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License:
CC-BY-NC-4.0
Steward:
MDC Curators
Task: MT
Release Date: 5/5/2026
Format: MP3, TSV
Size: 12.56 GB
Share
CoVoST 2 is a large-scale multilingual speech to text translation corpus based on Mozilla Common Voice 4.0. This segment of the corpus contains the English audio and the translations in Slovenian.
Licensing
Creative Commons Attribution Non Commercial 4.0 International (CC-BY-NC-4.0)
https://spdx.org/licenses/CC-BY-NC-4.0.htmlRestrictions/Special Constraints
Research and non-commercial use only
Forbidden Usage
You agree not to attempt to determine the identity of speakers in this dataset. You agree not to train models for public distribution on this dataset. Any attempt to clone the voice or train models that imitate the speakers in this dataset is forbidden
Ethical Review
This dataset contains data from speakers who have asked to be removed from the Mozilla Common Voice dataset, we expect that you will treat it with care. We expect it to be used only for research and non-commercial purposes only.
Intended Use
Replication experiments involving the CoVoST 2 datasets.
End-to-end speech-to-text translation (ST) has recently witnessed an increased interest given its system simplicity, lower inference latency and less compounding errors compared to cascaded ST (i.e. speech recognition + machine translation). End-to-end ST model training, however, is often hampered by the lack of parallel data. Thus, we created CoVoST, a large-scale multilingual ST corpus based on Common Voice, to foster ST research with the largest ever open dataset. Its latest version covers translations from English into 15 languages---Arabic, Catalan, Welsh, German, Estonian, Persian, Indonesian, Japanese, Latvian, Mongolian, Slovenian, Swedish, Tamil, Turkish, Chinese---and from 21 languages into English, including the 15 target languages as well as Spanish, French, Italian, Dutch, Portuguese, Russian. It has total 2,880 hours of speech and is diversified with 78K speakers.
path: Filename of the audio file
sentence: The sentence in the source language
translation: The sentence in the target language
client_id: The ID of the speaker of the source language, used for maintaining hygiene in the splits.
path sentence translation client_id
common_voice_en_18540003.mp3 When water is scarce, avoid wasting it. Varčuj z vodo, ko je primanjkuje. d277a1f3904ae00b09b73122b87674e7c2c78e08120721f37b5577013ead08d1ea0c053ca5b5c2fb948df2c81f27179aef2c741057a17249205d251a8fe0e658
common_voice_en_18540005.mp3 You will drive with her to her door. Z njo se boš peljal do njenih vrat. d277a1f3904ae00b09b73122b87674e7c2c78e08120721f37b5577013ead08d1ea0c053ca5b5c2fb948df2c81f27179aef2c741057a17249205d251a8fe0e658
common_voice_en_18540006.mp3 Celia shrank back, shivering. Celia je skočila nazaj in drhtela. d277a1f3904ae00b09b73122b87674e7c2c78e08120721f37b5577013ead08d1ea0c053ca5b5c2fb948df2c81f27179aef2c741057a17249205d251a8fe0e658
common_voice_en_65557.mp3 Have you got a ring? Imaš prstan? d28566a5d710dbd7e6c2ab4686ad5bd22ec86588a3abd11cefe0e93182e39a6f9da80550916fadb13e5ef051c7819e5aa5fc2e0ebbddc1b847c14926106c3fe3
If you use this dataset in your work please cite
@misc{wang2020covost,
title={CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus},
author={Changhan Wang and Anne Wu and Juan Pino},
year={2020},
eprint={2007.10310},
archivePrefix={arXiv},
primaryClass={cs.CL}
}