# Config Defaults Reference

_Auto-generated by `scripts/gen_defaults_table.py` from 482 `config_val*()` call sites across `nkululeko/`. Do not edit by hand - run `python scripts/gen_defaults_table.py --write` to refresh._

This is a contributor-facing cross-check, not user documentation - see [ini_file.md](ini_file.md) for the hand-written configuration guide. "Inconsistent defaults" below are candidates worth a human look, not necessarily bugs: some reflect genuinely different, context-appropriate choices (e.g. an SVM and an SVR using different default C values).

**⚠ 26 key(s) have inconsistent defaults across call sites:**

* `'DATA'.'no_reuse'`: `'False'` (nkululeko/augment.py:43), `'False'` (nkululeko/experiment.py:161), `False` (nkululeko/utils/dataframe.py:250)
* `'DATA'.'target'`: `'emotion'` (nkululeko/augment.py:81), `'emotion'` (nkululeko/augmenting/resampler.py:76), `'emotion'` (nkululeko/bundle.py:56), `'emotion'` (nkululeko/bundle.py:218), `None` (nkululeko/data/dataset.py:35), `None` (nkululeko/data/dataset_csv.py:84), `None` (nkululeko/experiment.py:123), `'emotion'` (nkululeko/experiment.py:617), `'emotion'` (nkululeko/experiment.py:627), `'emotion'` (nkululeko/experiment.py:637), `'emotion'` (nkululeko/export.py:108), `'emotion'` (nkululeko/feat_extract/feats_analyser.py:26), `'emotion'` (nkululeko/models/model.py:34), `'emotion'` (nkululeko/plots.py:31), `'class_label'` (nkululeko/plots.py:893), `'emotion'` (nkululeko/testing_predictor.py:71), `None` (nkululeko/utils/util.py:613)
* `'DATA'.'tests'`: `'False'` (nkululeko/data/dataset.py:725), `False` (nkululeko/nkululeko.py:46), `False` (nkululeko/testing_predictor.py:59)
* `'DATA'.'type'`: `False` (nkululeko/data/dataset.py:817), `'dummy'` (nkululeko/data/datasplitter.py:236)
* `'EXP'.'epochs'`: `1` (nkululeko/modelrunner.py:129), `1` (nkululeko/models/model_tuned.py:65), `'50'` (nkululeko/optimizers/scheduler_factory.py:84)
* `'EXP'.'language'`: `'en'` (nkululeko/autopredict/ap_text.py:31), `False` (nkululeko/bundle.py:101)
* `'EXP'.'sample_selection'`: `'all'` (nkululeko/data/datasplitter.py:36), `'train'` (nkululeko/experiment.py:476), `'all'` (nkululeko/experiment.py:497), `'all'` (nkululeko/feat_extract/feats_analyser.py:388), `'all'` (nkululeko/plots.py:636), `'all'` (nkululeko/predict.py:577), `'all'` (nkululeko/resample.py:127), `'all'` (nkululeko/segment.py:242)
* `'EXP'.'type'`: `None` (nkululeko/data/dataset.py:146), `'classification'` (nkululeko/utils/util.py:297)
* `'EXPL'.'dist_type'`: `'hist'` (nkululeko/plots.py:149), `'kde'` (nkululeko/plots.py:425)
* `'EXPL'.'model'`: `False` (nkululeko/explore.py:91), `"['log_reg']"` (nkululeko/feat_extract/feats_analyser.py:172)
* `'EXPL'.'plot_tree'`: `'False'` (nkululeko/explore.py:92), `False` (nkululeko/feat_extract/feats_analyser.py:96)
* `'FEATS'.'audmodel.id'`: `False` (nkululeko/feat_extract/feats_audmodel.py:34), `'audmodel'` (nkululeko/feat_extract/feats_audmodel.py:167)
* `'FEATS'.'no_reuse'`: `'False'` (nkululeko/feat_extract/feats_cqcc.py:114), `'False'` (nkululeko/feat_extract/feats_emotion2vec.py:78), `'False'` (nkululeko/feat_extract/feats_lfcc.py:124), `False` (nkululeko/feat_extract/feats_mld.py:45), `'False'` (nkululeko/feat_extract/feats_textclassifier.py:57), `False` (nkululeko/feat_extract/featureset.py:48)
* `'FEATS'.'type'`: `['os']` (nkululeko/bundle.py:220), `'os'` (nkululeko/data/datasplitter.py:410), `['os']` (nkululeko/experiment.py:227), `['os']` (nkululeko/infer.py:208), `None` (nkululeko/predict.py:215), `None` (nkululeko/predict.py:911)
* `'MODEL'.'C_val'`: `'1.0'` (nkululeko/feat_extract/feats_analyser.py:233), `'1'` (nkululeko/models/model_svm.py:16), `'0.001'` (nkululeko/models/model_svr.py:16)
* `'MODEL'.'adm.branches'`: `'time,spectral,phase,lfcc,cqcc'` (nkululeko/models/model_adm.py:140), `'time,spectral,phase,lfcc,cqcc'` (nkululeko/models/model_adm.py:546), `'time,spectral,phase'` (nkululeko/utils/naming.py:100)
