FAMA: The First Large-Scale Open-Science Speech Foundation Model for
  English and Italian
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.
6839680e3e5dd928f0576da0
Sara Papi
6839680e3e5dd928f0576da1
Marco Gaido
6839680e3e5dd928f0576da2
Luisa Bentivogli
6839680e3e5dd928f0576da3
Alessio Brutti
6839680e3e5dd928f0576da4
Mauro Cettolo
6839680e3e5dd928f0576da5
Roberto Gretter
6839680e3e5dd928f0576da6
Marco Matassoni
6839680e3e5dd928f0576da7
Mohamed Nabih
claimed_verified
2025-06-02T07:45:37.798Z
659eab38d55b5c4fa49172d7
https://cdn-avatars.huggingface.co/v1/production/uploads/659eab38d55b5c4fa49172d7/b6FHt_DIw2rHfcLumjv4f.png
Mohamed Nabih Ali
user
mnabihali
6839680e3e5dd928f0576da8
Matteo Negri
FAMA: The First Large-Scale Open-Science Speech Foundation Model for
  English and Italian
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.
FAMA, an open science family of speech foundation models, provides transparency and competitive performance by leveraging open-source training data and code.
speech foundation models Whisper SeamlessM4T open science open-source code open-source data FAMA pseudo-labeled speech