More details about the dataset (#6)

- More details about the dataset (baa1e74e4326e5461258f0316ce1a4381bff537d)


Co-authored-by: Smirnova Alisa <Aliskin@users.noreply.huggingface.co>

README.md

@@ -37,4 +37,78 @@ tags:
 
 ### Dataset Summary
 
-CrowdSpeech is the first publicly available large-scale dataset of crowdsourced audio transcriptions.
+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},
+}
+```