Common Voice Scripted Speech 25.0 - Turkish

Türkçe — Turkish (tr)

This datasheet is for cv-corpus-25.0-2026-03-09 of the Mozilla Common Voice Scripted Speech dataset for Turkish [Türkçe - tr]. The dataset contains 126510 clips representing 135.29 hours of recorded speech (129.22 hours validated) from 1816 speakers, recorded from a text corpus of 413,900 sentences.

Language

Turkish is the most widely spoken language among Turkic languages and has around 100 million L1 speakers, which makes it the 18th most spoken language. It is the national language of Turkey and one of two official languages of Cyprus, and secondary languages of some neighboring countries. Many smaller groups in other countries exist, through migrations or communities from Ottoman era. These smaller groups should usually be categorized as a variant.

Variants

There are currently no variants defined for Common Voice Turkish dataset. It is worth noting that, until now, this dataset focused on literary Turkish, often called "Turkish of Turkey". There are also some L2 voices, mostly from immigrants coming into the country, but these can be categorized as "foreign accents".

Accents

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: 93,952 of 126,510 clips (74.3%), 448 of 1,816 speakers (24.7%)

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: 95,316 of 126,510 clips (75.3%), 491 of 1,816 speakers (27.0%)

Data splits for modelling

Clip buckets

Training splits

Training split coverage: 64,431 of 120,832 validated clips (53.3%)

The dataset contains 120832 validated, 5018 invalidated, and 660 unresolved clips. The average clip duration is 3.85 seconds.

Text corpus

Actual values of text corpus from the previous version (please check the text corpus tab) show that:

• Average Characters/Sentence of the whole corpus is 62.25, but validated set average is 35.915. Median values are 61 and 27 respectively.

• Average Words/Sentence of the whole corpus is 8.379, but validated set average is 5.096. Median values are 8 and 4 respectively.

• Shorter sentences in the older text corpus resulted in shorter recording durations, which is 3.8 seconds on the average (in the previous dataset). This value is increasing with each version, and we aim to reach 5 sec, which is the ideal minimum of most SotA model architectures.

Validated sentences: 410,410

The corpus contains 413,900 sentences: 410,410 validated and 3,490 unvalidated (40 pending review, 3,450 rejected), with 486 reported for review.

Writing system

Turkish uses an extended Latin alphabet.

Symbol table

Official Alphabet:

• Lowercase: a b c ç d e f g ğ h ı i j k l m n o ö p r s ş t u ü v y z

• Uppercase: A B C Ç D E F G Ğ H I İ J K L M N O Ö P R S Ş T U Ü V Y Z

Auxilary Characters (Arabic/Farsi loanwords): â î û Â Î Û

Sample

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

1. Bitinya'da Olimpos Dağı'nda vaftiz edildiği manastırda keşiş oldu.

2. Seçilmesinden beş gün sonra ani bir şekilde öldü.

3. Tahta resim kalıbı üstüne çizdiği Fransa haritası türünün ilkidir.

4. Lafın gelişi.

5. Mesleğe başladığı sırada birinci Dünya Savaşı başladı ve silah altına alındı.

Sources

The whole history of the text-corpora added throughout the years can be found in this forum post (Turkish). They mainly consist of:

• Pre 2021: SETimes => ~5k sentences (only these are recorded multiple times between 2018-2021, ~30h audio)

• 2021/10: Turkish proverbs => ~2.5k

• 2021-2022: Extracted sentences from books of Sabahattin Ali => ~18-19k sentences

• 2021-2024: Community generated conversational sentences => ~33k sentences

• 2023/11: Wikipedia random selection through cv-sentence-extractor => 348.5k

Please note that:

• Not all of these sentences are recorded. The validated set only includes ~63k unique sentences.

• Until inclusion of Wikipedia sentences, we did not put a minimum limit to the sentence length. Especially after adding short conversational sentences, the average recording duration dropped. For this reason, although they are descriptive statements, we put a 3 word/20 char minimum limit to Wikipedia sentences.

• We currently have ~110k sentences extracted from public works of deceased authors, and 20k community generated sentences, waiting to be cross checked.

Text domains

Until this version, we did not work on specialized sentence domains, except entries coming from individuals and ~300 numbers we added.

Processing

Except entries coming from individuals, we followed the following procedure for bulk additions to the text-corpus:

• Extraction using Common Voice rules (e.g. 14 words)

• Using Google Sheets to create conversational sentences in volunteer groups

• Every entry has been check twice by the datasheet author, then at least one person checked it again, fully.

• Wikipedia data extracted using the cv-sentence-extractor has been done through a long and carefull process to keep the corpus quality high. We don't expect more than 2.5% error rate in these sentences, and most will be based on the bad grammer in originals, which we could not correct at that time due to the random nature of the algorithm.

Please note that Turkish in Common Voice does not have a special language based validator, it uses the defaults.

Recommended post-processing

• Because there has been no sentence validation, there are some non-alphabet characters in the text corpus, especially coming from the SETimes corpus via proper names. You may like to remove these.

• Don't normalize extra characters used for Arabic/Farsi loanwords (â, î, û), normalizing them to a/ı/u will change the meaning and intonation.

• There are some proper names containing x and w, there are few and can be removed. We usually keep them because they are also used in minority languages of Turkey and in some proper names.

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

• 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.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

Main Channels:

• Telegram (IM / Support)

• Discourse (Documents)

Social media channels used during campaigns:

• Facebook (Community & Support & Guides)

• Youtube (Video Guides)

• X/Twitter

• Instagram

• LinkedIn Page

• Common Voice translators on Pontoon

• Common Voice Communities

Discussions

Most info can be found in Turkish language in the Discourse Turkish sub-forum. Other discussions are in Discourse main forum in English. Current discussions are on the Telegram group.

• Common Voice on Matrix

• Common Voice on Discourse

• Common Voice on Discord

• Common Voice on Telegram

Contribute

"How to contribute?", "What to avoid?", and similar topics for the newcomers can be found in the following forum post in Turkish: The process, rights & wrongs and dataset improvement

If you want to contribute, please first join the Telegram group.

Our future plans include:

• Adding more conversational sentences, validating extracted 110k sentences, adding longer sentences.

• Providing domain based text-corpora

• Adding pre-defined variants and accents

• Adding validators

• Prepare a global campaign

You can find more information about how to participate in the Common Voice Project on the following page:
Community Participation Guidelines

• Speak

• Write

• Listen

• Review

Acknowledgements

Datasheet authors

Bülent Özden

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