---
license: mit
pipeline_tag: audio-classification
---

# Model Card for DualCounter

Class-agnostic audio repetition counting: counting repeated sounds in an audio waveform (a clock striking, hammer blows, a heartbeat, etc.) without training on those specific sound classes.

Code: https://github.com/Hamozwa/audio-counting

Paper: [link] · Demo: https://huggingface.co/spaces/Hamozwa/dualcounter

Datasets: https://huggingface.co/datasets/Hamozwa/RepeatSynth · https://huggingface.co/datasets/Hamozwa/RepeatReal

## Model Description

DualCounter runs two independent counting architectures and combines their predictions:

- **WavCounter** regresses a repetition heatmap over the waveform and derives a count from it via a Schmitt trigger.
- **TSSMCounter** builds a temporal self-similarity matrix from Wav2Vec2 features and classifies the count directly with a DINO vision transformer.

DualCounter runs both and falls back to TSSMCounter's prediction when the WavCounter heatmap is judged low quality.

## Repository Contents

```
wav_counter/    checkpoint for the WavCounter model
tssm_counter/   checkpoint for the TSSMCounter model
```

## Usage

Load and run these checkpoints using the training/inference code in the linked GitHub repo (`WavCounter_tester.py`, `TSSMCounter_tester.py`, `DualCounter.py`). DINO weights are pulled automatically via `torch.hub` (`facebookresearch/dino`) when running TSSMCounter.

## Training Data

Trained on the synthetic datasets in RepeatSynth. Evaluated zero-shot on the real-world recordings in RepeatReal, covering mechanical, medical, and ecological domains.

## Citation

```bibtex
[Your paper's BibTeX entry here]
```