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Updates

  • 2024/07/09: we also uploaded a new version of YODAS as YODAS2, it provides unsegmented audios and higher sampling rate (24k)

README

This is the YODAS manual/automatic subset from our YODAS dataset, it has 369,510 hours of speech.

This dataset contains audio utterances and corresponding captions (manual or automatic) from YouTube. Note that manual caption only indicates that it is uploaded by users, but not necessarily transcribed by a human

For more details about YODAS dataset, please refer to our paper

Usage:

Considering the extremely large size of the entire dataset, we support two modes of dataset loadings:

standard mode: each subset will be downloaded to the local dish before first iterating.

from datasets import load_dataset

# Note this will take very long time to download and preprocess
# you can try small subset for testing purpose
ds = load_dataset('espnet/yodas', 'en000')
print(next(iter(ds['train'])))

streaming mode most of the files will be streamed instead of downloaded to your local deivce. It can be used to inspect this dataset quickly.

from datasets import load_dataset

# this streaming loading will finish quickly
ds = load_dataset('espnet/yodas', 'en000', streaming=True)


#{'id': '9774', 'utt_id': 'YoRjzEnRcqu-00000-00000716-00000819', 'audio': {'path': None, 'array': array([-0.009552  , -0.01086426, -0.012146  , ..., -0.01992798,
#       -0.01885986, -0.01074219]), 'sampling_rate': 16000}, 'text': 'There is a saying'}
print(next(iter(ds['train'])))

Subsets/Shards

There are 149 languages in this dataset, each language is sharded into at least 1 shard to make it easy for our processing and uploading purposes. The raw data of each shard contains 500G at most.

Statistics of each shard can be found in the last section.

We distinguish manual caption subset and automatic caption subset by the first digit in each shard's name. The first digit is 0 if it contains manual captions, 1 if it contains automatic captions.

For example, en000 to en005 are the English shards containing manual subsets, and en100 to en127 contains the automatic subsets.

Reference

@inproceedings{li2023yodas,
  title={Yodas: Youtube-Oriented Dataset for Audio and Speech},
  author={Li, Xinjian and Takamichi, Shinnosuke and Saeki, Takaaki and Chen, William and Shiota, Sayaka and Watanabe, Shinji},
  booktitle={2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
  pages={1--8},
  year={2023},
  organization={IEEE}
}

Contact

If you have any questions, feel free to contact us at the following email address.

We made sure that our dataset only consisted of videos with CC licenses during our downloading. But in case you find your video unintentionally included in our dataset and would like to delete it, you can send a delete request to the following email.

Remove the parenthesis () from the following email address

(lixinjian)(1217)@gmail.com

Statistics

Note that there are no overlappings across different subsets, each audio can be included in the dataset at most once.

Subset name Hours
aa000 0.171472
ab000 0.358342
af000 0.880497
ak000 0.250858
am000 0.924708
ar000 289.707
as000 0.548239
ay000 0.0342722
az000 3.8537
ba000 0.0210556
be000 48.1537
bg000 46.8375
bh000 0.0127111
bi000 0.0125556
bm000 0.00214722
bn000 27.064
bo000 0.746211
br000 0.729914
bs000 9.36959
ca000 74.1909
co000 0.0418639
cr000 0.00584167
cs000 167.604
cy000 5.20017
da000 27.4345
de000 3063.81
de100 4998.11
de101 4995.08
de102 955.389
dz000 0.06365
ee000 0.0411722
el000 126.75
en000 4999.73
en001 5032.69
en002 5039.9
en003 5001.4
en004 5054.66
en005 4027.02
en100 5147.07
en101 5123.05
en102 5117.68
en103 5127.3
en104 5126.33
en105 5097.65
en106 5131.47
en107 5135.6
en108 5136.84
en109 5112.94
en110 5109
en111 5118.69
en112 5122.57
en113 5122.31
en114 5112.36
en115 5112.27
en116 5123.77
en117 5117.31
en118 5117.94
en119 5133.05
en120 5127.79
en121 5129.08
en122 5130.22
en123 5097.56
en124 5116.59
en125 5109.76
en126 5136.21
en127 2404.89
eo000 12.6874
es000 3737.86
es100 5125.25
es101 5130.44
es102 5145.66
es103 5138.26
es104 5139.57
es105 5138.95
es106 2605.26
et000 14.4129
eu000 19.6356
fa000 42.6734
ff000 0.0394972
fi000 212.899
fj000 0.0167806
fo000 0.183244
fr000 2423.7
fr100 5074.93
fr101 5057.79
fr102 5094.14
fr103 3222.95
fy000 0.0651667
ga000 1.49252
gd000 0.01885
gl000 9.52575
gn000 0.181356
gu000 1.99355
ha000 0.102931
hi000 480.79
hi100 2.74865
ho000 0.0562194
hr000 25.9171
ht000 1.07494
hu000 181.763
hy000 1.64412
ia000 0.0856056
id000 1420.09
id100 4902.79
id101 3560.82
ie000 0.134603
ig000 0.086875
ik000 0.00436667
is000 5.07075
it000 1454.98
it100 4989.62
it101 4242.87
iu000 0.0584278
iw000 161.373
ja000 1094.18
ja100 2929.94
jv000 1.08701
ka000 26.9727
ki000 0.000555556
kk000 3.72081
kl000 0.00575556
km000 3.98273
kn000 2.36041
ko000 2774.28
ko100 5018.29
ko101 5048.49
ko102 5018.27
ko103 2587.85
ks000 0.0150444
ku000 1.93419
ky000 14.3917
la000 7.26088
lb000 0.1115
lg000 0.00386111
ln000 0.188739
lo000 0.230986
lt000 17.6507
lv000 2.47671
mg000 0.169653
mi000 1.10089
mk000 5.54236
ml000 13.2386
mn000 2.0232
mr000 7.11602
ms000 28.0219
my000 2.35663
na000 0.0397056
nd000 0.00111111
ne000 2.34936
nl000 413.044
nl100 2490.13
no000 129.183
nv000 0.00319444
oc000 0.166108
om000 0.148478
or000 0.421436
pa000 1.58188
pl000 757.986
ps000 0.9871
pt000 1631.44
pt100 5044.57
pt101 5038.33
pt102 5041.59
pt103 3553.28
qu000 0.748772
rm000 0.192933
rn000 0.00401111
ro000 99.9175
ru000 4968.37
ru001 627.679
ru100 5098.3
ru101 5098
ru102 5119.43
ru103 5107.29
ru104 5121.73
ru105 5088.05
ru106 3393.44
rw000 0.640825
sa000 0.354139
sc000 0.00801111
sd000 0.0768722
sg000 0.000472222
sh000 0.250914
si000 4.2634
sk000 30.0155
sl000 22.9366
sm000 0.102333
sn000 0.0134722
so000 3.36819
sq000 3.48276
sr000 15.2849
st000 0.00324167
su000 0.0404639
sv000 127.411
sw000 1.93409
ta000 59.4805
te000 5.66794
tg000 0.272386
th000 497.14
th100 1.87429
ti000 0.343897
tk000 0.0651806
tn000 0.112181
to000 0.000555556
tr000 588.698
tr100 4067.68
ts000 0.00111111
tt000 0.0441194
ug000 0.0905
uk000 396.598
uk100 450.411
ur000 22.4373
uz000 5.29325
ve000 0.00355278
vi000 779.854
vi100 4963.77
vi101 4239.37
vo000 0.209436
wo000 0.0801528
xh000 0.126628
yi000 0.0810111
yo000 0.322206
zh000 299.368
zu000 0.139931
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