Update README.md

README.md

@@ -1,4 +1,3 @@
----
 license: cc-by-nc-sa-4.0
 language:
 - en
@@ -15,136 +14,29 @@ tags:
 - audio-comprehension
 - benchmark
 - audio-question-answering
-
----
-
-# AudioSpan: Spanning the Duration and Depth of Audio Comprehension
-
-<p align="center">
-  <a href="https://arxiv.org/abs/ARXIV_ID"><img src="https://img.shields.io/badge/Paper-arXiv-b31b1b?style=flat-square&logo=arxiv&logoColor=white" alt="arXiv"></a>
-  <a href="https://huggingface.co/datasets/holvan/AudioSpan"><img src="https://img.shields.io/badge/Dataset-AudioSpan-ffd21e?style=flat-square&logo=huggingface&logoColor=000" alt="Hugging Face Dataset"></a>
-  <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><img src="https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-3da639?style=flat-square" alt="License: CC BY-NC-SA 4.0"></a>
-</p>
-
-
-## Introduction
-
-**AudioSpan** is a benchmark for **long-form audio comprehension**, spanning
-diverse durations and cognitive depths.
-
-<p align="center">
-  <img src="figs/audio_span.png" alt="AudioSpan overview" width="100%">
-</p>
-
-
-Questions come from two complementary paths:
-
-- **Native QA**: questions drawn from the audio's natural content.
-- **Anchor QA**: questions built around acoustic anchors planted into the
-  audio.
-
-Each path is scored in its own mode:
-
-- **Accuracy**: multiple choice questions on native audio, scored by exact
-  match.
-- **Rubric**: open-ended questions on native audio, graded by rubric-based
-  LLM judges against criteria.
-- **Chain**: multiple-choice question chains on anchor audio; an answer is
-  credited only up to the first error in the chain.
-
-## Quick Start
-
-Download the release (~18 GB) and work from its root:
-
-```bash
-pip install -U huggingface_hub
-hf download holvan/AudioSpan --repo-type dataset --local-dir AudioSpan
-cd AudioSpan
-```
-
-### 1. Prepare Audio Data
-
-Native recordings and sound events ship as tar archives
-(`audio/audio_native.part*.tar`, `audio/audio_events.tar`; the native parts
-are concatenated automatically). From the release root (the directory with
-`prepare/` and `audio/`), verify them against `audio/CHECKSUMS.sha256` and
-unpack into `audio/`:
-
-```bash
-# accuracy / rubric: native recordings only
-python prepare/unpack_audio.py --buckets native
-# -> audio/native/*.flac
-
-# chain: also rebuild the anchor audio from the native recordings and the
-# exact edit recipes in metadata/media/anchor/anchor_manifest.jsonl
-# (needs only ffmpeg)
-python prepare/unpack_audio.py --buckets native,events
-python prepare/prepare_anchor.py
-# -> audio/anchor/*.flac
-```
-
-(`unpack_audio.py` resolves the release root from its own location; pass
-`--root` if you run it from elsewhere.)
-
-### 2. Prepare Inference Results
-
-The questions live in `metadata/{accuracy,rubric,chain}/{S,M,L}.jsonl`; each
-record carries the question, its `audio_path`, and (for multiple choice) the
-four options. Run your model over each record and write one answer per
-question — only `qa_id` and the model's output; ground truth stays in the
-metadata and is joined in by the scorer:
-
-**accuracy / chain** (multiple choice):
-
-```json
-{"qa_id": "S_EN_001_P", "answer": "D"}
-```
-
-**rubric** (open-ended):
-
-```json
-{"qa_id": "S_EN_001_P", "answer": "The narrator first says HDR at about 06:04 ..."}
-```
-
-- `answer` — the model's raw output; for multiple choice the scorer
-  extracts the option letter (A/B/C/D) from it.
-- Questions missing from your file count as wrong (or score zero).
-
-### 3. Run Evaluation
-
-```bash
-# accuracy + chain: stdlib-only, no dependencies
-python evaluate/score.py --mode accuracy --input your_accuracy.jsonl
-python evaluate/score.py --mode chain    --input your_chain.jsonl
-
-# rubric: needs an LLM judge (default: gpt-5.4-2026-03-05; API key from the
-# matching provider env var, e.g. OPENAI_API_KEY; or pass --api-base/--api-key)
-pip install openai tenacity
-export OPENAI_API_KEY="your-api-key-here"
-python evaluate/score_rubric.py --input your_rubric.jsonl
-```
-
-Both scorers join your answers against the ground truth in `metadata/` by
-`qa_id` — no reference answers needed in your submission.
-
-## License and Data Use
-
-AudioSpan is released for **non-commercial research and evaluation**. Our
-artifacts (QA items, rubric criteria, anchor manifests, sound events, and
-evaluation scripts) are under CC BY-NC-SA 4.0; copyright of the source
-recordings remains with their original creators. The recordings are
-included only for evaluation, and downloading the dataset constitutes
-agreement not to redistribute the audio or use it in commercial products.
-
-## Citation
-
-If you find AudioSpan useful for your research, please consider citing:
-
-```bibtex
-@article{huang2026audiospan,
-  title   = {AudioSpan: Spanning the Duration and Depth of Audio Comprehension},
-  author  = {Huang, Wen and Chu, Yunfei and Gao, Meng and He, Haolin and Xu, Jin},
-  journal = {arXiv preprint arXiv:ARXIV_ID},
-  year    = {2026}
-}
-```
+configs:
+- config_name: accuracy
+  default: true
+  data_files:
+  - split: S
+    path: metadata/accuracy/S.jsonl
+  - split: M
+    path: metadata/accuracy/M.jsonl
+  - split: L
+    path: metadata/accuracy/L.jsonl
+- config_name: rubric
+  data_files:
+  - split: S
+    path: metadata/rubric/S.jsonl
+  - split: M
+    path: metadata/rubric/M.jsonl
+  - split: L
+    path: metadata/rubric/L.jsonl
+- config_name: chain
+  data_files:
+  - split: S
+    path: metadata/chain/S.jsonl
+  - split: M
+    path: metadata/chain/M.jsonl
+  - split: L
+    path: metadata/chain/L.jsonl