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Release Date: 9/17/2026
Format: MP3
Size: 205.89 MB
A collection of read speech recordings in Losso (Losso).
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.
nmz)This datasheet is for cv-corpus-27.0-2026-09-11 of the Mozilla Common Voice Scripted Speech dataset for Losso [Losso - nmz]. The dataset contains 14117 clips representing 11.55 hours of recorded speech (11.21 hours validated) from 35 speakers, recorded from a text corpus of 2,447 sentences.
Nawdm (also known as Nawdem, Losso, Losu, Naoudem) is a Gur language spoken in northern Togo and southern Ghana. It belongs to the Niger‑Congo family, under the Oti‑Volta subgroup. The language is used in daily communication, religious texts, poetry, oral tradition, and common expressions.
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 | 78 (0.6%) | 1 (2.9%) |
| transgender | Transgender | - | - |
| non-binary | Non-binary | - | - |
| do_not_wish_to_say | Prefer not to say | - | - |
| - | Unspecified | 14,039 (99.4%) | 34 (97.1%) |
Gender declared: 78 of 14,117 clips (0.6%), 1 of 35 speakers (2.9%)
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 | - | - |
| thirties | Thirties | 1,157 (8.2%) | 2 (5.7%) |
| fourties | Fourties | - | - |
| fifties | Fifties | - | - |
| sixties | Sixties | - | - |
| seventies | Seventies | - | - |
| eighties | Eighties | - | - |
| nineties | Nineties | - | - |
| - | Unspecified | 12,960 (91.8%) | 33 (94.3%) |
Age declared: 1,157 of 14,117 clips (8.2%), 2 of 35 speakers (5.7%)
Clip buckets
| Bucket | Clips |
|---|---|
| Validated | 13,698 (97.0%) |
| Invalidated | 106 (0.8%) |
| Other | 313 (2.2%) |
Training splits
| Split | Clips |
|---|---|
| Train | 846 (6.2%) |
| Dev | 795 (5.8%) |
| Test | 795 (5.8%) |
Training split coverage: 2,436 of 13,698 validated clips (17.8%)
The dataset contains 13698 validated, 106 invalidated, and 313 unresolved clips. The average clip duration is 2.947 seconds.
Validated sentences: 2,446
| Category | Count |
|---|---|
| Unvalidated sentences | 1 |
| Pending sentences | 1 |
| Rejected sentences | - |
| Reported sentences | 2 |
The corpus contains 2,447 sentences: 2,446 validated and 1 unvalidated (1 pending review, 0 rejected), with 2 reported for review.
Nawdm is a tonal language, with at least two level tones:
High tone: marked with an acute accent (á)
Low tone: marked with a grave accent (à)
Tone marking is not always used, typically reserved for pronouns, religious or formal texts.
Uppercase letters:
A B D E Ɛ F G Gw Gb H Ĥ I J K Kw Kp L M N Ny Ŋ Ŋm O Ɔ R S T U V W Y
Lowercase letters:
a b d e ɛ f g gw gb h ɦ i j k kw kp l m n ny ŋ ŋm o ɔ r s t u v w y
There follows a randomly selected sample of five sentences from the corpus.
Hiwung na lutu.
Nuuta.
Ha bawra blihbka na kom nin madi wu.
Bilu.
Ma kon ji, ma ci na nangkpang.
The dataset has been compiled from:
Religious texts and translations
Dictionaries of Nawdm
Common expressions collected from everyday life and fieldwork
| Source | Sentences |
|---|---|
| self | 2,446 (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
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.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)
Common Voice Nawdm (nmz) page (link to be activated when available)
Justin Bakoubolo
Guedela, PhD
Justin Bakoubolo — supervised the compilation and coordination of the dataset.
Justin Bakoubolo
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