Hugging Face's logo

Datasets:
SamagraDataGov
/
word-boundry-detection-dataset

Modalities:
Tabular
Text
Formats:
csv
Size:
10K - 100K
Libraries:
Datasets
pandas
Dataset card Data Studio Files
xet
Community
1
Dataset Viewer
Auto-converted to Parquet
Unnamed: 0
int64
0
10.2k
audio_embedding
stringlengths
64
103
label
int64
0
1
audio_path
stringclasses
41 values
0
tensor([[-1.2933, -1.4311, 0.7780, ..., 0.9516, -0.6364, -1.1266]])
0
844424930730670-326-f.wav
1
tensor([[-1.2933, -1.4311, 0.7780, ..., 0.9516, -0.6364, -1.1266]])
0
844424930730670-326-f.wav
2
tensor([[ 0.9597, -2.9257, 0.2614, ..., 1.1113, -1.6006, -1.0215]])
0
844424930730670-326-f.wav
3
tensor([[-3.2353, 1.0206, 2.4142, ..., -0.0530, -0.3050, -0.4316]])
0
844424930730670-326-f.wav
4
tensor([[-1.6416, -0.0363, 1.8862, ..., 1.0093, -0.3377, -0.6687]])
0
844424930730670-326-f.wav
5
tensor([[-2.9321, 0.0840, 2.6524, ..., 1.4912, -0.5050, 0.2037]])
0
844424930730670-326-f.wav
6
tensor([[-2.9123, -0.0493, 2.7093, ..., 1.3595, -0.4228, 0.2702]])
0
844424930730670-326-f.wav
7
tensor([[-2.5121, -0.4988, 2.4721, ..., 1.3517, -0.7283, 0.2166]])
0
844424930730670-326-f.wav
8
tensor([[-2.6799, -0.4689, 2.3364, ..., 0.9131, -0.3532, -0.4260]])
0
844424930730670-326-f.wav
9
tensor([[-2.5791, -0.5682, 1.9388, ..., 1.0015, -0.5167, -0.7791]])
0
844424930730670-326-f.wav
10
tensor([[-2.1948, -0.7697, 2.6331, ..., 1.3679, -0.5123, 0.0714]])
0
844424930730670-326-f.wav
11
tensor([[-1.6825, -0.8952, 2.0741, ..., 0.6963, -0.7514, -1.0439]])
0
844424930730670-326-f.wav
12
tensor([[-1.5661, -0.9975, 1.0945, ..., 0.0172, -1.0020, -1.9941]])
0
844424930730670-326-f.wav
13
tensor([[-1.2508, -0.5466, 1.4952, ..., 0.3090, -0.8608, -2.1309]])
0
844424930730670-326-f.wav
14
tensor([[-1.3463, -1.0668, 2.7528, ..., -0.7155, 0.5570, 0.2898]])
1
844424930730670-326-f.wav
15
tensor([[-3.5805, 1.9928, 5.5398, ..., 2.3325, 0.5204, 4.7564]])
0
844424930730670-326-f.wav
16
tensor([[-2.1639, 2.9085, 8.0996, ..., -0.3868, 0.0855, 3.8671]])
0
844424930730670-326-f.wav
17
tensor([[-2.6274, -0.1496, 9.6595, ..., -1.4928, 0.4729, 4.0801]])
0
844424930730670-326-f.wav
18
tensor([[-3.6960, 1.1585, 10.2220, ..., -1.7962, 1.2885, 5.2145]])
0
844424930730670-326-f.wav
19
tensor([[-2.6173, 3.9398, 3.1165, ..., -4.6525, 1.7087, 5.1862]])
0
844424930730670-326-f.wav
20
tensor([[-4.1668, -0.0781, 5.7995, ..., 0.9995, 3.6395, 1.9006]])
0
844424930730670-326-f.wav
21
tensor([[-4.2120, 0.0654, 5.2422, ..., -0.0694, 2.3128, 1.1667]])
0
844424930730670-326-f.wav
22
tensor([[-2.9316, 0.3739, 3.0009, ..., 1.1944, 0.3824, 0.2177]])
0
