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common_voice_8_0

Tasks:
Automatic Speech Recognition
Modalities:
Audio
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Size:
1M - 10M
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metadata
pretty_name: Common Voice Corpus 8.0
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language_bcp47:
  - ab
  - ar
  - as
  - az
  - ba
  - bas
  - be
  - bg
  - br
  - ca
  - ckb
  - cnh
  - cs
  - cv
  - cy
  - da
  - de
  - dv
  - el
  - en
  - eo
  - es
  - et
  - eu
  - fa
  - fi
  - fr
  - fy-NL
  - ga-IE
  - gl
  - gn
  - ha
  - hi
  - hsb
  - hu
  - hy-AM
  - ia
  - id
  - ig
  - it
  - ja
  - ka
  - kab
  - kk
  - kmr
  - ky
  - lg
  - lt
  - lv
  - mdf
  - mk
  - ml
  - mn
  - mr
  - mt
  - myv
  - nl
  - nn-NO
  - or
  - pa-IN
  - pl
  - pt
  - rm-sursilv
  - rm-vallader
  - ro
  - ru
  - rw
  - sah
  - sat
  - sk
  - sl
  - sr
  - sv-SE
  - sw
  - ta
  - th
  - tr
  - tt
  - ug
  - uk
  - ur
  - uz
  - vi
  - vot
  - zh-CN
  - zh-HK
  - zh-TW
license:
  - cc0-1.0
multilinguality:
  - multilingual
size_categories:
  ab:
    - 10K<n<100K
  ar:
    - 100K<n<1M
  as:
    - n<1K
  az:
    - n<1K
  ba:
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  bas:
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  ckb:
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  cs:
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  cv:
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  cy:
    - 100K<n<1M
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    - 1K<n<10K
  de:
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  dv:
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  el:
    - 10K<n<100K
  en:
    - 1M<n<10M
  eo:
    - 1M<n<10M
  es:
    - 100K<n<1M
  et:
    - 10K<n<100K
  eu:
    - 100K<n<1M
  fa:
    - 100K<n<1M
  fi:
    - 10K<n<100K
  fr:
    - 100K<n<1M
  fy-NL:
    - 10K<n<100K
  ga-IE:
    - 1K<n<10K
  gl:
    - 10K<n<100K
  gn:
    - 1K<n<10K
  ha:
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  hi:
    - 10K<n<100K
  hsb:
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  hu:
    - 10K<n<100K
  hy-AM:
    - 1K<n<10K
  ia:
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  id:
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  ig:
    - n<1K
  it:
    - 100K<n<1M
  ja:
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  ka:
    - 1K<n<10K
  kab:
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  kk:
    - 1K<n<10K
  kmr:
    - 10K<n<100K
  ky:
    - 10K<n<100K
  lg:
    - 100K<n<1M
  lt:
    - 10K<n<100K
  lv:
    - 1K<n<10K
  mdf:
    - n<1K
  mk:
    - n<1K
  ml:
    - 1K<n<10K
  mn:
    - 10K<n<100K
  mr:
    - 1K<n<10K
  mt:
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  myv:
    - 1K<n<10K
  nl:
    - 10K<n<100K
  nn-NO:
    - n<1K
  or:
    - 1K<n<10K
  pa-IN:
    - 1K<n<10K
  pl:
    - 100K<n<1M
  pt:
    - 100K<n<1M
  rm-sursilv:
    - 1K<n<10K
  rm-vallader:
    - 1K<n<10K
  ro:
    - 10K<n<100K
  ru:
    - 100K<n<1M
  rw:
    - 1M<n<10M
  sah:
    - 1K<n<10K
  sat:
    - n<1K
  sk:
    - 10K<n<100K
  sl:
    - 10K<n<100K
  sr:
    - 1K<n<10K
  sv-SE:
    - 10K<n<100K
  sw:
    - 100K<n<1M
  ta:
    - 100K<n<1M
  th:
    - 100K<n<1M
  tr:
    - 10K<n<100K
  tt:
    - 10K<n<100K
  ug:
    - 10K<n<100K
  uk:
    - 10K<n<100K
  ur:
    - 1K<n<10K
  uz:
    - 100K<n<1M
  vi:
    - 10K<n<100K
  vot:
    - n<1K
  zh-CN:
    - 10K<n<100K
  zh-HK:
    - 100K<n<1M
  zh-TW:
    - 10K<n<100K
source_datasets:
  - extended|common_voice
task_categories:
  - automatic-speech-recognition
task_ids: []
paperswithcode_id: common-voice
extra_gated_prompt: >-
  By clicking on “Access repository” below, you also agree to not attempt to
  determine the identity of speakers in the Common Voice dataset.

Dataset Card for Common Voice Corpus 8.0

Table of Contents

Dataset Description

Dataset Summary

The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 18243 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines.

The dataset currently consists of 14122 validated hours in 87 languages, but more voices and languages are always added. Take a look at the Languages page to request a language or start contributing.

Supported Tasks and Leaderboards

The results for models trained on the Common Voice datasets are available via the 🤗 Speech Bench

Languages 

Abkhaz, Arabic, Armenian, Assamese, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Breton, Bulgarian, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Moksha, Mongolian, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Tamil, Tatar, Thai, Turkish, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh

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.

{
  'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5', 
  'path': 'et/clips/common_voice_et_18318995.mp3', 
  'audio': {
    'path': 'et/clips/common_voice_et_18318995.mp3', 
    'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32), 
    'sampling_rate': 48000
  }, 
  'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.', 
  'up_votes': 2, 
  'down_votes': 0, 
  'age': 'twenties', 
  'gender': 'male', 
  'accent': '', 
  'locale': 'et', 
  'segment': ''
}

Data Fields

client_id (string): An id for which client (voice) made the recording

path (string): The path to the audio file

audio (dict): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: dataset[0]["audio"] the audio file is automatically decoded and resampled to dataset.features["audio"].sampling_rate. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. dataset[0]["audio"] should always be preferred over dataset["audio"][0].

sentence (string): The sentence the user was prompted to speak

up_votes (int64): How many upvotes the audio file has received from reviewers

down_votes (int64): How many downvotes the audio file has received from reviewers

age (string): The age of the speaker (e.g. teens, twenties, fifties)

gender (string): The gender of the speaker

accent (string): Accent of the speaker

locale (string): The locale of the speaker

segment (string): Usually an empty field

Data Splits

The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.

The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.

The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality.

The reported data is data that has been reported, for different reasons.

The other data is data that has not yet been reviewed.

The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.

Data Preprocessing Recommended by Hugging Face

The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.

Many examples in this dataset have trailing quotations marks, e.g “the cat sat on the mat.“. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: the cat sat on the mat.

In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.

from datasets import load_dataset

ds = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True)

def prepare_dataset(batch):
  """Function to preprocess the dataset with the .map method"""
  transcription = batch["sentence"]
  
  if transcription.startswith('"') and transcription.endswith('"'):
    # we can remove trailing quotation marks as they do not affect the transcription
    transcription = transcription[1:-1]
  
  if transcription[-1] not in [".", "?", "!"]:
    # append a full-stop to sentences that do not end in punctuation
    transcription = transcription + "."
  
  batch["sentence"] = transcription
  
  return batch

ds = ds.map(prepare_dataset, desc="preprocess dataset")

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.

Considerations for Using the Data

Social Impact of Dataset

The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

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
}