Loading datasets...
Task: ASR
Release Date: 3/22/2026
Format: MP3
Size: 175.21 MB
Share
A collection of read speech recordings in Hazargi (haz).
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.
haz)This datasheet is for cv-corpus-25.0-2026-03-09 of the Mozilla Common Voice Scripted Speech dataset for Hazargi [haz - haz]. The dataset contains 8325 clips representing 10.53 hours of recorded speech (10.52 hours validated) from 9 speakers, recorded from a text corpus of 1,361 sentences.
Hazargi is the language of Hazara people who live in Pakistan, Afghanistan, Iran, Europe, Australia and America. It contains 32 alphabets and the script is in Arabic with some additional characters which are Hazargi based.
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 | - | - |
| transgender | Transgender | - | - |
| non-binary | Non-binary | - | - |
| do_not_wish_to_say | Prefer not to say | - | - |
| - | Unspecified | 8,325 (100.0%) | 9 (100.0%) |
Gender declared: 0 of 8,325 clips (0.0%), 0 of 9 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 | 2,688 (32.3%) | 4 (44.4%) |
| thirties | Thirties | 2,707 (32.5%) | 2 (22.2%) |
| fourties | Fourties | - | - |
| fifties | Fifties | - | - |
| sixties | Sixties | - | - |
| seventies | Seventies | - | - |
| eighties | Eighties | - | - |
| nineties | Nineties | - | - |
| - | Unspecified | 2,930 (35.2%) | 4 (44.4%) |
Age declared: 5,395 of 8,325 clips (64.8%), 5 of 9 speakers (55.6%)
Clip buckets
| Bucket | Clips |
|---|---|
| Validated | 8,319 (99.9%) |
| Invalidated | 1 (0.0%) |
| Other | 5 (0.1%) |
Training splits
| Split | Clips |
|---|---|
| Train | 823 (9.9%) |
| Dev | 86 (1.0%) |
| Test | 446 (5.4%) |
Training split coverage: 1,355 of 8,319 validated clips (16.3%)
The dataset contains 8319 validated, 1 invalidated, and 5 unresolved clips. The average clip duration is 4.556 seconds.
As mentioned before that there has not been enough work on Hazargi so I tried to gather different books from the people around, arranged them and made the dataset. I gathered some 19 Hazargi books which are in different contexts like poems, folk stories, history and literature, the word count is around 893,112.
Validated sentences: 1,361
| Category | Count |
|---|---|
| Unvalidated sentences | - |
| Pending sentences | - |
| Rejected sentences | - |
| Reported sentences | 29 |
The corpus contains 1,361 sentences: 1,361 validated and 0 unvalidated (0 pending review, 0 rejected), with 29 reported for review.
The writing system is in Hazargi with an Arabic script that includes some additional Hazargi based characters.
ا ب پ ت ݖ ج چ خ د ۮ ر ز ژ س ش غ ف ق ک گ ل م ن و ۉ ۆ ۂ ی ې ݷ ئ
There follows a randomly selected sample of five sentences from the corpus.
بئل زیمینی خۉد خو مونی موزدوری بېگا سوبا
زیندئگی پور جۉب جیرئگۂ زیندئگی
مئگئم بېبولغۂ بارۉ شود چی بئد شود
دئرئگ تو دیدۂ لاکین گوم ، نۂ مې یی
نومودی وجودی
| Source | Sentences |
|---|---|
| تئلوئس | 1,361 (100.0%) |
General
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)
Mushtaq Mughul Ali Toorani Jawad Khawari Raziq Kohzad Mustafa Elkhani Shawkat shaoor Hussain Ali Yosufi Aziz Fayaz Qadir Nayil Yaseen Zameer Loyaqath Ajiz Manzoor poya T. Malistani Amir Shah Haidri Farhad Zahidi Doc Zaibul Nisa
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