--- 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 - feature-extraction tags: - video - audio - multimodal - webdataset - embeddings - ocr - asr pretty_name: microvent-features size_categories: - n<1K configs: - config_name: ocr_ppocrvl15 data_files: - split: train path: ocr/ppocrvl15/shard_*.tar - config_name: asr_qwen3asr1p7b data_files: - split: train path: asr/qwen3asr1p7b/shard_*.tar - config_name: emb_kf_uni5s_vizemb_qwen3vlemb2b data_files: - split: train path: embeddings/kf_uni5s-vizemb_qwen3vlemb2b/shard_*.tar - config_name: emb_kf_uni5s_vizemb_qwen3vlemb8b data_files: - split: train path: embeddings/kf_uni5s-vizemb_qwen3vlemb8b/shard_*.tar - config_name: emb_kf_uni5s_vizemb_siglip2so400m512 data_files: - split: train path: embeddings/kf_uni5s-vizemb_siglip2so400m512/shard_*.tar - config_name: emb_kf_uni5s_ocr_ppocrvl15_txtemb_qwen3emb8b data_files: - split: train path: embeddings/kf_uni5s-ocr_ppocrvl15-txtemb_qwen3emb8b/shard_*.tar - config_name: emb_audemb_glap data_files: - split: train path: embeddings/audemb_glap/shard_*.tar - config_name: emb_audemb_jinav5omnismall data_files: - split: train path: embeddings/audemb_jinav5omnismall/shard_*.tar - config_name: emb_audemb_largerclapgeneral data_files: - split: train path: embeddings/audemb_largerclapgeneral/shard_*.tar - config_name: emb_audemb_lcoomni7b data_files: - split: train path: embeddings/audemb_lcoomni7b/shard_*.tar - config_name: emb_audemb_omniembed01 data_files: - split: train path: embeddings/audemb_omniembed01/shard_*.tar - config_name: emb_audemb_omninemotron3b data_files: - split: train path: embeddings/audemb_omninemotron3b/shard_*.tar - config_name: emb_videmb_lcoomni7b data_files: - split: train path: embeddings/videmb_lcoomni7b/shard_*.tar - config_name: emb_videmb_omninemotron3b data_files: - split: train path: embeddings/videmb_omninemotron3b/shard_*.tar - config_name: emb_videmb_qwen3vlemb8b data_files: - split: train path: embeddings/videmb_qwen3vlemb8b/shard_*.tar - config_name: emb_omniemb_lcoomni7b data_files: - split: train path: embeddings/omniemb_lcoomni7b/shard_*.tar - config_name: emb_omniemb_omniembed01 data_files: - split: train path: embeddings/omniemb_omniembed01/shard_*.tar - config_name: emb_omniemb_omninemotron3b data_files: - split: train path: embeddings/omniemb_omninemotron3b/shard_*.tar --- # microvent-features Derived signals for the **microvent** core release: per-keyframe OCR text, per-chunk ASR transcripts, and an embedding zoo (keyframe-level vision, keyframe-OCR text, audio-level, video-level, omni-modal). This card covers only the features. For the source videos, audio, keyframes, and the public eval annotations, see the **microvent** dataset card. All artifacts here key on the same `chunk_id` and follow the same WebDataset shard layout, so joining feature shards back to the core release is a straight tar-member lookup. --- ## Directory layout ``` microvent-features/ ├── README.md │ ├── ocr/ │ └── ppocrvl15/ ← per-frame OCR text (PaddleOCR-VL-1.5, cleaned) │ ├── catalog.csv │ └── shard_NNNNNN.tar (×5) │ ├── asr/ │ └── qwen3asr1p7b/ ← per-chunk ASR (Qwen3-ASR-1.7B) │ ├── catalog.csv │ └── shard_NNNNNN.tar (×5) │ └── embeddings/ ← per-chunk .npz, keyed by chunk_id │ │ ── vision over uniform-5s keyframes ── ├── kf_uni5s-vizemb_qwen3vlemb2b/ ← Qwen3-VL-Embedding-2B, dim 2048 ├── kf_uni5s-vizemb_qwen3vlemb8b/ ← Qwen3-VL-Embedding-8B, dim 4096 ├── kf_uni5s-vizemb_siglip2so400m512/ ← SigLIP2-So400M/512, dim 1152 │ │ ── text embedding of keyframe OCR ── ├── kf_uni5s-ocr_ppocrvl15-txtemb_qwen3emb8b/ ← Qwen3-Embedding-8B over ppocrvl15 text, dim 4096 │ │ ── audio-level (one vector(s) per chunk's audio) ── ├── audemb_glap/ ← GLAP, dim 1024 ├── audemb_jinav5omnismall/ ← Jina-v5-omni-small, dim 1024 ├── audemb_largerclapgeneral/ ← Larger-CLAP-general, dim 512 ├── audemb_lcoomni7b/ ← LCO-Embedding-Omni-7B (audio), dim 3584 ├── audemb_omniembed01/ ← OmniEmbed-v0.1 (audio), dim 3584 ├── audemb_omninemotron3b/ ← Omni-Embed-Nemotron-3B (audio), dim 2048 │ │ ── video-level (one vector per chunk's full video) ── ├── videmb_lcoomni7b/ ← LCO-Embedding-Omni-7B (video), dim 3584 ├── videmb_omninemotron3b/ ← Omni-Embed-Nemotron-3B (video), dim 2048 ├── videmb_qwen3vlemb8b/ ← Qwen3-VL-Embedding-8B, dim 4096 │ │ ── omni-modal (joint audio+video per chunk) ── ├── omniemb_lcoomni7b/ ← LCO-Embedding-Omni-7B (omni), dim 3584 ├── omniemb_omniembed01/ ← OmniEmbed-v0.1 (omni), dim 3584 └── omniemb_omninemotron3b/ ← Omni-Embed-Nemotron-3B (omni), dim 2048 ``` Model cards for everything listed above are linked from the embedding table further down. Each artifact directory contains the same two-file pattern: a `catalog.csv` and the `shard_NNNNNN.tar` WebDataset shards. The newer embedding directories ship 3 shards (~314 chunks each); the older keyframe-vision and OCR-text-embedding directories, plus `ocr/` and `asr/`, ship 5 shards (~189 chunks each) matching the core release. Some newer embedding directories may be missing their `catalog.csv` pending a backfill; the chunk membership is always recoverable from the tar TOC in that case. --- ## Identifiers and join keys Same `chunk_id` / `video_id` / `tNNNNNN` scheme as the core release. The filename of every tar member starts with the `chunk_id` of the source chunk, so a WebDataset loader will group features and core artifacts into the same sample automatically when you `wds.WebDataset(...)` over both shard sets. --- ## In-shard file names ``` .. ``` | artifact directory | tag | member | |----------------------------------------------------------|----------------------------------------------|--------| | `ocr/ppocrvl15/` | `kf_uni5s.ocr_ppocrvl15` | `.jsonl` (one line per frame) | | `asr/qwen3asr1p7b/` | `asr_qwen3asr1p7b` | `.json` | | `embeddings/kf_uni5s-vizemb_qwen3vlemb2b/` | `kf_uni5s.vizemb_qwen3vlemb2b` | `.npz` | | `embeddings/kf_uni5s-vizemb_qwen3vlemb8b/` | `kf_uni5s.vizemb_qwen3vlemb8b` | `.npz` | | `embeddings/kf_uni5s-vizemb_siglip2so400m512/` | `kf_uni5s.vizemb_siglip2so400m512` | `.npz` | | `embeddings/kf_uni5s-ocr_ppocrvl15-txtemb_qwen3emb8b/` | `kf_uni5s.ocr_ppocrvl15.txtemb_qwen3emb8b` | `.npz` | | `embeddings/audemb_*/` | `audemb_` | `.npz` | | `embeddings/videmb_*/` | `videmb_` | `.npz` | | `embeddings/omniemb_*/` | `omniemb_` | `.npz` | The stem before the first `.