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Datasets:
samarrik
/
talkingcelebs

Tasks:
Video Classification
Size:
100K<n<1M
Tags:
deepfakes
temporal-motion-signatures
video-analysis
talking-faces
celebrities
License:
Dataset card Files
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Files changed (6) hide show
  1. .gitignore +14 -0
  2. README.md +157 -3
  3. collect_dataset.py +212 -0
  4. pyproject.toml +10 -0
  5. sources.yaml +1695 -0
  6. uv.lock +182 -0
.gitignore ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Python-generated files
2
+ __pycache__/
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+ .python-version
4
+ *.py[oc]
5
+ build/
6
+ dist/
7
+ wheels/
8
+ *.egg-info
9
+
10
+ # Virtual environments
11
+ .venv
12
+
13
+ # Data
14
+ data/
README.md CHANGED
@@ -1,3 +1,157 @@
1
- ---
2
- license: mit
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: cc-by-nc-4.0
3
+ task_categories:
4
+ - video-classification
5
+ - computer-vision
6
+ tags:
7
+ - deepfakes
8
+ - temporal-motion-signatures
9
+ - video-analysis
10
+ - talking-faces
11
+ - celebrities
12
+ size_categories:
13
+ - 100K<n<1M
14
+ ---
15
+
16
+ # TalkingCelebs Dataset
17
+
18
+ ## Dataset Description
19
+
20
+ TalkingCelebs is a manually curated video dataset designed specifically for Talking Motion Signatures (TMS) analysis and deepfake detection research. The dataset contains 500 high-quality video clips from five prominent public figures, addressing the limitations of existing datasets by providing more clips per identity and longer duration clips with continuous speech.
21
+
22
+ ### Dataset Summary
23
+
24
+ - **Total clips**: 500 (100 clips per identity)
25
+ - **Identities**: 5 public figures (Barack Obama, Angela Merkel, Volodymyr Zelenskyy, Elon Musk, Emma Watson)
26
+ - **Clip duration**: 30 seconds each
27
+ - **Video sources**: 13-18 unique videos per person
28
+ - **Resolution**: 720p
29
+ - **Frame rate**: 25 FPS
30
+ - **Split**: 80% training (400 clips) / 20% test (100 clips)
31
+
32
+ ## Dataset Structure
33
+
34
+ ### Data Instances
35
+
36
+ Each data instance represents a 30-second video clip containing continuous speech from one of the five identities. The clips capture diverse speaking environments, camera angles, and contexts.
37
+
38
+ ### Data Fields
39
+ ```
40
+ TalkingCelebs/
41
+ ├── train/
42
+ │ ├── id00000/ # identity id
43
+ │ │ ├── ZNYmK19-d0U/ # 11-char YouTube video id
44
+ │ │ ... ├── 000.mp4/ # clip id within a video
45
+ │ │ ...
46
+ │ ...
47
+ └── test/
48
+ ├── id00000/
49
+ ...
50
+ ```
51
+
52
+ Each identity folder contains subdirectories organized by source video ID, with individual clips numbered sequentially (e.g., `000.mp4`, `001.mp4`, etc.).
