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pip install personalitylinmult — no MLflow, no repo clone.1from personalitylinmult import PersonalityModel
2
3model = PersonalityModel.from_pretrained("wavlm_best-ccc")
4scores = model.predict({"wavlm": wavlm_features}) # {trait: score in [0, 1]}model.feature_names lists exactly which features a given checkpoint needs;
model.traits lists the five Big Five traits in output order
(openness, conscientiousness, extraversion, agreeableness,
emotional_stability).pip install personalitylinmult[onnx])1from personalitylinmult.onnx import PersonalityModelONNX
2
3model = PersonalityModelONNX.from_pretrained("wavlm_best-ccc")
4scores = model.predict_from_audio("clip.wav") # raw audio/video -> predictions,
5 # no torch/transformers/exordiumPersonalityModel — installing the plain
package never pulls in onnxruntime. WavLM's own ONNX export
(wavlm-base-plus.onnx, shared across every WavLM-based champion) downloads
on first use. See docs/experiments.md (Blocks 5-6) for the accuracy/speed
benchmark and the small, measured prediction drift from ffmpeg-based
resampling (~0.001-0.004 per trait).{features joined by "_", multiword feature names use "-"}_best-{metric},
e.g.:wavlm_best-ccc — single-stream LinT, WavLM audio only.wavlm-emotion2vec_best-ccc — cross-modal LinMulT, audio fusion
(WavLM + emotion2vec).avt_best-ccc — cross-modal LinMulT, all 7 features (audio + visual +
text).PersonalityModel.from_pretrained(...) reads it off the
downloaded checkpoint automatically._best-{metric} names which validation metric the run was selected on —
ccc (Lin's concordance correlation) is preferred over mae/loss for this
task, since elementwise losses collapse prediction variance toward the
training mean (see docs/experiments.md for the full comparison and the
std_ratio metric that catches this).wavlm_best-cccccc loss at
batch_size=256 — the batch size that resolves CCC's per-batch statistical
noise problem (see docs/experiments.md, Block 3, for the full diagnosis).| Metric | Value |
|---|---|
test mean(1 - MAE) | 0.8918 |
test mean CCC | 0.5604 |
test mean Pearson r | 0.5609 |
test mean std_ratio | 1.0013 |
fi_lint_wavlm-ccc-bs256efb88223fdd04753bb8cefc46a5b51f7fi_lint_wavlm-ccc-bs256 v1, @champion0f60e83f35061e8ee26af61e5a58ad1950986446make train-fi-wavlm ARGS="--set train.loss=ccc --set data.batch_size=256"make promote-champion and published via
make push-champion-model, never automatically.