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Release Date: 9/17/2026
Format: MP3
Size: 214.46 MB
A collection of spontaneous responses to questions in Betawi (Betawi).
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-5.0-2026-09-11 of the Mozilla Common Voice Spontaneous Speech dataset for Betawi [Betawi - 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 | 854 | 63.9% |
| Dev | 214 | 16.0% |
| Test | 202 | 15.1% |
| 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.
Adé kegiatan apé di sekitar rumé pas malem ari?
Seberapé penting bahasé daerah bagi budaya Ente?
Apé wajar orang-orang tidur tengeh malem? (lebih dari jem 10 malem)
*Menurut Ente dewek, seberapé besar peran kesenian tuh buat acaré khusus ntuh? *
Kalo Ente punya kesempetan ke luar negeri, Ente mao cobé ngerasain musim apé di negaré mané?
There follows a randomly selected sample of transcribed responses from the corpus.
ya perlu lah, buat jaga-jaga aja sih takutnya kan pas di tempat wisata makananye ade yang kita ga suka, jadi kita gausah repot-repot nyari lagih, udah ada gitu makanannya yang kita suka, ga ngeluarin duit juga, irit.
Ngejaganya kita tuh jangan buang sampah sembarangan dah. Itu aja tuh yang paling penting biar ngga banjir juga ni udah mau musim ujan. Jangan buang sampah sembarangan sering-sering nyapuin jalan kayak gitu sih.
Banyak taneman mah dirumah noh daonnye ampe pada rontok ga diurusin. Jambu buahnya kaga tapi apa ya nanem kelengkeng ya rontok doang daonnya buahnya kaga, mau ditebang sayang takutnya kagak adem. Ada kembang apa namanya [uhm] kamboja dibilang nya kayak kuburan, terus pu'un apa tu yang buat nangkal jin, Pohon Bidara gak gede gede. Tau ah kudu diapain biarin ah yang penting idup aja.
*Cita-cita saya sih pas kecil dulu pengennyé jadi orang yang sukses, terus bisa ngebahagiain kedua orang tué, terus bisa jiarah kemané gitu pegi umroh tuh wajib. jadi orang yang bériman itu cita-cita saya tapi ya bégini émang adenyé dikampung kita nerimain tapi kita syukuri dengan keadaan seperti ini *
Taneman di sini paling kita seringnya nanem biji cabe tuh, ntar jadinya tumbuh-tumbuh liar tuh, tomat. Ya gitu-gitu doanglah di depan-depan rumah, iseng-iseng kita tanem. Ada juga tuh di depan tuh kebon, ada singkong, pohon pisang. Ya gitu-gitu doanglah. Namanya di kampung.
(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 <[email protected]>
Riska Legistari Febri <[email protected]>
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