Datasets:
Formats:
json
Languages:
English
Size:
100K - 1M
Tags:
audio
audio-deepfake-detection
audio-language-model
audio-question-answering
acoustic-forensics
asvspoof
| language: | |
| - en | |
| pretty_name: "SEAR: Spoofing Evidence-Grounded Audio Reasoning" | |
| task_categories: | |
| - audio-classification | |
| - question-answering | |
| tags: | |
| - audio | |
| - audio-deepfake-detection | |
| - audio-language-model | |
| - audio-question-answering | |
| - acoustic-forensics | |
| - asvspoof | |
| - datasets | |
| size_categories: | |
| - 100K<n<1M | |
| configs: | |
| - config_name: t1 | |
| default: true | |
| data_files: | |
| - split: 19LA_train | |
| path: t1/19LA_train.jsonl | |
| - split: 19LA_dev | |
| path: t1/19LA_dev.jsonl | |
| - split: 19LA_eval | |
| path: t1/19LA_eval.jsonl | |
| - split: 21LA_eval | |
| path: t1/21LA_eval.jsonl | |
| - config_name: t2 | |
| data_files: | |
| - split: 19LA_train | |
| path: t2/19LA_train.jsonl | |
| - split: 19LA_dev | |
| path: t2/19LA_dev.jsonl | |
| - split: 19LA_eval | |
| path: t2/19LA_eval.jsonl | |
| - split: 21LA_eval | |
| path: t2/21LA_eval.jsonl | |
| - config_name: t3 | |
| data_files: | |
| - split: 19LA_train | |
| path: t3/19LA_train.jsonl | |
| - split: 19LA_dev | |
| path: t3/19LA_dev.jsonl | |
| - split: 19LA_eval | |
| path: t3/19LA_eval.jsonl | |
| - split: 21LA_eval | |
| path: t3/21LA_eval.jsonl | |
| - config_name: t4 | |
| data_files: | |
| - split: 19LA_train | |
| path: t4/19LA_train.jsonl | |
| - split: 19LA_dev | |
| path: t4/19LA_dev.jsonl | |
| - split: 19LA_eval | |
| path: t4/19LA_eval.jsonl | |
| - split: 21LA_eval | |
| path: t4/21LA_eval.jsonl | |
| # SEAR: Spoofing Evidence-Grounded Audio Reasoning | |
| SEAR is an audio question-answering benchmark for testing whether audio language models | |
| can identify and quantify signal-level acoustic anomalies and use them as evidence for | |
| audio deepfake detection. Instead of evaluating only a final authenticity verdict, SEAR | |
| separates deepfake detection, forgery-cue identification, acoustic measurement, and | |
| forensic rationale generation. | |
| SEAR contains four complementary tasks covering acoustic evidence identification and | |
| quantification, forensic rationale grounding, and deepfake detection. | |
| ## Tasks | |
| | Config | Task | Format | Evaluation target | | |
| |---|---|---|---| | |
| | `t1` | Binary classification | One multiple-choice question per audio | Deepfake detection | | |
| | `t2` | Forgery cue identification | One multiple-choice question per audio | Acoustic evidence identification | | |
| | `t3` | Acoustic feature measurement | Zero or more multiple-choice questions per audio | Acoustic evidence quantification | | |
| | `t4` | Forensic rationale generation | One open-ended question per audio | Forensic rationale grounding | | |
| T3 contains one question for each applicable reference finding. If no selected feature | |
| is anomalous, the audio record is retained with an empty `qa_pairs` list and | |
| `task_status.T3 = "not_applicable"`; no artificial measurement question is introduced. | |
| T4 asks the model to summarize signal-level acoustic evidence in natural language. Its | |
| reference rationale verbalizes the verified feature names, measured values, and deviation | |
| directions without revealing the authenticity label or attack identity. | |
| ## Scale | |
| | Source partition | Audio | T1 | T2 | T3 questions | T4 | | |
| |---|---:|---:|---:|---:|---:| | |
| | ASVspoof 2019 LA train | 25,380 | 25,380 | 25,380 | 96,318 | 25,380 | | |
| | ASVspoof 2019 LA dev | 24,844 | 24,844 | 24,844 | 138,549 | 24,844 | | |
| | ASVspoof 2019 LA eval | 71,237 | 71,237 | 71,237 | 639,204 | 71,237 | | |
| | ASVspoof 2021 LA eval | 101,921 | 101,921 | 101,921 | 353,664 | 101,921 | | |
| | **Total** | **223,382** | **223,382** | **223,382** | **1,227,735** | **223,382** | | |
| The complete benchmark therefore contains 1,897,881 AQA items over 223,382 audio | |
| recordings. | |
| ## Data format | |
| Each JSONL row represents one audio recording. Questions for the same recording remain | |
| grouped in `qa_pairs`, allowing an ALM runner to load the audio once and answer all | |
| applicable questions. | |
| ```json | |
| { | |
| "partition": "19LA_dev", | |
| "file_id": "LA_D_1000752", | |
