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More details about the dataset (#6)

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- More details about the dataset (baa1e74e4326e5461258f0316ce1a4381bff537d)


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

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  ### Dataset Summary
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- CrowdSpeech is the first publicly available large-scale dataset of crowdsourced audio transcriptions.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Dataset Summary
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+ CrowdSpeech is the first publicly available large-scale dataset of crowdsourced audio transcriptions.
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+ The dataset was constructed by annotation [LibriSpeech](https://www.openslr.org/12) on [Toloka crowdsourcing platform](https://toloka.ai).
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+ CrowdSpeech consists of 22K instances having around 155K annotations obtained from crowd workers.
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+
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+
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+ ### Supported Tasks and Leaderboards
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+ Aggregation of crowd transcriptions.
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+
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+ ### Languages
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+ English
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ A data instance contains a url to the audio recording, a list of transcriptions along with the corresponding performers identifiers and ground truth.
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+ For each data instance, seven crowdsourced transcriptions are provided.
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+
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+ ```
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+ {'task': 'https://tlk.s3.yandex.net/annotation_tasks/librispeech/train-clean/0.mp3',
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+ '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",
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+ 'performers': '1154 | 3449 | 3097 | 461 | 3519 | 920 | 3660',
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+ '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"}
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+ ```
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+
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+ ### Data Fields
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+
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+ * task: a string containing a url of the audio recording
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+ * transcriptions: a list of the crowdsourced transcriptions separated by '|'
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+ * performers: the corresponding performers' identifiers.
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+ * gt: ground truth transcription
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+
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+ ### Data Splits
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+
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+ There are five splits in the data: train, test, test.other, dev.clean and dev.other.
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+ Splits train, test and dev.clean correspond to *clean* part of LibriSpeech that contains audio recordings of higher quality with accents
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+ of the speaker being closer to the US English. Splits dev.other and test.other correspond to *other* part of LibriSpeech with
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+ the recordings more challenging for recognition. The audio recordings are gender-balanced.
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+
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+ ## Dataset Creation
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+
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+ ### Source Data
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+
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+ [LibriSpeech](https://www.openslr.org/12) is a corpus of approximately 1000 hours of 16kHz read English speech.
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+
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+ ### Annotations
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+
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+ Annotation was done on [Toloka crowdsourcing platform](https://toloka.ai) with overlap of 7 (that is, each task was performed by 7 annotators).
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+
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+ Only annotators who self-reported the knowledge of English had access to the annotation task.
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+ Additionally, annotators had to pass *Entrance Exam*. For this, we ask all incoming eligible workers to annotate ten audio
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+ recordings. We then compute our target metric — Word Error Rate (WER) — on these recordings and accept to the main task all workers
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+ who achieve WER of 40% or less (the smaller the value of the metric, the higher the quality of annotation).
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+
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+ The Toloka crowdsourcing platform associates workers with unique identifiers and returns these identifiers to the requester.
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+ To further protect the data, we additionally encode each identifier with an integer that is eventually reported in our released datasets.
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+
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+ See more details in the [paper](https://arxiv.org/pdf/2107.01091.pdf).
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+
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+ ### Citation Information
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+
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+ ```
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+ @inproceedings{CrowdSpeech,
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+ author = {Pavlichenko, Nikita and Stelmakh, Ivan and Ustalov, Dmitry},
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+ title = {{CrowdSpeech and Vox~DIY: Benchmark Dataset for Crowdsourced Audio Transcription}},
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+ year = {2021},
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+ booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
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+ eprint = {2107.01091},
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+ eprinttype = {arxiv},
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+ eprintclass = {cs.SD},
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+ url = {https://openreview.net/forum?id=3_hgF1NAXU7},
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+ language = {english},
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+ pubstate = {forthcoming},
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+ }
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+ ```