Create README.md

README.md

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+---
+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]
+```