Loading datasets...
Release Date: 9/17/2026
Format: MP3
Size: 21.40 GB
A collection of read speech recordings in Chinese (China) (汉语（中国大陆）).
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.
zh-CN)This datasheet is for cv-corpus-27.0-2026-09-11 of the Mozilla Common Voice Scripted Speech dataset for Chinese (China) [汉语（中国大陆） - zh-CN]. The dataset contains 852410 clips representing 1074.96 hours of recorded speech (240.11 hours validated) from 7601 speakers, recorded from a text corpus of 60,191 sentences.
| Code | Accent | Clips | Speakers |
|---|---|---|---|
| 110000 | 出生地：11 北京市 | 7,204 (0.8%) | 130 (1.7%) |
| 360000 | 出生地：36 江西省 | 5,764 (0.7%) | 43 (0.6%) |
| 440000 | 出生地：44 广东省 | 4,634 (0.5%) | 97 (1.3%) |
| 230000 | 出生地：23 黑龙江省 | 3,986 (0.5%) | 46 (0.6%) |
| 320000 | 出生地：32 江苏省 | 3,923 (0.5%) | 97 (1.3%) |
| 370000 | 出生地：37 山东省 | 3,701 (0.4%) | 98 (1.3%) |
| 310000 | 出生地：31 上海市 | 3,621 (0.4%) | 60 (0.8%) |
| 330000 | 出生地：33 浙江省 | 2,987 (0.4%) | 88 (1.2%) |
| 120000 | 出生地：12 天津市 | 2,914 (0.3%) | 22 (0.3%) |
| 210000 | 出生地：21 辽宁省 | 2,904 (0.3%) | 42 (0.6%) |
| 510000 | 出生地：51 四川省 | 2,713 (0.3%) | 76 (1.0%) |
| 410000 | 出生地：41 河南省 | 2,275 (0.3%) | 68 (0.9%) |
| 130000 | 出生地：13 河北省 | 1,950 (0.2%) | 60 (0.8%) |
| 350000 | 出生地：35 福建省 | 1,855 (0.2%) | 38 (0.5%) |
| 420000 | 出生地：42 湖北省 | 1,817 (0.2%) | 55 (0.7%) |
| 450000 | 出生地：45 广西壮族自治区 | 1,685 (0.2%) | 24 (0.3%) |
| 140000 | 出生地：14 山西省 | 1,498 (0.2%) | 32 (0.4%) |
| 340000 | 出生地：34 安徽省 | 1,411 (0.2%) | 42 (0.6%) |
| 500000 | 出生地：50 重庆市 | 1,409 (0.2%) | 22 (0.3%) |
| 430000 | 出生地：43 湖南省 | 1,058 (0.1%) | 52 (0.7%) |
| 610000 | 出生地：61 陕西省 | 685 (0.1%) | 37 (0.5%) |
| 220000 | 出生地：22 吉林省 | 534 (0.1%) | 24 (0.3%) |
| 640000 | 出生地：64 宁夏回族自治区 | 414 (0.0%) | 6 (0.1%) |
| 650000 | 出生地：65 新疆维吾尔自治区 | 355 (0.0%) | 18 (0.2%) |
| 460000 | 出生地：46 海南省 | 331 (0.0%) | 2 (0.0%) |
| 150000 | 出生地：15 内蒙古自治区 | 277 (0.0%) | 17 (0.2%) |
| 530000 | 出生地：53 云南省 | 240 (0.0%) | 14 (0.2%) |
| 620000 | 出生地：62 甘肃省 | 187 (0.0%) | 17 (0.2%) |
| 520000 | 出生地：52 贵州省 | 164 (0.0%) | 13 (0.2%) |
| 810000 | 出生地：81 香港特别行政区 | 113 (0.0%) | 4 (0.1%) |
| 710000 | 出生地：71 台湾省 | 85 (0.0%) | 2 (0.0%) |
| 630000 | 出生地：63 青海省 | 5 (0.0%) | 1 (0.0%) |
| 540000 | 出生地：54 西藏自治区 | 5 (0.0%) | 1 (0.0%) |
| - | Other | 3,918 (0.5%) | 82 (1.1%) |
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 | 48,547 (5.7%) | 975 (12.8%) |
| female_feminine | Female, feminine | 12,350 (1.4%) | 260 (3.4%) |
| transgender | Transgender | - | - |
| non-binary | Non-binary | 238 (0.0%) | 2 (0.0%) |
| do_not_wish_to_say | Prefer not to say | 536 (0.1%) | 9 (0.1%) |
| - | Unspecified | 790,739 (92.8%) | 6,484 (85.3%) |