* `'MODEL'.'batch_size'`: `32` (nkululeko/models/model_adm.py:190), `8` (nkululeko/models/model_cnn.py:71), `8` (nkululeko/models/model_mlp.py:79), `8` (nkululeko/models/model_mlp_regression.py:71), `'8'` (nkululeko/models/model_tuned.py:58)
* `'MODEL'.'class_weight'`: `False` (nkululeko/models/model.py:278), `'False'` (nkululeko/models/model.py:302), `False` (nkululeko/models/model.py:317), `'auto'` (nkululeko/models/model_adm.py:93), `'False'` (nkululeko/models/model_svm.py:17), `False` (nkululeko/models/model_tuned.py:437), `False` (nkululeko/models/model_xgb.py:137)
* `'MODEL'.'device'`: `'cpu'` (nkululeko/autopredict/ap_sid.py:31), `device` (nkululeko/autopredict/ap_text.py:28), `cuda` (nkululeko/feat_extract/feats_agender.py:38), `cuda` (nkululeko/feat_extract/feats_agender_agender.py:36), `cuda` (nkululeko/feat_extract/feats_ast.py:19), `cuda` (nkululeko/feat_extract/feats_auddim.py:34), `cuda` (nkululeko/feat_extract/feats_audmodel.py:45), `cuda` (nkululeko/feat_extract/feats_audwav2vec2.py:38), `cuda` (nkululeko/feat_extract/feats_bert.py:22), `'cpu'` (nkululeko/feat_extract/feats_clap.py:17), `cuda` (nkululeko/feat_extract/feats_emotion2vec.py:31), `cuda` (nkululeko/feat_extract/feats_hubert.py:29), `cuda` (nkululeko/feat_extract/feats_lfcc.py:97), `'cpu'` (nkululeko/feat_extract/feats_mos.py:33), `cuda` (nkululeko/feat_extract/feats_spkrec.py:29), `cuda` (nkululeko/feat_extract/feats_sptk.py:39), `cuda` (nkululeko/feat_extract/feats_squim.py:36), `cuda` (nkululeko/feat_extract/feats_textclassifier.py:24), `cuda` (nkululeko/feat_extract/feats_wav2vec2.py:32), `cuda` (nkululeko/feat_extract/feats_wavlm.py:29), `cuda` (nkululeko/feat_extract/feats_whisper.py:19), `cuda` (nkululeko/models/model_adm.py:62), `cuda` (nkululeko/models/model_adm.py:510), `'cpu'` (nkululeko/models/model_cnn.py:49), `cuda` (nkululeko/models/model_cnn.py:232), `cuda` (nkululeko/models/model_cnn.py:249), `cuda` (nkululeko/models/model_mlp.py:45), `cuda` (nkululeko/models/model_mlp.py:271), `cuda` (nkululeko/models/model_mlp.py:304), `cuda` (nkululeko/models/model_mlp_regression.py:48), `'cpu'` (nkululeko/models/model_mlp_regression.py:251), `'cpu'` (nkululeko/models/model_mlp_regression.py:269), `False` (nkululeko/models/model_tuned.py:43), `'cpu'` (nkululeko/segmenting/seg_pyannote.py:38)
* `'MODEL'.'drop'`: `False` (nkululeko/models/model_cnn.py:57), `False` (nkululeko/models/model_cnn.py:235), `False` (nkululeko/models/model_cnn.py:252), `'False'` (nkululeko/models/model_mlp.py:53), `'False'` (nkululeko/models/model_mlp.py:274), `'False'` (nkululeko/models/model_mlp.py:307), `False` (nkululeko/models/model_mlp_regression.py:55), `False` (nkululeko/models/model_mlp_regression.py:253), `False` (nkululeko/models/model_mlp_regression.py:272), `False` (nkululeko/models/model_tuned.py:67)
* `'MODEL'.'learning_rate'`: `'0.0001'` (nkululeko/models/model_tuned.py:61), `0.3` (nkululeko/models/model_xgb.py:32), `str(default_lr)` (nkululeko/optimizers/optimizer_factory.py:32)
* `'MODEL'.'loss'`: `'cross'` (nkululeko/models/model.py:93), `'bce'` (nkululeko/models/model_adm.py:65), `'mse'` (nkululeko/models/model_mlp_regression.py:29), `'cross'` (nkululeko/models/model_tuned.py:434), `'1-ccc'` (nkululeko/models/model_tuned.py:453)
* `'MODEL'.'measure'`: `'mse'` (nkululeko/models/model_mlp_regression.py:232), `'ccc'` (nkululeko/models/model_tuned.py:56), `'uar'` (nkululeko/reporting/reporter.py:81), `'mse'` (nkululeko/reporting/reporter.py:92), `'mse'` (nkululeko/reporting/reporter.py:636), `'uar'` (nkululeko/reporting/run_plotter.py:79), `'uar'` (nkululeko/runmanager.py:166), `'uar'` (nkululeko/utils/util.py:622), `'mse'` (nkululeko/utils/util.py:629)
* `'MODEL'.'scheduler.gamma'`: `'0.5'` (nkululeko/optimizers/scheduler_factory.py:46), `'0.95'` (nkululeko/optimizers/scheduler_factory.py:54)
* `'MODEL'.'type'`: `'svm'` (nkululeko/bundle.py:224), `''` (nkululeko/utils/naming.py:118)
* `'REPORT'.'latex'`: `False` (nkululeko/experiment.py:90), `'nkululeko_latex'` (nkululeko/reporting/latex_writer.py:39)

