Datasets:
video_Name stringlengths 11 11 | Time_start float64 0 297 | Time_Stop float64 3 300 |
|---|---|---|
W6lCUO2cV4s | 81 | 87.967 |
_guMbBQbs1A | 158.266 | 162.3 |
I1RBJhJI_HU | 270 | 275.533 |
GaA2KdZStM8 | 258.867 | 262.233 |
Bv7udsazUs8 | 146.58 | 149.983 |
FEBa6vxjrHk | 106.083 | 119.375 |
ZyTGFziTw7k | 102.602 | 111.152 |
jwYytjbn4CQ | 41.88 | 48.44 |
8FOPINv55uc | 21.533 | 24.533 |
FH3SbZjWWBY | 217.3 | 226.333 |
HzuiYdtc0Oo | 109.45 | 120 |
O85FlilqIi4 | 53.787 | 59.993 |
UVYk_MkBp38 | 60.72 | 65.08 |
QlcZohxNRUE | 162.371 | 166.541 |
OrtfWSu5I6M | 183.153 | 186.153 |
bVW9ElGQTq8 | 13.067 | 16.067 |
CWZgt8a7a-k | 152.185 | 158.625 |
HAMSlg9Zz5Q | 138.166 | 145.1 |
7xrvS9_Nnf8 | 54.432 | 57.432 |
QJoTn5q1RLQ | 112.412 | 117.817 |
lhkCIbIp8nU | 131.6 | 134.6 |
fJdGcTFvdZY | 245.212 | 256.256 |
WVo2UpVFECE | 256.867 | 262.2 |
vGjudwFCQjo | 46.716 | 49.716 |
r6PxOY6XGKk | 137.68 | 141.08 |
AfxtZfN6OX8 | 226.359 | 233.133 |
e-j9FOO-i84 | 51.706 | 56.044 |
iDG4QXb5Yqk | 227.64 | 234.32 |
TtPEYakuMFw | 280.183 | 283.183 |
sTX6FlaEsqw | 150.217 | 155.822 |
TF_BlESEAsQ | 30 | 34.533 |
1ULE8PeqG0Y | 110.043 | 113.18 |
RbG63d8VcI4 | 90 | 93.1 |
o6dOxsVqZ8M | 120.02 | 129.83 |
-m7ckPQlQjA | 77.744 | 89.99 |
bdkiJT24us0 | 222.889 | 230.229 |
wg6cfsnmqyg | 290.724 | 298.532 |
1JlSuyggHrs | 185.815 | 198.346 |
0YgL-zg41_Y | 189.222 | 192.893 |
dar59aSA7FU | 90.883 | 101.059 |
4AQnh4QNmY4 | 30.03 | 37.971 |
J3IdvbbAXKs | 136.773 | 139.773 |
tSZnQKGlEjQ | 152.986 | 156.456 |
FtoHJAfVLws | 69.403 | 74.074 |
IycpOCdzTIc | 69.82 | 75.617 |
PAGV1NxveKU | 86.375 | 90 |
9zT885ld-WQ | 200.617 | 205.163 |
BeTwbxsszSU | 111.348 | 114.348 |
6SIAMj7ii_4 | 150.017 | 156.456 |
9EE5EJflcoI | 82.619 | 85.619 |
ejAxBy51uxA | 270.4 | 273.4 |
uSajZFDObHs | 66.099 | 70.737 |
TcDihLO4cNo | 8.267 | 13.167 |
WhjghdZRET0 | 160.961 | 173.606 |
s_9fQqDgvms | 291.5 | 299.967 |
FH3SbZjWWBY | 140.1 | 149.067 |
3hUpo5FGZkk | 228.067 | 238.567 |
1kKWfLK6RXU | 210.01 | 214.547 |
-l6idQEaSAA | 111.178 | 114.648 |
qaPBo5yKUO4 | 45.867 | 56.967 |
9t6s3Z3k0j0 | 4.238 | 8.709 |
kUM0lYIqMZk | 152.552 | 161.128 |
3Nlsd0TcJeI | 255.455 | 264.397 |
XaqedUL5XdA | 288.989 | 296.096 |
exNSSJGgWtk | 42.809 | 49.783 |
olnTE7ZrB_s | 276.26 | 280.28 |
7PKx3kS7f4A | 282.32 | 289.04 |
PFeGNcWyLMc | 270.303 | 280.313 |
zzHSzRqjELY | 192.833 | 202.292 |
kkHAIEwOv0o | 120.12 | 134.468 |
LwfYxoU7sVM | 203.94 | 206.94 |
2okFvv43cqU | 54.255 | 59.993 |
6Em246vApBM | 81.582 | 89.965 |
h_cRskK3mr8 | 247.314 | 255.088 |
V1r-dZTlNmw | 61.662 | 74.908 |
hyfwM_vtXlk | 19.119 | 29.997 |
IdyJDI52DUE | 183.92 | 190.48 |
Xv9vT3QcRIU | 120 | 132.92 |
c_RIKaXq9ro | 192.733 | 195.733 |
8U0tR3J4ac0 | 113.547 | 119.252 |
vhMt-KYSlDA | 215 | 229.24 |
O5V6_pHoGnw | 240.157 | 243.285 |
