Stable Audio Open
Open generative models are vitally important for the community, allowing for
fine-tunes and serving as baselines when presenting new models. However, most
current text-to-audio models are private and not accessible for artists and
researchers to build upon. Here we describe the architecture and training
process of a new open-weights text-to-audio model trained with Creative Commons
data. Our evaluation shows that the model's performance is competitive with the
state-of-the-art across various metrics. Notably, the reported FDopenl3 results
(measuring the realism of the generations) showcase its potential for
high-quality stereo sound synthesis at 44.1kHz.
669dbec5a8b62d05158f7d8b
Zach Evans
admin_assigned
2024-07-22T08:50:33.087Z
6306bca3cb5492c985a30588
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Zach Evans
user
Fauno15
669dbec5a8b62d05158f7d8c
Julian D. Parker
admin_assigned
2024-07-22T08:51:05.023Z
657c2cf25c6f0b1f36077033
/avatars/af538ee6d185d49efc0b7cf8660217ad.svg
Julian Parker
user
julian-parker
669dbec5a8b62d05158f7d8d
CJ Carr
admin_assigned
2024-07-22T08:51:14.822Z
64932b4a7ed33d93550902bb
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/RAF-iCFqukRnjZ1VbtVZU.jpeg
CJ Carr
user
cjus13
669dbec5a8b62d05158f7d8e
Zack Zukowski
admin_assigned
2024-07-22T08:51:20.602Z
666431010875aaaa91b2f1cb
/avatars/52b4941a8fdfabe7058d6d7f0e4909e9.svg
Zack Zukowski
user
drawordoodle
669dbec5a8b62d05158f7d8f
Josiah Taylor
admin_assigned
2024-07-22T08:51:26.239Z
642c16a0c3694d2b74558f14
https://cdn-avatars.huggingface.co/v1/production/uploads/642c16a0c3694d2b74558f14/ILnrIBeO_tA3BjkEmyyFZ.png
Josiah Taylor
user
AtOdds
669dbec5a8b62d05158f7d90
Jordi Pons
admin_assigned
2024-07-22T08:51:32.700Z
6682a312d17f4c29394f6c97
/avatars/18f943e694ce54c4949d467d73cfba14.svg
Jordi Pons
user
jordipons
Stable Audio Open
Open generative models are vitally important for the community, allowing for
fine-tunes and serving as baselines when presenting new models. However, most
current text-to-audio models are private and not accessible for artists and
researchers to build upon. Here we describe the architecture and training
process of a new open-weights text-to-audio model trained with Creative Commons
data. Our evaluation shows that the model's performance is competitive with the
state-of-the-art across various metrics. Notably, the reported FDopenl3 results
(measuring the realism of the generations) showcase its potential for
high-quality stereo sound synthesis at 44.1kHz.
An open-access text-to-audio model trained with Creative Commons data achieves competitive performance, particularly in high-quality stereo sound synthesis.
open generative models fine-tunes text-to-audio models Creative Commons data open-weights FDopenl3 stereo sound synthesis