| Namespace | Key | Default | Source |
|---|---|---|---|
| 'AUGMENT' | 'augment' | `False` | nkululeko/aug_train.py:40, nkululeko/augment.py:57, nkululeko/utils/naming.py:108 |
| 'AUGMENT' | 'augmentations' | `defaults` | nkululeko/augmenting/augmenter_audiomentations.py:31 |
| 'AUGMENT' | 'bypass_prob' | `0.3` | nkululeko/augmenting/augmenter_auglib.py:60 |
| 'AUGMENT' | 'crop_dur' | `1.0` | nkululeko/augmenting/augmenter_auglib.py:126 |
| 'AUGMENT' | 'p_reverse' | `'0.3'` | nkululeko/augmenting/randomsplicer.py:44 |
| 'AUGMENT' | 'result' | `'augmented.csv'` | nkululeko/augment.py:41 |
| 'AUGMENT' | 'top_db' | `'12'` | nkululeko/augmenting/randomsplicer.py:45 |
| 'AUGMENT' | 'transformations' | `'["room", "music", "noise", "babble", "crop", "cough"]'` | nkululeko/augmenting/augmenter_auglib.py:52 |
| 'DATA' | 'balance' | `False` | nkululeko/data/dataset.py:539 |
| 'DATA' | 'bins' | `False` | nkululeko/data/dataset.py:529 |
| 'DATA' | 'check_size' | `False` | nkululeko/file_checker.py:37 |
| 'DATA' | 'check_vad' | `False` | nkululeko/file_checker.py:11, nkululeko/file_checker.py:67 |
| 'DATA' | 'filter' | `False` | nkululeko/filter_data.py:135 |
| 'DATA' | 'label_data' | `False` | nkululeko/experiment.py:740, nkululeko/testing_predictor.py:31 |
| 'DATA' | 'label_result' | `False` | nkululeko/experiment.py:741 |
| 'DATA' | 'labels' | `False` | nkululeko/data/dataset.py:805, nkululeko/experiment.py:134 |
| 'DATA' | 'limit_samples' | `False` | nkululeko/filter_data.py:26 |
| 'DATA' | 'limit_samples_per_speaker' | `False` | nkululeko/filter_data.py:49 |
| 'DATA' | 'max_duration_of_sample' | `False` | nkululeko/filter_data.py:76 |
| 'DATA' | 'min_duration_of_sample' | `False` | nkululeko/filter_data.py:75 |
| 'DATA' | 'no_reuse' | `'False'` / `False` | nkululeko/augment.py:43, nkululeko/experiment.py:161, nkululeko/utils/dataframe.py:250 |
| 'DATA' | 'root_folders' | `False` | nkululeko/utils/util.py:70 |
| 'DATA' | 'sample_selection' | `'all'` | nkululeko/experiment.py:343 |
| 'DATA' | 'size_diff_weight' | `'1'` | nkululeko/data/dataset.py:558 |
| 'DATA' | 'target' | `'class_label'` / `'emotion'` / `None` | nkululeko/augment.py:81, nkululeko/augmenting/resampler.py:76, nkululeko/bundle.py:56, +14 more |
| 'DATA' | 'target_divide_by' | `False` | nkululeko/data/datasplitter.py:311 |
| 'DATA' | 'tests' | `'False'` / `False` | nkululeko/data/dataset.py:725, nkululeko/nkululeko.py:46, nkululeko/testing_predictor.py:59 |
| 'DATA' | 'trains' | `False` | nkululeko/utils/naming.py:52 |
| 'DATA' | 'type' | `'dummy'` / `False` | nkululeko/data/dataset.py:817, nkululeko/data/datasplitter.py:236 |
| 'DATA' | <f'{stratif_var}_bins'> | `False` | nkululeko/data/dataset.py:551 |
| 'EXP' | 'balancing' | `False` | nkululeko/modelrunner.py:256 |
| 'EXP' | 'epochs' | `'50'` / `1` | nkululeko/modelrunner.py:129, nkululeko/models/model_tuned.py:65, nkululeko/optimizers/scheduler_factory.py:84 |
| 'EXP' | 'export_onnx' | `'False'` | nkululeko/nkululeko.py:131 |
| 'EXP' | 'filter.sample_selection' | `'all'` | nkululeko/data/datasplitter.py:194 |
| 'EXP' | 'language' | `'en'` / `False` | nkululeko/autopredict/ap_text.py:31, nkululeko/bundle.py:101 |
| 'EXP' | 'no_warnings' | `False` | nkululeko/aug_train.py:35, nkululeko/augment.py:36, nkululeko/explore.py:62, +5 more |
| 'EXP' | 'run' | `0` | nkululeko/utils/util.py:217 |
| 'EXP' | 'runs' | `1` | nkululeko/experiment.py:729, nkululeko/reporting/run_plotter.py:43, nkululeko/runmanager.py:66 |
| 'EXP' | 'sample_selection' | `'all'` / `'train'` | nkululeko/data/datasplitter.py:36, nkululeko/experiment.py:476, nkululeko/experiment.py:497, +5 more |
| 'EXP' | 'save' | `True` | nkululeko/experiment.py:706 |
| 'EXP' | 'save_test' | `False` | nkululeko/experiment.py:716 |
| 'EXP' | 'traindevtest' | `'False'` | nkululeko/experiment.py:54, nkululeko/models/model_xgb.py:74, nkululeko/runmanager.py:59 |
| 'EXP' | 'type' | `'classification'` / `None` | nkululeko/data/dataset.py:146, nkululeko/utils/util.py:297 |
