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Task: ASR
Release Date: 3/22/2026
Format: MP3
Size: 264.17 MB
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A collection of read speech recordings in Medumba (byv).
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.
byv)This datasheet is for cv-corpus-25.0-2026-03-09 of the Mozilla Common Voice Scripted Speech dataset for Medumba [byv - byv]. The dataset contains 7719 clips representing 13.24 hours of recorded speech (13.24 hours validated) from 96 speakers, recorded from a text corpus of 2,017 sentences.
Medumba (Mə̀dʉ̂mbɑ̀) is a Grassfield Bantu language spoken in the Nde Division, West Region in Cameroon.
The dataset includes the following self-declared age and gender distributions. A coverage summary is shown below each table.
Self-declared gender information. The table shows clip and speaker counts with percentages. Speakers who did not declare a gender are listed as Unspecified. A dash (-) indicates zero.
| Code | Gender | Clips | Speakers |
|---|---|---|---|
| male_masculine | Male, masculine | - | - |
| female_feminine | Female, feminine | 497 (6.4%) | 1 (1.0%) |
| transgender | Transgender | - | - |
| non-binary | Non-binary | - | - |
| do_not_wish_to_say | Prefer not to say | - | - |
| - | Unspecified | 7,222 (93.6%) | 96 (100.0%) |
Gender declared: 497 of 7,719 clips (6.4%), 0 of 96 speakers (0.0%)
Self-declared age information. The table shows clip and speaker counts with percentages. Speakers who did not declare an age are listed as Unspecified. A dash (-) indicates zero.
| Code | Age | Clips | Speakers |
|---|---|---|---|
| teens | Teens | - | - |
| twenties | Twenties | 497 (6.4%) | 1 (1.0%) |
| thirties | Thirties | 948 (12.3%) | 1 (1.0%) |
| fourties | Fourties | - | - |
| fifties | Fifties | - | - |
| sixties | Sixties | - | - |
| seventies | Seventies | - | - |
| eighties | Eighties | - | - |
| nineties | Nineties | - | - |
| - | Unspecified | 6,274 (81.3%) | 96 (100.0%) |
Age declared: 1,445 of 7,719 clips (18.7%), 0 of 96 speakers (0.0%)
Clip buckets
| Bucket | Clips |
|---|---|
| Validated | 7,719 (100.0%) |
| Invalidated | - |
| Other | - |
Training splits
| Split | Clips |
|---|---|
| Train | 336 (4.4%) |
| Dev | 327 (4.2%) |
| Test | 327 (4.2%) |
Training split coverage: 990 of 7,719 validated clips (12.8%)
The dataset contains 7719 validated, 0 invalidated, and 0 unresolved clips. The average clip duration is 6.178 seconds.
Validated sentences: 990
| Category | Count |
|---|---|
| Unvalidated sentences | 1,027 |
| Pending sentences | 1,027 |
| Rejected sentences | - |
| Reported sentences | - |
The corpus contains 2,017 sentences: 990 validated and 1,027 unvalidated (1,027 pending review, 0 rejected), with 0 reported for review.
The collection of sentence prompts provided by the language representatives aligns with the General Alphabet of Cameroonian Languages
There follows a randomly selected sample of five sentences from the corpus.
Mə́ bɑ́mə́ mɛ̀n zə̄ á nǔm mbə́ mbrə̀ ɔ́m. Ahmadou bə́ á yə̀k nsí.
Mba tswemanko’ na’ mbe ke ghù ju z’a lo’ tô nkàb yi ngùnyàm nà.
À se’ mfà nkàb i yi.
Á wʉ́ Nùmí nɔ́ɔ̀ cúp mbʉ̀ á swɛ́ɛ̀n Nùŋgɛ̀ dí.
Bà ŋkrʉ̌n lá' nǔm rə̂ bù lɛ̌n mbā á nǔ tɔ́k bə́ lá.
| Source | Sentences |
|---|---|
| Own Submission | 990 (100.0%) |
Each row of a tsv file represents a single audio clip, and contains the following information:
client_id - hashed UUID of a given user
path - relative path of the audio file
text - supposed transcription of the audio
up_votes - number of people who said audio matches the text
down_votes - number of people who said audio does not match text
age - age of the speaker1
gender - gender of the speaker1
accents - accents of the speaker1
variant - variant of the language1
segment - if sentence belongs to a custom dataset segment, it will be listed here
prompt_upvotes - number of upvotes the sentence prompt received
prompt_reports - number of reports the sentence prompt received
is_edited - whether the clip's transcription has been edited
validated_sentences.tsvThe validated_sentences.tsv file contains one row per validated sentence in the text corpus:
sentence_id - unique identifier for the sentence
sentence - the sentence text
variant - the variant of the language
sentence_domain - the domain(s) the sentence belongs to
source - the source the sentence was collected from
is_used - whether the sentence is still in circulation for recording
clips_count - number of clips recorded for this sentence
unvalidated_sentences.tsvThe unvalidated_sentences.tsv file contains one row per unvalidated sentence in the text corpus:
sentence_id - unique identifier for the sentence
sentence - the sentence text
variant - the variant of the language
sentence_domain - the domain(s) the sentence belongs to
source - the source the sentence was collected from
up_votes - number of upvotes the sentence received
down_votes - number of downvotes the sentence received
status - current status of the sentence (pending or rejected)
https://commonvoice.mozilla.org/byv/
Emmanuel Ngué Um , Jeff Sterline Ngami Kamagoua , Minette Yimga Mbamen
Ngué Um E, Ngo Tjomb EEC, Dibengue FL, Banum Manguele BM, Abo Djoulde B, Nyambe MA, Atangana Eloundou BM, Ngami Kamagoua JS, Mpouda Avom J, Nyobe Z, Eloundou Eyenga EG, Likwai AP (2025) Speech Technologies Datasets for African Under-Served Languages. Proceedings of the Eight Workshop on the Use of Computational Methods in the Study of Endangered Languages, edited by Lachler J, Agyapong G, Arppe A, Moeller S, Chaudhary A, Rijhwani S, Rosenblum D. URL Association for Computational Linguistics (ACL).
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 ↩3 ↩4