Audio Event Triage Baseline Baseline Model
Model Description
This repository contains a small, transparent prototype model for
Operations teams need an explainable starting point for classifying alarms, machinery noise, and speech-like events.
The model combines per-label token weights with IDF-weighted evidence
retrieval. It was generated for reproducible architecture demonstrations and
does not call a hosted LLM.
Evaluation
- Held-out synthetic examples: 4
- Accuracy: 1
- Intended metrics: classification_accuracy, macro_recall, review_coverage
Intended Use
- Architecture prototyping
- CI and evaluation examples
- Local baseline comparisons
- Educational experimentation
Hugging Face Task Coverage
audio-classificationautomatic-speech-recognitionfeature-extractionaudio-to-audio
Limitations and Risks
The included records are synthetic feature vectors and do not replace evaluation on licensed real audio.
The dataset is synthetic and small. Do not use this model for consequential
decisions without representative data, expert review, and production-grade
evaluation.
Reproducibility
The linked GitHub repository includes train.py, the exact dataset split,
evaluation code, and the model JSON format.
