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wlasl_reduced

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train · 334 rows

filepath stringlengths 18 29	gloss stringclasses 35 values	fps float64 24 24	duration float64 0.83 5.96	num_frames int64 20 143	video_width int64 288 1.92k	video_height int64 180 1.08k	bbox_x float64 0.03 0.44	bbox_y float64 0 0.16	bbox_w float64 0.3 0.89	bbox_h float64 0.84 1
videos/candy/65300.mp4	candy	24	2.67	64	656	370	0.297256	0.043243	0.414634	0.956757
videos/computer/12320.mp4	computer	24	3.42	82	640	480	0.115625	0.039583	0.767188	0.960417
videos/help/27206.mp4	help	24	2.38	57	736	414	0.286685	0.038647	0.42663	0.961353
videos/help/27216.mp4	help	24	1.75	42	288	192	0.232639	0.026042	0.520833	0.973958
videos/bowling/07393.mp4	bowling	24	2.04	49	1,920	1,080	0.222917	0.043519	0.652083	0.956481
videos/i/28789.mp4	i	24	3.83	92	640	480	0.253125	0.0625	0.640625	0.9375
videos/computer/12318.mp4	computer	24	1.5	36	1,920	1,080	0.190104	0.041667	0.68125	0.958333
videos/cool/13200.mp4	cool	24	0.96	23	1,920	1,080	0.305208	0.087963	0.509375	0.912037
videos/thank you/69502.mp4	thank you	24	2.75	66	1,280	720	0.295313	0.058333	0.404688	0.941667
videos/no/38532.mp4	no	24	1.46	35	288	192	0.256944	0.114583	0.454861	0.885417
videos/cool/69281.mp4	cool	24	2.58	62	640	480	0.23125	0.01875	0.714063	0.98125
videos/thin/57936.mp4	thin	24	1.21	29	1,920	1,080	0.4375	0.069444	0.39375	0.925926
videos/wait/62113.mp4	wait	24	2.92	70	720	400	0.280556	0.13	0.494444	0.87
videos/accident/00625.mp4	accident	24	1.42	34	1,920	1,080	0.351563	0.046296	0.409375	0.953704
videos/candy/08921.mp4	candy	24	1.92	46	640	480	0.103125	0.05	0.790625	0.95
videos/drink/17722.mp4	drink	24	1.58	38	288	192	0.229167	0.098958	0.496528	0.901042
videos/no/38529.mp4	no	24	3.58	86	640	480	0.1375	0	0.646875	1
videos/bad/04712.mp4	bad	24	0.83	20	1,920	1,080	0.384896	0.062037	0.467708	0.937963
videos/help/65891.mp4	help	24	2.46	59	656	370	0.286585	0.037838	0.419207	0.962162
videos/thank you/66598.mp4	thank you	24	2.79	67	656	370	0.27439	0.083784	0.425305	0.916216
videos/wait/66740.mp4	wait	24	2.67	64	656	370	0.295732	0.081081	0.492378	0.918919
videos/thanksgiving/57638.mp4	thanksgiving	24	1.92	46	288	192	0.326389	0.098958	0.5	0.901042
videos/good/25072.mp4	good	24	1.67	40	288	192	0.274306	0.052083	0.475694	0.947917
videos/thanksgiving/57630.mp4	thanksgiving	24	2.25	54	1,280	720	0.314063	0.077778	0.328906	0.922222
videos/cool/13202.mp4	cool	24	2.54	61	640	480	0.178125	0.0625	0.617188	0.9375
videos/pizza/42961.mp4	pizza	24	1.67	40	1,920	1,080	0.3	0.073148	0.558333	0.924074
videos/cousin/13642.mp4	cousin	24	1.29	31	288	192	0.1875	0.046875	0.583333	0.953125
videos/thank you/57655.mp4	thank you	24	2.5	60	1,920	1,080	0.363021	0.036111	0.429688	0.951852
videos/bed/05633.mp4	bed	24	1.12	27	1,920	1,080	0.363021	0.075926	0.493229	0.912963
videos/bowling/07394.mp4	bowling	24	3.25	78	640	480	0.165625	0.047917	0.704688	0.952083
videos/hospital/28033.mp4	hospital	24	2.75	66	320	240	0.234375	0	0.496875	1
videos/cousin/13631.mp4	cousin	24	2.38	57	1,280	720	0.361719	0.093056	0.307031	0.906944
videos/now/69413.mp4	now	24	2	48	1,280	720	0.265625	0.055556	0.479688	0.944444
videos/before/05732.mp4	before	24	2.88	69	640	480	0.160938	0.052083	0.664063	0.947917
videos/tall/56848.mp4	tall	24	1.17	28	288	192	0.225694	0.052083	0.506944	0.947917
videos/son/66532.mp4	son	24	2.46	59	656	370	0.242378	0.043243	0.466463	0.956757
videos/computer/12327.mp4	computer	24	1.54	37	288	192	0.21875	0	0.538194	1
videos/thin/57937.mp4	thin	24	1.04	25	1,920	1,080	0.385417	0.068519	0.463021	0.927778
videos/tall/56838.mp4	tall	24	3.21	77	736	414	0.247283	0.002415	0.467391	0.997585
videos/help/65890.mp4	help	24	2.46	59	656	370	0.277439	0.037838	0.431402	0.962162
videos/accident/00623.mp4	accident	24	4.17	100	320	240	0.16875	0.025	0.68125	0.975
videos/no/69411.mp4	no	24	2.25	54	1,280	720	0.214063	0.059722	0.469531	0.940278
videos/pizza/42959.mp4	pizza	24	3.42	82	1,280	720	0.274219	0.081944	0.365625	0.918056
videos/thin/57935.mp4	thin	24	3	72	1,280	720	0.341406	0.079167	0.303125	0.920833
videos/man/34733.mp4	man	24	2.92	70	736	414	0.248641	0.041063	0.467391	0.958937
videos/candy/08918.mp4	candy	24	1	24	1,920	1,080	0.403125	0.141667	0.377604	0.858333
videos/cool/13209.mp4	cool	24	2.25	54	288	192	0.253472	0.109375	0.440972	0.890625