| 'EXPL' | 'dist_type' | `'hist'` / `'kde'` | nkululeko/plots.py:149, nkululeko/plots.py:425 |
| 'EXPL' | 'feature_distributions' | `'False'` | nkululeko/experiment.py:495, nkululeko/explore.py:85 |
| 'EXPL' | 'ignore_gender' | `'False'` | nkululeko/plots.py:798 |
| 'EXPL' | 'max_feats' | `'10'` | nkululeko/feat_extract/feats_analyser.py:174 |
| 'EXPL' | 'model' | `"['log_reg']"` / `False` | nkululeko/explore.py:91, nkululeko/feat_extract/feats_analyser.py:172 |
| 'EXPL' | 'pca' | `'False'` | nkululeko/experiment.py:635, nkululeko/explore.py:88 |
| 'EXPL' | 'permutation' | `False` | nkululeko/feat_extract/feats_analyser.py:176 |
| 'EXPL' | 'plot_features' | `None` | nkululeko/feat_extract/feats_analyser.py:383 |
| 'EXPL' | 'plot_tree' | `'False'` / `False` | nkululeko/explore.py:92, nkululeko/feat_extract/feats_analyser.py:96 |
| 'EXPL' | 'print_colvals' | `'False'` | nkululeko/experiment.py:516 |
| 'EXPL' | 'print_stats' | `'False'` | nkululeko/plots.py:35, nkululeko/reporting/run_plotter.py:44 |
| 'EXPL' | 'regplot' | `None` | nkululeko/feat_extract/feats_analyser.py:399 |
| 'EXPL' | 'scatter' | `'False'` | nkululeko/experiment.py:556, nkululeko/explore.py:89 |
| 'EXPL' | 'scatter.dim' | `2` | nkululeko/plots.py:633 |
| 'EXPL' | 'scatter.target' | `default_scatter_target` | nkululeko/experiment.py:568 |
| 'EXPL' | 'shap' | `'False'` | nkululeko/experiment.py:548, nkululeko/explore.py:90, nkululeko/flags.py:297 |
| 'EXPL' | 'spotlight' | `'False'` | nkululeko/experiment.py:523 |
| 'EXPL' | 'tsne' | `'False'` | nkululeko/experiment.py:615, nkululeko/explore.py:86 |
| 'EXPL' | 'umap' | `'False'` | nkululeko/experiment.py:625, nkululeko/explore.py:87 |
| 'EXPL' | 'value_counts' | `False` | nkululeko/experiment.py:514, nkululeko/plots.py:129 |
| 'EXPL' | <f'{att}.bin_reals'> | `'False'` | nkululeko/plots.py:322 |
| 'EXPORT' | 'bundle_path' | `os.path.join(expr.root, expr.name, 'export')` | nkululeko/bundle.py:233 |
| 'EXPORT' | 'data_name' | `'export'` | nkululeko/export.py:99 |
| 'EXPORT' | 'orig_root' | `None` | nkululeko/export.py:98 |
| 'EXPORT' | 'root' | `'./exported_data/'` | nkululeko/export.py:97 |
| 'EXPORT' | 'segments_as_files' | `'False'` | nkululeko/export.py:100 |
| 'FEATS' | 'Spkrec.model' | `f'speechbrain/{self.feat_type}'` | nkululeko/feat_extract/feats_spkrec.py:39 |
| 'FEATS' | 'WavLM.model' | `f'microsoft/{self.feat_type}'` | nkululeko/feat_extract/feats_wavlm.py:38 |
| 'FEATS' | 'agender.model' | `'./audmodel_agender/'` | nkululeko/feat_extract/feats_agender.py:29, nkululeko/feat_extract/feats_agender_agender.py:27 |
| 'FEATS' | 'assignments' | `10` | nkululeko/feat_extract/feats_oxbow.py:62 |
| 'FEATS' | 'ast.model' | `'MIT/ast-finetuned-audioset-10-10-0.4593'` | nkululeko/feat_extract/feats_ast.py:25 |
| 'FEATS' | 'aud.model' | `'./audmodel/'` | nkululeko/feat_extract/feats_auddim.py:27, nkululeko/feat_extract/feats_audwav2vec2.py:31 |
| 'FEATS' | 'audmodel.embeddings_name' | `'hidden_states'` | nkululeko/feat_extract/feats_audmodel.py:39 |
| 'FEATS' | 'audmodel.id' | `'audmodel'` / `False` | nkululeko/feat_extract/feats_audmodel.py:34, nkululeko/feat_extract/feats_audmodel.py:167 |
| 'FEATS' | 'balancing' | `False` | nkululeko/modelrunner.py:378 |
| 'FEATS' | 'balancing_random_state' | `42` | nkululeko/modelrunner.py:384 |
| 'FEATS' | 'bert.layer' | `'0'` | nkululeko/feat_extract/feats_bert.py:39 |
| 'FEATS' | 'bert.model' | `f'{self.feat_type}'` | nkululeko/feat_extract/feats_bert.py:31, nkululeko/feat_extract/feats_bert.py:79 |
| 'FEATS' | 'bert.text_column' | `'text'` | nkululeko/feat_extract/feats_bert.py:57 |
| 'FEATS' | 'cqcc.frame_period' | `FRAME_PERIOD` | nkululeko/feat_extract/feats_cqcc.py:92 |
| 'FEATS' | 'cqcc.n_cqcc' | `N_CQCC` | nkululeko/feat_extract/feats_cqcc.py:97 |
| 'FEATS' | 'cqcc.n_cqt_bins' | `N_CQT_BINS` | nkululeko/feat_extract/feats_cqcc.py:99 |
| 'FEATS' | 'cqcc.sample_rate' | `SAMPLING_RATE` | nkululeko/feat_extract/feats_cqcc.py:95 |
