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Task: ASR
Release Date: 3/22/2026
Format: MP3
Size: 375.12 MB
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A collection of read speech recordings in Khowar (کھوار).
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.
khw)This datasheet is for cv-corpus-25.0-2026-03-09 of the Mozilla Common Voice Scripted Speech dataset for Khowar [کھوار - khw]. The dataset contains 9845 clips representing 18.53 hours of recorded speech (16 hours validated) from 50 speakers, recorded from a text corpus of 7,251 sentences.
The Khowar-speaking people are the largest group in Chitral and also use as a lingua franca in in the valley. This language is also known as Qashqari by Pashto speakers. It is classified within the Indo-Aryan branch of the Indo-European family. Besides Chitral, Khowar is also spoken in Gilgit-Baltistan and the Swat Valley. The estimated number of Khowar speakers in all regions is more than 600,000, with a population of 400,000 in Chitral alone. Khowar is a literate language, with books, magazines, radio and tive programs, and audio/video documentation. It has been included in the school curriculum since 2017.
| Code | Accent | Clips | Speakers |
|---|---|---|---|
| - | 89 (0.9%) | 5 (10.0%) |
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 | 10 (0.1%) | 1 (2.0%) |
| transgender | Transgender | - | - |
| non-binary | Non-binary | - | - |
| do_not_wish_to_say | Prefer not to say | - | - |
| - | Unspecified | 9,835 (99.9%) | 50 (100.0%) |
Gender declared: 10 of 9,845 clips (0.1%), 0 of 50 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 | 1,119 (11.4%) | 4 (8.0%) |
| thirties | Thirties | 2,606 (26.5%) | 7 (14.0%) |
| fourties | Fourties | 5,424 (55.1%) | 7 (14.0%) |
| fifties | Fifties | 5 (0.1%) | 1 (2.0%) |
| sixties | Sixties | - | - |
| seventies | Seventies | - | - |
| eighties | Eighties | - | - |
| nineties | Nineties | - | - |
| - | Unspecified | 691 (7.0%) | 44 (88.0%) |
Age declared: 9,154 of 9,845 clips (93.0%), 6 of 50 speakers (12.0%)
Clip buckets
| Bucket | Clips |
|---|---|
| Validated | 8,500 (86.3%) |
| Invalidated | 207 (2.1%) |
| Other | 1,138 (11.6%) |
Training splits
| Split | Clips |
|---|---|
| Train | 2,642 (31.1%) |
| Dev | 1,607 (18.9%) |
| Test | 1,535 (18.1%) |
Training split coverage: 5,784 of 8,500 validated clips (68.0%)
The dataset contains 8500 validated, 207 invalidated, and 1138 unresolved clips. The average clip duration is 6.777 seconds.
The text come from the books FLI and its partners organisation published. I also wrote my own around 1000 sentences.
Validated sentences: 7,051
| Category | Count |
|---|---|
| Unvalidated sentences | 200 |
| Pending sentences | 189 |
| Rejected sentences | 11 |
| Reported sentences | 4 |
The corpus contains 7,251 sentences: 7,051 validated and 200 unvalidated (189 pending review, 11 rejected), with 4 reported for review.
I used the standard writing system that is Perso-Arabic with standard symbol for specific sounds of Khowar
In addition to all letters of Urdu we use the following additional letters:
ݱ ݰ ݯ ځ څ
There follows a randomly selected sample of five sentences from the corpus.
غلام نبی ہیہ لُوؤ کارکوری اوا کورمہ اسوم اچی پِیسہ سُم لُودوم رے ٹیلی فونو بند اریر
شوروئی دی آخر بیتی اشوئے
نہ اوغ شیر،نہ بجلی وا نہ راہ صحیح
تہ ژور بابا ،بابا، رے ہر کا گیکو قوشد کویان
مہ ژور مہ دُوری اسور
Angrestan by Zafar Ullah Pervaz
Robinson Cruso, by Fardi
Oraya by Farid
Translation of MTB MLE material in Khowar by FLI
Khowar Material by Farid
Human and Children Rights Translation by Farid.
Khowar Folktales by Zahoor
100 Sentence by myself
| Source | Sentences |
|---|---|
| اُورائے | 2,233 (31.7%) |
| انگریستان | 2,039 (29.0%) |
| کھوار شیلوغ | 1,339 (19.0%) |
| روبن سن کروسو | 1,017 (14.4%) |
| تان حوالہ | 175 (2.5%) |
| کھوار زبان | 91 (1.3%) |
| Other | 147 (2.1%) |
General
| Code | Domain | Clips | Speakers |
|---|---|---|---|
| general | General | 252 (2.6%) | 13 (26.0%) |
| agriculture_food | Agriculture and Food | 18 (0.2%) | 6 (12.0%) |
| automotive_transport | Automotive and Transport | 53 (0.5%) | 8 (16.0%) |
| finance | Finance | 2 (0.0%) | 2 (4.0%) |
| service_retail | Service and Retail | - | - |
| healthcare | Healthcare | 4 (0.0%) | 4 (8.0%) |
| history_law_government | History, Law and Government | 8 (0.1%) | 5 (10.0%) |
| media_entertainment | Media and Entertainment | 4 (0.0%) | 4 (8.0%) |
| nature_environment | Nature and Environment | 9 (0.1%) | 5 (10.0%) |
| news_current_affairs | News and Current Affairs | 4 (0.0%) | 3 (6.0%) |
| technology_robotics | Technology and Robotics | 1 (0.0%) | 1 (2.0%) |
| language_fundamentals | Language Fundamentals | 30 (0.3%) | 8 (16.0%) |
Collected soft books and got copy waiver from authors. Put on Excel sheet and reviewed the sentences for length and correction. Sent to Meesum Alam. In my own case upload the sentence directly. Voice over the sentences by different by people. Validated the sentences by different people.
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)
Common Voice Community
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