Update dataset card

README.md

@@ -1,3 +1,88 @@
----
-license: apache-2.0
----
+---
+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