| 'FEATS' | 'emotion2vec.model' | `model_mapping.get(self.feat_type, 'emotion2vec/emotion2vec_base')` | nkululeko/feat_extract/feats_emotion2vec.py:56 |
| 'FEATS' | 'fail_threshold' | `str(default)` | nkululeko/feat_extract/featureset.py:61 |
| 'FEATS' | 'fft_hop_dur' | `'10'` | nkululeko/feat_extract/feats_spectra.py:31 |
| 'FEATS' | 'fft_nbands' | `'64'` | nkululeko/feat_extract/feats_spectra.py:29 |
| 'FEATS' | 'fft_win_dur' | `'25'` | nkululeko/feat_extract/feats_spectra.py:30 |
| 'FEATS' | 'hubert.layer' | `'0'` | nkululeko/feat_extract/feats_hubert.py:32 |
| 'FEATS' | 'hubert.model' | `f'facebook/{self.feat_type}'` | nkululeko/feat_extract/feats_hubert.py:39 |
| 'FEATS' | 'import_file' | `False` | nkululeko/feat_extract/feats_import.py:26 |
| 'FEATS' | 'import_files_append' | `True` | nkululeko/feat_extract/feats_import.py:22 |
| 'FEATS' | 'level' | `'functionals'` | nkululeko/feat_extract/feats_opensmile.py:69 |
| 'FEATS' | 'lfcc.fft_length' | `N_FFT` | nkululeko/feat_extract/feats_lfcc.py:104 |
| 'FEATS' | 'lfcc.frame_length' | `FRAME_LENGTH` | nkululeko/feat_extract/feats_lfcc.py:99 |
| 'FEATS' | 'lfcc.frame_period' | `FRAME_PERIOD` | nkululeko/feat_extract/feats_lfcc.py:102 |
| 'FEATS' | 'lfcc.n_lfcc' | `N_LFCC` | nkululeko/feat_extract/feats_lfcc.py:108 |
| 'FEATS' | 'lfcc.sample_rate' | `SAMPLING_RATE` | nkululeko/feat_extract/feats_lfcc.py:106 |
| 'FEATS' | 'mld.df' | `'Mld'` | nkululeko/feat_extract/feats_mld.py:33 |
| 'FEATS' | 'mld.model' | `None` | nkululeko/feat_extract/feats_mld.py:17 |
| 'FEATS' | 'nan_strategy' | `'zero'` | nkululeko/utils/util.py:361 |
| 'FEATS' | 'needs_feature_extraction' | `False` | nkululeko/feat_extract/feats_cqcc.py:113, nkululeko/feat_extract/feats_emotion2vec.py:77, nkululeko/feat_extract/feats_lfcc.py:123, +2 more |
| 'FEATS' | 'no_reuse' | `'False'` / `False` | nkululeko/feat_extract/feats_cqcc.py:114, nkululeko/feat_extract/feats_emotion2vec.py:78, nkululeko/feat_extract/feats_lfcc.py:124, +3 more |
| 'FEATS' | 'os.set' | `'eGeMAPSv02'` | nkululeko/bundle.py:63, nkululeko/bundle.py:114 |
| 'FEATS' | 'print_feats' | `'False'` | nkululeko/feat_extract/feats_opensmile.py:90, nkululeko/feat_extract/feats_praat.py:22, nkululeko/feat_extract/feats_sptk.py:81 |
| 'FEATS' | 'scale' | `False` | nkululeko/bundle.py:221, nkululeko/experiment.py:649, nkululeko/predict.py:918, +1 more |
| 'FEATS' | 'set' | `'eGeMAPSv02'` | nkululeko/feat_extract/feats_opensmile.py:53, nkululeko/feat_extract/feats_oxbow.py:22 |
| 'FEATS' | 'size' | `500` | nkululeko/feat_extract/feats_oxbow.py:60 |
| 'FEATS' | 'sptk.features' | `"['stft', 'fbank']"` | nkululeko/feat_extract/feats_sptk.py:53 |
| 'FEATS' | 'sptk.fft_length' | `N_FFT` | nkululeko/feat_extract/feats_sptk.py:48 |
| 'FEATS' | 'sptk.frame_length' | `FRAME_LENGTH` | nkululeko/feat_extract/feats_sptk.py:43 |
| 'FEATS' | 'sptk.frame_period' | `FRAME_PERIOD` | nkululeko/feat_extract/feats_sptk.py:46 |
| 'FEATS' | 'sptk.n_channel' | `N_CHANNEL` | nkululeko/feat_extract/feats_sptk.py:49 |
| 'FEATS' | 'sptk.sample_rate' | `SR` | nkululeko/feat_extract/feats_sptk.py:50 |
| 'FEATS' | 'store_format' | `'pkl'` | nkululeko/feat_extract/feats_agender.py:46, nkululeko/feat_extract/feats_agender_agender.py:48, nkululeko/feat_extract/feats_auddim.py:41, +14 more |
| 'FEATS' | 'trill.model' | `'https://tfhub.dev/google/nonsemantic-speech-benchmark/trill/3'` | nkululeko/feat_extract/feats_trill.py:37 |
| 'FEATS' | 'type' | `'os'` / `None` / `['os']` | nkululeko/bundle.py:220, nkululeko/data/datasplitter.py:410, nkululeko/experiment.py:227, +3 more |
| 'FEATS' | 'wav2vec2.layer' | `'0'` | nkululeko/feat_extract/feats_audmodel.py:30, nkululeko/feat_extract/feats_wav2vec2.py:38 |
| 'FEATS' | 'wav2vec2.model' | `f'facebook/{self.feat_type}'` | nkululeko/feat_extract/feats_wav2vec2.py:43 |
| 'FEATS' | 'wavlm.layer' | `'0'` | nkululeko/feat_extract/feats_wavlm.py:32 |
| 'FEATS' | 'with_os' | `False` | nkululeko/feat_extract/feats_oxbow.py:79 |