` is always the `chunk_id`. --- ## Per-artifact details ### OCR (`ocr/ppocrvl15/`) [PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5) run per keyframe, then lightly cleaned. Each chunk contributes one `.kf_uni5s.ocr_ppocrvl15.jsonl` file whose lines are one frame each, in `tNNNNNN` order. Each line is a JSON object with these fields: | field | type | meaning | |-----------|--------|---------| | `frame` | str | the `tNNNNNN` second-offset label for the keyframe | | `raw` | str | the model's raw output, with bounding-box location tokens like `<|LOC_NNN|>` interleaved with the recognized text | | `cleaned` | str | the same string after light post-processing (the cleanup is conservative; for many frames `cleaned == raw`) | | `txt` | str | the recognized text only, with all `<|LOC_NNN|>` tokens stripped; this is what you want for downstream text indexing | ### ASR (`asr/qwen3asr1p7b/`) [Qwen3-ASR-1.7B](https://huggingface.co/Qwen/Qwen3-ASR-1.7B) run per chunk on the audio track. Each chunk contributes one `.asr_qwen3asr1p7b.json` with whole-chunk transcript text plus per-segment timings. Chunks with `has_audio=False` (10 of 943) have no JSON member. ### Embeddings (`embeddings/`) Every `.npz` has the same two-array schema regardless of model or modality: | key | shape | dtype | meaning | |----------------|-------------|---------|---------| | `keyframe_ids` | `(N,)` | `` for chunk-level | | `embeddings` | `(N, D)` | float32 | one row per `keyframe_ids` entry; `D` is the model's output dim | * **Keyframe-level** (`kf_uni5s-...`): `N == frame_count` from the keyframe catalog; `keyframe_ids` are the `tNNNNNN` strings. One row per keyframe. * **Chunk-level** (`audemb_*`, `videmb_*`, `omniemb_*`): `N == 1` for most backends; `keyframe_ids` carries the `chunk_id`. A couple of audio backends segment internally and emit one row per internal window instead (`audemb_glap` and `audemb_largerclapgeneral`); for those, `keyframe_ids` carries window labels. Embedding dims and model cards: | family | dir tag | dim | model card | |--------------|-----------------------------------|------|------------| | vision (kf) | `vizemb_qwen3vlemb2b` | 2048 | [Qwen/Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) | | vision (kf) | `vizemb_qwen3vlemb8b` | 4096 | [Qwen/Qwen3-VL-Embedding-8B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B) | | vision (kf) | `vizemb_siglip2so400m512` | 1152 | [google/siglip2-so400m-patch16-512](https://huggingface.co/google/siglip2-so400m-patch16-512) | | text (kf) | `txtemb_qwen3emb8b` over ppocrvl15| 4096 | [Qwen/Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) | | audio | `audemb_glap` | 1024 | [mispeech/GLAP](https://huggingface.co/mispeech/GLAP) | | audio | `audemb_jinav5omnismall` | 1024 | [jinaai/jina-embeddings-v5-omni-small](https://huggingface.co/jinaai/jina-embeddings-v5-omni-small) | | audio | `audemb_largerclapgeneral` | 512 | [laion/larger_clap_general](https://huggingface.co/laion/larger_clap_general) | | audio | `audemb_lcoomni7b` | 3584 | [LCO-Embedding/LCO-Embedding-Omni-7B](https://huggingface.co/LCO-Embedding/LCO-Embedding-Omni-7B) | | audio | `audemb_omniembed01` | 3584 | [Tevatron/OmniEmbed-v0.1](https://huggingface.co/Tevatron/OmniEmbed-v0.1) | | audio | `audemb_omninemotron3b` | 2048 | [nvidia/omni-embed-nemotron-3b](https://huggingface.co/nvidia/omni-embed-nemotron-3b) | | video | `videmb_qwen3vlemb8b` | 4096 | [Qwen/Qwen3-VL-Embedding-8B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B) | | video | `videmb_lcoomni7b` | 3584 | [LCO-Embedding/LCO-Embedding-Omni-7B](https://huggingface.co/LCO-Embedding/LCO-Embedding-Omni-7B) | | video | `videmb_omninemotron3b` | 2048 | [nvidia/omni-embed-nemotron-3b](https://huggingface.co/nvidia/omni-embed-nemotron-3b) | | omni | `omniemb_lcoomni7b` | 3584 | [LCO-Embedding/LCO-Embedding-Omni-7B](https://huggingface.co/LCO-Embedding/LCO-Embedding-Omni-7B) | | omni | `omniemb_omniembed01` | 3584 | [Tevatron/OmniEmbed-v0.1](https://huggingface.co/Tevatron/OmniEmbed-v0.1) | | omni | `omniemb_omninemotron3b` | 2048 | [nvidia/omni-embed-nemotron-3b](https://huggingface.co/nvidia/omni-embed-nemotron-3b) | Catalog columns (where the file exists): ``` chunk_id, shard_index, input_shard, source_member, video_id, chunk_index, embedding_dim, embedding_rows, artifact_id ``` --- ## Sharding and joins Chunk → shard assignment for `ocr/`, `asr/`, and the older keyframe-vision / OCR-text-embedding directories matches the core microvent release (5 shards). Newer embedding directories were processed in a different pass with 3 shards; the chunk-membership union is still the same 943 chunks, but the shard index will differ. If you need a single chunk-keyed table across everything, join on `chunk_id` (not on `shard_index`). --- ## Pulling the data locally Mirror the whole feature release or any subset with the `hf` CLI: ```bash # everything hf download hltcoe/microvent-features --repo-type dataset --local-dir ./microvent-features # just OCR + ASR (skip the embedding zoo) hf download hltcoe/microvent-features --repo-type dataset --local-dir ./microvent-features \ --include "ocr/*" "asr/*" # one specific embedding config hf download hltcoe/microvent-features --repo-type dataset --local-dir ./microvent-features \ --include "embeddings/kf_uni5s-vizemb_qwen3vlemb8b/*" ``` `--local-dir` writes plain files (no blob/symlink indirection); drop it to land in the standard `~/.cache/huggingface/hub/` layout instead. --- ## Loading with `datasets` Every feature directory is exposed as a separate config (so you only pay to download what you need): ```python import datasets ocr = datasets.load_dataset("hltcoe/microvent-features", "ocr_ppocrvl15", split="train", streaming=True) asr = datasets.load_dataset("hltcoe/microvent-features", "asr_qwen3asr1p7b", split="train", streaming=True) viz = datasets.load_dataset("hltcoe/microvent-features", "emb_kf_uni5s_vizemb_qwen3vlemb8b", split="train", streaming=True) ``` To join features with the core artifacts, point `webdataset` at both shard sets and let the chunk-id stem do the grouping: ```python import webdataset as wds ds = wds.WebDataset([ "videos/shard_{000000..000004}.tar", "embeddings/kf_uni5s-vizemb_qwen3vlemb8b/shard_{000000..000002}.tar", ]).decode() ``` --- ## License * HLTCOE-authored content (this README, the `catalog.csv` files, and all of the OCR / ASR / embedding outputs produced by HLTCOE-run pipelines) is released under Apache-2.0. * The upstream models used to generate these features (PaddleOCR-VL-1.5, Qwen3-ASR-1.7B, Qwen3-VL-Embedding-2B/8B, Qwen3-Embedding-8B, SigLIP2, GLAP, Jina-v5-omni-small, laion CLAP, Tevatron OmniEmbed, nvidia omni-embed-nemotron, LCO-Embedding-Omni) carry their own licenses; consult each model's card (linked in the embeddings table above) before redistributing the embedding vectors in a commercial setting. * The source video, audio, and keyframe content that these features describe lives in the **microvent** core release and is copyrighted by its respective original owners. Distributing these features alongside the source media is research / fair-use only.