Final V12 system:

Version-name map:

The .pkl model is therefore the learned ranker inside the final V12 system. The V12 part is the deterministic rule fusion applied by inference.py.

The model selects the most empathetic candidate response audio for each question. Two-option questions are handled as direct pairwise comparisons. Three-option MELD questions are handled by round-robin pairwise comparisons.

This final V12 system does not use test-time LLM calls. DeepSeek text fusion was explored as a diagnostic add-on but is not included. Deterministic tone alignment is included at the fixed weight 0.10.

Current Hugging Face repository layout:

This verification package uses precomputed augmented feature artifacts. For the final Phase 2 submission, the released inference path starts from test_features_v11_augmented_phase2.csv and test_rule_alignment_scores_phase2.csv; it does not regenerate acoustic or listener-state features from raw audio.

The string v11_augmented is retained as a feature-table compatibility name because the selected learned ranker was trained in the v11 ablation stage. It does not mean the submitted system is v11-only.

The original verification package was first prepared with the earlier 530-question test release. If the organizer releases a replacement Phase 2 test set, do not change the final V12 system. Keep using:

Only regenerate the two input artifacts for the new raw data:

Then run inference.py with those new artifact paths. The legacy v2/v3 scripts used in the emergency Phase 2 runbook are raw-audio feature extractors only; they are not old submitted models.

After rerunning Phase 2, the uploaded HF package should include the regenerated 542-question prediction and Phase 2 artifacts:

The one-click Colab notebook and raw Phase 2 regeneration helpers are included under scripts/phase2_raw_regeneration/. They are not required for running the minimal packaged HF inference artifact, because the regenerated Phase 2 feature tables are already included here. They are provided for reproducibility from the raw replacement Phase 2 data.

Minimal requirements for packaged inference from the included feature tables:

Additional packages used only by the raw Phase 2 regeneration notebook/scripts include PyTorch, Transformers, faster-whisper, openSMILE, ffmpeg, and the saved listener-state checkpoint/tokenizer. Those are Colab/GPU-side preprocessing dependencies, not requirements for loading the final XGBoost model from the included feature tables.

The pickle contains:

The final inference expects the augmented feature table and rule-score table used by the final V12 system. For the final Phase 2 submission these are:

The feature table contains V3-derived relative/candidate features and V10 listener-state features.

Example command:

Running this command from the repository root produces 542 Phase 2 predictions. The result should match the included Lenormand_Team.jsonl.

Output format:

The included official Phase 2 prediction is already named with the registered Team ID:

Lenormand_Team_Phase1.jsonl is retained only as an archive of the earlier 530-question release and should not be submitted for Phase 2.

Lenormand_Team.scores.csv is a debug/verification artifact and is not the official submission file.

To regenerate the Phase 2 feature artifacts from the raw replacement test set, use:

The raw regeneration path performs:

Expected replacement Phase 2 counts:

The main training script is:

It creates four feature variants:

Selected learned ranker:

The submitted system subsequently applies deterministic rule alignment at the fixed weight 0.10. OOF validation uses fixed 5-fold splits.

Main report table:

Summary:

Although D has the highest train-domain OOF score, E is selected because it combines the domain-robust relative feature design with listener-state context information and performs best on manually audited MELD/context-sensitive cases.

The rule-only system obtains 0.6594 OOF accuracy. At weight 0.10, fusion changes the selected ranker's OOF accuracy from 0.9693 to 0.9700, while AUC decreases from 0.9958 to 0.9927. We retain this low-weight rule as a conservative decision-level correction and report the ranking and cross-domain limitations explicitly.

We use a 130-example personal golden set for diagnostic model selection. This is not official test labeling and should not be interpreted as a leaderboard score.

Main report table:

The larger full_metric_matrix.csv is provided as an appendix/debug artifact. It contains N/A entries for variants that do not have valid train-side OOF signals, such as DeepSeek-only test-side fusion. Do not treat those N/A values as failed OOF runs.

Main diagnostic finding:

The following were explored:

Reason:

DeepSeek did not improve over the selected learned ranker and lacks train-side OOF comparability. Rule alignment also decreased manual diagnostic accuracy by one example; it is retained only because its fixed 0.10 weight slightly improves formal OOF discrete-choice accuracy.

This repository is prepared for AEQ/HumOmni Track 1 final model verification. The repository should remain publicly accessible until July 15 AOE, per competition requirements.

The repository is released as a competition verification artifact. The license: other metadata is intentional; no broader license is asserted for the underlying competition data or third-party components.