53
+
54
+ ### Data Splits
55
+
56
+ | Split | Clips per Identity | Total Clips | Source Videos per Identity |
57
+ |-------|-------------------|-------------|--------------------------------|
58
+ | Train | 80 | 400 | 10-15 |
59
+ | Test | 20 | 100 | ≥3 (no overlap w/ train) |
60
+
61
+ ## Dataset Creation
62
+
63
+ ### Curation Rationale
64
+
65
+ TalkingCelebs was created to overcome limitations in existing datasets for TMS analysis:
66
+
67
+ 1. **Quantity**: Provides 100 clips per identity - a quantity rarely to be found for a single person with the following critirea
68
+ 2. **Duration**: 30-second clips
69
+ 3. **Quality**: All clips contain continuous speech throughout
70
+ 4. **Diversity**: Multiple source videos per identity capture various contexts
71
+
72
+ ### Source Data
73
+
74
+ - **Initial Data Collection**: YouTube videos of public speeches, interviews, and presentations
75
+ - **Data Processing**: Automated clip extraction using timestamps defined in `sources.yaml`
76
+ - **Quality Control**: Manual verification of speech continuity and video quality
77
+
78
+ ### Preprocessing
79
+ 1. Frame resclaing to 720p
80
+ 2. Standardization to 25 FPS
81
+
82
+ ## Considerations for Using the Data
83
+
84
+ ### Social Impact of Dataset
85
+
86
+ This dataset contains videos of real public figures and should be used responsibly:
87
+
88
+ - **Intended Use**: Research on deepfake detection, biometric authentication, and video analysis
89
+ - **Ethical Considerations**: Should not be used to create deepfakes or misleading content
90
+ - **Privacy**: All content is from publicly available sources
91
+
92
+ ### Discussion of Biases
93
+
94
+ - **Representation and Demographics**: Limited to 5 individuals, not representative of global population
95
+ - **Context**: Clips primarily from formal speaking contexts (interviews, speeches)
96
+ - **Language**: English-language content
97
+
98
+ ## Usage
99
+
100
+ To collect and process the TalkingCelebs dataset, follow these steps:
101
+
102
+ ### Prerequisites
103
+
104
+ 1. **Install uv package manager** (if not already installed):
105
+ ```bash
106
+ curl -LsSf https://astral.sh/uv/install.sh | sh
107
+ ```
108
+
109
+ 2. **Setup the environment**:
110
+ ```bash
111
+ uv sync
112
+ ```
113
+ This will install all dependencies specified in the `uv.lock` file.
114
+
115
+ 3. **Install FFmpeg** (if not already available):
116
+
117
+ **On Ubuntu/Debian:**
118
+ ```bash
119
+ sudo apt update && sudo apt install ffmpeg
120
+ ```
121
+
122
+ **On macOS:**
123
+ ```bash
124
+ brew install ffmpeg
125
+ ```
126
+
127
+ **On Windows:**
128
+ Download from [https://ffmpeg.org/download.html](https://ffmpeg.org/download.html) or use:
129
+ ```bash
130
+ winget install FFmpeg
131
+ ```
132
+
133
+ ### Dataset Collection
134
+
135
+ Run the dataset collection script:
136
+ ```bash
137
+ uv run collect_dataset.py
138
+ ```
139
+
140
+ This will download and process the video clips according to the specifications in `sources.yaml`, creating the structured dataset ready for use in TMS analysis and deepfake detection research.
141
+
142
+ ## Additional Information
143
+
144
+ ### Licensing Information
145
+
146
+ All video content in this dataset remains the intellectual property of the original content creators, the individuals depicted, and the platforms (YouTube) where the videos were originally published. The TalkingCelebs dataset provides only curated metadata and organizational structure for research purposes; no original video files are redistributed. Any use of the dataset must comply with YouTube’s Terms of Service and the rights of the individuals featured in the videos. The dataset annotations and structure are made available under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license for non-commercial research use only.