| "audio_path": "data/LA/ASVspoof2019_LA_dev/flac/LA_D_1000752.flac", | |
| "label_offline": "spoof", | |
| "attack_metadata_offline": "A05", | |
| "qa_pairs": [ | |
| { | |
| "qid": "LA_D_1000752_T2", | |
| "task": "forgery_cue_identification", | |
| "new_task": "T2", | |
| "question_type": "multiple_choice", | |
| "question": "Which acoustic finding best matches the deterministic analysis of this audio?", | |
| "options": {"A": "...", "B": "...", "C": "...", "D": "..."}, | |
| "answer": "C", | |
| "answer_text": "...", | |
| "metadata": {"target_feature": "voiced_ratio", "no_anomaly_target": false} | |
| } | |
| ] | |
| } | |
| ``` | |
| The paths are repository-relative identifiers for alignment with locally obtained source | |
| audio. This repository does **not** redistribute ASVspoof audio. | |
| ## Loading | |
| Replace `YOUR_NAMESPACE/SEAR` with this dataset repository's identifier: | |
| ```python | |
| from datasets import load_dataset | |
| t1 = load_dataset("YOUR_NAMESPACE/SEAR", "t1") | |
| t2 = load_dataset("YOUR_NAMESPACE/SEAR", "t2") | |
| t3 = load_dataset("YOUR_NAMESPACE/SEAR", "t3") | |
| t4 = load_dataset("YOUR_NAMESPACE/SEAR", "t4") | |
| example = t2["19LA_eval"][0] | |
| print(example["audio_path"], example["qa_pairs"]) | |
| ``` | |
| Users must obtain ASVspoof 2019 LA and ASVspoof 2021 LA from their official distribution | |
| channels and resolve `audio_path` relative to their local copy. | |
| ## Evaluation protocol and leakage prevention | |
| `label_offline`, `attack_metadata_offline`, `answer`, and `answer_text` are evaluation-side | |
| fields. They must not be included in the model prompt. In particular: | |
| - do not reveal authenticity labels or attack identities during inference; | |
| - do not derive normalization statistics from development or evaluation data; | |
| - use bona-fide training speech only when constructing a deployable reference prior; | |
| - use development data only for model selection or threshold selection; | |
| - reserve evaluation partitions for final reporting. | |
| The included reference evidence is an offline benchmark target constructed with dataset | |
| labels and available attack metadata. It is oracle supervision for evaluation, not the | |
| output of a deployable detector. | |
| ## Construction and validation | |
| SEAR uses 35 utterance-level descriptors spanning cepstral, spectral, prosodic, temporal, | |
| and energy characteristics. Candidate evidence was statistically screened globally and, | |
| when annotations were available, attack-wise. T2 presents a verified finding among | |
| controlled distractors and a neutral no-anomaly option. T3 masks the measured value of a | |
| specified finding and asks the model to recover it from four candidates. | |
| All 223,382 audio records passed automated checks for partition and audio-path alignment, | |
| task cardinality, answer mapping, option uniqueness, feature/value traceability, value | |
| masking, and exclusion of attack identifiers from model-visible text. File counts and | |
| SHA-256 checksums are provided in `manifest.json`. | |
| For T4, references are generated solely from the verified structured findings and checked | |
| for feature coverage, exact measured values, deviation directions, sentence count, and | |
| forbidden class or attack inferences before release. | |
| ## Intended use and limitations | |
| SEAR is intended for evaluating evidence-grounded acoustic reasoning in ALMs and for | |
| controlled research on audio deepfake detection. The reference findings are statistical | |
| descriptors rather than exhaustive causal explanations of spoofing. Performance may vary | |
| under unseen recording conditions, languages, codecs, attacks, and demographic groups. | |
| The source benchmarks and their collection processes may introduce additional biases. | |
| ## Source data and terms | |
| This release provides AQA annotations and source-audio identifiers only. Users are | |
| responsible for obtaining the corresponding ASVspoof data and complying with its terms of | |
| use. No license is asserted here over the underlying audio recordings. | |
| ## Citation | |
| The paper citation will be added upon publication. Until then, please cite the SEAR | |
| dataset repository and the corresponding ASVspoof source benchmarks. | |