README.md

---
license: cc-by-nc-4.0
language:
- en
task_categories:
- feature-extraction
- audio-classification
- video-classification
tags:
- humor
- stand-up-comedy
- multimodal
- alignment
- laughter-detection
- pose-estimation
- shot-detection
- topic-modeling
pretty_name: "TIC-TALK (Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics)"
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: unified_humor_dataset.parquet
---

# TIC-TALK — Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics

Time-aligned text, audio, and vision features from 90 stand-up specials
(2015–2024). Show identities are anonymized (`SHOW_XXXX`); no source audio,
video, or transcripts are distributed.

Dataset contains:

| | |
|---|---|
| Shows | 90 |
| Total runtime | ≈ 94 h (mean 63 min / show) |
| Rows (60 s blocks) | 5 416 |
| Topics (BERTopic) | 24 |
| Video frames (1 fps) | 322 973 (22 % full-body) |
| Laughter coverage (mean) | 17.8 % of block duration, ≈ 1.2 events / 10 s |
| Embedding dim | 384 |
| File | single Parquet, 44 MB (zstd) |

## Loading

Single split named `train`

```python
from datasets import load_dataset
ds = load_dataset("ENC-PSL/TIC-TALK", split="train")
```

## Schema (1 row = 1 block)

| field | type | description |
|---|---|---|
| `show_id` | string | `SHOW_0001`…`SHOW_0090` |
| `block_id` | int32 | block index within the show |
| `start`, `end` | float32 | block window, seconds from show start |
| `topic_id` | int32 | BERTopic id (24 topics, `-1` = outlier) |
| `embedding` | float32[384] | sentence-BERT embedding of block text |
| `laugh_events` | list&lt;struct&gt; | `start`, `end`, `type`, `confidence` |
| `pose_keypoints` | list&lt;struct&gt; | `time`, `has_detection`, `bbox_*`, `keypoints[17][2]` (COCO-17 order, `[x,y]` in pixels) |
| `shot_events` | list&lt;struct&gt; | `time`, `label`, `class_id`, `score` |
| `show_duration_sec`, `show_n_blocks` | float32 / int32 | show-level |
| `show_has_laugh` / `_pose` / `_shots` | bool | modality flags |
| `topic_show_id` | string | per-show topic model id |

Shot labels: `full_shot`, `medium_close-up`, `medium_long_shot`,
`medium_shot`, `other_angles`, `other` (6 classes, average F1 = 0.91).

All timestamps are in seconds on the same absolute axis (from show start).
Block assignment is by event start: `pose_keypoints[i].time`,
`shot_events[i].time`, `laugh_events[i].start` ∈ `[start, end)`; a
`laugh_events[i].end` may extend ≤ 0.8 s past `end` (Whisper-AT stride).

## Example

```python
from datasets import load_dataset
ds = load_dataset("ENC-PSL/TIC-TALK", split="train")

def pose_near(row, t, window=1.0):
    return [p for p in row["pose_keypoints"]
            if p["has_detection"] and abs(p["time"] - t) <= window]

for row in ds:
    for ev in row["laugh_events"]:
        poses_at_onset = pose_near(row, ev["start"])
        # ... features at laughter onset
```

## Provenance

Derived features only (no source media is redistributed).

- **Text / topics**: subtitle-derived 60 s blocks, sentence-BERT
  `sentence-transformers/all-MiniLM-L6-v2` (384-dim), BERTopic with UMAP
  (`n_neighbors=15`, `n_components=5`, `min_dist=0.1`) + HDBSCAN
  (`min_cluster_size=15`, `min_samples=5`), 24 topics.
- **Laughter**: Whisper-AT (AudioSet audio tagging), 0.8 s stride,
  high-recall configuration; contiguous positive windows merged into events.
- **Pose**: YOLOv8s-pose (pretrained), 17-keypoint output in canonical COCO
  order, 1 fps, restricted to full-body / frontal frames.
- **Shot**: YOLOv8-cls fine-tuned on 594 manually annotated frames
  (val on 128 held-out frames, average F1 = 0.91), 1 fps.

## License

CC-BY-NC-4.0 for research use. Underlying audiovisual material remains the
property of its rights holders; only derived features are distributed.

## How to cite

Paper accepted for the 2nd Workshop on Computational Humor (CHum 2026) @ACL 2026.

Preprint :

```bibtex
@misc{zribi2026timingstandupcomedytext,
      title={Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics (TIC-TALK): Pipeline and Database for the Multimodal Study of Comedic Timing}, 
      author={Yaelle Zribi and Florian Cafiero and Vincent Lépinay and Chahan Vidal-Gorène},
      year={2026},
      eprint={2603.21803},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.21803}, 
}
```

## Acknowledgements

This study was conducted as part of the DH master’s program at École nationale des chartes–PSL (Yaelle Zribi master thesis) and with the support of the PSL Research University’s Major Research Program CultureLab, implemented by the ANR (reference ANR-10-IDEX-0001).