|
--- |
|
pretty_name: Common Voice Corpus 10.0 |
|
annotations_creators: |
|
- crowdsourced |
|
language_creators: |
|
- crowdsourced |
|
language_bcp47: |
|
- ab |
|
- ar |
|
- as |
|
- ast |
|
- az |
|
- ba |
|
- bas |
|
- be |
|
- bg |
|
- bn |
|
- 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 |
|
- mhr |
|
- mk |
|
- ml |
|
- mn |
|
- mr |
|
- mt |
|
- myv |
|
- nan-tw |
|
- ne-NP |
|
- nl |
|
- nn-NO |
|
- or |
|
- pa-IN |
|
- pl |
|
- pt |
|
- rm-sursilv |
|
- rm-vallader |
|
- ro |
|
- ru |
|
- rw |
|
- sah |
|
- sat |
|
- sc |
|
- sk |
|
- sl |
|
- sr |
|
- sv-SE |
|
- sw |
|
- ta |
|
- th |
|
- tig |
|
- tok |
|
- tr |
|
- tt |
|
- ug |
|
- uk |
|
- ur |
|
- uz |
|
- vi |
|
- vot |
|
- yue |
|
- zh-CN |
|
- zh-HK |
|
- zh-TW |
|
license: |
|
- cc0-1.0 |
|
multilinguality: |
|
- multilingual |
|
size_categories: |
|
ab: |
|
- 10K<n<100K |
|
ar: |
|
- 100K<n<1M |
|
as: |
|
- 1K<n<10K |
|
ast: |
|
- n<1K |
|
az: |
|
- n<1K |
|
ba: |
|
- 100K<n<1M |
|
bas: |
|
- 1K<n<10K |
|
be: |
|
- 100K<n<1M |
|
bg: |
|
- 1K<n<10K |
|
bn: |
|
- 100K<n<1M |
|
br: |
|
- 10K<n<100K |
|
ca: |
|
- 1M<n<10M |
|
ckb: |
|
- 100K<n<1M |
|
cnh: |
|
- 1K<n<10K |
|
cs: |
|
- 10K<n<100K |
|
cv: |
|
- 10K<n<100K |
|
cy: |
|
- 100K<n<1M |
|
da: |
|
- 1K<n<10K |
|
de: |
|
- 100K<n<1M |
|
dv: |
|
- 10K<n<100K |
|
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: |
|
- 1K<n<10K |
|
hi: |
|
- 10K<n<100K |
|
hsb: |
|
- 1K<n<10K |
|
hu: |
|
- 10K<n<100K |
|
hy-AM: |
|
- 1K<n<10K |
|
ia: |
|
- 10K<n<100K |
|
id: |
|
- 10K<n<100K |
|
ig: |
|
- 1K<n<10K |
|
it: |
|
- 100K<n<1M |
|
ja: |
|
- 10K<n<100K |
|
ka: |
|
- 1K<n<10K |
|
kab: |
|
- 100K<n<1M |
|
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 |
|
mhr: |
|
- 10K<n<100K |
|
mk: |
|
- n<1K |
|
ml: |
|
- 1K<n<10K |
|
mn: |
|
- 10K<n<100K |
|
mr: |
|
- 10K<n<100K |
|
mt: |
|
- 10K<n<100K |
|
myv: |
|
- 1K<n<10K |
|
nan-tw: |
|
- 10K<n<100K |
|
ne-NP: |
|
- n<1K |
|
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 |
|
sc: |
|
- 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 |
|
tig: |
|
- n<1K |
|
tok: |
|
- 1K<n<10K |
|
tr: |
|
- 10K<n<100K |
|
tt: |
|
- 10K<n<100K |
|
ug: |
|
- 10K<n<100K |
|
uk: |
|
- 10K<n<100K |
|
ur: |
|
- 100K<n<1M |
|
uz: |
|
- 100K<n<1M |
|
vi: |
|
- 10K<n<100K |
|
vot: |
|
- n<1K |
|
yue: |
|
- 10K<n<100K |
|
zh-CN: |
|
- 100K<n<1M |
|
zh-HK: |
|
- 100K<n<1M |
|
zh-TW: |
|
- 100K<n<1M |
|
source_datasets: |
|
- extended|common_voice |
|
task_categories: |
|
- automatic-speech-recognition |
|
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 10.0 |
|
|
|
## Table of Contents |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
|
- [Data Fields](#data-fields) |
|
- [Data Splits](#data-splits) |
|
- [Dataset Creation](#dataset-creation) |
|
- [Curation Rationale](#curation-rationale) |
|
- [Source Data](#source-data) |
|
- [Annotations](#annotations) |
|
- [Personal and Sensitive Information](#personal-and-sensitive-information) |
|
- [Considerations for Using the Data](#considerations-for-using-the-data) |
|
- [Social Impact of Dataset](#social-impact-of-dataset) |
|
- [Discussion of Biases](#discussion-of-biases) |
|
- [Other Known Limitations](#other-known-limitations) |
|
- [Additional Information](#additional-information) |
|
- [Dataset Curators](#dataset-curators) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
- [Contributions](#contributions) |
|
|
|
## Dataset Description |
|
|
|
- **Homepage:** https://commonvoice.mozilla.org/en/datasets |
|
- **Repository:** https://github.com/common-voice/common-voice |
|
- **Paper:** https://arxiv.org/abs/1912.06670 |
|
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice |
|
- **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co) |