844424930730670-326-f.wav
23
tensor([[-2.9515, 0.2487, 3.0710, ..., 0.2237, 1.4717, 0.8556]])
0
844424930730670-326-f.wav
24
tensor([[-2.3884, 0.8606, 1.8196, ..., 0.5007, -1.4439, 1.5936]])
0
844424930730670-326-f.wav
25
tensor([[ 1.0093, -2.2261, -1.7674, ..., 2.1664, -1.6762, 0.9170]])
0
844424930730670-326-f.wav
26
tensor([[-0.8143, -1.1985, -1.1718, ..., 0.4055, -1.8267, 1.8034]])
0
844424930730670-326-f.wav
27
tensor([[-2.0449, -1.4718, -1.2309, ..., 1.3057, 0.9248, 0.4963]])
0
844424930730670-326-f.wav
28
tensor([[-2.6814, 0.1288, 1.2864, ..., 0.2788, -1.1083, -0.6422]])
0
844424930730670-326-f.wav
29
tensor([[-1.5816, -0.5263, -0.4142, ..., 1.5958, -0.3124, 1.4358]])
0
844424930730670-326-f.wav
30
tensor([[-0.1165, -1.6580, -0.3805, ..., 2.7445, -0.9526, -0.0707]])
0
844424930730670-326-f.wav
31
tensor([[-3.2121, -2.0928, -2.4460, ..., 0.4892, -0.4946, -0.3495]])
0
844424930730670-326-f.wav
32
tensor([[-2.2094, -2.0816, -0.6965, ..., 0.3604, 1.2960, 0.7090]])
1
844424930730670-326-f.wav
33
tensor([[-0.8871, -1.8171, 0.6843, ..., 1.0175, 0.2690, -1.3972]])
0
844424930730670-326-f.wav
34
tensor([[-1.5447, -1.2250, 1.8437, ..., 0.7887, 0.2635, -1.2734]])
0
844424930730670-326-f.wav
35
tensor([[-1.6037, -1.6920, 1.6745, ..., 1.0454, 0.0871, -1.1065]])
0
844424930730670-326-f.wav
36
tensor([[-1.9277, -1.1019, 1.0315, ..., -0.1436, -0.5092, -0.9351]])
0
844424930730670-326-f.wav
37
tensor([[-1.5742, -1.0398, 0.6001, ..., -0.0811, -0.8577, -1.6808]])
1
844424930730670-326-f.wav
38
tensor([[ 0.5307, 2.6937, 3.7190, ..., 0.3949, -1.8985, 0.8315]])
0
844424930730670-326-f.wav
39
tensor([[-1.7083, -0.9920, 3.0386, ..., 0.9950, 2.3936, -0.9659]])
0
844424930730670-326-f.wav
40
tensor([[-2.7732, 1.1377, 9.4932, ..., 0.2721, 1.1340, 2.5417]])
0
844424930730670-326-f.wav
41
tensor([[-3.0510, 1.9065, 6.4600, ..., 1.1766, 1.4000, 2.3409]])
0
844424930730670-326-f.wav
42
tensor([[-3.5747, 1.5008, 6.8441, ..., 0.6196, 2.2912, 3.7656]])
0
844424930730670-326-f.wav
43
tensor([[-3.3719, 2.8709, 6.3091, ..., -0.1809, 1.4459, 4.3157]])
0
844424930730670-326-f.wav
44
tensor([[-3.1551, 0.5982, 4.3514, ..., 0.0074, 2.2082, 3.0417]])
0
844424930730670-326-f.wav
45
tensor([[-0.4005, 0.5563, 3.6483, ..., 1.0132, 1.4425, 3.1018]])
0
844424930730670-326-f.wav
46
tensor([[-1.3002, -0.1427, 5.1112, ..., 0.6897, -0.2105, 3.6563]])
0
844424930730670-326-f.wav
47
tensor([[-0.6931, 0.8149, 9.5419, ..., 1.3716, -0.5545, 3.0817]])
0
844424930730670-326-f.wav
48
tensor([[-3.9573, 0.6823, 9.3046, ..., 0.0656, 0.6251, 6.5317]])
0
844424930730670-326-f.wav
49