| 'FEATS' | 'xbow.model' | `'openXBOW'` | nkululeko/feat_extract/feats_oxbow.py:54 |
| 'FEATS' | <f'{ft}.model'> | `''` | nkululeko/bundle.py:66, nkululeko/bundle.py:117 |
| 'MODEL' | 'C_val' | `'0.001'` / `'1'` / `'1.0'` | nkululeko/feat_extract/feats_analyser.py:233, nkululeko/models/model_svm.py:16, nkululeko/models/model_svr.py:16 |
| 'MODEL' | 'GMM_components' | `'4'` | nkululeko/feat_extract/feats_analyser.py:197, nkululeko/models/model_gmm.py:17 |
| 'MODEL' | 'GMM_covariance_type' | `'full'` | nkululeko/feat_extract/feats_analyser.py:199, nkululeko/models/model_gmm.py:18 |
| 'MODEL' | 'KNN_weights' | `'uniform'` | nkululeko/feat_extract/feats_analyser.py:217, nkululeko/feat_extract/feats_analyser.py:266, nkululeko/models/model_knn.py:16, +1 more |
| 'MODEL' | 'K_val' | `'5'` | nkululeko/feat_extract/feats_analyser.py:218, nkululeko/feat_extract/feats_analyser.py:267, nkululeko/models/model_knn.py:17, +1 more |
| 'MODEL' | 'activation' | `'relu'` | nkululeko/models/model_mlp.py:89, nkululeko/models/model_mlp_regression.py:81 |
| 'MODEL' | 'adm.branches' | `'time,spectral,phase'` / `'time,spectral,phase,lfcc,cqcc'` | nkululeko/models/model_adm.py:140, nkululeko/models/model_adm.py:546, nkululeko/utils/naming.py:100 |
| 'MODEL' | 'adm.fusion' | `'weighted'` | nkululeko/models/model_adm.py:134, nkululeko/models/model_adm.py:541 |
| 'MODEL' | 'adm.hidden_dim' | `'256'` | nkululeko/models/model_adm.py:136, nkululeko/models/model_adm.py:543 |
| 'MODEL' | 'balancing' | `False` | nkululeko/models/model_tuned.py:73 |
| 'MODEL' | 'batch_size' | `'8'` / `32` / `8` | nkululeko/models/model_adm.py:190, nkululeko/models/model_cnn.py:71, nkululeko/models/model_mlp.py:79, +2 more |
| 'MODEL' | 'class_weight' | `'False'` / `'auto'` / `False` | nkululeko/models/model.py:278, nkululeko/models/model.py:302, nkululeko/models/model.py:317, +4 more |
| 'MODEL' | 'device' | `'cpu'` / `False` / `cuda` / `device` | nkululeko/autopredict/ap_sid.py:31, nkululeko/autopredict/ap_text.py:28, nkululeko/feat_extract/feats_agender.py:38, +31 more |
| 'MODEL' | 'drop' | `'False'` / `False` | nkululeko/models/model_cnn.py:57, nkululeko/models/model_cnn.py:235, nkululeko/models/model_cnn.py:252, +7 more |
| 'MODEL' | 'early_stopping_rounds' | `False` | nkululeko/models/model_xgb.py:24 |
| 'MODEL' | 'eval_metric' | `'logloss'` | nkululeko/models/model_xgb.py:27 |
| 'MODEL' | 'feature_noise' | `'0.0'` | nkululeko/models/model_adm.py:198 |
| 'MODEL' | 'focal.alpha' | `0.25` | nkululeko/models/model_adm.py:78 |
| 'MODEL' | 'focal.gamma' | `2.0` | nkululeko/models/model_adm.py:80 |
| 'MODEL' | 'hf_token' | `None` | nkululeko/autopredict/ap_sid.py:22, nkululeko/segmenting/seg_pyannote.py:29 |
| 'MODEL' | 'k_fold_cross' | `False` | nkululeko/experiment.py:71, nkululeko/models/model.py:38 |
| 'MODEL' | 'kernel' | `'rbf'` | nkululeko/models/model_svm.py:21, nkululeko/models/model_svr.py:18 |
| 'MODEL' | 'label_smoothing' | `'False'` | nkululeko/models/model.py:48 |
| 'MODEL' | 'layers' | `False` | nkululeko/utils/naming.py:87 |
| 'MODEL' | 'learning_rate' | `'0.0001'` / `0.3` / `str(default_lr)` | nkululeko/models/model_tuned.py:61, nkululeko/models/model_xgb.py:32, nkululeko/optimizers/optimizer_factory.py:32 |
| 'MODEL' | 'logo' | `False` | nkululeko/experiment.py:70, nkululeko/models/model.py:37 |
| 'MODEL' | 'loso' | `False` | nkululeko/experiment.py:69 |
| 'MODEL' | 'loss' | `'1-ccc'` / `'bce'` / `'cross'` / `'mse'` | nkululeko/models/model.py:93, nkululeko/models/model_adm.py:65, nkululeko/models/model_mlp_regression.py:29, +2 more |
| 'MODEL' | 'max_depth' | `6` | nkululeko/models/model_xgb.py:31 |
| 'MODEL' | 'max_duration' | `'8.0'` | nkululeko/models/model_tuned.py:63 |
| 'MODEL' | 'max_grad_norm' | `'0.0'` | nkululeko/models/model_adm.py:195 |
| 'MODEL' | 'measure' | `'ccc'` / `'mse'` / `'uar'` | nkululeko/models/model_mlp_regression.py:232, nkululeko/models/model_tuned.py:56, nkululeko/reporting/reporter.py:81, +6 more |
| 'MODEL' | 'momentum' | `'0.9'` | nkululeko/optimizers/optimizer_factory.py:51 |
