---
pretty_name: FormulaBank-28K
size_categories:
- 10K<n<100K
task_categories:
- feature-extraction
tags:
- audio
- procedural-audio
- synthetic-audio
- audio-pretraining
---

# FormulaBank-28K

FormulaBank-28K is a deterministic procedural-audio corpus for audio representation pre-training. It contains **28,000** mono clips at 16 kHz, each exactly 10.24 seconds long.

## Configuration

- **Version:** `C125-I224-v1`
- **Formula classes:** 125
- **Renderings per class:** 224
- **Total clips:** 28,000
- **Audio format:** lossless 24-bit FLAC
- **Source:** frozen `AudioPG-Atomic-H7-C224-R0-Clean` FormulaBank manifest

Each formula class specifies an acoustic rendering rule. Each rendering varies controlled nuisance factors such as pitch, duration, gain, onset, phase, and family-specific parameters. The published metadata records the class identity, instance ID, deterministic render seed, sampled parameters, and audio checksum for every clip.

## Layout and loading

Audio is stored below `audio/`, with `metadata.jsonl` at repository root. The `file_name` field is relative to the repository root.

```python
from datasets import load_dataset

ds = load_dataset("KevinJustin/FormulaBank-28K", split="train")
audio = ds[0]["audio"]
```

## Reproducibility

`metadata/subset_c125_i224_v1.json` freezes the selected 125 classes. `metadata/generation_manifest.json` records the source FormulaBank and generator hashes. Together with `reproduce/export_formulabank_28k_hf.py`, these files reproduce the procedural source configuration without relying on model checkpoints.

## Intended use and limitations

The corpus is intended for controlled audio pre-training, scaling studies, and analysis of synthetic-to-real transfer. It does not contain natural recordings, speech transcripts, environmental labels, or benchmark test data. It is not a substitute for natural-audio coverage and should not be used to infer real-world demographic, linguistic, or environmental distributions.

## License

The release license will be specified by the dataset maintainers before a public research release. Until then, please do not redistribute derived copies outside the Hugging Face repository.