README.md

---
license: apache-2.0
task_categories:
  - automatic-speech-recognition
language:
  - en
tags:
  - asr
  - robustness
  - benchmark
  - out-of-distribution
  - hallucination
  - speech
pretty_name: WildASR
size_categories:
  - 1K<n<10K
---

# WildASR

Official dataset for **Back to Basics: Revisiting ASR in the Age of Voice Agents**.

Code: [github.com/boson-ai/WildASR-public](https://github.com/boson-ai/WildASR-public)

## Overview

WildASR is a multilingual diagnostic benchmark built from **real human speech** to stress-test ASR robustness under real-world out-of-distribution (OOD) conditions. We decompose robustness into three axes:

- **Environmental Degradation** (the *where*): reverberation, far-field, phone codec, noise gap, clipping
- **Demographic Shift** (the *who*): children, older adults, accented speech
- **Linguistic Diversity** (the *what*): short utterances, incomplete audio, code-switching

## Dataset

Due to licensing constraints, we currently release 7 splits covering environment degradation (clean, clipping, far-field, noise gap, phone codec, reverberation) and demographic shift (accent). 10,058 samples, ~30 hours total. Each sample contains `audio` (16kHz WAV), `transcript`, and metadata (`category`, `subset`, `language`, etc.). More splits and languages will be added as licenses are cleared.

## Usage

```python
from datasets import load_dataset

# Load all splits
ds = load_dataset("bosonai/WildASR")

# Load a specific split
clean = load_dataset("bosonai/WildASR", split="environment_degradation__en__fleurs_clean_en")

# Play audio (in a notebook)
clean[0]["audio"]
```

### Run evaluation with WildASR toolkit

```bash
pip install git+https://github.com/boson-ai/WildASR-public.git

# Save a split as parquet for the eval toolkit
clean.to_parquet("data/fleurs_clean.parquet")
```

```python
from run_eval.eval import create_client, run_asr_evaluation, ASREvalConfig

client = create_client("whisper-large-v3", "en")
cfg = ASREvalConfig(
    model_name="whisper-large-v3",
    data_path="data/fleurs_clean.parquet",
    output_dir="results/whisper-large-v3",
    language="en",
    wer_method="qwen",
)
run_asr_evaluation(client=client, config=cfg)
```

## Citation

```bibtex
@misc{wildasr2026,
  title   = {Back to Basics: Revisiting ASR in the Age of Voice Agents},
  author  = {Geeyang Tay and Wentao Ma and Jaewon Lee and Yuzhi Tang and Daniel Lee and Weisu Yin and Dongming Shen and Yi Zhu and Mu Li and Alex Smola},
  year    = {2026},
  note    = {arXiv:TODO}
}
```

## License

Apache 2.0