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Golos dataset


You can read the Russian version of the documentation.

Golos is a Russian corpus suitable for speech research. The dataset mainly consists of recorded audio files manually annotated on the crowd-sourcing platform. The total duration of the audio is about 1240 hours. We have made the corpus freely available for downloading, along with the acoustic model prepared on this corpus. Also we create 3-gram KenLM language model using an open Common Crawl corpus.

Dataset structure

DomainTrain filesTrain hoursTest filesTest hours
Crowd979 7961 0959 99411.2
Farfield124 003132.41 9161.4
Total1 103 7991 227.411 91012.6


Audio files in opus format

golos_opus.tar20.5 Gbhttps://sc.link/JpD

Audio files in wav format

Manifest files with all the training transcription texts are in the train_crowd9.tar archive listed in the table:

train_farfield.tar15.4 Gbhttps://sc.link/1Z3
train_crowd0.tar11 Gbhttps://sc.link/Lrg
train_crowd1.tar14 Gbhttps://sc.link/MvQ
train_crowd2.tar13.2 Gbhttps://sc.link/NwL
train_crowd3.tar11.6 Gbhttps://sc.link/Oxg
train_crowd4.tar15.8 Gbhttps://sc.link/Pyz
train_crowd5.tar13.1 Gbhttps://sc.link/Qz7
train_crowd6.tar15.7 Gbhttps://sc.link/RAL
train_crowd7.tar12.7 Gbhttps://sc.link/VG5
train_crowd8.tar12.2 Gbhttps://sc.link/WJW
train_crowd9.tar8.08 Gbhttps://sc.link/XKk
test.tar1.3 Gbhttps://sc.link/Kqr

Acoustic and language models

Acoustic model built using QuartzNet15x5 architecture and trained using NeMo toolkit.

Three n-gram language models created using KenLM Language Model Toolkit.

  • LM built on Common Crawl Russian dataset.
  • LM built on Golos train set.
  • LM built on Common Crawl and Golos datasets together (50/50).
QuartzNet15x5_golos.nemo68 MBhttps://sc.link/ZMv
KenLMs.tar4.8 Gbhttps://sc.link/YL0

Golos data and models are also available in the hub of pre-trained models, datasets, and containers — DataHub ML Space. You can train the model and deploy it on the high-performance SberCloud infrastructure in ML Space — full-cycle machine learning development platform for DS-teams collaboration based on the Christofari Supercomputer.


Percents of Word Error Rate for different test sets.

Decoder / Test setCrowd testFarfield testMCV devMCV test
Greedy decoder4.389%14.949%9.314%11.278%
Beam Search with Common Crawl LM4.709%12.503%6.341%7.976%
Beam Search with Golos train set LM3.548%12.384%
Beam Search with Common Crawl and Golos LM3.318%11.488%6.4%8.06%

MCV — Mozilla Common Voice — Mozilla's initiative to help teach machines how real people speak.


Golos: Russian Dataset for Speech Research.


Public license.


  • Aleksandr Denisenko.
  • Angelina Kovalenko.
  • Nikolaj Karpov.
  • Fedor Min'kin.


Ask your questions by email SmartSpeech@sberbank.ru.

Обновлено 19 мая 2022