft3_nnet_num_epochs=10
84+
ft3_margin_warmup=20
85+
ft3_reg_weight_embed=0.1
86+
ft3_reg_weight_enc=0.1
87+
ft3_min_chunk=10
88+
ft3_max_chunk=60
89+
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ft3_opt_opt="--opt-optimizer sgd --opt-lr $ft3_lr --opt-momentum 0.9 --opt-weight-decay 1e-5 --use-amp --var-batch-size"
91+
ft3_lrs_opt="--lrsch-lrsch-type cos_lr --lrsch-t 2500 --lrsch-t-mul 2 --lrsch-warm-restarts --lrsch-gamma 0.75 --lrsch-min-lr 1e-4 --lrsch-warmup-steps 100 --lrsch-update-lr-on-opt-step"
92+
ft3_nnet_name=${ft2_nnet_name}.ft_reg_wenc${ft3_reg_weight_enc}_we${ft3_reg_weigth_embed}_${ft3_min_chunk}_${ft3_max_chunk}_sgdcos_lr${ft3_lr}_b${ft3_eff_batch_size}_amp.v2
93+
ft3_nnet_name=${ft2_nnet_name}.ft_${ft3_min_chunk}_${ft3_max_chunk}_sgdcos_lr${ft3_lr}_b${ft3_eff_batch_size}_amp.v2
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ft3_nnet_dir=exp/xvector_nnets/$ft3_nnet_name
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ft3_nnet=$ft3_nnet_dir/model_ep0010.pth
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97+
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# back-end
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plda_aug_config=conf/noise_aug.yml
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plda_num_augs=0
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# if [ $plda_num_augs -eq 0 ]; then
102+
# plda_data=sre_tel
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# plda_adapt_data=sre18_cmn2_adapt_lab
104+
# else
105+
# plda_data=sre_tel_augx${plda_num_augs}
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# plda_adapt_data=sre18_cmn2_adapt_lab_augx${plda_num_augs}
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# fi
108+
# plda_type=splda
109+
# lda_dim=200
110+
# plda_y_dim=150
111+
# plda_z_dim=200
112+

‎egs/sre20-cts/v1/config_fbank64_stmn_resnet34_eina_hln_chattstatsi128_arcs30m0.3_trn_alllangs_nocv_nocnceleb_adam_lr0.01.amp.v1.sh‎

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# ResNet34 x-vector with mixed precision training
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# acoustic features
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feat_config=conf/fbank64_mvn_8k.pyconf
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feat_type=fbank64_stmn
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7+
8+
# x-vector training
9+
nnet_data=alllangs_nocv_nocnceleb
10+
nnet_num_augs=4
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aug_opt="--train-aug-cfg conf/reverb_noise_aug.yml --val-aug-cfg conf/reverb_noise_aug.yml"
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13+
batch_size_1gpu=32
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eff_batch_size=512 # effective batch size
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ipe=$nnet_num_augs
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min_chunk=4
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max_chunk=4
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lr=0.01
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nnet_type=resnet34
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dropout=0
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embed_dim=256
23+
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s=30
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margin_warmup=20
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margin=0.3
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attstats_inner=128
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nnet_opt="--resnet-type $nnet_type --in-feats 64 --in-channels 1 --in-kernel-size 3 --in-stride 1 --no-maxpool --norm-layer instance-norm-affine --head-norm-layer layer-norm --pool-type ch-wise-att-mean-stddev --pool-inner-feats $attstats_inner"
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opt_opt="--opt-optimizer adam --opt-lr $lr --opt-beta1 0.9 --opt-beta2 0.95 --opt-weight-decay 1e-5 --opt-amsgrad" # --use-amp"
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lrs_opt="--lrsch-lrsch-type exp_lr --lrsch-decay-rate 0.5 --lrsch-decay-steps 10000 --lrsch-hold-steps 40000 --lrsch-min-lr 1e-5 --lrsch-warmup-steps 1000 --lrsch-update-lr-on-opt-step"
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nnet_name=${feat_type}_${nnet_type}_eina_hln_chattstatsi128_e${embed_dim}_arcs${s}m${margin}_do${dropout}_adam_lr${lr}_b${eff_batch_size}_amp.v1.$nnet_data
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nnet_num_epochs=50
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nnet_dir=exp/xvector_nnets/$nnet_name
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nnet=$nnet_dir/model_ep0050.pth
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# xvector full net finetuning with out-of-domain
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ft_batch_size_1gpu=4
43+
ft_eff_batch_size=128 # effective batch size
44+
ft_min_chunk=10
45+
ft_max_chunk=60
46+
ft_ipe=1
47+
ft_lr=0.05
48+
ft_nnet_num_epochs=21
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ft_margin=0.3
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ft_margin_warmup=3
