Common Voice 15
Common Voice is a massively-multilingual collection of transcribed speech intended for speech technology research and development. Version 15.0 contains 28,750 recorded hours across 114 languages, consisting of crowdsourced voice recordings with corresponding transcriptions.
Qwen2.5-Omni-7B from Alibaba Cloud / Qwen Team currently leads the Common Voice 15 leaderboard with a score of 0.076 across 1 evaluated AI models.
What Common Voice 15 measures
Common Voice 15 is a audio benchmark that evaluates large language models on speech to text, language, and audio tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 100. Current average across reported models is 0.1, with the leader reaching 0.1.
Compare leaders on the best AI for speech to text, best AI for language and best AI for audio leaderboards.
Publication
- Paper
- Common Voice: A Massively-Multilingual Speech Corpus
- Authors
- Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, and 6 others
- Published
- arXiv
- 1912.06670
Abstract
The Common Voice corpus is a massively-multilingual collection of transcribed speech intended for speech technology research and development. Common Voice is designed for Automatic Speech Recognition purposes but can be useful in other domains (e.g. language identification). To achieve scale and sustainability, the Common Voice project employs crowdsourcing for both data collection and data validation. The most recent release includes 29 languages, and as of November 2019 there are a total of 38 languages collecting data. Over 50,000 individuals have participated so far, resulting in 2,500 hours of collected audio. To our knowledge this is the largest audio corpus in the public domain for speech recognition, both in terms of number of hours and number of languages. As an example use case for Common Voice, we present speech recognition experiments using Mozilla's DeepSpeech Speech-to-Text toolkit. By applying transfer learning from a source English model, we find an average Character Error Rate improvement of 5.99 +/- 5.48 for twelve target languages (German, French, Italian, Turkish, Catalan, Slovenian, Welsh, Irish, Breton, Tatar, Chuvash, and Kabyle). For most of these languages, these are the first ever published results on end-to-end Automatic Speech Recognition.
Qwen2.5-Omni-7B leads with 0.1%.
Progress Over Time
Interactive timeline showing model performance evolution on Common Voice 15
Common Voice 15 Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 7B | — | — |
FAQ
Common questions about Common Voice 15.
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