Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
crowdsourced
Annotations Creators:
found
Source Datasets:
original
ArXiv:
License:
annotations_creators: | |
- found | |
language_creators: | |
- crowdsourced | |
language: | |
- en | |
license: | |
- cc-by-4.0 | |
multilinguality: | |
- monolingual | |
size_categories: | |
- unknown | |
source_datasets: | |
- original | |
task_categories: | |
- summarization | |
- automatic-speech-recognition | |
- text2text-generation | |
task_ids: [] | |
paperswithcode_id: crowdspeech | |
pretty_name: CrowdSpeech | |
language_bcp47: | |
- en-US | |
tags: | |
- conditional-text-generation | |
- stuctured-to-text | |
- speech-recognition | |
# Dataset Card for CrowdSpeech | |
## Dataset Description | |
- **Repository:** [GitHub](https://github.com/Toloka/CrowdSpeech) | |
- **Paper:** [Paper](https://openreview.net/forum?id=3_hgF1NAXU7) | |
- **Point of Contact:** research@toloka.ai | |
### Dataset Summary | |
CrowdSpeech is the first publicly available large-scale dataset of crowdsourced audio transcriptions. | |
The dataset was constructed by annotation [LibriSpeech](https://www.openslr.org/12) on [Toloka crowdsourcing platform](https://toloka.ai). | |
CrowdSpeech consists of 22K instances having around 155K annotations obtained from crowd workers. | |
### Supported Tasks and Leaderboards | |
Aggregation of crowd transcriptions. | |
### Languages | |
English | |
## Dataset Structure | |
### Data Instances | |
A data instance contains a url to the audio recording, a list of transcriptions along with the corresponding performers identifiers and ground truth. | |
For each data instance, seven crowdsourced transcriptions are provided. | |
``` | |
{'task': 'https://tlk.s3.yandex.net/annotation_tasks/librispeech/train-clean/0.mp3', | |
'transcriptions': "had laid before her a pair of alternatives now of course you're completely your own mistress and are as free as the bird on the bough i don't mean you were not so before but you're at present on a different footing | had laid before her a pair of alternatives now of course you are completely your own mistress and are as free as the bird on the bowl i don't mean you were not so before but you were present on a different footing | had laid before her a pair of alternatives now of course you're completely your own mistress and are as free as the bird on the bow i don't mean you are not so before but you're at present on a different footing | had laid before her a pair of alternatives now of course you're completely your own mistress and are as free as the bird on the bow i don't mean you are not so before but you're at present on a different footing | laid before her a pair of alternativesnow of course you're completely your own mistress and are as free as the bird on the bow i don't mean you're not so before but you're at present on a different footing | had laid before her a peril alternatives now of course your completely your own mistress and as free as a bird as the back bowl i don't mean you were not so before but you are present on a different footing | a lady before her a pair of alternatives now of course you're completely your own mistress and rs free as the bird on the ball i don't need you or not so before but you're at present on a different footing", | |
'performers': '1154 | 3449 | 3097 | 461 | 3519 | 920 | 3660', | |
'gt': "had laid before her a pair of alternatives now of course you're completely your own mistress and are as free as the bird on the bough i don't mean you were not so before but you're at present on a different footing"} | |
``` | |
### Data Fields | |
* task: a string containing a url of the audio recording | |
* transcriptions: a list of the crowdsourced transcriptions separated by '|' | |
* performers: the corresponding performers' identifiers. | |
* gt: ground truth transcription | |
### Data Splits | |
There are five splits in the data: train, test, test.other, dev.clean and dev.other. | |
Splits train, test and dev.clean correspond to *clean* part of LibriSpeech that contains audio recordings of higher quality with accents | |
of the speaker being closer to the US English. Splits dev.other and test.other correspond to *other* part of LibriSpeech with | |
the recordings more challenging for recognition. The audio recordings are gender-balanced. | |
## Dataset Creation | |
### Source Data | |
[LibriSpeech](https://www.openslr.org/12) is a corpus of approximately 1000 hours of 16kHz read English speech. | |
### Annotations | |
Annotation was done on [Toloka crowdsourcing platform](https://toloka.ai) with overlap of 7 (that is, each task was performed by 7 annotators). | |
Only annotators who self-reported the knowledge of English had access to the annotation task. | |
Additionally, annotators had to pass *Entrance Exam*. For this, we ask all incoming eligible workers to annotate ten audio | |
recordings. We then compute our target metric — Word Error Rate (WER) — on these recordings and accept to the main task all workers | |
who achieve WER of 40% or less (the smaller the value of the metric, the higher the quality of annotation). | |
The Toloka crowdsourcing platform associates workers with unique identifiers and returns these identifiers to the requester. | |
To further protect the data, we additionally encode each identifier with an integer that is eventually reported in our released datasets. | |
See more details in the [paper](https://arxiv.org/pdf/2107.01091.pdf). | |
### Citation Information | |
``` | |
@inproceedings{CrowdSpeech, | |
author = {Pavlichenko, Nikita and Stelmakh, Ivan and Ustalov, Dmitry}, | |
title = {{CrowdSpeech and Vox~DIY: Benchmark Dataset for Crowdsourced Audio Transcription}}, | |
year = {2021}, | |
booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks}, | |
eprint = {2107.01091}, | |
eprinttype = {arxiv}, | |
eprintclass = {cs.SD}, | |
url = {https://openreview.net/forum?id=3_hgF1NAXU7}, | |
language = {english}, | |
pubstate = {forthcoming}, | |
} | |
``` |