README.md

---
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

```
<chunk_id>.<artifact_tag>.<extension>
```

| 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_<model>`                             | `.npz` |
| `embeddings/videmb_*/`                                   | `videmb_<model>`                             | `.npz` |
| `embeddings/omniemb_*/`                                  | `omniemb_<model>`                            | `.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
`<chunk_id>.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
`<chunk_id>.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,)`      | `<U*`   | row labels: `tNNNNNN` for keyframe-level embeddings, `<chunk_id>` 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.