147
+
148
+ ### Citation Information
149
+
150
+ ```bibtex
151
+ @dataset{talkingcelebs2024,
152
+ title={TalkingCelebs: A Dataset for Temporal Speech Motion Signatures Analysis },
153
+ author={[Ivan Samarskyi]},
154
+ year={2025},
155
+ note={Dataset available at "https://huggingface.co/datasets/samarrik/talkingcelebs"}
156
+ }
157
+ ```
collect_dataset.py ADDED
@@ -0,0 +1,212 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import re
2
+ import subprocess
3
+ import time
4
+ from multiprocessing import Value
5
+ from pathlib import Path
6
+ from pydoc import cli
7
+ from typing import Any, Dict, List
8
+
9
+ import yaml
10
+ import yt_dlp
11
+ from pydantic import BaseModel, field_validator
12
+ from rich import print as rprint
13
+ from rich.progress import (
14
+ BarColumn,
15
+ Progress,
16
+ TextColumn,
17
+ TimeElapsedColumn,
18
+ TimeRemainingColumn,
19
+ )
20
+ from yt_dlp import download_range_func
21
+
22
+
23
+ class ClipData(BaseModel):
24
+ id: int
25
+ start: int
26
+ end: int
27
+
28
+ @field_validator("start", "end", mode="before")
29
+ @classmethod
30
+ def validate_and_transform(cls, v):
31
+ if not re.match(r"^\d{2}:\d{2}:\d{2}$", str(v)):
32
+ raise ValueError("Time must be in H:MM:SS or HH:MM:SS format")
33
+ # Transform to seconds
34
+ h, m, s = map(int, v.split(":"))
35
+ return h * 3600 + m * 60 + s
36
+
37
+
38
+ class VideoData(BaseModel):
39
+ id: str
40
+ clips: List[ClipData]
41
+
42
+ @field_validator("id", mode="before")
43
+ @classmethod
44
+ def validity_check(cls, v: Any) -> str:
45
+ if not isinstance(v, str):
46
+ raise ValueError("Video ID is not a string")
47
+
48
+ if len(v) != 11:
49
+ raise ValueError("Video ID is not 11-char")
50
+
51
+ return v
52
+
53
+
54
+ class IdentityData(BaseModel):
55
+ id: int
56
+ name: str
57
+ videos: List[VideoData]
58
+
59
+
60
+ class SubdatasetData(BaseModel):
61
+ name: str
62
+ identities: List[IdentityData]
63
+
64
+
65
+ class SourcesData(BaseModel):
66
+ subdatasets: List[SubdatasetData]
67
+
68
+
69
+ def collect_dataset(
70
+ sources: SourcesData,
71
+ data_dir: Path = Path("./data"),
72
+ dataset_name: str = "TalkingCelebs",
73
+ ):
74
+ def download_clip(
75
+ video_id: str,
76
+ output: str,
77
+ start: int,
78
+ end: int,
79
+ max_retries: int = 3,
80
+ retry_delay: int = 0.5,
81
+ ):
82
+ """Downloads a clip from a video."""
83
+ for attempt in range(max_retries):
84
+ try:
85
+ ydl_opts = {
86
+ "outtmpl": str(
87
+ output
88
+ ), # output should already have the desired suffix
89
+ "download_ranges": download_range_func(None, [(start, end)]),
90
+ "force_keyframes_at_cuts": True,
91
+ "format": "bestvideo[height<=480]+bestaudio[ext=m4a]/mp4",
92
+ "quiet": True,
93
+ "merge_output_format": "mp4",
94
+ # Using android client if defualt one stops working
95
+ # "extractor_args": {
96
+ # "youtube": {
97
+ # "player_client": [
98
+ # "android"
99
+ # ],
100
+ # }
101
+ # },
102
+ }
103
+
104
+ with yt_dlp.YoutubeDL(ydl_opts) as ydl:
105
+ ydl.download(f"https://www.youtube.com/watch?v={video_id}")
106
+ break
107
+ except Exception as e:
108
+ if attempt == max_retries - 1:
109
+ rprint(
110
+ f"[red]Failed to download video {video_id} after {max_retries} attempts: {e}[/]"
111
+ )
112
+ rprint(f"[red]Attempt {attempt + 1} failed: {e}[/]")
113
+ time.sleep(retry_delay)
114
+
115
+ def preprocess_clip(clip_path: Path):
116
+ """Preprocess a video clip to 720p resolution and 25 FPS while maintaining duration, in-place."""
