eval_gvd_both.yaml

root_dir: .
exp_dir: !ref <root_dir>/exp

datasets: !include:datasets_vpc2022_official_eval.yaml

eval_steps:  # all metrics in this list will be computed in the evaluation. Remove entry to skip
  utility:
    - gvd

anon_data_suffix: anon  # suffix for dataset to signal that it is anonymized
eval_data_dir: !PLACEHOLDER  # path to anonymized evaluation data in kaldi format, e.g. <eval_data_dir>/libri_test_enrolls/wav.scp etc.

privacy:
  asv:
    model_dir: !ref <exp_dir>/asv_anon_<privacy[asv][vec_type]>_scratch  # path to existing ASV model or output for trained ASV model
    vec_type: ecapa  # ecapa or xvector

    evaluation:
      results_dir: !ref <privacy[asv][model_dir]>  # path to save evaluation results
      distance: cosine  # cosine or plda
      plda:   # ignored if distance is not plda
        model_dir: null  # path to trained PLDA or output of PLDA training
        train_data_dir: null # path to PLDA training data
        anon: !ref <privacy[asv][training][anon]>  # trained on anonymized (true) or original (false) data

utility:
  gvd:
    num_utt: all  # all or specific number, number of utterances per speaker
    asv_params:  # settings for ASV using for GVD evaluation
      # model_dir: !ref <privacy[asv][model_dir]>, can be used if ASV model for original and anon speakers is same
      orig_model_dir: !ref <exp_dir>/asv_ori_ecapa_model  # path to ASV model for original speakers
      anon_model_dir: !ref <privacy[asv][model_dir]>  # path to ASV model for anon speakers
      vec_type: !ref <privacy[asv][vec_type]>
      evaluation:
        plda: !ref <privacy[asv][evaluation][plda]>
        distance: !ref <privacy[asv][evaluation][distance]>
    orig_results_dir: !ref <utility[gvd][asv_params][orig_model_dir]>  # path to dir for similarity matrices for original speakers
    anon_results_dir: !ref <utility[gvd][asv_params][anon_model_dir]>  # path to dir for similarity matrices for anon speakers
    results_dir: !ref <exp_dir>/gvd_both  # path to dir for GVD scores results
    recompute: false  # recompute similarity matrices in any case (true) or skip if matrices exist (false)