--- license: apache-2.0 license_name: mixed-content language: - en - es - ru - zh - pt - ar - fr task_categories: - video-classification - visual-question-answering - text-retrieval tags: - video - audio - multimodal - webdataset - retrieval - claim-extraction pretty_name: microvent size_categories: - n<1K configs: - config_name: videos data_files: - split: train path: videos/shard_*.tar - config_name: audio data_files: - split: train path: audio/shard_*.tar - config_name: keyframes_uniform_5s data_files: - split: train path: keyframes/uniform_5s/shard_*.tar --- # microvent A compact development set for video retrieval, claim extraction, and report generation. It uses the same schema as the larger `multivent-raw`, so scripts that target one transfer straight to the other. This dataset card covers the **core release**: videos, audio, keyframes, and the public evaluation annotations. Derived signals (OCR text, ASR transcripts, visual / audio / video / omni embeddings) live in a companion release, **microvent-features**, with its own dataset card (`FEATURES_README.md` while the two are co-located on disk). A **chunk** is the unit of retrieval here: roughly the video analogue of a "passage" in text IR, a contiguous slice of one source video short enough to be a useful retrieval target on its own. Short videos are a single chunk; long-form sources split into several. Every artifact, including the annotations, is keyed by `chunk_id`. A `video_id` is just the prefix of its `chunk_id`s; the mapping (`video_id` → `[chunk_id, ...]`) is fully recoverable from `videos/catalog.csv` for clients that retrieve at video grain. --- ## At a glance | | | |---|---| | Queries | 31 | | Topics | 23 | | Positives (relevance:1) | 279 | | Hard negatives (relevance:0) | 730 | | Source videos | 933 | | Total chunks | 943 | | Shards | 5 | --- ## Directory layout ``` microvent/ ├── README.md │ ├── annotations/ ← public eval inputs │ ├── queries.jsonl │ ├── judgments.jsonl │ └── reference.json │ ├── videos/ ← .mp4 + per-chunk JSON │ ├── catalog.csv │ └── shard_NNNNNN.tar (×5) │ ├── audio/ ← .m4a (AAC, demuxed from .mp4) │ ├── catalog.csv │ └── shard_NNNNNN.tar (×5) │ └── keyframes/uniform_5s/ ← .jpg frames, one every 5 s ├── catalog.csv └── shard_NNNNNN.tar (×5) ``` Each artifact directory contains exactly two kinds of file: one `catalog.csv` and the `shard_NNNNNN.tar` WebDataset shards. The `annotations/` subtree is unique to microvent for now; multivent-raw's annotations are pending upload. Derived artifacts (`ocr/`, `asr/`, `embeddings/`) ship in **microvent-features**. --- ## Identifiers Three IDs let you locate, group, and time-align everything. Same schema as `multivent-raw`. | field | example | what it identifies | |-----------------|----------------------------------|--------------------| | `chunk_id` | `XM5xOIzL_vSkGAKR_0000` | one chunk; the join key across artifacts | | `video_id` | `XM5xOIzL_vSkGAKR` | the source video the chunk came from | | frame `tNNNNNN` | `t000005` | a keyframe within a chunk, at second NNNNNN of the chunk | * `chunk_id == f"{video_id}_{chunk_index:04d}"`. Always 4-digit padded, even for single-chunk videos. * `tNNNNNN` is the integer second offset **within the chunk** (zero-padded to 6 digits). Keyframes are sampled every 5 s. * No `chunk_id` or `video_id` starts with `-`, so filenames are safe to pass to `tar`, `find`, `xargs`, etc. without escaping. --- ## Annotations (`annotations/`) ``` annotations/ ├── queries.jsonl 31 rows, one per query ├── judgments.jsonl 279 positives + 730 hard negatives = 1009 rows └── reference.json 23 topics with per-claim chunk-level evidence ``` ### `queries.jsonl` One JSON object per line, 31 rows total: ```json { "query_id": "1", "query_type": "unbiased", // or "biased" "language": "english", "topic_id": "TTdFH8QvqAzM", // joins to reference.json "persona_title": "Statistician for North American Elections", "background": "I am a statistician who monitors...", "query": "Help me compile parliamentary and vote share statistics..." } ``` Each query carries a unique `persona_title` + `background`. The `topic_id` joins to `reference.json` (a many-to-one relationship: biased/unbiased query pairs share a topic). Source-pool prefixes (`multivent_`, `anomaly_`, `magmar_`) have been stripped to prevent provenance peeking. ### `judgments.jsonl` 1009 rows, keyed by `chunk_id`. Positives and negatives mixed. Positive (`relevance: 