Jp38WsuW74E | 1.8 | 10.067 |
QTtH_od4vMQ | 74.641 | 78.612 |
mFm3yA1nslE | 17.601 | 22.105 |
6aLhO8HDCac | 198.5 | 209.867 |
HeWRfrxUmPA | 55.333 | 59.499 |
JaAtPifTz2I | 195.662 | 209.943 |
rSdreNjiYvU | 217.417 | 224.191 |
foxfR3pCe9M | 191.066 | 194.945 |
h_jPlA2qLX4 | 155.733 | 167.483 |
VvpvSWsdHws | 55.856 | 59.993 |
nMLK15f2Avg | 219.01 | 225.058 |
Dq6-USM0fLY | 115.6 | 119.9 |
LUbfTAgP1vI | 20.633 | 29.5 |
gOISS5aMnHs | 215.549 | 225.726 |
F5zG0G_Fu3g | 96.066 | 99.066 |
Fy8-F1Q1a54 | 4.104 | 9.877 |
CIS0osvIeEk | 137.237 | 143.31 |
gGKc-pmvx7s | 167.433 | 173.233 |
WildVid-LIP: In-The-Wild Temporal Anchors for Visual Speech Recognition
WildVid-LIP is a large-scale, open-source dataset mapping over 100,000 curated temporal segments from unconstrained, real-world YouTube videos. It provides precise timestamp anchors optimized for training Visual Speech Recognition (VSR / Lip-Reading), audio-visual synchronization, and multimodal self-supervised models.
Instead of distributing heavy, monolithic video files—which introduces platform friction and restrictive redistribution licensing—WildVid-LIP distributes high-fidelity metadata anchors. This allows researchers to stream or crop localized face tracks directly from source media while maintaining absolute compliance with digital copyright frameworks.
Key Features
- Real-World Complexity: Unlike laboratory-controlled data (e.g., GRID, TCD-TIMIT), these anchors capture dynamic variations in head pose, natural lighting transitions, speaker diversity, compression artifacts, and occlusions.
- Scale-Optimized: Expanded to 100,000+ precise windows, making it ideal for deep spatial-temporal networks (3D-CNNs, Vision Transformers, and Conformers).
- Self-Supervised & Multimodal Ready: Designed intentionally without rigid text baselines, enabling pipelines to dynamically generate pseudo-labels using state-of-the-art Automated Speech Recognition (ASK) models like Whisper or Wav2Vec2 during ingestion.
Dataset Structure
The corpus is contained within a single manifest mapping (train.csv). The schema tracks highly accurate temporal bounds across active speech windows:
| Column Name | Data Type | Description |
|---|---|---|
video_name |
string |
The unique 11-character YouTube video alphanumeric identifier. |
time_start |
float |
The precise timestamp (in seconds) where the targeted visual speech sequence begins. |
time_end |
float |
The precise timestamp (in seconds) where the targeted visual speech sequence terminates. |
Sample Schema Entry
video_name,time_start,time_end
dQw4w9WgXcQ,42.15,44.50
Future Updates
WildVid-Lip would be regularly updated with transcripts, download scripts, preprocessed videos for direct downloads and much more!
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