Gender declared: 61,671 of 852,410 clips (7.2%), 1,117 of 7,601 speakers (14.7%)
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 | 11,818 (1.4%) | 232 (3.1%) |
| twenties | Twenties | 41,824 (4.9%) | 883 (11.6%) |
| thirties | Thirties | 11,782 (1.4%) | 198 (2.6%) |
| fourties | Fourties | 2,197 (0.3%) | 60 (0.8%) |
| fifties | Fifties | 165 (0.0%) | 10 (0.1%) |
| sixties | Sixties | 6 (0.0%) | 2 (0.0%) |
| seventies | Seventies | 5 (0.0%) | 1 (0.0%) |
| eighties | Eighties | - | - |
| nineties | Nineties | 30 (0.0%) | 2 (0.0%) |
| - | Unspecified | 784,583 (92.0%) | 6,360 (83.7%) |
Age declared: 67,827 of 852,410 clips (8.0%), 1,241 of 7,601 speakers (16.3%)
Clip buckets
| Bucket | Clips |
|---|---|
| Validated | 190,406 (22.3%) |
| Invalidated | 59,382 (7.0%) |
| Other | 602,622 (70.7%) |
Training splits
| Split | Clips |
|---|---|
| Train | 29,814 (15.7%) |
| Dev | 10,679 (5.6%) |
| Test | 10,679 (5.6%) |
Training split coverage: 51,172 of 190,406 validated clips (26.9%)
The dataset contains 190406 validated, 59382 invalidated, and 602622 unresolved clips. The average clip duration is 4.54 seconds.
Validated sentences: 59,170
| Category | Count |
|---|---|
| Unvalidated sentences | 1,021 |
| Pending sentences | 132 |
| Rejected sentences | 889 |
| Reported sentences | 1,214 |
The corpus contains 60,191 sentences: 59,170 validated and 1,021 unvalidated (132 pending review, 889 rejected), with 1,214 reported for review.
There follows a randomly selected sample of five sentences from the corpus.
毕业于西南财经大学经济管理专业。
崇祯三年庚午科河南乡试解元。
因为此时迷途指针所指向的内存现在包含的已经完全是不同的数据。
山东战事仍为全局关键。
温卤面是一种中国朝鲜族面食。
| Source | Sentences |
|---|---|
| wiki | 54,638 (92.3%) |
| cn | 2,881 (4.9%) |
| Other | 1,650 (2.8%) |
| Code | Domain | Clips | Speakers |
|---|---|---|---|
| general | General | 978 (0.1%) | 184 (2.4%) |
| agriculture_food | Agriculture and Food | 60 (0.0%) | 41 (0.5%) |
| automotive_transport | Automotive and Transport | 81 (0.0%) | 60 (0.8%) |
| finance | Finance | 121 (0.0%) | 62 (0.8%) |
| service_retail | Service and Retail | 61 (0.0%) | 47 (0.6%) |
| healthcare | Healthcare | 155 (0.0%) | 80 (1.1%) |
| history_law_government | History, Law and Government | 453 (0.1%) | 135 (1.8%) |
| media_entertainment | Media and Entertainment | 1,825 (0.2%) | 204 (2.7%) |
| nature_environment | Nature and Environment | 77 (0.0%) | 54 (0.7%) |
| news_current_affairs | News and Current Affairs | 222 (0.0%) | 91 (1.2%) |
| technology_robotics | Technology and Robotics | 267 (0.0%) | 100 (1.3%) |
| language_fundamentals | Language Fundamentals | 120 (0.0%) | 69 (0.9%) |
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)
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