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Super-squash branch 'main' using huggingface_hub

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LICENSE ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ YouTube_Talking is licensed under the Creative Commons
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+ Attribution-NonCommercial 4.0 International License (CC-BY-NC-4.0).
3
+
4
+ Full license text: https://creativecommons.org/licenses/by-nc/4.0/legalcode
5
+
6
+ This release redistributes NO source videos, audio, video frames, or derived
7
+ media. It provides only: source YouTube URLs, TalkNet active-speaker results
8
+ (pywork), data splits, and a download script. The source videos remain subject
9
+ to their original YouTube terms of service and the rights of their owners.
10
+
11
+ Provided for non-commercial research only. Do not use for commercial purposes
12
+ and do not attempt to re-identify speakers.
README.md ADDED
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+ ---
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+ pretty_name: YouTube_Talking
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+ license: cc-by-nc-4.0
4
+ language:
5
+ - en
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+ task_categories:
7
+ - other
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+ tags:
9
+ - co-speech
10
+ - talking-head
11
+ - facial-animation
12
+ - body-motion
13
+ - FLAME
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+ - SMPL-X
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+ - in-the-wild
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+ size_categories:
17
+ - 1K<n<10K
18
+ ---
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+
20
+ # YouTube_Talking
21
+
22
+ YouTube_Talking is a large in-the-wild conversational dataset: **2,983 English
23
+ single-speaker talking-head videos** (interviews + speeches) with derived
24
+ multimodal annotations — speech audio, FLAME face motion, SMPL-X body, HaMeR
25
+ hands, active-speaker segments, and motion tokens. It is one of the datasets used
26
+ by [ViBES](https://github.com/Juzezhang/ViBES) (CVPR 2026).
27
+
28
+ > ⚠️ **Research use only.** The source **videos are copyrighted YouTube content
29
+ > and are NOT redistributed** — you download them yourself from the URL list. We
30
+ > *do* ship the **expensive model-output annotations** (TalkNet active-speaker,
31
+ > 4D-Humans / MHR body, FLAME face, fitted SMPL-X, HaMeR hands) so you can skip
32
+ > hundreds of GPU-hours of re-processing. Cheap/deterministic steps (audio
33
+ > extraction, tokenization, HF packing) are reproduced with the shipped scripts.
34
+ > License: CC-BY-NC-4.0.
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+
36
+ ## Contents
37
+
38
+ ```
39
+ YouTube_Talking/
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+ ├── video_urls.csv (2,983 rows: id,url,language,type,body_parts,num_people,duration_min)
41
+ ├── download_youtube_talking.py (downloader for the source videos)
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+ ├── talknet_pywork.tar (TalkNet active-speaker results, pywork/ per video)
43
+ ├── 4d_humans_results.tar (4D-Humans body recovery, per-video .pkl)
44
+ ├── mhr_results.tar (MHR body, per-frame .npz)
45
+ ├── smplxflame_25.tar (fitted SMPL-X body+face @25fps)
46
+ ├── FLAME_coeffs_25.tar (FLAME face coefficients @25fps)
47
+ ├── hamer_results.tar (HaMeR hand recovery, per-frame .pkl)
48
+ ├── train_processed.txt (346 fully-processed clip ids)
49
+ ├── val_processed.txt / test_processed.txt (9 / 11)
50
+ ├── train_unprocessed.txt / val_unprocessed.txt / test_unprocessed.txt
51
+ ├── README.md
52
+ └── LICENSE
53
+ ```
54
+
55
+ Each `*.tar` extracts to its directory in place (`tar -xf <name>.tar`). The
56
+ `talknet_pywork.tar` gives `talknet_output/<id>/pywork/{scores,tracks,faces,scene}.pckl`.
57
+ The `*_processed.txt` splits list the clips with full multimodal processing.
58
+
59
+ > **Not shipped (regenerate locally):** the videos themselves, the extracted
60
+ > audio (`audios/`, `audios_original/`), speaking-segment crops, transcripts, and
61
+ > the motion/audio tokens — all cheap to recompute from the downloaded videos +
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+ > shipped annotations using the scripts below.
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+
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+ ## How to use
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+
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+ 1. **Download videos** — `python download_youtube_talking.py --url_csv video_urls.csv --output_dir videos`
67
+ 2. **Extract the shipped annotations** — `for f in *.tar; do tar -xf "$f"; done`
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+ 3. **Reconstruct audio / tokens / HF dataset** from the videos + annotations —
69
+ follow the step-by-step recipe in the ViBES repo:
70
+ [`docs/1-data/youtube_talking.md`](https://github.com/Juzezhang/ViBES/blob/main/docs/1-data/youtube_talking.md).
71
+
72
+ All preprocessing scripts live in the ViBES repo under `preprocess/`.
