Common Voice Scripted Speech 27.0 - Bamun

Shüpamom — Bamun (bax)

This datasheet is for cv-corpus-27.0-2026-09-11 of the Mozilla Common Voice Scripted Speech dataset for Bamun [Shüpamom - bax]. The dataset contains 8669 clips representing 11.84 hours of recorded speech (10.62 hours validated) from 14 speakers, recorded from a text corpus of 1,030 sentences.

Language

Bamun or Shüpamom/Shupamem is a Bantu-Grassfield language spoken in the Noun Divison, West Region in Cameroon.

Variants

The Bamun language is quite homogeneous within their indigenous territory, the Noun Administrative Division. However, the Administrative Atlas of Cameroon's Languages (Breton and Bikia Fohtung, 1991) indicates a few "islands" outside the Noun Department where the Bamun language exhibits minor variations. These include Bapi in the Mifi Division in the West Region and Bamalang and Bangolan in the Mezam Division in the Northwest Region.

The variant represented in the collection of sentence prompts is that spoken in the Noun Division.

Demographic information

The dataset includes the following self-declared age and gender distributions. A coverage summary is shown below each table.

Gender

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.

Gender declared: 0 of 8,669 clips (0.0%), 0 of 14 speakers (0.0%)

Age

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.

Age declared: 0 of 8,669 clips (0.0%), 0 of 14 speakers (0.0%)

Data splits for modelling

Clip buckets

Training splits

Training split coverage: 1,030 of 7,780 validated clips (13.2%)

The dataset contains 7780 validated, 62 invalidated, and 827 unresolved clips. The average clip duration is 4.917 seconds.

Text corpus

Validated sentences: 1,030

The corpus contains 1,030 sentences: 1,030 validated and 0 unvalidated (0 pending review, 0 rejected), with 0 reported for review.

Writing system

The collection of sentence prompts provided by the language representatives aligns with the General Alphabet of Cameroonian Languages

Sample

There follows a randomly selected sample of five sentences from the corpus.

1. A puâ’ njap mbe yùe i ndi’ ṅi me yin kwet ṅaṅâ na.
2. Me na sha’a mi Ndam lié.
3. Ngam ka shèt mengap, i nue nzé put.
4. Lo’ tùtù nguo fa’ !
5. Me na nküt mfendap po ndû ne mbùm-ùe me njù pinsié na.

Sources

Fields

Clips

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
• sentence - the sentence to be read aloud
• sentence_id - unique identifier for the sentence
• sentence_domain - domain classification(s) of the sentence
• 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
• locale - locale code of the language
• segment - if sentence belongs to a custom dataset segment, it will be listed here

validated_sentences.tsv

The 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.tsv

The 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)

Get involved

Community links

• Common Voice translators on Pontoon
• Common Voice Communities

Discussions

• Common Voice on Matrix
• Common Voice on Discourse
• Common Voice on Discord
• Common Voice on Telegram

Contribute

• Speak
• Write
• Listen
• Review

Acknowledgements

Datasheet authors

Emmanuel Ngue Um , Germaine Shuewam , Njutapmvoui Ismaïla

Citation guidelines

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).

Funding

This dataset was partially funded by the Open Multilingual Speech Fund managed by Mozilla Common Voice.

Licence

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.

Footnotes

1. 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