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  ### Dataset Summary
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- VoxDIY RusNews is a dataset for crowdsourced text aggregation collected via VoxDIY pipeline. RusNews dataset was used as a source for the speech generation step.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Dataset Summary
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+ VoxDIY RusNews is the first publicly available large-scale dataset of crowdsourced audio transcriptions in Russian language.
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+ The dataset was constructed by annotating audio recordings of Russian sentences from news domain on [Toloka crowdsourcing platform](https://toloka.ai).
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+ VoxDIY RusNews consists of 3091 instances having around 21K annotations obtained from crowd workers.
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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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+ Russian
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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
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+ ground truth. 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/russian/1003.wav',
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+ 'transcriptions': 'в список так же попали мэрлин монро джон ленон и альберт эйнштейн | в список также попали мерлин монро джон ленон и альберт энштейн | в список также попали мерилин монро джон леннон и альберт энтштейн | в список также попали мэрилин монро джон леннон и альберт эпштейн | в список также попали мэрилин монро джон леннон и альберт эйнштейн | в список так же попали мерелин монро джон ленон и альберт нштейн | в список также попали мэрилин монро джон леннон и альберт эйнштейн',
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+ 'performers': '1743 | 784 | 1014 | 1572 | 744 | 2187 | 1208',
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+ 'gt': 'в список также попали мэрилин монро джон леннон и альберт эйнштейн'}
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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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+ ## Dataset Creation
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+
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+ ### Source Data
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+
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+ The audio recordings were obtained using a [speech synthesis tool](https://cloud.yandex.com/en-ru/services/speechkit).
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+ The source sentences come from the Russian test set of the machine translation shared task executed as a part of the
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+ Eights and Ninth Workshops on Statistical Machine Translation ([WMT 2013](https://www.statmt.org/wmt13/) and [WMT 2014](https://www.statmt.org/wmt14/)).
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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 Russian 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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+ ```