Datasets:
Human Preference
This component contains 100 prompts derived from ASG-Bench, each used to independently generate three audio candidates with SeedAudio 1.0 (300 candidates in total), together with prompt-derived rubrics and annotations from seven human annotators. All released audio files are in WAV format.
The release contains all 100 prompt groups and their individual annotation records. The paper evaluation uses 279 pairwise comparisons with a unique winning candidate and 84 three-candidate ranking groups with a unique modal ranking among valid complete annotations.
Files
candidate_groups.jsonl
One unlabeled group per prompt. Each row contains group_id, language,
category, prompt, and three nested candidates with candidate_id, audio,
and duration_seconds. It intentionally contains no preference label so the
groups can be independently re-annotated.
prompt_rubrics.jsonl
One prompt and its nested rubric list per row. Rubric fields are rubric_id,
question, axis, scope, subtype, domain, basis, and parent_ids.
The rubric questions are produced from each prompt using the structured
rubric decomposition method proposed in FineAudio-Judge.
human_preference_annotations.jsonl
This file contains 700 individual annotation records, seven per prompt
group. Annotators are identified by stable anonymous IDs
annotator_01 through annotator_07.
Each row contains the annotation status, unable_to_judge, the
best-to-worst candidate ranking, and the candidate presentation_order.
human_preference_pairwise.jsonl
This file contains 279 pairwise comparisons with a unique modal preference among the 300 possible candidate pairs across 100 prompt groups, based on the annotators' complete rankings.
label identifies the winning candidate. votes records the vote counts
for both candidates, together with unable-to-judge and missing or invalid
annotations. vote_pattern records winner_votes:loser_votes.
Consensus strata are:
| Level | Definition | Observed vote patterns | Comparisons |
|---|---|---|---|
high |
loser votes = 0 | 7:0, 6:0, 5:0, 4:0 |
68 |
medium |
loser votes = 1 | 6:1, 5:1, 4:1, 3:1 |
91 |
low |
loser votes >= 2 | 5:2, 4:3, 4:2, 3:2 |
120 |
Vote patterns are shown as winner_votes:loser_votes, with unable-to-judge
and missing or invalid annotations counted separately.
human_preference_ranking.jsonl
This file contains the 84 three-candidate ranking groups used in the paper evaluation. Each reference ranking is the unique mode among the valid complete rankings submitted by the seven annotators. Groups with tied modal rankings are excluded.
label gives the candidate order from best to worst. vote_distribution
records each observed complete ranking and its count. consensus_level
is strict_majority when the modal ranking receives at least four votes,
and unique_plurality when the unique mode receives two or three votes.
Categories
| Category | Groups |
|---|---|
Cinematic |
18 |
Environmental |
20 |
Field Reporting |
19 |
Narrational |
19 |
Podcast_Broadcast |
24 |
Evaluation protocol
For each candidate audio, the judge jointly evaluates the rubrics derived
from its prompt and predicts yes or no for each rubric. The proportion
of yes predictions forms the candidate's prompt–audio consistency score:
score = number of yes predictions / number of rubrics
Candidates generated from the same prompt are compared and ranked by
their consistency scores. Pairwise accuracy is evaluated against
human_preference_pairwise.jsonl. A three-candidate ranking is counted
as correct when the predicted order exactly matches the reference
order in human_preference_ranking.jsonl.
For pairwise evaluation, equal consistency scores for the two candidates count as an incorrect judgment. For three-candidate ranking evaluation, equal consistency scores for any two candidates count as an incorrect ranking.
Evaluation is performed at the candidate-audio level, with multiple rubrics for the same candidate evaluated jointly in each model request. Longer rubric lists are divided into chunks of up to 80 questions by default. Predictions from all chunks are combined into a single consistency score for that candidate.