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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. 😉
Languages
Of the 500+ languages listed at BloomLibrary.org, 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:
print(dataset['train']['text'][0])
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:
dataset['train']['text']
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.
Changelog
- 26 September 2022 Page initiated
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