Loading datasets...
Task: ASR
Release Date: 9/15/2025
Format: MP3
Size: 11.20 MB
Share
A collection of spontaneous spoken phrases in Catalan.
Restrictions/Special Constraints
You agree that you will not re-host or re-share this dataset
Forbidden Usage
You agree not to attempt to determine the identity of speakers in the Common Voice 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.
ca)This datasheet has been generated automatically, we would love to include more information, if you would like to help out, get in touch!
This datasheet is for version 23.0 of the the Mozilla Common Voice Spontaneous Speech dataset
for Catalan (ca). The dataset contains 137 clips representing 1 hours of recorded
speech (1 hours validated) from 11 speakers.
The dataset includes the following distribution of age and gender.
Self-declared gender information, frequency refers to the number of clips annotated with this gender.
Self-declared age information, frequency refers to the number of clips annotated with this age band.
Prompts: 57
Duration: 2101320[ms]
Avg. Transcription Len: 74
Avg. Duration: 15.34[s]
Valid Duration: 605.95[s]
Total hours: 0.58[h]
Valid hours: 0.17[h]
There follows a randomly selected sample of questions used in the corpus.
Tʼinformes de lʼactualitat sense fer servir televisió convencional? Com?
Quin és el teu plat preferit?
Explica alguna tradició de Sant Jordi que trobis especial.
Què significa lʼèxit per a tu?
Què creus que és important per a un bon barri?
There follows a randomly selected sample of transcribed responses from the corpus.
Els avenços que s'han fet en el camp de la diabetis, eh... on te... cada vegada hi ha sensors per me... mesurar la glucosa en sang, i bombes per a... administrar la insulina, diferents tecnologies que... la veritat és que... ajuden molt al, al dia a dia del... dels pacients i... i que fa un temps enrere no els ah tenien i és un camp qualitatiu molt gran.
Doncs en els últims anys, la veritat és que viatjo molt menys, perquè trobo que tot s'ha encarit moltíssim, tant l'allotjament com els vols o la gasolina pel cotxe, etcètera. La qual cosa fa que, francament, viatgi potser la meitat del que acostumava a viatjar abans.
Per seguir l'actualitat, normalment utilitzo l'aplicació del 324. De 3cat.
Each row of a tsv file represents a single audio clip, and contains the following information:
client_id - hashed UUID of a given user
audio_id - numeric id for audio file
audio_file - audio file name
duration_ms - duration of audio in milliseconds
prompt_id - numeric id for prompt
prompt - question for user
transcription - transcription of the audio response
votes - number of people that who approved a given transcript
age - age of the speaker1
gender - gender of the speaker1
language - language name
split - for data modelling, which subset of the data does this clip pertain to
char_per_sec - how many characters of transcription per second of audio
quality_tags - some automated assessment of the transcription--audio pair, separated by |
transcription-length - character per second under 3 characters per second
speech-rate - characters per second over 30 characters per second
short-audio - audio length under 2 seconds
long-audio - audio length over 30 seconds
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 demograpics spec. These will only be reported if the speaker opted in to provide that information. ↩ ↩2