Common Voice Scripted Speech 25.0 - French
License:
CC0-1.0
Steward:
Common VoiceTask: ASR
Release Date: 3/25/2026
Format: MP3
Size: 28.39 GB
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Description
A collection of read speech recordings in French (Français).
Specifics
Considerations
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.
Processes
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.
Metadata
Français — French (fr)
This datasheet is for cv-corpus-25.0-2026-03-09 of the Mozilla Common Voice Scripted Speech dataset for French [Français - fr]. The dataset contains 864728 clips representing 1209.71 hours of recorded speech (1095.87 hours validated) from 21003 speakers, recorded from a text corpus of 1,692,862 sentences.
Language
French is a Romance language. It is the official language of 26 countries and is spoken across around 50 countries.
Variants
| Code | Variant | Clips | Speakers |
|---|---|---|---|
| fr-metro | Français de métropole | 542,845 (62.8%) | 4,639 (22.1%) |
| fr-europe | Français d'Europe | 26,543 (3.1%) | 442 (2.1%) |
| fr-namerica | Français d'Amérique du Nord | 14,424 (1.7%) | 309 (1.5%) |
| fr-safrica | Français d'Afrique subsaharienne et des îles africaines | 2,066 (0.2%) | 87 (0.4%) |
| fr-droum | Français des départements et régions d'outre-mer | 1,936 (0.2%) | 40 (0.2%) |
| fr-nafrica | Français du nord de l'Afrique | 1,342 (0.2%) | 61 (0.3%) |
| fr-samerica | Français d'Amérique du Sud et des Caraïbes | 90 (0.0%) | 6 (0.0%) |
Accents
| Code | Accent | Clips | Speakers |
|---|---|---|---|
| canada | Français du Canada | 12,869 (1.5%) | 275 (1.3%) |
| belgium | Français de Belgique | 11,381 (1.3%) | 226 (1.1%) |
| switzerland | Français de Suisse | 5,916 (0.7%) | 141 (0.7%) |
| united_states | Français des États-Unis | 1,610 (0.2%) | 41 (0.2%) |
| reunion | Français de La Réunion | 1,307 (0.2%) | 16 (0.1%) |
| benin | Français du Bénin | 1,073 (0.1%) | 7 (0.0%) |
| algeria | Français d’Algérie | 1,070 (0.1%) | 26 (0.1%) |
| germany | Français d’Allemagne | 552 (0.1%) | 26 (0.1%) |
| fr-metro-north | Français du nord de la France | 535 (0.1%) | 2 (0.0%) |
| united_kingdom | Français du Royaume-Uni | 502 (0.1%) | 25 (0.1%) |
| haiti | Français d’Haïti | 498 (0.1%) | 7 (0.0%) |
| madagascar | Français de Madagascar | 283 (0.0%) | 12 (0.1%) |
| fr-metro-south | Français du sud de la France | 229 (0.0%) | 9 (0.0%) |
| morocco | Français du Maroc | 211 (0.0%) | 30 (0.1%) |
| fr-metro-east | Français de l'est de la France | 209 (0.0%) | 3 (0.0%) |
| cote_d_ivoire | Français de Côte d’Ivoire | 201 (0.0%) | 18 (0.1%) |
| senegal | Français du Sénégal | 197 (0.0%) | 16 (0.1%) |
| french_guiana | Français de Guyane | 188 (0.0%) | 3 (0.0%) |
| guadeloupe | Français de Guadeloupe | 175 (0.0%) | 13 (0.1%) |
| italy | Français d’Italie | 171 (0.0%) | 9 (0.0%) |
| fr-metro-west | Français de l'ouest de la France | 166 (0.0%) | 7 (0.0%) |
| cameroon | Français du Cameroun | 163 (0.0%) | 16 (0.1%) |
| new_caledonia | Français de Nouvelle-Calédonie | 159 (0.0%) | 3 (0.0%) |
| romania | Français de Roumanie | 150 (0.0%) | 6 (0.0%) |
| tunisia | Français de Tunisie | 121 (0.0%) | 16 (0.1%) |
| monaco | Français de Monaco | 111 (0.0%) | 3 (0.0%) |
| netherlands | Français des Pays-Bas | 101 (0.0%) | 4 (0.0%) |
| martinique | Français de Martinique | 100 (0.0%) | 7 (0.0%) |
| congo_kinshasa | Français du Congo (Kinshasa) | 45 (0.0%) | 5 (0.0%) |
| mali | Français du Mali | 39 (0.0%) | 4 (0.0%) |
| luxembourg | Français du Luxembourg | 20 (0.0%) | 3 (0.0%) |
| st_pierre_et_miquelon | Français de Saint-Pierre-et-Miquelon | 15 (0.0%) | 1 (0.0%) |
| mayotte | Français de Mayotte | 12 (0.0%) | 1 (0.0%) |
| mauritius | Français de l’Île Maurice | 10 (0.0%) | 2 (0.0%) |
| - | Other | 7,511 (0.9%) | 244 (1.2%) |
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.
