标题： FAMA：首个面向英语和意大利语的大型开放科学语音基础模型 显示英文标题

标题： FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian

摘要： 像Whisper和SeamlessM4T这样的语音基础模型（SFM）的发展显著推动了语音处理领域的发展。然而，它们的封闭性质——无法获取训练数据和代码——带来了重大可重复性和公平评估的挑战。虽然其他领域通过开发完全透明的模型，在开源（OS）代码和数据上进行训练，已经取得了实质性进展，但语音领域的类似努力仍然有限。为填补这一空白，我们推出了FAMA，这是首个针对英语和意大利语的开放科学SFM系列，基于超过15万小时的开源语音数据进行训练。此外，我们还发布了一个包含两语言各16,000小时清理后且伪标注语音的新数据集。结果显示，FAMA的性能与现有SFM相当，同时速度提升高达8倍。所有成果，包括代码、数据集和模型，均以符合开源许可的方式发布，促进了语音技术研究中的开放性。 显示英文摘要

摘要： The development of speech foundation models (SFMs) like Whisper and SeamlessM4T has significantly advanced the field of speech processing. However, their closed nature--with inaccessible training data and code--poses major reproducibility and fair evaluation challenges. While other domains have made substantial progress toward open science by developing fully transparent models trained on open-source (OS) code and data, similar efforts in speech remain limited. To fill this gap, we introduce FAMA, the first family of open science SFMs for English and Italian, trained on 150k+ hours of OS speech data. Moreover, we present a new dataset containing 16k hours of cleaned and pseudo-labeled speech for both languages. Results show that FAMA achieves competitive performance compared to existing SFMs while being up to 8 times faster. All artifacts, including code, datasets, and models, are released under OS-compliant licenses, promoting openness in speech technology research.