models/model_metadata.json

{
  "model_name": "whisper-tiny-frozen-encoder-logreg",
  "architecture": [
    "16kHz mono audio",
    "Whisper Tiny frozen encoder (openai/whisper-tiny, loaded via transformers at runtime, NOT bundled here)",
    "mean pooling over encoder time axis (384-dim vector for whisper-tiny)",
    "Logistic Regression (StandardScaler -> LogisticRegression, class_weight=balanced, C=1.0)",
    "threshold/hysteresis decision layer (src/turn_detector/inference.py: TurnDecisionConfig)"
  ],
  "classifier_artifact": "whisper_classifier.joblib",
  "classifier_trained_on": {
    "experiment": "EXP-004",
    "run_location": "Google Colab (notebooks/EXP004_whisper_baseline.ipynb)",
    "sample": "Fresh 250-clip HF-streaming sample from pipecat-ai/smart-turn-data-v3.2-train, independent of the Phase 3 sample",
    "dev_n": 175,
    "val_n": 75,
    "seed": 42
  },
  "measured_performance_val_set": {
    "n": 75,
    "f1": 0.693,
    "accuracy": 0.693,
    "false_end_rate": 0.257,
    "false_continue_rate": 0.350,
    "note": "Measured on ONE 75-clip validation split from ONE 250-clip sample. Not full-dataset or official-test-set performance. See docs/RESULTS.md."
  },
  "encoder_param_count": 8208384,
  "encoder_approx_size_fp32_mb": 32.8,
  "classifier_param_count": 92,
  "classifier_approx_size_bytes": 2920,
  "sklearn_version_trained_with": "1.6.1",
  "sklearn_version_note": "This classifier was pickled with scikit-learn 1.6.1 in the Colab environment. Loading it with a different sklearn version (e.g. this project's dev sandbox, which has 1.8.0) raises an InconsistentVersionWarning — observed and non-fatal in testing, but worth pinning scikit-learn==1.6.1 in a production deployment to avoid any risk of silent incompatibility.",
  "whisper_weights_not_included": true,
  "whisper_weights_loading": "TurnDetector loads openai/whisper-tiny via transformers.WhisperModel.from_pretrained() at runtime, which downloads/caches the weights (~151MB, separate from this classifier). Requires network access to Hugging Face on first run."
}