| 'MODEL' | 'n_estimators' | `100` | nkululeko/models/model_xgb.py:30 |
| 'MODEL' | 'n_jobs' | `'8'` | nkululeko/feat_extract/featureset.py:34, nkululeko/models/model.py:39 |
| 'MODEL' | 'nan_strategy' | `'zero'` | nkululeko/models/model.py:380 |
| 'MODEL' | 'only_test' | `False` | nkululeko/modelrunner.py:128 |
| 'MODEL' | 'optimizer' | `default_optimizer` | nkululeko/optimizers/optimizer_factory.py:33 |
| 'MODEL' | 'patience' | `False` | nkululeko/modelrunner.py:134 |
| 'MODEL' | 'pretrained_model' | `model_path` | nkululeko/models/model_tuned.py:78 |
| 'MODEL' | 'push_to_hub' | `'False'` | nkululeko/models/model_tuned.py:72 |
| 'MODEL' | 'random_seed' | `'False'` | nkululeko/models/model_adm.py:51, nkululeko/models/model_mlp.py:33, nkululeko/models/model_mlp_regression.py:42 |
| 'MODEL' | 'save' | `'True'` | nkululeko/modelrunner.py:197 |
| 'MODEL' | 'scheduler' | `default_scheduler` | nkululeko/optimizers/scheduler_factory.py:37 |
| 'MODEL' | 'scheduler.gamma' | `'0.5'` / `'0.95'` | nkululeko/optimizers/scheduler_factory.py:46, nkululeko/optimizers/scheduler_factory.py:54 |
| 'MODEL' | 'scheduler.step_size' | `'10'` | nkululeko/optimizers/scheduler_factory.py:45 |
| 'MODEL' | 'subsample' | `1.0` | nkululeko/models/model_xgb.py:33 |
| 'MODEL' | 'threshold' | `'0.5'` | nkululeko/models/model_adm.py:200 |
| 'MODEL' | 'tuning_params' | `False` | nkululeko/models/model.py:283 |
| 'MODEL' | 'type' | `''` / `'svm'` | nkululeko/bundle.py:224, nkululeko/utils/naming.py:118 |
| 'MODEL' | 'validation_split' | `0.2` | nkululeko/models/model_xgb.py:99 |
| 'MODEL' | 'warmup_epochs' | `'5'` | nkululeko/optimizers/scheduler_factory.py:86 |
| 'MODEL' | 'weight_decay' | `'0.01'` | nkululeko/optimizers/optimizer_factory.py:36 |
| 'PLOT' | 'anim_progression' | `0` | nkululeko/runmanager.py:107 |
| 'PLOT' | 'ccc' | `'False'` | nkululeko/plots.py:32 |
| 'PLOT' | 'combine_per_speaker' | `False` | nkululeko/experiment.py:721 |
| 'PLOT' | 'epoch_progression' | `0` | nkululeko/runmanager.py:116 |
| 'PLOT' | 'epochs' | `False` | nkululeko/modelrunner.py:127 |
| 'PLOT' | 'fill_areas' | `'False'` | nkululeko/plots.py:426 |
| 'PLOT' | 'format' | `'png'` | nkululeko/feat_extract/feats_analyser.py:141, nkululeko/feat_extract/feats_analyser.py:339, nkululeko/plots.py:30, +2 more |
| 'PLOT' | 'fps' | `'1'` | nkululeko/reporting/reporter.py:650 |
| 'PLOT' | 'kind' | `'violin'` | nkululeko/plots.py:786 |
| 'PLOT' | 'name' | `plot_name_suggest` | nkululeko/experiment.py:273, nkululeko/reporting/reporter.py:663, nkululeko/runmanager.py:100, +7 more |
| 'PLOT' | 'runs_compare' | `False` | nkululeko/experiment.py:728 |
| 'PLOT' | 'titles' | `'True'` | nkululeko/plots.py:34, nkululeko/reporting/run_plotter.py:21 |
| 'PLOT' | 'uncertainty_threshold' | `False` | nkululeko/reporting/reporter.py:283 |
| 'PREDICT' | 'sample_selection' | `'all'` | nkululeko/experiment.py:373 |
| 'PREDICT' | 'target_language' | `'en'` | nkululeko/autopredict/ap_translate.py:25 |
| 'PREDICT' | 'targets' | `None` | nkululeko/experiment.py:385 |
| 'PREDICT' | 'textclassifier.candidates' | `False` | nkululeko/feat_extract/feats_textclassifier.py:39 |
| 'REPORT' | 'author' | `'anon'` | nkululeko/reporting/latex_writer.py:16 |
| 'REPORT' | 'fresh' | `'False'` | nkululeko/experiment.py:56 |
| 'REPORT' | 'latex' | `'nkululeko_latex'` / `False` | nkululeko/experiment.py:90, nkululeko/reporting/latex_writer.py:39 |
| 'REPORT' | 'show' | `'False'` | nkululeko/experiment.py:88, nkululeko/reporting/report.py:33 |
| 'REPORT' | 'title' | `'Nkululeko report'` | nkululeko/reporting/latex_writer.py:15 |
| 'RESAMPLE' | 'replace' | `'False'` | nkululeko/augmenting/resampler.py:24, nkululeko/resample.py:140 |
| 'RESAMPLE' | 'target' | `'resampled.csv'` | nkululeko/augmenting/resampler.py:72 |
| 'SEGMENT' | 'audio_dir' | `'segments'` | nkululeko/segment.py:65 |
| 'SEGMENT' | 'audio_format' | `'wav'` | nkululeko/segment.py:68 |
| 'SEGMENT' | 'include_silence_borders' | `'False'` | nkululeko/segment.py:284 |
