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Dataset Summary

Bloom is free, open-source software and an associated website Bloom Library, app, and services developed by SIL International. Bloom’s primary goal is to equip non-dominant language communities and their members to create the literature they want for their community and children. Bloom also serves organizations that help such communities develop literature and education or other aspects of community development.

This version of the Bloom Library data is developed specifically for the automatic speech recognition and speech-to-text tasks. It includes data from 56 languages across 18 language families. There is a mean of 458 and median of 138 audio records per language.

Note: If you speak one of these languages and can help provide feedback or corrections, please let us know!

Note: Although data from bloom-lm was used in the training of the BLOOM model, the dataset only represents a small portion of the data used to train that model. Data from "Bloom Library" was combined with a large number of other datasets to train that model. "Bloom Library" is a project that existed prior to the BLOOM model, and is something separate. All that to say... We were using the "Bloom" name before it was cool. 😉


Of the 500+ languages listed at, there are 56 languages available in this dataset. Here are the corresponding ISO 639-3 codes:

ajz, bam, bis, bjn, boz, bze, bzi, cak, ceb, chd, chp, clo, csw, eng, fli, fra, guj, hbb, hin, ind, jmx, jra, kan, kbq, kek, kjb, kmu, kqr, kwu, loh, mai, mal, mam, mar, mle, mya, myk, nas, nsk, nsn, oji, omw, por, quc, sdk, snk, spa, stk, taj, tam, tbj, tdc, tgl, tpi, tuz, tzj

Dataset Statistics

Some of the languages included in the dataset include few audio cuts. These are not split between training, validation, and test. For those with higher numbers of available stories we include the following numbers of stories in each split:

ISO 639-3 Name Train Cuts Validation Cuts Test Cuts
ajz Amri Karbi 135 34 50
bam Bamanankan 203 50 50
bis Bislama 0 0 46
bjn Banjar 80 20 50
boz Bozo, Tieyaxo 427 50 52
bze Bozo, Jenaama 101 26 50
bzi Bisu 1363 50 157
cak Kaqchikel 989 50 115
ceb Cebuano 553 50 67
chd Chontal, Highland Oaxaca 205 50 50
chp Dene 0 0 14
clo Chontal, Lowland Oaxaca 120 30 50
csw Cree, Swampy 0 0 45
eng English 4143 48 455
fli Fali Muchella 59 15 50
fra French 261 49 50
guj Gujarati 27 0 48
hbb Nya Huba 558 50 67
hin Hindi 62 15 49
ind Indonesian 0 0 14
jmx Mixtec, Western Juxtlahuaca 39 0 50
jra Jarai 203 50 50
kan Kannada 281 43 50
kbq Kamano 0 0 27
kek Q’eqchi’ 1676 49 190
kjb Q’anjob’al 770 50 91
kmu Kanite 0 0 28
kqr Kimaragang 0 0 18
kwu Kwakum 58 15 50
loh Narim 0 0 15
mai Maithili 0 0 11
mal Malayalam 125 31 44
mam Mam 1313 50 151
mar Marathi 25 0 49
mle Manambu 0 0 8
mya Burmese 321 50 50
myk Sénoufo, Mamara 669 50 80
nas Naasioi 13 0 50
nsk Naskapi 0 0 15
nsn Nehan 0 0 31
oji Ojibwa 0 0 25
omw Tairora, South 0 0 34
por Portuguese 0 0 34
quc K’iche’ 1460 50 167
sdk Sos Kundi 312 50 50
snk Soninke 546 50 66
spa Spanish 1816 50 207
stk Aramba 180 45 50
taj Tamang, Eastern 0 0 24
tam Tamil 159 39 46
tbj Tiang 0 0 24
tdc Ẽpẽra Pedea 0 0 19
tgl Tagalog 352 48 50
tpi Tok Pisin 1061 50 123
tuz Turka 48 13 50
tzj Tz’utujil 0 0 41

Dataset Structure

Data Instances

The examples look like this for Hindi:

from datasets import load_dataset

# Specify the language code.
dataset = load_dataset('sil-ai/bloom-speech', 'hin', use_auth_token=True) #note you must login to HuggingFace via the huggingface hub or huggingface cli

# A data point consists of transcribed audio in the specified language code.
# To see a transcription:

This would produce an output:

चित्र: बो और शैम्पू की बोतल

Whereas if you wish to gather all the text for a language you may use this:


Data Fields

The metadata fields are below. In terms of licenses, all stories included in the current release are released under a Creative Commons license (even if the individual story metadata fields are missing).

  • file: the local path to the audio file
  • audio: a dictionary with a path, array, and sampling_rate as is standard for Hugging Face audio
  • text: the transcribed text
  • book: title of the book, e.g. "बो मेस्सी और शैम्पू".
  • instance: unique ID for each book/translation assigned by Bloom Library. For example the Hindi version of 'बो मेस्सी और शैम्पू' is 'eba60f56-eade-4d78-a66f-f52870f6bfdd'
  • license: specific license used, e.g. "cc-by-sa" for "Creative Commons, by attribution, share-alike".
  • credits: attribution of contributors as described in the book metadata, including authors, editors, etc. if available
  • original_lang_tag: the language tag originally assigned in Bloom Library. This may include information on script type, etc.

Data Splits

All languages include a train, validation, and test split. However, for language having a small number of stories, certain of these splits maybe empty. In such cases, we recommend using any data for testing only or for zero-shot experiments.


  • 26 September 2022 Page initiated
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