--- pretty_name: AV-Dense-60k language: - en task_categories: - text-to-video - text-to-audio - video-text-to-text tags: - audio-video - multimodal - dense-captioning - reinforcement-learning - webdataset license: other --- # AV-Dense-60k AV-Dense-60k contains paired video, audio, raw text, and dense audio-video captions. The media is packaged as deterministic WebDataset TAR shards, with portable metadata and checksums for streaming or restoration. > **Rights and license notice:** this repository uses `license: other`. Public > availability does not relicense the underlying media and does not grant rights that > the publisher does not hold. Before downloading, redistributing, or using any sample, > users must independently confirm the media's provenance, copyright and neighboring > rights, platform terms, privacy/consent requirements, and suitability for their > intended jurisdiction and use. ## Verified dataset inventory | Split | Records | Video | Audio | Intended role | | --- | ---: | ---: | ---: | --- | | train | 44,178 | 44,178 | 44,178 | model training | | validation | 5,522 | 5,522 | 5,522 | held-out validation | | test | 5,523 | 5,523 | 5,523 | final evaluation only | The split IDs are unique and pairwise disjoint. Every manifest ID has exactly one MP4 and one WAV in `processed_gt_data`; no missing, orphan, or zero-byte files were found. `AV60KDenseBench.csv` contains the same 5,523 IDs as `test.jsonl` and must not be used for training. The additional `480p_grpo` variant contains the 5,522 validation IDs. The upstream metadata contained obsolete machine-specific media locations. Release staging reconstructs every video/audio field from the split and `video_id`/`id`, so all structured paths in this repository are canonical and relative to the restored dataset root, for example `processed_gt_data/train/videos/.mp4`. ## Repository contents This data repository contains only the following release surfaces: ```text README.md metadata/ train.jsonl val.jsonl test.jsonl AV60KDenseBench.csv AV69kDense-vanilla-max4s.pt align_csv.py data/ processed/train/avdense-train-*.tar processed/val/avdense-val-*.tar processed/test/avdense-test-*.tar legacy_480p_grpo/avdense-val480p-*.tar manifests/ shards.json SHA256SUMS ``` Training, extraction, and manifest-building code is distributed separately from this dataset repository. No model source, checkpoint, experiment report, or presentation is claimed as part of this public data release. ## WebDataset sample format Each processed sample has three same-stem members: ```text .mp4 .wav .json ``` The JSON member contains `sample_id`, `split`, `variant`, `prompt_av`, `prompt_v`, `prompt_a`, `video_path`, and `audio_path`. `prompt_av`/`prompt_v` use `dense_caption`; `prompt_a` uses `raw_text`; both media paths are relative. ## Stream shards without extraction Install the optional `webdataset` package, then run this example from the downloaded data repository root: ```bash python -m pip install webdataset python - <<'PY' import json from pathlib import Path import webdataset as wds shards = sorted(Path("data/processed/train").glob("avdense-train-*.tar")) if not shards: raise SystemExit("no train shards found; run from the data repository root") samples = wds.WebDataset([str(path) for path in shards], shardshuffle=False).to_tuple( "__key__", "mp4", "wav", "json" ) for sample_id, mp4_bytes, wav_bytes, metadata_bytes in samples: metadata = json.loads(metadata_bytes) print(sample_id, len(mp4_bytes), len(wav_bytes), metadata["prompt_av"]) break PY ``` ## Integrity verification Before extraction, run the checksum gate from the data repository root: ```bash sha256sum --check manifests/SHA256SUMS ``` Do not extract or train if any shard is missing or is not reported as `OK`. For a stronger, read-only audit, use `extract_avdense_webdataset.py` from the separately distributed JavisR1 code package. The `--verify-only` mode rechecks shard digests, manifest totals, TAR structure, sample counts, relative JSON paths, and every MP4/WAV/JSON triplet without writing output. One reproducible relative workspace arrangement is: ```text workspace/ AV-Dense-60k/ # this data repository code/jarvisr1/ # separately distributed code package datasets/ # restoration destination ``` From `workspace/code/jarvisr1`: ```bash python3 scripts/extract_avdense_webdataset.py \ --repo-root ../../AV-Dense-60k \ --verify-only ``` ## Restore the training layout Run restoration only after both checksum and verify-only gates succeed. From the same separately distributed code-package directory: ```bash python3 scripts/extract_avdense_webdataset.py \ --repo-root ../../AV-Dense-60k \ --output-root ../../datasets/AV-Dense-60k python3 scripts/build_avdense_manifests.py \ --dataset-root ../../datasets/AV-Dense-60k ``` The extractor restores the normalized metadata plus the following default media tree: ```text ../../datasets/AV-Dense-60k/ train.jsonl val.jsonl test.jsonl processed_gt_data/train/{videos,audios}/... processed_gt_data/val/{videos,audios}/... processed_gt_data/test/{videos,audios}/... ``` To additionally restore the legacy validation-resolution variant, add `--include-legacy-480p` to the extraction command. The canonical builder produces: ```text sample_id,prompt_av,prompt_v,prompt_a,video_path,audio_path,split ``` ## Responsible use and limitations - `license: other` is intentional; do not infer a permissive media license from public repository visibility. - Users are responsible for rights clearance, attribution obligations, privacy review, consent, and compliance with applicable law and source-platform terms. - Keep train, validation, and test IDs disjoint. Test and benchmark rows are evaluation only. - Dense captions may contain factual, temporal, or synchronization errors and should not be treated as ground truth without task-appropriate review. - Dataset inclusion does not imply endorsement of a depicted person, action, statement, or downstream application. - This repository makes no model-quality claim.