1`): ```json {"query_id": "1", "chunk_id": "_Ffutvei9wgoxMYS_0000", "relevance": 1, "language": "english"} ``` Positives were annotated at video grain (annotators marked a whole video as relevant for a query) and expanded to chunk grain here: every chunk of a video relevant to query Q inherits that relevance. A multi-chunk video contributes one row per chunk. Negative (`relevance: 0`, hard negative from the retrieval pool): ```json { "query_id": "1", "chunk_id": "IY_y1OVmryOyKNAw_0000", "relevance": 0, "distractor_type": "other", // or "same_camera" "rank_source": "qwen3vl8b" // also "ppocr" or "qwen3asr" } ``` Distractors were mined at chunk grain, so each row points at one specific chunk of one source video. `rank_source` identifies which retrieval signal mined the negative, so you can weight or hold-out negatives per signal: | `rank_source` | signal | model | |---------------|--------|-------| | `qwen3vl8b` | visual (keyframe embedding) | [Qwen/Qwen3-VL-Embedding-8B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B) | | `ppocr` | OCR text from keyframes | [PaddlePaddle/PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5) | | `qwen3asr` | ASR text from audio | [Qwen/Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B) | ### `reference.json` Single JSON document with a `version` field and a `topics` list: ```json { "version": "1.0", "topics": [ { "topic_id": "TInVWzp25aIM", "query_id": 18, // joins to queries.jsonl "query_type": "biased", // or "unbiased" "language": "english", "article": null, // non-null only on magmar topics "chunks": ["", ...], // oracle relevant set, chunk grain "claims": [ { "claim_id": "TInVWzp25aIM_c0", // stable, `_c` "text": "Emmonak, Alaska is being affected by the typhoon.", "evidence": { // chunk_id → list of modalities used "ls22tjnDj3GN8Jfj_0000": ["video-text"], "kkH5Nopcv9waN9dl_0000": ["audio-speech"] } } ] } ] } ``` Each claim's `evidence` maps a supporting `chunk_id` to the list of modalities used to support the claim. Annotators worked at chunk grain, so a multi-chunk video can have different claims attributed to its different chunks (e.g. a satellite-launch video's orbital-burn chunk vs. its landing chunk). The set of supporting chunks for a claim is just `evidence.keys()`; there is no separate `supporting_chunks` field. Modality labels are preserved verbatim from upstream annotators: `video-text`, `video-non-text`, `audio-speech`, `audio-non-speech`. Lookup by topic_id: ```python import json ref = json.load(open("annotations/reference.json")) topics_by_id = {t["topic_id"]: t for t in ref["topics"]} ``` --- ## video_id ↔ chunk_id `chunk_id` is the primary key throughout the release. Every artifact and every annotation uses it. A `video_id` is the prefix of one or more `chunk_id`s (`{video_id}_{NNNN}`); most videos contribute one chunk (`{video_id}_0000`), but long-form sources (e.g. anomaly streams) split into multiple. The mapping each way is fully recoverable from `videos/catalog.csv`: ```python import pandas as pd cat = pd.read_csv("videos/catalog.csv") video_to_chunks = cat.groupby("video_id")["chunk_id"].agg(list).to_dict() # {"XM5xOIzL_vSkGAKR": ["XM5xOIzL_vSkGAKR_0000"], # "PxRXEWfLiL3w_E7y": ["PxRXEWfLiL3w_E7y_0000", "PxRXEWfLiL3w_E7y_0001"], ...} chunk_to_video = dict(zip(cat["chunk_id"], cat["video_id"])) ``` Eval clients that want to roll chunk-level scores up to video grain can use `chunk_to_video` to group. --- ## In-shard file names Same convention as multivent-raw: ``` .. ``` | artifact directory | tag | per-chunk members | |-----------------------------|------------|-------------------| | `videos/` | *(none)* | `.mp4`, `.json` | | `audio/` | *(none)* | `.m4a` (absent if `has_audio=False`) | | `keyframes/uniform_5s/` | `kf_uni5s` | `.kf_uni5s.tNNNNNN.jpg` (one per 5 s) | The stem before the first `.` is always the `chunk_id`. WebDataset uses this prefix to group multi-artifact records into one sample. Feature artifacts in **microvent-features** follow the same convention so they join cleanly with these shards. --- ## Per-artifact details ### Videos (`videos/`) `.mp4` is the video clip itself; `.json` carries the per-chunk metadata (duration, codec, source-chunk offsets) that's also summarized in `videos/catalog.csv`. Catalog columns: ``` chunk_id, video_id, chunk_index, chunk_count, shard_index, duration_sec, chunk_start_sec, chunk_end_sec, size_bytes, vcodec, acodec ``` ### Audio (`audio/`) Each `.m4a` is the raw AAC track demuxed from the matching `.mp4` with `ffmpeg -vn -c:a copy`. The audio is not re-encoded; it is byte-identical to the bitstream inside the source mp4. 