videos/help/27217.mp4	help	24	2.08	50	288	192	0.211806	0.0625	0.527778	0.9375
videos/candy/65298.mp4	candy	24	2.46	59	656	370	0.283537	0.040541	0.428354	0.959459
videos/bed/05637.mp4	bed	24	1.67	40	288	192	0.256944	0.09375	0.409722	0.90625
videos/who/63232.mp4	who	24	5.17	124	640	480	0.175	0.058333	0.617188	0.941667
videos/short/66469.mp4	short	24	1.58	38	656	370	0.208841	0.083784	0.571646	0.916216
videos/like/33285.mp4	like	24	3.42	82	720	400	0.2625	0.1425	0.4875	0.8575
videos/me/35546.mp4	me	24	2.92	70	640	480	0.142188	0	0.61875	1
videos/go/24952.mp4	go	24	1.08	26	288	192	0.111111	0.072917	0.597222	0.927083
videos/computer/12326.mp4	computer	24	2.42	58	288	192	0.239583	0.046875	0.503472	0.953125
videos/cousin/13641.mp4	cousin	24	1.42	34	288	192	0.274306	0.041667	0.496528	0.958333
videos/cool/13208.mp4	cool	24	1.92	46	288	192	0.295139	0.104167	0.465278	0.895833
videos/now/38990.mp4	now	24	3.12	75	320	240	0.18125	0.008333	0.6375	0.991667
videos/like/69389.mp4	like	24	1.96	47	1,280	720	0.26875	0.05	0.415625	0.95
videos/before/05731.mp4	before	24	1.88	45	1,920	1,080	0.296875	0.090741	0.502604	0.909259
videos/pizza/42960.mp4	pizza	24	1.25	30	1,920	1,080	0.416146	0.062963	0.424479	0.92037
videos/trade/59210.mp4	trade	24	2.46	59	1,920	1,080	0.359375	0.082407	0.530729	0.917593
videos/drink/17733.mp4	drink	24	3.08	74	720	400	0.258333	0.1575	0.506944	0.8425
videos/before/05734.mp4	before	24	2.71	65	640	480	0.201563	0	0.665625	1
videos/go/24961.mp4	go	24	1.5	36	288	192	0.270833	0.130208	0.472222	0.869792
videos/me/35544.mp4	me	24	2.62	63	320	240	0.0875	0	0.734375	1
videos/drink/17723.mp4	drink	24	1.96	47	288	192	0.225694	0.104167	0.496528	0.895833
videos/candy/08925.mp4	candy	24	1.38	33	288	192	0.270833	0.078125	0.538194	0.921875
videos/bed/05630.mp4	bed	24	2.92	70	1,280	720	0.322656	0.079167	0.307813	0.920833
videos/before/05742.mp4	before	24	0.92	22	288	192	0.232639	0.072917	0.517361	0.927083
videos/thanksgiving/57633.mp4	thanksgiving	24	2.92	70	640	480	0.204688	0	0.670313	1
videos/go/24947.mp4	go	24	2.08	50	640	480	0.090625	0.004167	0.779688	0.995833
videos/before/05729.mp4	before	24	1.75	42	1,920	1,080	0.15625	0.011111	0.469792	0.988889
videos/trade/59214.mp4	trade	24	1.58	38	288	192	0.184028	0.052083	0.565972	0.947917
videos/good/25076.mp4	good	24	2.88	69	720	400	0.338889	0.1075	0.354167	0.8925
videos/accident/00626.mp4	accident	24	1.83	44	1,920	1,080	0.252604	0.078704	0.584896	0.921296
videos/short/51221.mp4	short	24	3.33	80	736	414	0.232337	0.033816	0.480978	0.966184
videos/please/69434.mp4	please	24	2.88	69	1,280	720	0.261719	0.063889	0.444531	0.934722
videos/who/63230.mp4	who	24	2.04	49	1,920	1,080	0.178125	0.018519	0.427083	0.981481
videos/cousin/13634.mp4	cousin	24	3.21	77	640	480	0.154688	0.05	0.645313	0.95
videos/doctor/17026.mp4	doctor	24	3.42	82	720	400	0.2625	0.1575	0.495833	0.8425
videos/trade/59215.mp4	trade	24	1.83	44	288	192	0.204861	0.057292	0.541667	0.942708
videos/trade/59207.mp4	trade	24	0.96	23	1,920	1,080	0.329167	0.043519	0.495833	0.956481
videos/now/38995.mp4	now	24	2.75	66	640	480	0.19375	0	0.6375	1
videos/tall/56843.mp4	tall	24	3	72	640	480	0.114063	0	0.75625	1
videos/bad/04718.mp4	bad	24	1.29	31	288	192	0.260417	0.078125	0.565972	0.921875
videos/thin/57949.mp4	thin	24	1.75	42	288	192	0.229167	0.109375	0.486111	0.890625
videos/son/53268.mp4	son	24	2.5	60	1,280	720	0.310156	0.098611	0.35625	0.901389
videos/before/05733.mp4	before	24	3.58	86	640	480	0.148438	0.047917	0.684375	0.952083
videos/find/21858.mp4	find	24	1.42	34	288	192	0.274306	0.052083	0.465278	0.947917
videos/man/66097.mp4	man	24	2.67	64	656	370	0.217988	0.018919	0.489329	0.981081
videos/bowling/07399.mp4	bowling	24	2.17	52	288	192	0.236111	0.052083	0.534722	0.947917
videos/tall/56842.mp4	tall	24	1.79	43	1,920	1,080	0.355729	0.059259	0.453646	0.936111
videos/drink/69302.mp4	drink	24	2.58	62	1,920	1,080	0.286979	0.062963	0.416146	0.937037
videos/tall/56852.mp4	tall	24	2.79	67	720	400	0.284722	0.1325	0.505556	0.8675
videos/me/35548.mp4	me	24	0.92	22	288	192	0.284722	0.067708	0.503472	0.932292
videos/pizza/42962.mp4	pizza	24	3	72	640	480	0.176563	0	0.639063	1
videos/bathroom/05300.mp4	bathroom	24	3.58	86	640	480	0.195313	0.047917	0.628125	0.952083
videos/go/69345.mp4	go	24	2.25	54	1,280	720	0.209375	0.052778	0.492188	0.947222

