Skip to content

Latest commit

 

History

History
103 lines (91 loc) · 6.74 KB

README.md

File metadata and controls

103 lines (91 loc) · 6.74 KB

Results

  • Setup: fbank80, num_frms200, epoch150, ArcMargin, aug_prob0.6, speed_perturb (no spec_aug)
  • Scoring: cosine (sub mean of vox2_dev), AS-Norm, QMF
  • Metric: EER(%)
  • 🔥 UPDATE 2024.09.03: We support the SimAM_ResNet pretrained on VoxBlink2 and Finetuned on Voxceleb2!
  • 🔥 UPDATE 2024.08.27: We support SSL models as the feature front-end, take a look at the WavLM recipe!
  • UPDATE 2022.07.19: We apply the same setups as the winning system of CNSRC 2022 (see cnceleb recipe for details), and obtain significant performance improvement.
    • LR scheduler warmup from 0
    • Remove one embedding layer in ResNet models
    • Add large margin fine-tuning strategy (LM)
Model Params Flops LM AS-Norm QMF vox1-O-clean vox1-E-clean vox1-H-clean
XVEC-TSTP-emb512 4.61M 0.53G × × × 1.989 1.950 3.412
× √ × 1.834 1.846 3.124
√ × × 1.749 1.721 2.944
√ √ × 1.590 1.641 2.726
ECAPA_TDNN_GLOB_c512-ASTP-emb192 6.19M 1.04G × × × 1.069 1.209 2.310
× √ × 0.957 1.128 2.105
√ × × 0.878 1.072 2.007
√ √ × 0.782 1.005 1.824
ECAPA_TDNN_GLOB_c1024-ASTP-emb192 14.65M 2.65G × × × 0.856 1.072 2.059
× √ × 0.808 0.990 1.874
√ × × 0.798 0.993 1.883
√ √ × 0.728 0.929 1.721
√ √ √ 0.707 0.894 1.615
ResNet34-TSTP-emb256 6.63M 4.55G × × × 0.867 1.049 1.959
× √ × 0.787 0.964 1.726
× √ √ 0.718 0.911 1.606
√ × × 0.797 0.937 1.695
√ √ × 0.723 0.867 1.532
√ √ √ 0.659 0.821 1.437
ResNet221-TSTP-emb256 23.79M 21.29G × × × 0.569 0.774 1.464
× √ × 0.479 0.707 1.290
√ × × 0.580 0.729 1.351
√ √ × 0.505 0.676 1.213
ResNet293-TSTP-emb256 28.62M 28.10G × × × 0.595 0.756 1.433
× √ × 0.537 0.701 1.276
√ × × 0.532 0.707 1.311
√ √ × 0.447 0.657 1.183
√ √ √ 0.425 0.641 1.146
RepVGG_TINY_A0 6.26M 4.65G × × × 0.909 1.034 1.943
× √ × 0.824 0.953 1.709
CAM++ 7.18M 1.15G × × × 0.803 0.932 1.860
× √ × 0.718 0.879 1.735
√ x × 0.707 0.845 1.664
√ √ × 0.659 0.803 1.569
ERes2Net34_Base 7.88M 3.43G × × × 0.914 1.065 1.986
× √ × 0.803 0.976 1.787
√ x × 0.824 0.968 1.776
√ √ × 0.744 0.896 1.603
Res2Net34_Base 4.68M 1.77G × × × 1.351 1.347 2.478
× √ × 1.234 1.232 2.162
Gemini_DFResNet114 6.53M 5.42G × × × 0.787 0.963 1.760
× √ × 0.707 0.889 1.546
√ x × 0.771 0.906 1.599
√ √ × 0.638 0.839 1.427
SimAM_ResNet34 (VoxBlink2 Pretrain) 25.2M √ x × 0.415 0.615 1.121
√ √ × 0.372 0.581 1.049
√ √ √ 0.372 0.559 0.997
SimAM_ResNet100 (VoxBlink2 Pretrain) 50.2M √ x × 0.229 0.458 0.868
√ √ × 0.207 0.424 0.804
√ √ √ 0.202 0.421 0.795
XI_VEC_ECAPA_TDNN_c512 5.9M 0.68G x x × 0.995 1.130 2.169
× √ × 0.883 1.056 1.976

PLDA results

If you are interested in the PLDA scoring (which is inferior to the simple cosine scoring under the margin based setting), simply run:

local/score_plda.sh --stage 1 --stop-stage 3 --exp_dir exp_name

The results on ResNet34 (large margin, no asnorm) are:

Scoring method vox1-O-clean vox1-E-clean vox1-H-clean
PLDA 1.207 1.350 2.528

WavLM results

  • Pre-trained frontend: the WavLM Large model, multilayer features are used
  • Speaker model: ECAPA_TDNN_GLOB_c512-ASTP-emb192
  • Training strategy: Frozen => Joint ft => Joint lmft
bash run_wavlm.sh --stage 3 --stop_stage 9
Training strategy AS-Norm QMF vox1-O-clean vox1-E-clean vox1-H-clean
Frozen × × 0.595 0.719 1.501
√ × 0.548 0.656 1.355
√ √ 0.489 0.619 1.224
Frozen => Joint ft × × 0.542 0.635 1.355
√ × 0.521 0.594 1.237
√ √ 0.494 0.576 1.205
Frozen => Joint ft => Joint lmft × × 0.521 0.626 1.344
√ × 0.495 0.588 1.247
√ √ 0.415 0.551 1.118