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audio
audioduration (s)
1.01
69.9
clip_id
stringlengths
15
15
duration
float64
1.01
69.9
ml_7740075335bb
1.822
ml_f02cd1ac51b7
4.034
ml_5071eb4b153b
23.844
ml_3cd40005c67c
6.075
ml_64c30699ae02
5.012
ml_4fce36c36182
11.34
ml_504d101bf644
2.463
ml_07443deae0fe
3.577
ml_1f11ca1c9e21
3.831
ml_ec82856f0d07
2.143
ml_c74fd5b8aa7b
5.147
ml_211e1641bad7
5.265
ml_12d338fce76f
7.189
ml_29c39b44cafb
1.823
ml_4bbe5b43add4
2.97
ml_980cc9d52528
2.548
ml_1468ff91ad7d
6.834
ml_4435815ab9be
7.645
ml_f1fbcd4ae2b3
3.189
ml_555bdc878b02
3.577
ml_3791030b3958
4.236
ml_71f22201c9ee
18.495
ml_e9c21a420d98
16.976
ml_a1bd19fccca0
7.07
ml_8d35051f72b3
8.421
ml_67a04fac76b3
1.587
ml_aa0d9d2bac69
5.855
ml_d97f7704ec04
2.683
ml_909a41740d93
4.235
ml_57f779871103
6.683
ml_43e88a4aa4a6
10.057
ml_ccc66c245a5d
5.906
ml_138334554a03
4.134
ml_38234dda2b17
7.088
ml_f5a2dd13bdf2
4.522
ml_99d3d0ba9405
18.006
ml_6401e7ddd62d
2.194
ml_85b79a3686a1
2.565
ml_af85f55bea84
6.666
ml_c80bd57c4d43
5.519
ml_e0308c01ad56
1.688
ml_5c94e74f7108
1.265
ml_33b4a26d8abf
1.316
ml_71f8868e19c2
7.088
ml_2d37c17ecb88
1.333
ml_3d82dcec0411
1.435
ml_a83b798bcf88
2.598
ml_713adb118e08
3.949
ml_bd35e9095d93
3.527
ml_d6d1345cc8e1
7.999
ml_40a4f1f81c44
7.746
ml_3b5d3e757b8c
3.628
ml_26485a7cdc0e
1.603
ml_a91d4e505dbe
10.834
ml_e4dcf98021b0
4.337
ml_c60e21244334
4.353
ml_ac0cd97e09a1
13.517
ml_7f7d6f0c3f69
9.282
ml_0dbe8302265a
1.62
ml_b947f7b0db4f
1.215
ml_d2a98415e455
7.189
ml_584e03e548c6
1.148
ml_37a6d3683481
10.175
ml_f882794c6192
12.183
ml_55bea0e2ed7f
1.755
ml_e4d0d7b59032
2.649
ml_7d4c275a9258
6.345
ml_0e52820b3a51
9.062
ml_fc8c20d0ab94
1.198
ml_f478724cef56
3.105
ml_f5adebe69a9c
1.586
ml_c656982d7f9a
4.809
ml_a81e6bf1cbf7
7.678
ml_00dd098b40c6
2.514
ml_94d266491062
1.671
ml_475feee7e474
3.898
ml_2975a8123433
1.924
ml_5386861cc397
5.096
ml_e90dffc7004a
14.85
ml_e322e5aba96c
1.738
ml_9c8dba6dcf1d
6.682
ml_2e10556d66e2
26.274
ml_51e3047b9f18
5.906
ml_6b8070747490
3.442
ml_cc65e04003d0
4.506
ml_53055cfcbc52
12.707
ml_50df834c373a
2.176
ml_60c30d1fbadb
2.97
ml_8130248fd32c
7.981
ml_8cf09755eca0
1.519
ml_a549d3580204
7.088
ml_9c8479b3e9bd
10.058
ml_e4571b641f14
5.484
ml_f8032be4a518
3.764
ml_4176d4af2095
7.053
ml_75dfae97e1fb
4.675
ml_b76d801638a6
10.952
ml_cd8235fa2f7a
3.426
ml_eec382aa9924
6.008
ml_32d49d61adbc
34.391
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Check out the documentation for more information.

Malayalam Speech — 179 hours (denoised, unlabeled)

86,799 Malayalam speech clips, 48 kHz stereo WAV, ~7.4 s average. No transcripts — this is unlabeled audio, intended for self-supervised pretraining, voice/speaker modelling, or as raw material for your own labelling pipeline.

Processing

Each clip passed through a full source-separation and enhancement chain:

  1. Vocal isolation — BS-RoFormer
  2. De-reverberation — UVR-DeEcho-DeReverb
  3. Noise removal — UVR-DeNoise-Lite
  4. Final enhancement

Clips shorter than 1 second were dropped. Clip IDs are randomly generated and carry no ordering or grouping information.

Source and licensing

The audio is derived from publicly available Malayalam video content on the internet, segmented and processed as described above. Original speakers did not consent to inclusion and the underlying recordings may be subject to copyright. It is published here for research use; verify your own legal position before using it commercially or redistributing it. If you hold rights to material in this dataset and want it removed, open a discussion on this repository.

Columns

  • audio — 48 kHz stereo waveform
  • clip_id — randomized identifier
  • duration — clip length in seconds
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