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Reduced WLASL Dataset

This dataset is a task-specific reduced version of the WLASL dataset, constructed for American Sign Language (ASL) recognition experiments.

videos/
Video clips organized by gloss label.
metadata.csv
Per-sample metadata including:
- file path
- gloss label
- fps (after normalization, if applied)
- video resolution
- normalized bounding box coordinates
gloss_map.json
Mapping from gloss labels to integer class IDs.

Dataset Construction

The dataset was generated from the original WLASL dataset using a custom CLI preprocessing script with the following criteria:

Glosses restricted to a selected subset
Samples without bounding boxes were excluded
Videos optionally normalized to a fixed FPS
Original videos preserved (no cropping applied)
Bounding boxes stored as metadata for downstream processing

Command Used

The dataset was generated using the following command:

python .\src\sign_language_model\scripts\build_reduced_wlasl.py --wlasl-root .\data\WLASL\ --output-root .\data\wlasl_reduced --glosses before,cool,thin,go,drink,help,computer,cousin,who,bowling,trade,bed,accident,tall,thanksgiving,candy,short,pizza,man,no,wait,good,bad,son,like,doctor,now,find,you,thank you,please,hospital,bathroom,me,i --target-fps 24 --dry-run

Notes

Bounding boxes are stored in normalized coordinates (0-1) with top-left origin.
FPS normalization (if applied) uses frame dropping (no interpolation).

Downloads last month: 4

Size of downloaded dataset files:

43.9 kB

Size of the auto-converted Parquet files:

24.8 kB

Number of rows:

394

Reduced WLASL Dataset

Contents

Dataset Construction

Command Used

Notes