Abstract
The rise of foundation models (FMs), coupled with regulatory efforts addressing their risks and impacts, has sparked significant interest in open-source models. However, existing speech FMs (SFMs) fall short of full compliance with the open-source principles, even if claimed otherwise, as no existing SFM has model weights, code, and training data publicly available under open-source terms. In this work, we take the first step toward filling this gap by focusing on the 24 official languages of the European Union (EU). We collect suitable training data by surveying automatic speech recognition datasets and unlabeled speech corpora under open-source compliant licenses, for a total of 950k hours. Additionally, we release automatic transcripts for 441k hours of unlabeled data under the permissive CC-BY license, thereby facilitating the creation of open-source SFMs for the EU languages.
Abstract (translated)
基础模型（FMs）的崛起，以及针对其风险和影响的监管努力，引发了对于开源模型的浓厚兴趣。然而，现有的语音FM（SFM）即使在宣称符合开源原则的情况下，也未能完全符合，因为目前没有开源许可证下公开可用的模型权重、代码和训练数据。在这项工作中，我们迈出了填补这一空白的第一步，专注于欧盟（EU）的24个官方语言。我们通过调查开源许可证下的自动语音识别数据集和开源语音语料库，收集了总计950k小时的合适训练数据。此外，我们针对441k小时的未标注数据发布了宽松的CC-BY许可证的自动转录，从而为欧盟语言创建了开源SFM提供了便利。
URL
https://arxiv.org/abs/2410.01036
