espnet/yodas

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