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Task: ASR
Release Date: 6/17/2026
Format: MP3
Size: 214.46 MB
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A collection of spontaneous responses to questions in Betawi (bew).
Restrictions/Special Constraints
None provided.
Forbidden Usage
It is forbidden to attempt to determine the identity of speakers in the Common Voice datasets. It is forbidden to re-host or re-share this dataset.
Intended Use
This dataset is intended to be used for training and evaluating automatic speech recognition (ASR) models. It may also be used for applications relating to computer-aided language learning (CALL) and language or heritage revitalisation.
bew)This datasheet is for sps-corpus-4.0-2026-06-12 of the Mozilla Common Voice Spontaneous Speech dataset for Betawi [bew - bew]. The dataset contains 1336 clips representing 10.49 hours of recorded speech (9.78 hours validated) from 21 speakers.
Betawi language originally belongs to Austronesian language with a full name of Melayu-Betawi. This language is considered as one of Malay dialects, but historically it grew together with other major languages, such as Arabic, Hokkien, Sundanese, Javanese, and Malay in Sumatra - a tiny portion with Portuguese and Dutch. The language vitality status is Endangered according to https://www.ethnologue.com/language/bew/. At the moment, Indonesian standard and English in general influence the native speakers, allowing code switching and code mixing happens in a spontaneous speech. The specific variation of this dataset is Betawi Ora or Betawi Pinggiran (Peripheral Betawi), taken from several locations of Bekasi District/City, West Java Province, Indonesia. This variation is unique in terms of geo-politics: language is spoken only in the community, but it is not taught at school. Instead, the community is taught Sundanese language, which is dominated in West Java Province in general.
The dataset clips are categorised by transcription status and training-set assignment. The following tables summarise the distribution.
| Bucket | Clips | % |
|---|---|---|
| Transcribed & Validated | 1,270 | 95.1% |
| Transcribed & Pending | 12 | 0.9% |
| Not transcribed | 54 | 4.0% |
| Bucket | Clips | % |
|---|---|---|
| Train | 800 | 59.9% |
| Dev | 231 | 17.3% |
| Test | 239 | 17.9% |
| Unassigned | 66 | 4.9% |
Training split coverage: 1,270 of 1,270 transcribed & validated clips (100.0%)
The transcription system uses general Latin script, but involves allophone variants of three /e/, these are /é/, /è/, and /e/.
Prompts: 199
Duration: 39396960[ms]
Avg. Transcription Len: 292
Avg. Duration: 28.28[s]
Valid Duration: 36776.84[s]
Total hours: 10.94[h]
Valid hours: 10.22[h]
| Bucket | Clips | % |
|---|---|---|
| Validated | 1,270 | 99.1% |
| Pending | 12 | 0.9% |
| Edited | 1,127 | 87.9% |
Historically, this language used Pegon, Arabic script, but now Latin is adapted.The writing system in this dataset uses general Latin script, but involves allophone variants of three /e/, these are /é/, /è/, and /e/.
a b c d é è ȇ e f g h i j k l m n o p q r s t u v w y z
There follows a randomly selected sample of questions used in the corpus.
Dari pagi ampe menggerib, apé ajé kegiatan Ente?
Kalo Ente, pernah belajar kesenian apé? Dan, kenapé belajar tuh kesenian?
Menurut Ente, seberapé penting kité belajar sejaré negaré kité? Dan napah dah?
Menurut Ente, sapa nyang tanggung jawab buat ngelestariin bahasé daerah Ente: pemerintah atau déwék-déwék?
Begimané Ente bisa tau cerité mitos nyang adé di negaré Ente?
There follows a randomly selected sample of transcribed responses from the corpus.
alat komunikasi yang bisa dipakai sih biasanyé yé hapé yang gampang menghubungi, cépét, tepat, dan singkat..
*Mungkin 50% lah, kan jamu juga ada yang buat nyembuhin penyakit juga. Jadi jamu itu baguslah, bagus juga dia bisa nyembuhin penyakit juga, bisa bikin badan sehat juga. *
kalo menurut ané soal èlmu apa yang bakal,penting buat kita pelajari dimasa depan ya, kalo orang kampung mah bilangnya ilmu ikhlas, ikhlas mènjalani hidup, ikhlas kaga punya duit.
Iya sangat penting sih basaha itu. Karena kan bahasa itu kan penting banget, apalagi khususnya daerah kite. Kite harus melestarikan bahasa kite. Kite harus bener-bener menjaga bahasa kite, jangan sampé ilang.
Kegiatan di pagi hari, ya biasa yang namanya ibu-ibu, mah. Ya pertama ya benah itu bebenah-benah rumah: nyuci, nyuci piring, ngejemurin pakean, ngurus anak tuh, sekolah. Terus abis itu masak. Habis masak-masal, rapi-rapi masak, biasa ngobrol, kongkow bareng temen-temen. Habis itu nunggu dah anak pulang sekolah. Habis anak pulang sekolah baru dah kita.
(1) Observe the non-linguistic aspects, such as filler, (2) Make sure your machine learning does not differ the suprasegmental aspect, like intonation which does not change the word and its meaning.
Each row of a tsv file represents a single audio clip, and contains the following information:
client_id - hashed UUID of a given user
audio_id - numeric id for audio file
audio_file - audio file name
duration_ms - duration of audio in milliseconds
prompt_id - numeric id for prompt
prompt - question for user
transcription - transcription of the audio response
votes - number of people that who approved a given transcript
age - age of the speaker1
gender - gender of the speaker1
language - language name
split - for data modelling, which subset of the data does this clip pertain to
char_per_sec - how many characters of transcription per second of audio
quality_tags - some automated assessment of the transcription--audio pair, separated by |
transcription-length - character per second under 3 characters per second
speech-rate - characters per second over 30 characters per second
short-audio - audio length under 2 seconds
long-audio - audio length over 5 minutes
non-allowed-script - transcription contains characters from a writing system not associated with the language
mixed-script-words - a single word contains characters from multiple writing systems
mixed-script-transcription - transcription spans multiple writing systems, but each word consistently uses only one
https://referensi.data.kemendikdasmen.go.id/budayakita/wbtb/objek/AA000491
https://petabahasa.kemdikbud.go.id/ (Web of peta bahasa does not consider Betawi language is part of Indonesia, particularly in Jakarta and West Jawa Province.
Yacub Fahmilda <yacub.fahmilda@gmail.com>
Riska Legistari Febri <riskalegistari25@gmail.com>
This dataset was partially funded by the Open Multilingual Speech Fund managed by Mozilla Common Voice.
This dataset is released under the Creative Commons Zero (CC-0) licence. By downloading this data you agree to not determine the identity of speakers in the dataset.
For a full list of age, gender, and accent options, see the demographics spec. These will only be reported if the speaker opted in to provide that information. ↩ ↩2