Upload AudioEval dataset package

README.md

@@ -13,167 +13,69 @@ tags:
 - text-to-audio
 - benchmark
 - evaluation
-- human-ratings
-- multimodal
 configs:
 - config_name: default
   data_files:
-  - split: train
-    path: data/train/**
-  - split: validation
-    path: data/validation/**
-  - split: test
-    path: data/test/**
+  - split: all
+    path: data/**
   drop_labels: true
 ---
 
 # AudioEval
 
-AudioEval is a benchmark for evaluating text-to-audio generation systems from two listener perspectives (`common` and `pro`) and five perceptual dimensions: `production_complexity`, `content_enjoyment`, `production_quality`, `textual_alignment`, and `content_usefulness`.
-This repository is packaged in an `AudioFolder`-compatible layout so it can be previewed directly on the Hugging Face Hub and later switched from private to public visibility without repacking.
+AudioEval is a text-to-audio evaluation benchmark with 4200 generated clips, 451 prompts, 24 systems, and 25200 per-rater annotations.
+This release uses one main clip table in `data/metadata.jsonl` and keeps the original `train` / `validation` / `test` assignment as a `split` column instead of separate split folders.
 
-## Dataset Summary
+## Files
 
-- 4200 generated audio clips from 24 text-to-audio systems and 451 prompts.
-- 11.712 total hours of audio, with an average clip duration of 10.039 seconds.
-- Official splits: train=3360, validation=420, test=420.
-- 25200 per-rater annotation rows in `annotations/ratings.csv`.
-- 126000 dimension-level human scores overall, because each annotation row contains five dimension scores.
-- 12 raters in total: common=9, pro=3.
-- Rating rows by rater type: common=12600, pro=12600.
-- Prompts span five coarse scene categories: daily life, art and cultural, natural and outdoor, work and production, and transportation and travel.
-
-## What Is In This Repository
-
-- `data/train`, `data/validation`, `data/test`: split directories containing `.wav` files and a `metadata.jsonl` file for each split.
+- `data/metadata.jsonl`: one clip-level table for all 4200 clips.
+- `data/*.wav`: audio files referenced by `file_name`.
 - `annotations/ratings.csv`: anonymized per-rater annotations.
-- `annotations/prompts.tsv`: prompt text plus prompt-side metadata such as `scene_category` and `audioset_ontology`.
-- `annotations/system_info.csv`: mapping from `system_id` to the evaluated system name.
-- `annotations/release_summary.json`: summary counts for this packaged release.
-- `stats/*.csv`: analysis tables such as ICC, Krippendorff's alpha, significance tests, and model result summaries.
-
-## Suggested Uses
-
-- Benchmarking automatic evaluators for text-to-audio generation.
-- Training clip-level regression or distribution-prediction models from prompt-audio pairs.
-- Studying rating differences between professional and non-professional listeners.
-- Analyzing inter-rater reliability, disagreement, and cross-dimension correlations.
-
-## Split Sizes
-
-| Split | Clips |
-| --- | ---: |
-| train | 3360 |
-| validation | 420 |
-| test | 420 |
-
-## Data Structure
-
-### Split-Level Metadata
-
-Each row in `data/*/metadata.jsonl` corresponds to one generated audio clip.
-
-| Field | Description |
-| --- | --- |
-| `file_name` | Relative path to the audio file inside the split directory. |
-| `wav_name` | Original clip filename. |
-| `split` | One of `train`, `validation`, or `test`. |
-| `prompt_id` | Prompt identifier such as `P0004` or `N001`. |
-| `prompt_text` | Natural language prompt used to generate the clip. |
-| `scene_category` | Coarse manually assigned prompt category. |
-| `sound_event_count` | Number of sound events recorded in the prompt metadata. |
-| `audioset_ontology` | Prompt-side ontology category. |
-| `system_id` | System identifier such as `S001`. |
-| `system_name` | Human-readable system name. |
-| `num_ratings_common` | Number of common-rater judgments for the clip. |
-| `num_ratings_pro` | Number of professional-rater judgments for the clip. |
-| `num_ratings_overall` | Total number of judgments for the clip. |
-| `common_*_mean` | Mean score from common raters for a given dimension. |
-| `pro_*_mean` | Mean score from professional raters for a given dimension. |
-| `common_*_raw_scores` | List of raw common-rater scores for a given dimension. |
-| `pro_*_raw_scores` | List of raw professional-rater scores for a given dimension. |
-
-The five dimensions are `production_complexity`, `content_enjoyment`, `production_quality`, `textual_alignment`, and `content_usefulness`.
-
-### Per-Rater Annotations
-
-Each row in `annotations/ratings.csv` contains one rater's five-dimensional judgment for one clip.
-
-| Field | Description |
-| --- | --- |
-| `wav_name` | Clip filename. |
-| `split` | Dataset split for the clip. |
-| `prompt_id` | Prompt identifier. |
-| `system_id` | System identifier. |
-| `rater_type` | `common` or `pro`. |
-| `rater_id` | Stable anonymized rater identifier within this release. |
-| `production_complexity` | Integer score from 1 to 10. |
-| `content_enjoyment` | Integer score from 1 to 10. |
-| `production_quality` | Integer score from 1 to 10. |
-| `textual_alignment` | Integer score from 1 to 10. |
-| `content_usefulness` | Integer score from 1 to 10. |
-
-## Loading the Data
-
-Once you have access to the repository on the Hub, you can load the split metadata and audio files with `datasets`:
+- `annotations/prompts.tsv`: prompt metadata.
+- `annotations/system_info.csv`: system name mapping.
+- `stats/*.csv`: reliability and model summary tables.
 
