pretty_name: Common Voice Corpus 16
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ha
language_bcp47:
- zh-CN
- zh-HK
- zh-TW
- sv-SE
- rm-sursilv
- rm-vallader
- pa-IN
- nn-NO
- ne-NP
- nan-tw
- hy-AM
- ga-IE
- fy-NL
license:
- cc0-1.0
multilinguality:
- multilingual
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 16
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://huggingface.co/datasets/Seon25/common_voice_16_0_/tree/main
- 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: Vaibhav Srivastav
Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 30328 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 19673 validated hours in 120 languages, but more voices and languages are always added. Take a look at the Languages page to request a language or start contributing.
Languages
Abkhaz, Afrikaans, 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, Hebrew, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latgalian, Latvian, Ligurian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Ossetian, 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, Telugu, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Western Sierra Puebla Nahuatl, Yiddish, 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 Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi):
from datasets import load_dataset
cv_16 = load_dataset("mozilla-foundation/common_voice_16_0", "hi", split="train")
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_16 = load_dataset("mozilla-foundation/common_voice_16_0", "hi", split="train", streaming=True)
print(next(iter(cv_16)))
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_16 = load_dataset("mozilla-foundation/common_voice_16_0", "hi", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_16), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_16, batch_sampler=batch_sampler)
Streaming
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_16 = load_dataset("mozilla-foundation/common_voice_16_0", "hi", split="train")
dataloader = DataLoader(cv_16, batch_size=32)
To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.
Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 16 with transformers - here.
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_16_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
}