futo-org/asr4all-s
Automatic Speech Recognition • 28.2M • Updated • 800 • 3
Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: RuntimeError
Message: Dataset scripts are no longer supported, but found yodas.py
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1182, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1142, in dataset_module_factory
raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
RuntimeError: Dataset scripts are no longer supported, but found yodas.pyNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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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