| Code | Gender | Clips | Speakers |
|---|---|---|---|
| male_masculine | Male, masculine | 491,443 (56.8%) | 3,878 (18.5%) |
| female_feminine | Female, feminine | 92,369 (10.7%) | 1,010 (4.8%) |
| transgender | Transgender | 5 (0.0%) | 1 (0.0%) |
| non-binary | Non-binary | 249 (0.0%) | 3 (0.0%) |
| do_not_wish_to_say | Prefer not to say | 302 (0.0%) | 4 (0.0%) |
| - | Unspecified | 280,360 (32.4%) | 16,786 (79.9%) |
Gender declared: 584,368 of 864,728 clips (67.6%), 4,217 of 21,003 speakers (20.1%)
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.
| Code | Age | Clips | Speakers |
|---|---|---|---|
| teens | Teens | 24,515 (2.8%) | 444 (2.1%) |
| twenties | Twenties | 147,928 (17.1%) | 1,732 (8.2%) |
| thirties | Thirties | 125,467 (14.5%) | 1,156 (5.5%) |
| fourties | Fourties | 122,937 (14.2%) | 855 (4.1%) |
| fifties | Fifties | 81,444 (9.4%) | 496 (2.4%) |
| sixties | Sixties | 28,974 (3.4%) | 326 (1.6%) |
| seventies | Seventies | 9,224 (1.1%) | 121 (0.6%) |
| eighties | Eighties | 212 (0.0%) | 7 (0.0%) |
| nineties | Nineties | 5 (0.0%) | 1 (0.0%) |
| - | Unspecified | 324,022 (37.5%) | 16,619 (79.1%) |
Age declared: 540,706 of 864,728 clips (62.5%), 4,384 of 21,003 speakers (20.9%)
Data splits for modelling
Clip buckets
| Bucket | Clips |
|---|---|
| Validated | 783,357 (90.6%) |
| Invalidated | 68,142 (7.9%) |
| Other | 13,229 (1.5%) |
Training splits
| Split | Clips |
|---|---|
| Train | 613,431 (78.3%) |
| Dev | 16,201 (2.1%) |
| Test | 16,201 (2.1%) |
Training split coverage: 645,833 of 783,357 validated clips (82.4%)
The dataset contains 783357 validated, 68142 invalidated, and 13229 unresolved clips. The average clip duration is 5.036 seconds.
Text corpus
Validated sentences: 1,649,097
| Category | Count |
|---|---|
| Unvalidated sentences | 43,765 |
| Pending sentences | 43,638 |
| Rejected sentences | 127 |
| Reported sentences | 7,562 |
The corpus contains 1,692,862 sentences: 1,649,097 validated and 43,765 unvalidated (43,638 pending review, 127 rejected), with 7,562 reported for review.
Writing system
The French language uses the 26 letters of the Latin alphabet with the addition of two ligatures (æ, œ) and five diacritics.
Symbol table
a à â æ b c ç d e é è ê ë f g h i î ï j k l m n ô œ p q r s t u ù û ü v w x y ÿ z
Sample
There follows a randomly selected sample of five sentences from the corpus.
Le canton de Tende était composé des communes de Tende et La Brigue.
Cette décision fait grand bruit.
Un paludier est un travailleur qui récolte le sel des marais salants.
Les jardins sont ouverts au public.
Église mononef, elle ne présente aucun caractère particulier.
Sources
| Source | Sentences |
|---|---|
| wiki-2 | 719,731 (43.8%) |
| wiki-1 | 717,145 (43.7%) |
| sentence-collector | 103,289 (6.3%) |
| issue2259_deleted_export_readd_fixed | 62,385 (3.8%) |
| Other | 39,075 (2.4%) |
Text domains
| Code | Domain | Clips | Speakers |
|---|---|---|---|
| general | General | 70 (0.0%) | 53 (0.3%) |
| agriculture_food | Agriculture and Food | - | - |
| automotive_transport | Automotive and Transport | 1 (0.0%) | 1 (0.0%) |
| finance | Finance | 1 (0.0%) | 1 (0.0%) |
| service_retail | Service and Retail | - | - |
| healthcare | Healthcare | 5 (0.0%) | 4 (0.0%) |
| history_law_government | History, Law and Government | 19 (0.0%) | 17 (0.1%) |
| media_entertainment | Media and Entertainment | 17 (0.0%) | 13 (0.1%) |
| nature_environment | Nature and Environment | 8 (0.0%) | 8 (0.0%) |
| news_current_affairs | News and Current Affairs | 2 (0.0%) | 2 (0.0%) |
| technology_robotics | Technology and Robotics | 18 (0.0%) | 12 (0.1%) |
| language_fundamentals | Language Fundamentals | 7 (0.0%) | 5 (0.0%) |
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 userpath- relative path of the audio filetext- supposed transcription of the audioup_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 language1segment- if sentence belongs to a custom dataset segment, it will be listed hereprompt_upvotes- number of upvotes the sentence prompt receivedprompt_reports- number of reports the sentence prompt receivedis_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 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 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 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 (pendingorrejected)
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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
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