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Results

  • Setup: fbank80, num_frms200, epoch150, ArcMargin, aug_prob0.6, speed_perturb (no spec_aug)
  • test_trials: CNC-Eval-Avg.lst
  • 🔥 UPDATE 2022.07.12: We update this recipe according to the setups in the winning system of CNSRC 2022, and get obvious performance improvement compared with the old recipe. Check the commit1, commit2 for details.
    • LR scheduler warmup from 0
    • Remove one embedding layer
    • Add large margin fine-tuning strategy (LM)
Model Params FLOPs LM AS-Norm EER (%) minDCF (p=0.01)
ResNet34-TSTP-emb256 (OLD) 6.70M 4.55 G × × 8.426 0.487
ResNet34-TSTP-emb256 6.63M 4.55 G × × 7.134 0.408
× 6.747 0.367
× 6.652 0.393
6.492 0.354
ResNet221-TSTP-emb256 23.86M 21.29 G × × 5.965 0.362
× 5.708 0.326
× 5.886 0.362
5.655 0.330
ECAPA_TDNN_GLOB_c512-ASTP-emb192 6.19M 1.04 G × × 8.313 0.432
× 7.644 0.390
× 8.004 0.422
7.417 0.379
ECAPA_TDNN_GLOB_c1024-ASTP-emb192 14.65M 2.65 G × × 7.879 0.420
× 7.412 0.379
× 7.986 0.417
7.395 0.372
RepVGG_TINY_A0 6.26M 4.65 G × × 6.883 0.399
× 6.550 0.355