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ft_opt_opt="--opt-optimizer sgd --opt-lr $ft_lr --opt-momentum 0.9 --opt-weight-decay 1e-5 --use-amp --var-batch-size"
53+
ft_lrs_opt="--lrsch-lrsch-type cos_lr --lrsch-t 2500 --lrsch-t-mul 2 --lrsch-warm-restarts --lrsch-gamma 0.75 --lrsch-min-lr 1e-4 --lrsch-warmup-steps 100 --lrsch-update-lr-on-opt-step"
54+
ft_nnet_name=${nnet_name}.ft_${ft_min_chunk}_${ft_max_chunk}_arcm${ft_margin}_sgdcos_lr${ft_lr}_b${ft_eff_batch_size}_amp.v2
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ft_nnet_dir=exp/xvector_nnets/$ft_nnet_name
56+
ft_nnet=$ft_nnet_dir/model_ep0021.pth
57+
58+
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# xvector last-layer finetuning realtel
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reg_layers_classif=0
61+
reg_layers_enc="0 1 2 3 4"
62+
nnet_adapt_data=realtel
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ft2_batch_size_1gpu=16
64+
ft2_eff_batch_size=128 # effective batch size
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ft2_ipe=1
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ft2_lr=0.01
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ft2_nnet_num_epochs=35
68+
ft2_margin_warmup=3
69+
ft2_reg_weight_embed=0.1
70+
ft2_min_chunk=10
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ft2_max_chunk=60
72+
73+
ft2_opt_opt="--opt-optimizer sgd --opt-lr $ft2_lr --opt-momentum 0.9 --opt-weight-decay 1e-5 --use-amp --var-batch-size"
74+
ft2_lrs_opt="--lrsch-lrsch-type cos_lr --lrsch-t 2500 --lrsch-t-mul 2 --lrsch-warm-restarts --lrsch-gamma 0.75 --lrsch-min-lr 1e-4 --lrsch-warmup-steps 100 --lrsch-update-lr-on-opt-step"
75+
ft2_nnet_name=${ft_nnet_name}.ft_eaffine_rege_w${ft2_reg_weight_embed}_${ft2_min_chunk}_${ft2_max_chunk}_sgdcos_lr${ft2_lr}_b${ft2_eff_batch_size}_amp.v2.$nnet_adapt_data
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ft2_nnet_dir=exp/xvector_nnets/$ft2_nnet_name
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ft2_nnet=$ft2_nnet_dir/model_ep0015.pth
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79+
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# xvector full nnet finetuning
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ft3_batch_size_1gpu=2
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ft3_eff_batch_size=128 # effective batch size
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ft3_ipe=1
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ft3_lr=0.01
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ft3_nnet_num_epochs=10
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ft3_margin_warmup=20
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ft3_reg_weight_embed=0.1
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ft3_reg_weight_enc=0.1
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ft3_min_chunk=10
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ft3_max_chunk=60
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ft3_opt_opt="--opt-optimizer sgd --opt-lr $ft3_lr --opt-momentum 0.9 --opt-weight-decay 1e-5 --use-amp --var-batch-size"
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ft3_lrs_opt="--lrsch-lrsch-type cos_lr --lrsch-t 2500 --lrsch-t-mul 2 --lrsch-warm-restarts --lrsch-gamma 0.75 --lrsch-min-lr 1e-4 --lrsch-warmup-steps 100 --lrsch-update-lr-on-opt-step"
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ft3_nnet_name=${ft2_nnet_name}.ft_reg_wenc${ft3_reg_weight_enc}_we${ft3_reg_weigth_embed}_${ft3_min_chunk}_${ft3_max_chunk}_sgdcos_lr${ft3_lr}_b${ft3_eff_batch_size}_amp.v2
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ft3_nnet_name=${ft2_nnet_name}.ft_${ft3_min_chunk}_${ft3_max_chunk}_sgdcos_lr${ft3_lr}_b${ft3_eff_batch_size}_amp.v2
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ft3_nnet_dir=exp/xvector_nnets/$ft3_nnet_name
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ft3_nnet=$ft3_nnet_dir/model_ep0010.pth
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# back-end
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plda_aug_config=conf/noise_aug.yml
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plda_num_augs=0
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# if [ $plda_num_augs -eq 0 ]; then
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# plda_data=sre_tel
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# plda_adapt_data=sre18_cmn2_adapt_lab
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# else
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# plda_data=sre_tel_augx${plda_num_augs}
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# plda_adapt_data=sre18_cmn2_adapt_lab_augx${plda_num_augs}
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# fi
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# plda_type=splda
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# lda_dim=200
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# plda_y_dim=150
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# plda_z_dim=200
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