Datasets:
metadata
license: cc0-1.0
language:
- ab
- af
- am
- ar
- as
- ast
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- dyu
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- gl
- gn
- ha
- he
- hi
- hsb
- hu
- ia
- id
- ig
- is
- it
- ja
- ka
- kab
- kk
- kmr
- ko
- ky
- lg
- lo
- lt
- lv
- mdf
- mhr
- mk
- ml
- mn
- mr
- mrj
- mt
- myv
- nl
- oc
- or
- pl
- ps
- pt
- quy
- ro
- ru
- rw
- sah
- sat
- sc
- sk
- skr
- sl
- sq
- sr
- sw
- ta
- th
- ti
- tig
- tk
- tok
- tr
- tt
- tw
- ug
- uk
- ur
- uz
- vi
- vot
- yue
- za
- zgh
- zh
- yo
task_categories:
- automatic-speech-recognition
pretty_name: Common Voice Corpus 22.0
size_categories:
- 100B<n<1T
tags:
- mozilla
- foundation
dataset_info:
- config_name: ab
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
splits:
- name: train
num_bytes: 821931235.695
num_examples: 21037
- name: validation
num_bytes: 397796991.848
num_examples: 9152
- name: test
num_bytes: 373536715.14
num_examples: 9132
- name: other
num_bytes: 531967636.306
num_examples: 16738
- name: invalidated
num_bytes: 197961922.03
num_examples: 5290
download_size: 1900457730
dataset_size: 2323194501.019
- config_name: af
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
splits:
- name: train
num_bytes: 5540295
num_examples: 139
- name: validation
num_bytes: 4973061
num_examples: 125
- name: test
num_bytes: 4958832
num_examples: 131
- name: other
num_bytes: 12549800
num_examples: 306
- name: invalidated
num_bytes: 4421447
num_examples: 198
download_size: 31592528
dataset_size: 32443435
- config_name: am
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
splits:
- name: train
num_bytes: 19268647
num_examples: 523
- name: validation
num_bytes: 8558518
num_examples: 248
- name: test
num_bytes: 9582994
num_examples: 252
- name: other
num_bytes: 23853466
num_examples: 579
- name: invalidated
num_bytes: 1250673
num_examples: 29
download_size: 61430333
dataset_size: 62514298
configs:
- config_name: ab
data_files:
- split: train
path: ab/train-*
- split: validation
path: ab/validation-*
- split: test
path: ab/test-*
- split: other
path: ab/other-*
- split: invalidated
path: ab/invalidated-*
- config_name: af
data_files:
- split: train
path: af/train-*
- split: validation
path: af/validation-*
- split: test
path: af/test-*
- split: other
path: af/other-*
- split: invalidated
path: af/invalidated-*
- config_name: am
data_files:
- split: train
path: am/train-*
- split: validation
path: am/validation-*
- split: test
path: am/test-*
- split: other
path: am/other-*
- split: invalidated
path: am/invalidated-*
Dataset Card for Common Voice Corpus 22.0
This dataset is an unofficial version of the Mozilla Common Voice Corpus 22. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/.
NOTE: currently converting to parquet for convenience.. WIP
Languages
Abkhaz, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
How to use
The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.
For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "en" for English):
from datasets import load_dataset
cv_22 = load_dataset("mort666/cv-corpus-v22", "en", split="train", trust_remote_code=True)
Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
from datasets import load_dataset
cv_22 = load_dataset("mort666/cv-corpus-v22", "en", split="train", streaming=True, trust_remote_code=True)
print(next(iter(cv_22)))
Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed).
Local
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
cv_22 = load_dataset("mort666/cv-corpus-v22", "en", split="train", trust_remote_code=True)
batch_sampler = BatchSampler(RandomSampler(cv_22), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_22, batch_sampler=batch_sampler)
Streaming
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_22 = load_dataset("mort666/cv-corpus-v22", "en", split="train", trust_remote_code=True)
dataloader = DataLoader(cv_22, batch_size=32)
To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.
Dataset Structure
Data Instances A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment.
Licensing Information
Public Domain, CC-0
Citation Information
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}