| 'SEGMENT' | 'max_length' | `'False'` | nkululeko/segmenting/seg_pyannote.py:92, nkululeko/segmenting/seg_silero.py:95 |
| 'SEGMENT' | 'method' | `'silero'` | nkululeko/segment.py:241 |
| 'SEGMENT' | 'min_length' | `2` | nkululeko/segmenting/seg_pyannote.py:95, nkululeko/segmenting/seg_silero.py:98 |
| 'SEGMENT' | 'output_audio' | `'False'` | nkululeko/segment.py:322 |
| 'SEGMENT' | 'result' | `'segmented'` | nkululeko/segment.py:239 |
| 'SEGMENT' | 'sampling_rate' | `None` | nkululeko/segment.py:69 |
| <section> | <key> | `str(default)` | nkululeko/utils/util.py:519 |
| <section> | <name> | `False` | nkululeko/utils/naming.py:37, nkululeko/utils/naming.py:38 |
| DATA.<d> | 'type' | `'audformat'` | nkululeko/experiment.py:107, nkululeko/experiment.py:170 |
| DATA.<data_name> | 'check_size' | `False` | nkululeko/file_checker.py:39 |
| DATA.<data_name> | 'check_vad' | `False` | nkululeko/file_checker.py:69 |
| DATA.<data_name> | 'filter' | `False` | nkululeko/filter_data.py:137 |
| DATA.<data_name> | 'limit_samples' | `False` | nkululeko/filter_data.py:28 |
| DATA.<data_name> | 'limit_samples_per_speaker' | `False` | nkululeko/filter_data.py:51 |
| DATA.<data_name> | 'max_duration_of_sample' | `False` | nkululeko/filter_data.py:81 |
| DATA.<data_name> | 'min_duration_of_sample' | `False` | nkululeko/filter_data.py:78 |
| DATA.<self.name> | '' | `''` | nkululeko/data/dataset.py:68, nkululeko/data/dataset_csv.py:21 |
| DATA.<self.name> | 'absolute_path' | `'True'` | nkululeko/data/dataset_csv.py:49 |
| DATA.<self.name> | 'as_test' | `'False'` | nkululeko/data/dataset.py:381, nkululeko/data/dataset.py:502 |
| DATA.<self.name> | 'as_train' | `'False'` | nkululeko/data/dataset.py:384, nkululeko/data/dataset.py:506 |
| DATA.<self.name> | 'audio_path' | `'./'` | nkululeko/data/dataset_csv.py:26 |
| DATA.<self.name> | 'colnames' | `False` | nkululeko/data/dataset.py:77, nkululeko/data/dataset.py:87, nkululeko/data/dataset_csv.py:44 |
| DATA.<self.name> | 'columns' | `False` | nkululeko/data/dataset.py:103 |
| DATA.<self.name> | 'dev' | `False` | nkululeko/data/dataset.py:642 |
| DATA.<self.name> | 'dev_size' | `20` | nkululeko/data/dataset.py:678, nkululeko/data/dataset.py:704 |
| DATA.<self.name> | 'dev_tables' | `False` | nkululeko/data/dataset.py:461 |
| DATA.<self.name> | 'files_tables' | `False` | nkululeko/data/dataset.py:53 |
| DATA.<self.name> | 'label' | `self.target` | nkululeko/data/dataset.py:165, nkululeko/data/dataset.py:203 |
| DATA.<self.name> | 'limit' | `0` | nkululeko/data/dataset.py:37 |
| DATA.<self.name> | 'mapping' | `False` | nkululeko/data/dataset.py:787 |
| DATA.<self.name> | 'rename_speakers' | `False` | nkululeko/data/dataset.py:266 |
| DATA.<self.name> | 'required' | `False` | nkululeko/data/dataset.py:253 |
| DATA.<self.name> | 'reverse' | `'False'` | nkululeko/data/dataset.py:281 |
| DATA.<self.name> | 'reverse.max' | `'False'` | nkululeko/data/dataset.py:283 |
| DATA.<self.name> | 'scale' | `False` | nkululeko/data/dataset.py:292 |
| DATA.<self.name> | 'split_strategy' | `'speaker_split'` | nkululeko/data/dataset.py:304, nkululeko/data/dataset.py:409 |
| DATA.<self.name> | 'target_tables' | `False` | nkululeko/data/dataset.py:49 |
| DATA.<self.name> | 'test' | `False` | nkululeko/data/dataset.py:621, nkululeko/data/dataset.py:636 |
| DATA.<self.name> | 'test_size' | `20` | nkululeko/data/dataset.py:524, nkululeko/data/dataset.py:668, nkululeko/data/dataset.py:677, +2 more |
| DATA.<self.name> | 'test_tables' | `False` | nkululeko/data/dataset.py:57, nkululeko/data/dataset.py:326, nkululeko/data/dataset.py:447 |
| DATA.<self.name> | 'train' | `False` | nkululeko/data/dataset.py:627, nkululeko/data/dataset.py:648 |
| DATA.<self.name> | 'train_tables' | `False` | nkululeko/data/dataset.py:61, nkululeko/data/dataset.py:333, nkululeko/data/dataset.py:454 |
| DATA.<self.name> | 'value_counts' | `False` | nkululeko/data/dataset.py:730, nkululeko/data/dataset.py:747 |
