Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
loss: double
ACC: double
EER: double
EER_CI: double
EER_CI_LOW: double
EER_CI_HIGH: double
F1_SCORE: double
habla_test: struct<ACC: double, EER: double, EER_CI: double, EER_CI_LOW: double, EER_CI_HIGH: double, F1_SCORE: (... 7 chars omitted)
child 0, ACC: double
child 1, EER: double
child 2, EER_CI: double
child 3, EER_CI_LOW: double
child 4, EER_CI_HIGH: double
child 5, F1_SCORE: double
asvspoof5_test: struct<ACC: double, EER: double, EER_CI: double, EER_CI_LOW: double, EER_CI_HIGH: double, F1_SCORE: (... 7 chars omitted)
child 0, ACC: double
child 1, EER: double
child 2, EER_CI: double
child 3, EER_CI_LOW: double
child 4, EER_CI_HIGH: double
child 5, F1_SCORE: double
to
{'loss': Value('float64'), 'ACC': Value('float64'), 'EER': Value('float64'), 'EER_CI': Value('float64'), 'EER_CI_LOW': Value('float64'), 'EER_CI_HIGH': Value('float64'), 'F1_SCORE': Value('float64'), 'asvspoof5_test': {'ACC': Value('float64'), 'EER': Value('float64'), 'EER_CI': Value('float64'), 'EER_CI_LOW': Value('float64'), 'EER_CI_HIGH': Value('float64'), 'F1_SCORE': Value('float64')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
loss: double
ACC: double
EER: double
EER_CI: double
EER_CI_LOW: double
EER_CI_HIGH: double
F1_SCORE: double
habla_test: struct<ACC: double, EER: double, EER_CI: double, EER_CI_LOW: double, EER_CI_HIGH: double, F1_SCORE: (... 7 chars omitted)
child 0, ACC: double
child 1, EER: double
child 2, EER_CI: double
child 3, EER_CI_LOW: double
child 4, EER_CI_HIGH: double
child 5, F1_SCORE: double
asvspoof5_test: struct<ACC: double, EER: double, EER_CI: double, EER_CI_LOW: double, EER_CI_HIGH: double, F1_SCORE: (... 7 chars omitted)
child 0, ACC: double
child 1, EER: double
child 2, EER_CI: double
child 3, EER_CI_LOW: double
child 4, EER_CI_HIGH: double
child 5, F1_SCORE: double
to
{'loss': Value('float64'), 'ACC': Value('float64'), 'EER': Value('float64'), 'EER_CI': Value('float64'), 'EER_CI_LOW': Value('float64'), 'EER_CI_HIGH': Value('float64'), 'F1_SCORE': Value('float64'), 'asvspoof5_test': {'ACC': Value('float64'), 'EER': Value('float64'), 'EER_CI': Value('float64'), 'EER_CI_LOW': Value('float64'), 'EER_CI_HIGH': Value('float64'), 'F1_SCORE': Value('float64')}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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DeepFense Paper Scores
Canonical, reproducible score bundle for the DeepFense camera-ready paper.
Hugging Face: DeepFense/prediction_scores
Directory layout
scores/
├── README.md
├── {train_recipe}/ # dataset the model was TRAINED on
│ ├── {backend}/ # AASIST | MLP | Nes2Net | TCM
│ │ └── {frontend}/ # Wav2Vec2 | HuBERT | WavLM | EAT
│ │ └── seed{2|42|240}/
│ │ └── {eval_benchmark}/ # held-out TEST set
│ │ ├── predictions.txt # per-utterance scores (TSV)
│ │ └── metrics.json # EER, ACC, F1, …
│ └── _summaries/{eval_benchmark}.json # optional cross-architecture tables
└── bias_fairness/
└── {accent|emotions|gender|language|quality}/
└── {eval_benchmark}/
└── {train_recipe}/{backend}/{frontend}/seed{N}/
└── utterances.txt # scores + subgroup metadata (TSV)
Example
From checkpoint DeepFense_ADD23_Wav2Vec2_TCM_NoAug_Seed240 evaluated on add22_test_track1:
ADD23/TCM/Wav2Vec2/seed240/add22_test_track1/predictions.txt
ADD23/TCM/Wav2Vec2/seed240/add22_test_track1/metrics.json
Naming conventions
| Token | Canonical form | Notes |
|---|---|---|
| Train recipe | ASV5, ASV19, ADD23, CodecFake, HABLA, PartialSpoof |
Training dataset (not the eval set). ASV5 = trained on ASVspoof 5; do not confuse with eval asvspoof5_test. |
| Frontend | Wav2Vec2, HuBERT, WavLM, EAT |
Always PascalCase; Hubert → HuBERT. |
| Backend | AASIST, MLP, Nes2Net, TCM |
Uppercase acronym. |
| Seed | seed2, seed42, seed240 |
Three seeds per recipe. |
| Eval benchmark | lowercase snake_case | e.g. asvspoof5_test, asvspoof2019_la_eval, mlaad_final, add22_test_track1. |
Eval benchmarks (20)
add22_test_track1, add22_test_track3, add23_test_R1, add23_test_R2, asvspoof2019_la_eval, asvspoof21_df_eval, asvspoof21_la_eval, asvspoof5_test, codecfake_eval, ctrsvdd_eval, fakemusiccaps_eval, habla_test, itw_eval, mlaad_final, odss_test, partialedit_eval, partialspoof_eval, replaydf_all_eval, spoofceleb_eval
File formats (.txt / .json only)
predictions.txt — clip-level (TSV)
utterance_id label score_spoof score_bonafide
LA_E_12345 0 -2.14895 3.14895
LA_T_67890 1 4.37140 -3.37140
label:0= spoof,1= bonafide- LLR =
score_bonafide − score_spoof
metrics.json
Per-run aggregated metrics (EER, ACC, F1, confidence intervals).
bias_fairness/.../utterances.txt
Per-utterance scores with subgroup columns (gender, accent, NISQA quality, etc.).
Score columns: score_spoof, score_bonafide (renamed from legacy class0/class1).
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