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Release Date: 9/17/2026
Format: MP3
Size: 376.04 MB
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-27.0-2026-09-11 of the Mozilla Common Voice Scripted Speech dataset for Khowar [کھوار - khw]. The dataset contains 9867 clips representing 18.58 hours of recorded speech (16.01 hours validated) from 51 speakers, recorded from a text corpus of 7,253 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 |
|---|---|---|---|
| - | 111 (1.1%) | 6 (11.8%) |
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 | 9,857 (99.9%) | 51 (100.0%) |
Gender declared: 10 of 9,867 clips (0.1%), 0 of 51 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 | 1,141 (11.6%) | 5 (9.8%) |
| thirties | Thirties | 2,606 (26.4%) | 7 (13.7%) |
| fourties | Fourties | 5,424 (55.0%) | 7 (13.7%) |
| fifties | Fifties | 5 (0.1%) | 1 (2.0%) |
| sixties | Sixties | - | - |
| seventies | Seventies | - | - |
| eighties | Eighties | - | - |
| nineties | Nineties | - | - |
| - | Unspecified | 691 (7.0%) | 44 (86.3%) |
Age declared: 9,176 of 9,867 clips (93.0%), 7 of 51 speakers (13.7%)
Clip buckets
| Bucket | Clips |
|---|---|
| Validated | 8,505 (86.2%) |
| Invalidated | 208 (2.1%) |
| Other | 1,154 (11.7%) |
Training splits
| Split | Clips |
|---|---|
| Train | 2,647 (31.1%) |
| Dev | 1,607 (18.9%) |
| Test | 1,535 (18.0%) |
Training split coverage: 5,789 of 8,505 validated clips (68.1%)
The dataset contains 8505 validated, 208 invalidated, and 1154 unresolved clips. The average clip duration is 6.779 seconds.
The text come from the books FLI and its partners organisation published. I also wrote my own around 1000 sentences.
Validated sentences: 7,059
| Category | Count |
|---|---|
| Unvalidated sentences | 194 |
| Pending sentences | 183 |
| Rejected sentences | 11 |
| Reported sentences | 4 |
The corpus contains 7,253 sentences: 7,059 validated and 194 unvalidated (183 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.
| Source | Sentences |
|---|---|
| اُورائے | 2,233 (31.7%) |
| انگریستان | 2,039 (28.9%) |
| کھوار شیلوغ | 1,339 (19.0%) |
| روبن سن کروسو | 1,017 (14.4%) |
| تان حوالہ | 178 (2.5%) |
| کھوار زبان | 91 (1.3%) |
| Other | 152 (2.2%) |
General
| Code | Domain | Clips | Speakers |
|---|---|---|---|
| general | General | 253 (2.6%) | 14 (27.5%) |
| agriculture_food | Agriculture and Food | 18 (0.2%) | 6 (11.8%) |
| automotive_transport | Automotive and Transport | 53 (0.5%) | 8 (15.7%) |
| finance | Finance | 2 (0.0%) | 2 (3.9%) |
| service_retail | Service and Retail | - | - |
| healthcare | Healthcare | 4 (0.0%) | 4 (7.8%) |
| history_law_government | History, Law and Government | 8 (0.1%) | 5 (9.8%) |
| media_entertainment | Media and Entertainment | 4 (0.0%) | 4 (7.8%) |
| nature_environment | Nature and Environment | 9 (0.1%) | 5 (9.8%) |
| news_current_affairs | News and Current Affairs | 4 (0.0%) | 3 (5.9%) |
| technology_robotics | Technology and Robotics | 1 (0.0%) | 1 (2.0%) |
| language_fundamentals | Language Fundamentals | 30 (0.3%) | 8 (15.7%) |
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 userpath - relative path of the audio filesentence - the sentence to be read aloudsentence_id - unique identifier for the sentencesentence_domain - domain classification(s) of the sentenceup_votes - number of people who said audio matches the textdown_votes - number of people who said audio does not match textage - age of the speaker1gender - gender of the speaker1accents - accents of the speaker1variant - variant of the language1locale - locale code of the languagesegment - if sentence belongs to a custom dataset segment, it will be listed herevalidated_sentences.tsvThe validated_sentences.tsv file contains one row per validated sentence in the text corpus:
sentence_id - unique identifier for the sentencesentence - the sentence textvariant - the variant of the languagesentence_domain - the domain(s) the sentence belongs tosource - the source the sentence was collected fromis_used - whether the sentence is still in circulation for recordingclips_count - number of clips recorded for this sentenceunvalidated_sentences.tsvThe unvalidated_sentences.tsv file contains one row per unvalidated sentence in the text corpus:
sentence_id - unique identifier for the sentencesentence - the sentence textvariant - the variant of the languagesentence_domain - the domain(s) the sentence belongs tosource - the source the sentence was collected fromup_votes - number of upvotes the sentence receiveddown_votes - number of downvotes the sentence receivedstatus - 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
Compensated · similar languages
A parallel corpus of over 10,000 sentences of Khowar and English. The sentences are derived from a multimodal corpus consisting of 152 transcribed and translated Khowar language videos.
A Khowar-English speech translation dataset with approximately 9.5 hours of Khowar audio with English text translations.