-```python
-from datasets import load_dataset
+## Summary
 
-train = load_dataset("Hui519/AudioEval", split="train")
-print(train[0]["audio"])
-print(train[0]["prompt_text"])
-```
-
-The anonymized annotation table can be loaded separately from `annotations/ratings.csv`.
-
-## Dataset Creation
+- 11.712 total hours of audio, about 10.039 seconds per clip on average.
+- Original split counts are preserved in the `split` column: train=3360, validation=420, test=420.
+- There are 9 common raters and 3 professional raters.
+- Rating rows by rater type: common=12600, pro=12600.
+- Each rating row contains 5 integer scores from 1 to 10.
 
-AudioEval is a benchmark release of machine-generated audio, not a collection of source recordings.
-Each clip is paired with its generation prompt, generating system identifier, and human evaluation results.
-This packaged Hub release was assembled from locally processed benchmark assets by:
+## Main Columns
 
-- standardizing prompt metadata into `annotations/prompts.tsv`,
-- merging raw MOS tables into a unified annotation table,
-- exporting split-level clip metadata into `metadata.jsonl`,
-- and anonymizing rater identities before publication.
+- `file_name`, `wav_name`, `split`, `prompt_id`, `prompt_text`
+- `scene_category`, `sound_event_count`, `audioset_ontology`
+- `system_id`, `system_name`
+- `common_*_mean`, `pro_*_mean`
+- `common_*_raw_scores`, `pro_*_raw_scores`
 
-## Annotation Process
+The five evaluation dimensions are `production_complexity`, `content_enjoyment`, `production_quality`, `textual_alignment`, and `content_usefulness`.
 
-- The release contains two listener groups: `common` and `pro`.
-- There are 9 common raters and 3 professional raters.
-- Each clip receives 3 common-rater and 3 professional-rater judgments for every dimension.
-- The five dimensions are production complexity, content enjoyment, production quality, textual alignment, and content usefulness.
-- Score values are integers from 1 to 10.
+## Loading
 
-## Biases, Risks, and Limitations
+Once you have access to the repository on the Hub, you can load the main table like this:
 
-- Prompt coverage is broad but not uniform across scene categories or sound-event complexity.
-- The prompts are written in English, but generated audio may contain non-speech sounds, speech-like content, music, or multilingual vocal content.
-- The benchmark is designed for evaluation research on text-to-audio systems and should not be treated as a general-purpose audio understanding dataset.
-- Because the audio is machine-generated, outputs may contain artifacts, distorted speech, or unsafe-sounding events that require additional filtering for downstream applications.
-- Anonymized `rater_id` values are release-specific identifiers and should not be linked back to source identities.
+```python
+from datasets import load_dataset
 
-## Privacy and Sensitive Information
+data = load_dataset("Hui519/AudioEval", split="all")
+print(data[0]["audio"])
+print(data[0]["split"])
+print(data[0]["prompt_text"])
+```
 
-- This release intentionally excludes the original rater demographic tables.
-- No direct personally identifying information is included in the published files.
-- `annotations/ratings.csv` keeps only anonymized rater IDs, rater type, system ID, prompt ID, split, and scores.
-- Although the audio is machine-generated, some clips may depict or imitate human speech, children, alarms, animals, vehicles, or other safety-relevant scenarios.
+- `annotations/ratings.csv` also keeps the original split assignment as a column.
+- Rater demographic tables are intentionally excluded from this release.
 
 ## License
 
 The repository is currently private.
-The YAML metadata uses `license: other` because the final public release license has not been fixed yet.
-Until a public license is selected, access and reuse are governed by the repository owner's access settings and release terms.
-Before switching the repository to public visibility, replace this section and the YAML metadata with the final license identifier if you choose a standard Hugging Face-supported license.
+The YAML metadata stays at `license: other` until the final public license is chosen.
 
 ## Citation
 
-If you use AudioEval, cite the paper below and, when relevant, the Hugging Face dataset repository.
-
-Paper:
-
 - Hui Wang, Jinghua Zhao, Junyang Cheng, Cheng Liu, Yuhang Jia, Haoqin Sun, Jiaming Zhou, and Yong Qin. *AudioEval: Automatic Dual-Perspective and Multi-Dimensional Evaluation of Text-to-Audio-Generation*. arXiv:2510.14570, 2025. DOI: 10.48550/arXiv.2510.14570.
 
 ```bibtex
@@ -185,9 +87,3 @@ Paper:
   doi={10.48550/arXiv.2510.14570}
 }
 ```
-
-## Release Status
-
-- The original source directory in this workspace remains unchanged.
-- The export directory can be regenerated by re-running `scripts/prepare_hf_audioeval_dataset.py`.
-- This card is written so the same package can remain useful both while private and after a later public release.