tensor([[-2.3505, 1.4832, 9.8059, ..., -1.2883, -0.5691, 4.8822]])
0
844424930730670-326-f.wav
50
tensor([[-3.3950, 0.8964, 9.3610, ..., 1.0993, 0.9942, 3.7403]])
0
844424930730670-326-f.wav
51
tensor([[-4.9008, 0.0295, 7.0474, ..., 0.7372, 1.4952, 4.1211]])
0
844424930730670-326-f.wav
52
tensor([[-3.2610, -0.1381, 6.7374, ..., 1.1967, 1.7845, 0.3525]])
0
844424930730670-326-f.wav
53
tensor([[-4.2822, 1.6798, 4.5573, ..., 1.9155, 1.0219, 3.8489]])
0
844424930730670-326-f.wav
54
tensor([[-4.5108, 2.3693, 4.4090, ..., 0.0818, 0.4734, 2.9995]])
0
844424930730670-326-f.wav
55
tensor([[-5.0126, 1.7873, 1.5501, ..., -0.4997, 2.0294, 3.2383]])
0
844424930730670-326-f.wav
56
tensor([[-2.8813, -0.5946, -0.5531, ..., 1.1629, -0.6834, 1.6982]])
0
844424930730670-326-f.wav
57
tensor([[-0.9802, -2.1884, -0.8996, ..., 1.8184, -1.0288, 0.5836]])
0
844424930730670-326-f.wav
58
tensor([[-2.3129, -3.0588, -1.7218, ..., -0.8067, -1.2122, -3.1270]])
1
844424930730670-326-f.wav
59
tensor([[-0.8634, -1.6304, -0.2420, ..., 0.8108, 1.2363, 0.0258]])
0
844424930730670-326-f.wav
60
tensor([[-2.3913, -1.0239, 1.0941, ..., 0.8335, -0.9298, 1.2219]])
0
844424930730670-326-f.wav
61
tensor([[-1.9347, -0.3314, 1.8497, ..., 1.7161, 0.0595, 1.1236]])
0
844424930730670-326-f.wav
62
tensor([[ 0.0738, -1.9656, 0.4251, ..., 1.0603, -1.2728, -2.9726]])
0
844424930730670-326-f.wav
63
tensor([[-2.4007, -0.8884, 1.9463, ..., -0.3769, -1.0658, -1.2995]])
1
844424930730670-326-f.wav
64
tensor([[-2.4715, -2.1384, -1.5278, ..., 1.4589, 0.0750, 0.0080]])
0
844424930730670-326-f.wav
65
tensor([[-1.9370, -2.1257, -0.7882, ..., 1.9042, -0.0593, -0.0598]])
0
844424930730670-326-f.wav
66
tensor([[-1.7778, -1.6330, -1.5274, ..., 2.3097, -0.1131, -1.6340]])
0
844424930730670-326-f.wav
67
tensor([[-4.0172, -2.2344, -0.9021, ..., -0.7397, 0.9987, -1.1576]])
1
844424930730670-326-f.wav
68
tensor([[-4.5936, -2.1459, -2.9446, ..., -0.9315, 2.4762, -0.4046]])
0
844424930730670-326-f.wav
69
tensor([[-1.7087, -2.2646, 3.5601, ..., 1.2001, -0.6420, 0.2588]])
0
844424930730670-326-f.wav
70
tensor([[-0.3104, -2.5639, 1.4796, ..., 1.6589, -0.9334, -0.6333]])
0
844424930730670-326-f.wav
71
tensor([[-1.5197, -0.8367, 1.0159, ..., 0.3119, -0.4528, -0.6881]])
0
844424930730670-326-f.wav
72
tensor([[-0.8183, -1.2080, 2.1948, ..., 1.6839, -0.9842, -0.2468]])
0
844424930730670-326-f.wav
73
tensor([[-0.8236, -0.7365, 0.8684, ..., 1.1025, -1.8906, -0.1255]])
1
844424930730670-326-f.wav
74
tensor([[-2.1181, -0.6494, 4.1252, ..., 0.9877, 1.0368, 0.0337]])
0
844424930730670-326-f.wav
75
tensor([[-4.1002, 0.0396, 4.5540, ..., 0.3744, 2.3168, 0.9407]])