|
|
|
### Dataset Summary |
|
|
|
The Common Voice dataset consists of a unique MP3 and corresponding text file. |
|
Many of the 20817 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 15234 validated hours in 96 languages, but more voices and languages are always added. |
|
Take a look at the [Languages](https://commonvoice.mozilla.org/en/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](https://huggingface.co/spaces/huggingface/hf-speech-bench) |
|
|
|
### Languages |
|
|
|
``` |
|
Abkhaz, 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, 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, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Toki Pona, 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`. |
|
|
|
```python |
|
{ |
|
'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. |
|
|
|
```python |
|
from datasets import load_dataset |
|
|
|
ds = load_dataset("mozilla-foundation/common_voice_10_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 |
|
|
|
The dataset is validated by "thumbs up" or "thumbs down" voting by listeners, as opposed to typed transcriptions. This biases speech recognition and pronunciation assessment systems against accented speakers (see e.g. [Kibishi and Nakagawa 2011](https://www.isca-speech.org/archive_v0/archive_papers/interspeech_2011/i11_1149.pdf), [Loukina et al. 2015](https://www.isca-speech.org/archive/pdfs/interspeech_2015/loukina15_interspeech.pdf), and [Gao et al. 2018](https://arxiv.org/pdf/1709.01713.pdf)). Such biases prevent accurate speech-to-text and pronunciation scoring for the accented, including in high stakes assessments such as for immigration qualification (e.g., [Australian Associated Press 2017](https://www.theguardian.com/australia-news/2017/aug/08/computer-says-no-irish-vet-fails-oral-english-test-needed-to-stay-in-australia), [Ferrier 2017](https://www.smh.com.au/technology/australian-exnews-reader-with-english-degree-fails-robots-english-test-20170809-gxsjv2.html), [Main and Watson 2022](https://www.bbc.com/news/uk-60264106)), forcing pronunciation assessment manufacturers to [overhaul their offerings with transcription data capable of measuring genuine listener intelligibility](https://www.english.com/blog/intelligibility-index-score-versant/). O’Brien _et al._ discuss this issue in ["Directions for the future of technology in pronunciation research and teaching"](https://www.jbe-platform.com/content/journals/10.1075/jslp.17001.obr) _Journal of Second Language Pronunciation_ **4**(2):182-207, e.g. on page 186, stating, "pronunciation researchers are primarily interested in improving L2 learners' intelligibility and comprehensibility, but they have not yet collected sufficient amounts of representative and reliable data (speech recordings with corresponding **annotations** and judgments) indicating which errors affect these speech dimensions and which do not." [Emphasis added; see also their discussion starting on page 192, "Collecting data through crowdsourcing."] Mozilla's EM Lewis-Jong discussed the trade-of of the greater quantity of data collection using binary voting at the expense of the greater quality of transcriptions typed by listeners during [the Q&A portion of this NVIDIA Speech AI Summit session.](https://event.on24.com/wcc/r/3907885/36548AA5E15A751C5D57FCF23B59140F) |
|
|
|
[More Information Needed] |
|
|
|
### Other Known Limitations |
|
|
|
[More Information Needed] |
|
|
|
## Additional Information |
|
|
|
### Dataset Curators |
|
|
|
[More Information Needed] |
|
|
|
### Licensing Information |
|
|
|
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) |
|
|
|
### 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 |
|
} |
|
``` |
|
|
