metadata
license: apache-2.0
library_name: pytorch
pipeline_tag: audio-classification
tags:
- hlm-micro
- polynomial-hopfield
- edge-ai
- keyword-spotting
- speech-commands
- tinyml
- real-data
datasets:
- speech-commands-v2
HLM-Micro Keyword Spotting v1 - Speech Commands
HLM-Micro Keyword Spotting v1 is a compact polynomial-Hopfield keyword spotter trained on the Google Speech Commands V2 12-class TinyML-style subset.
Results
| Field | Value |
|---|---|
| Parameters | 184,449 |
| Classes | 12 |
| Reported best validation accuracy | 92.09% |
| Final validation accuracy | 92.01% |
| Best checkpoint | epoch 6 of 8 |
Classes: yes, no, up, down, left, right, on, off, stop, go, _silence_, _unknown_.
This is a realistic small keyword-spotting result, not a state-of-the-art accuracy claim. The test split is intentionally not claimed here.
Files
| File | Purpose |
|---|---|
model.pt |
Sanitized model-only PyTorch checkpoint |
config.json |
Public architecture, task, classes, and metric metadata |
metrics.jsonl |
Training/evaluation metrics from the local run |
Intended Use
- Research on compact keyword spotting.
- TinyML and edge-audio experiments.
- Baseline for adding replayable audit metadata to edge classifiers.
Limitations
- Metric reported here is validation accuracy, not final held-out test accuracy.
- Not optimized with the full augmentation/training recipe used by top keyword-spotting systems.
- Not a wake-word product or production speech interface.