| # 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 | | |
| ## **Downloads** | |
| ### **Audio files in opus format** | |
| | Archive | Size | Link | | |
| |:----------------:|:----------:|:-------------------:| | |
| | golos_opus.tar | 20.5 GB | https://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: | |
| | Archives | Size | Links | | |
| |-------------------|------------|---------------------| | |
| | train_farfield.tar| 15.4 GB | https://sc.link/1Z3 | | |
| | train_crowd0.tar | 11 GB | https://sc.link/Lrg | | |
| | train_crowd1.tar | 14 GB | https://sc.link/MvQ | | |
| | train_crowd2.tar | 13.2 GB | https://sc.link/NwL | | |
| | train_crowd3.tar | 11.6 GB | https://sc.link/Oxg | | |
| | train_crowd4.tar | 15.8 GB | https://sc.link/Pyz | | |
| | train_crowd5.tar | 13.1 GB | https://sc.link/Qz7 | | |
| | train_crowd6.tar | 15.7 GB | https://sc.link/RAL | | |
| | train_crowd7.tar | 12.7 GB | https://sc.link/VG5 | | |
| | train_crowd8.tar | 12.2 GB | https://sc.link/WJW | | |
| | train_crowd9.tar | 8.08 GB | https://sc.link/XKk | | |
| | test.tar | 1.3 GB | https://sc.link/Kqr | | |
| ### **Acoustic and language models** | |
| Acoustic model built using [QuartzNet15x5](https://arxiv.org/pdf/1910.10261.pdf) architecture and trained using [NeMo toolkit](https://github.com/NVIDIA/NeMo/tree/r1.0.0b4) | |
| Three n-gram language models created using [KenLM Language Model Toolkit](https://kheafield.com/code/kenlm) | |
| * LM built on [Common Crawl](https://commoncrawl.org) Russian dataset | |
| * LM built on Golos train set | |
| * LM built on [Common Crawl](https://commoncrawl.org) and Golos datasets together (50/50) | |
| | Archives | Size | Links | | |
| |--------------------------|------------|-----------------| | |
| | QuartzNet15x5_golos.nemo | 68 MB | https://sc.link/ZMv | | |
| | KenLMs.tar | 4.8 GB | https://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](https://sbercloud.ru/ru/aicloud/mlspace) - full-cycle machine learning development platform for DS-teams collaboration based on the Christofari Supercomputer. | |
| ## **Evaluation** | |
| Percents of Word Error Rate for different test sets | |
| | Decoder \ Test set | Crowd test | Farfield test | MCV<sup>1</sup> dev | MCV<sup>1</sup> 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 % | | |
| <sup>1</sup> [Common Voice](https://commonvoice.mozilla.org) - Mozilla's initiative to help teach machines how real people speak. | |
| ## **Resources** | |
| [[arxiv.org] Golos: Russian Dataset for Speech Research](https://arxiv.org/abs/2106.10161) | |
| [[habr.com] Golos — самый большой русскоязычный речевой датасет, размеченный вручную, теперь в открытом доступе](https://habr.com/ru/company/sberdevices/blog/559496/) | |
| [[habr.com] Как улучшить распознавание русской речи до 3% WER с помощью открытых данных](https://habr.com/ru/company/sberdevices/blog/569082/) | |