10 of 943 chunks have no audio stream (silent captures or upload-side stripping); these have `has_audio=False` in `audio/catalog.csv` and no member in the tar. Sample rate / channel count vary per source (most are 44.1 kHz stereo from web video) and are recorded per-row in the catalog: ``` chunk_id, video_id, chunk_index, chunk_count, shard_index, has_audio, acodec, asample_rate_hz, achannels, duration_sec, size_bytes ``` ### Keyframes (`keyframes/uniform_5s/`) JPEG keyframes sampled uniformly at one frame per 5 s of chunk duration. Member name `.kf_uni5s.tNNNNNN.jpg`, where `NNNNNN` is the integer-second offset within the chunk (zero-padded to 6 digits, e.g. `t000005`, `t000010`, ...). Catalog columns: ``` chunk_id, video_id, chunk_index, shard_index, chunk_count, frame_count, duration_sec ``` `frame_count` is the exact number of `.jpg` members for that chunk and should match `ceil(duration_sec / 5)` modulo edge-case rounding. Schema details (chunk JSON shape, exact catalog dtypes) are identical to multivent-raw's; see that dataset's README for the canonical reference. --- ## Eval suite The standard eval client for microvent is **MiRAGE** ([Martin et al., 2025](https://arxiv.org/abs/2510.24870)), a claim-centric framework for evaluating multimodal retrieval-augmented generation. It scores system output against `annotations/reference.json` along two axes: **InfoF1** (claim-level information coverage and factuality) and **CiteF1** (whether generated citations actually support the claims they attach to). --- ## Sharding 5 shards of ~189 chunks each. Every artifact in this core release shards identically: chunk `C` in shard `N` of `videos/` lives in shard `N` of `audio/` and `keyframes/uniform_5s/`. Same join invariants as multivent-raw. The feature release uses the same chunk → shard assignment for the artifacts that were processed by the same pipeline; newer embeddings in microvent-features may reshard (see that card). --- ## Pulling the data locally The entire core release (or any subset of it) can be mirrored with the `hf` CLI from `huggingface_hub`: ```bash # everything hf download hltcoe/microvent --repo-type dataset --local-dir ./microvent # just the public annotations (small, fast) hf download hltcoe/microvent --repo-type dataset --local-dir ./microvent \ --include "annotations/*" "README.md" # just videos + audio shards hf download hltcoe/microvent --repo-type dataset --local-dir ./microvent \ --include "videos/*" "audio/*" ``` `--local-dir` writes plain files (no blob/symlink indirection); drop it to land in the standard `~/.cache/huggingface/hub/` layout instead. --- ## Loading with `datasets` The repo is a plain WebDataset, so `huggingface/datasets` auto-detects it when you ask for a config name (each top-level artifact dir is exposed as one config in the YAML frontmatter): ```python import datasets vids = datasets.load_dataset("hltcoe/microvent", "videos", split="train", streaming=True) audios = datasets.load_dataset("hltcoe/microvent", "audio", split="train", streaming=True) frames = datasets.load_dataset("hltcoe/microvent", "keyframes_uniform_5s", split="train", streaming=True) ``` If you prefer to drive `webdataset` directly, point it at the shard glob: ```python import webdataset as wds ds = wds.WebDataset("videos/shard_{000000..000004}.tar").decode() ``` The `annotations/` subtree is plain JSONL/JSON and should be read with `json` / `pandas` rather than the WebDataset loader. --- ## Provenance protection All `video_id`s are anonymized (token_urlsafe-derived, leading-dash sanitized). The release contains no original YouTube/X/TikTok/Instagram URLs, no uploader names, no `.info.json` files, and no source-pool labels. The private mapping back to original identifiers stays in HLTCOE-internal storage and is not redistributed. --- ## License * HLTCOE-authored content (this README, the `catalog.csv` files, the `annotations/` JSON/JSONL, and the chunk JSON sidecars in `videos/`) is released under Apache-2.0. * Video, audio, and keyframe content in the shards is copyrighted by its respective original owners and is redistributed here under research / fair-use terms only. Do not redistribute the raw shards outside research contexts; cite the upstream owners where known.