0
844424930730670-326-f.wav
76
tensor([[-4.4868, -0.0645, 3.6025, ..., 0.1294, 2.2475, 0.9803]])
0
844424930730670-326-f.wav
77
tensor([[-5.0278, 1.1821, 5.9276, ..., -0.4161, 2.8903, 1.6091]])
0
844424930730670-326-f.wav
78
tensor([[-5.1279, 0.3625, 8.1530, ..., -0.6285, 2.8572, 2.4057]])
0
844424930730670-326-f.wav
79
tensor([[-4.3559, 1.0983, 5.0295, ..., 0.4004, 3.0462, 2.7162]])
0
844424930730670-326-f.wav
80
tensor([[-2.8711, 0.2703, 3.6774, ..., 0.8508, 0.7907, 2.3048]])
0
844424930730670-326-f.wav
81
tensor([[-3.1642, 2.6470, 0.3198, ..., -0.2329, 0.1315, 1.3212]])
0
844424930730670-326-f.wav
82
tensor([[-1.0860, -1.1297, 1.1710, ..., 0.5103, -0.8240, 1.5864]])
0
844424930730670-326-f.wav
83
tensor([[-3.4182, 0.3672, 2.0794, ..., 0.7018, 0.8714, 0.2975]])
0
844424930730670-326-f.wav
84
tensor([[-2.1762, -0.7002, 3.1546, ..., 1.7848, 0.5530, -0.8849]])
0
844424930730670-326-f.wav
85
tensor([[-1.1290, -0.0531, 4.4370, ..., 1.0670, 3.0076, -0.4676]])
1
844424930730670-326-f.wav
86
tensor([[-1.9393, 0.5366, 3.7250, ..., 2.7280, 2.8460, -1.1687]])
0
844424930730670-326-f.wav
87
tensor([[-2.5213, 1.1915, 5.1336, ..., 1.6355, 1.7489, -1.7389]])
0
844424930730670-326-f.wav
88
tensor([[-3.3328, 1.0759, 4.2558, ..., 1.2533, 3.6294, -0.3716]])
1
844424930730670-326-f.wav
89
tensor([[-0.4425, 1.2310, 8.0573, ..., -1.0638, -1.3332, -1.5721]])
0
844424930730670-326-f.wav
90
tensor([[-3.2472, 4.8269, 3.4069, ..., -5.0689, 1.5179, 3.0810]])
0
844424930730670-326-f.wav
91
tensor([[-1.2167, 1.5769, 5.8563, ..., -1.5954, -0.1622, 3.1862]])
0
844424930730670-326-f.wav
92
tensor([[-1.7154, -0.3428, 2.5037, ..., 2.2031, 1.8823, 2.5366]])
0
844424930730670-326-f.wav
93
tensor([[-2.6303, -0.4003, 2.6878, ..., 1.5499, 0.3374, 0.0828]])
0
844424930730670-326-f.wav
94
tensor([[-2.7404, -0.0883, 2.6400, ..., 2.0275, 1.0872, -0.3208]])
0
844424930730670-326-f.wav
95
tensor([[-2.3725, 0.3873, 2.1011, ..., 1.5836, -0.1621, -0.0765]])
0
844424930730670-326-f.wav
96
tensor([[-3.9099, 1.2726, 2.4131, ..., 1.3795, 1.0263, 1.3274]])
0
844424930730670-326-f.wav
97
tensor([[-0.3362, -1.1824, 1.2139, ..., -0.0451, -1.7579, 0.0377]])
0
844424930730670-326-f.wav
98
tensor([[-1.3131, -1.6272, -2.9958, ..., 0.3793, -1.4417, 0.2673]])
0
844424930730670-326-f.wav
99
tensor([[-0.1322, -2.0270, -2.6094, ..., 0.7415, -1.9831, 0.1046]])
0
844424930730670-326-f.wav
End of preview. Expand in Data Studio

No dataset card yet

Downloads last month
8
Size of downloaded dataset files:
1.08 MB
Size of the auto-converted Parquet files:
460 kB
Number of rows:
10,181