73
+
74
+ ## License
75
+
76
+ [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/) — non-commercial
77
+ research use only. Source videos remain under their original YouTube terms; this
78
+ release redistributes none of them.
79
+
80
+ ## Citation
81
+
82
+ ```bibtex
83
+ @inproceedings{zhang2026vibes,
84
+ title={ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body},
85
+ author={Juze Zhang and Changan Chen and Xin Chen and Heng Yu and Tiange Xiang and Ali Sartaz Khan and Shrinidhi Kowshika Lakshmikanth and Ehsan Adeli},
86
+ booktitle={CVPR},
87
+ year={2026},
88
+ }
89
+ ```
download_youtube_talking.py ADDED
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+ #!/usr/bin/env python3
2
+ """Download the source videos for the YouTube_Talking dataset.
3
+
4
+ The YouTube_Talking annotations (FLAME / SMPL-X / HaMeR / speaking segments /
5
+ tokens) are released openly, but the **source videos are not redistributed**.
6
+ This script downloads them from YouTube using the shipped ``video_urls.csv``
7
+ (one row per clip: ``id,url,...``). Videos that have since been removed or made
8
+ private are skipped and logged.
9
+
10
+ Requires `yt-dlp` (``pip install yt-dlp``) and ``ffmpeg`` on PATH.
11
+
12
+ Usage:
13
+ python download_youtube_talking.py \
14
+ --url_csv video_urls.csv \
15
+ --output_dir <YOUTUBE_TALKING_ROOT>/videos \
16
+ [--workers 4]
17
+
18
+ Each video is saved as ``<output_dir>/<id>.mp4``. Re-running skips ids that
19
+ already exist, so the download is resumable.
20
+ """
21
+
22
+ import argparse
23
+ import csv
24
+ import os
25
+ import subprocess
26
+ from concurrent.futures import ThreadPoolExecutor, as_completed
27
+
28
+
29
+ def download_one(row, output_dir):
30
+ vid = row["id"]
31
+ url = row["url"]
32
+ out_path = os.path.join(output_dir, f"{vid}.mp4")
33
+ if os.path.exists(out_path):
34
+ return vid, "skip-exists"
35
+ cmd = [
36
+ "yt-dlp",
37
+ "-f", "bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best",
38
+ "--merge-output-format", "mp4",
39
+ "-o", out_path,
40
+ "--no-playlist",
41
+ "--quiet", "--no-warnings",
42
+ url,
43
+ ]
44
+ try:
45
+ subprocess.run(cmd, check=True, capture_output=True, timeout=1800)
46
+ return vid, "ok" if os.path.exists(out_path) else "no-file"
47
+ except subprocess.CalledProcessError:
48
+ return vid, "failed-unavailable"
49
+ except subprocess.TimeoutExpired:
50
+ return vid, "failed-timeout"
51
+ except Exception as e: # noqa: BLE001
52
+ return vid, f"failed-{type(e).__name__}"
53
+
54
+
55
+ def main():
56
+ ap = argparse.ArgumentParser()
57
+ ap.add_argument("--url_csv", required=True, help="video_urls.csv (id,url,...)")
58
+ ap.add_argument("--output_dir", required=True, help="where to save <id>.mp4")
59
+ ap.add_argument("--workers", type=int, default=4, help="parallel downloads")
60
+ args = ap.parse_args()
61
+
62
+ os.makedirs(args.output_dir, exist_ok=True)
63
+ rows = list(csv.DictReader(open(args.url_csv)))
64
+ print(f"{len(rows)} videos to fetch -> {args.output_dir}")
65
+
66
+ results = {}
67
+ with ThreadPoolExecutor(max_workers=args.workers) as ex:
68
+ futs = [ex.submit(download_one, r, args.output_dir) for r in rows]
69
+ for i, fut in enumerate(as_completed(futs), 1):
70
+ vid, status = fut.result()
71
+ results[status] = results.get(status, 0) + 1
72
+ if i % 50 == 0 or status.startswith("failed"):
73
+ print(f"[{i}/{len(rows)}] {vid}: {status}")
74
+
75
+ print("\n=== summary ===")
76
+ for status, n in sorted(results.items()):
77
+ print(f" {status}: {n}")
78
+
79
+ # Write the list of ids that are missing after the run (for a retry pass).
80
+ missing = [
81
+ r["id"] for r in rows
82
+ if not os.path.exists(os.path.join(args.output_dir, f"{r['id']}.mp4"))
83
+ ]
84
+ if missing:
85
+ miss_path = os.path.join(args.output_dir, "_missing_ids.txt")
86
+ with open(miss_path, "w") as f:
87
+ f.write("\n".join(missing) + "\n")
88
+ print(f"\n{len(missing)} videos unavailable; ids written to {miss_path}")
89
+
90
+
91
+ if __name__ == "__main__":
92
+ main()
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