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The paper proposes GSLM2, a generative spoken language model that uses multiple discrete speech-unit streams instead of one, and reports gains on sWuggy, sBLIMP, and tSC. The results are measured on benchmarks across training-data and model-size scales, and the authors open-source code and checkpoints, so others can verify the claims. Readers should note that the reported gap reduction is relative to specific speech benchmarks and does not show that spoken models match text LMs in general.
GSLM2: Multi-Stream Speech Representation Learning and Generative Spoken Language Modeling
arXiv:2611.01143v1 Announce Type: new Abstract: Generative spoken language models (SLMs) offer a route to learning linguistic structure purely from speech, yet they lag far behind text-based models in both capability and data scaling efficiency. We identify two bottlenecks underlying this gap: the limited linguistic quality of existing speech units and the reliance on a single stream of discrete units to represent speech. Multiple codebooks are widely used to encode acoustic features but rarely to represent speech semantics. We present GSLM2, which learns and models complementary properties of speech at multiple levels of abstraction. Its encoder, SpidR 2.0, uses self-supervised learning with differentiable prediction heads to induce high-quality discrete units at multiple levels. A multi-stream SLM then jointly models these units using a shared temporal backbone, stream-specific predictors, and an autoregressive head that captures cross-stream dependencies. Across training data sizes and model scales, up to 6M hours and 6.5B parameters, GSLM2 consistently improves linguistic competence while scaling more efficiently with data. As a result, it substantially narrows the gap between spoken and text-based LMs; across read and conversational speech domains, we shrink the gap by about two orders of magnitude on sWuggy, sBLIMP, and tSC, while also outperforming competitive speech-text systems on all three tasks. We open-source the training code and model checkpoints at https://github.com/facebookresearch/gslm2.
Abstract:Generative spoken language models (SLMs) offer a route to learning linguistic structure purely from speech, yet they lag far behind text-based models in both capability and data scaling efficiency. We identify two bottlenecks underlying this gap: the limited linguistic quality of existing speech units and the reliance on a single stream of discrete units to represent speech. Multiple codebooks are widely used to encode acoustic features but rarely to represent speech semantics. We present GSLM2, which learns and models complementary properties of speech at multiple levels of abstraction. Its encoder, SpidR 2.0, uses self-supervised learning with differentiable prediction heads to induce high-quality discrete units at multiple levels. A multi-stream SLM then jointly models these units using a shared temporal backbone, stream-specific predictors, and an autoregressive head that captures cross-stream dependencies. Across training data sizes and model scales, up to 6M hours and 6.5B parameters, GSLM2 consistently improves linguistic competence while scaling more efficiently with data. As a result, it substantially narrows the gap between spoken and text-based LMs; across read and conversational speech domains, we shrink the gap by about two orders of magnitude on sWuggy, sBLIMP, and tSC, while also outperforming competitive speech-text systems on all three tasks. We open-source the training code and model checkpoints at this https URL.
Equal contribution by Yosuke Higuchi and Tom Fizycki. Core contribution by Mahi Luthra, Charles-Éric Saint-James and Azeez Syed
Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
arXiv:2611.01143 [cs.CL]
(or arXiv:2611.01143v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.01143 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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