The dataset is currently empty. Upload or create new data files. Then, you will be able to explore them in the Dataset Viewer.
YAML Metadata Warning: empty or missing yaml metadata in repo card (

Golos dataset

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

Domain Train files Train hours Test files Test hours
Crowd 979 796 1 095 9 994 11.2
Farfield 124 003 132.4 1 916 1.4
Total 1 103 799 1 227.4 11 910 12.6


Audio files in opus format

Archive Size Link
golos_opus.tar 20.5 GB

Audio files in wav format

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

Archives Size Links
train_farfield.tar 15.4 GB
train_crowd0.tar 11 GB
train_crowd1.tar 14 GB
train_crowd2.tar 13.2 GB
train_crowd3.tar 11.6 GB
train_crowd4.tar 15.8 GB
train_crowd5.tar 13.1 GB
train_crowd6.tar 15.7 GB
train_crowd7.tar 12.7 GB
train_crowd8.tar 12.2 GB
train_crowd9.tar 8.08 GB
test.tar 1.3 GB

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)
Archives Size Links
QuartzNet15x5_golos.nemo 68 MB
KenLMs.tar 4.8 GB

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 set Crowd test Farfield test MCV1 dev MCV1 test
Greedy decoder 4.389 % 14.949 % 9.314 % 11.278 %
Beam Search with Common Crawl LM 4.709 % 12.503 % 6.341 % 7.976 %
Beam Search with Golos train set LM 3.548 % 12.384 % - -
Beam Search with Common Crawl and Golos LM 3.318 % 11.488 % 6.4 % 8.06 %

1 Common Voice - Mozilla's initiative to help teach machines how real people speak.


[] Golos: Russian Dataset for Speech Research

[] Golos — самый большой русскоязычный речевой датасет, размеченный вручную, теперь в открытом доступе

[] Как улучшить распознавание русской речи до 3% WER с помощью открытых данных

Edit dataset card
Evaluate models HF Leaderboard

Models trained or fine-tuned on SberDevices/Golos