117
+ try:
118
+ # Ensure the parent directory exists
119
+ clip_path.parent.mkdir(parents=True, exist_ok=True)
120
+
121
+ # Use a temporary file for output, then replace the original
122
+ # Ensure the temp file has a proper .mp4 extension for FFmpeg muxer detection
123
+ tmp_path = clip_path.with_suffix(".tmp.mp4")
124
+
125
+ cmd = [
126
+ "ffmpeg",
127
+ "-i",
128
+ str(clip_path),
129
+ "-vf",
130
+ "scale=-2:720",
131
+ "-r",
132
+ "25",
133
+ "-c:v",
134
+ "libx264",
135
+ "-c:a",
136
+ "aac",
137
+ "-preset",
138
+ "fast",
139
+ "-y",
140
+ str(tmp_path),
141
+ ]
142
+
143
+ result = subprocess.run(cmd, capture_output=True, text=True)
144
+
145
+ if result.returncode != 0:
146
+ rprint(f"[red]Failed to preprocess {clip_path}: {result.stderr}[/]")
147
+ return
148
+
149
+ # Replace the original file with the processed one
150
+ if tmp_path.exists():
151
+ clip_path.unlink(missing_ok=True)
152
+ tmp_path.rename(clip_path)
153
+
154
+ except Exception as e:
155
+ rprint(f"[red]Error preprocessing {clip_path}: {e}[/]")
156
+
157
+ dataset_dir = data_dir / dataset_name
158
+
159
+ # First, count total number of clips for progress bar
160
+ total_clips = 0
161
+ for subdataset in sources.subdatasets:
162
+ for identity in subdataset.identities:
163
+ for video in identity.videos:
164
+ total_clips += len(video.clips)
165
+
166
+ with Progress(
167
+ TextColumn("[progress.description]{task.description}"),
168
+ BarColumn(),
169
+ "[progress.percentage]{task.percentage:>3.0f}%",
170
+ "•",
171
+ TimeElapsedColumn(),
172
+ "•",
173
+ TimeRemainingColumn(),
174
+ transient=False,
175
+ ) as progress:
176
+ task = progress.add_task("[cyan]Collecting the dataset...", total=total_clips)
177
+ for subdataset in sources.subdatasets:
178
+ subdataset_dir = dataset_dir / subdataset.name
179
+
180
+ for identity in subdataset.identities:
181
+ identity_dir = subdataset_dir / f"id{identity.id:05d}"
182
+
183
+ for video in identity.videos:
184
+ video_dir = identity_dir / video.id
185
+
186
+ for clip in video.clips:
187
+ clip_path = video_dir / f"{clip.id:03d}.mp4"
188
+
189
+ # Download the clip first
190
+ download_clip(video.id, clip_path, clip.start, clip.end)
191
+
192
+ # Preprocess the clip to 720p and 25 FPS
193
+ preprocess_clip(clip_path)
194
+
195
+ progress.update(task, advance=1)
196
+
197
+
198
+ def load_sources(path: Path = Path("sources.yaml")) -> Dict:
199
+ # Load the data
200
+ with open(path, "r") as file:
201
+ data = yaml.safe_load(file)
202
+
203
+ # Validate and transform the data into sources
204
+ rprint("[cyan]Verification of videos in the dataset...[/]")
205
+ sources = SourcesData(**data)
206
+
207
+ return sources
208
+
209
+
210
+ if __name__ == "__main__":
211
+ sources = load_sources()
212
+ collect_dataset(sources)
pyproject.toml ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ [project]
2
+ name = "talkingcelebs"
3
+ version = "0.1.0"
4
+ readme = "README.md"
5
+ requires-python = ">=3.11"
6
+ dependencies = [
7
+ "pydantic>=2.11.7",
8
+ "rich>=14.0.0",
9
+ "yt-dlp>=2025.7.21",
10
+ ]
sources.yaml ADDED
@@ -0,0 +1,1695 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ subdatasets:
2
+ - name: train
3
+ identities:
4
+ - id: 0
5
+ name: "Obama"
6
+ videos:
7
+ - id: "ZNYmK19-d0U"
8
+ clips:
9
+ - id: 0
10
+ start: "00:00:30"
11
+ end: "00:01:00"
12
+ - id: 1
13
+ start: "00:01:00"
14
+ end: "00:01:30"
15
+ - id: 2
16
+ start: "00:01:30"
17
+ end: "00:02:00"
18
+ - id: 3
19
+ start: "00:02:00"
20
+ end: "00:02:30"
21
+ - id: 4
22
+ start: "00:02:30"
23
+ end: "00:03:00"
24
+ - id: 5
25
+ start: "00:03:00"
26
+ end: "00:03:30"
27
+ - id: 6
28
+ start: "00:03:30"
29
+ end: "00:04:00"
30
+ - id: 7
31
+ start: "00:04:00"
32
+ end: "00:04:30"
33
+ - id: 8
34
+ start: "00:04:30"
35
+ end: "00:05:00"
36
+ - id: 9
37
+ start: "00:05:00"
38
+ end: "00:05:30"
39
+ - id: "sUmNkhmQWW4"
40
+ clips:
41
+ - id: 0
42
+ start: "00:01:30"
43
+ end: "00:02:00"
44
+ - id: 1
45
+ start: "00:02:00"
46
+ end: "00:02:30"
47
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