Existing Speech Language Model (SLM) scaling analysis paints a bleak picture.
They predict that SLMs require much more compute and data compared to text,
leading some to question the feasibility of training high-quality SLMs.
However, modern SLMs are often initialised from pre-trained TextLMs using
speech-text interleaving to allow knowledge transfer. This raises the question
– Do interleaved SLMs scale more efficiently than textless-SLMs? In this paper
we answer a resounding, yes! We conduct scaling analysis of interleaved SLMs by
training several dozen and analysing the scaling trends. We see that under this
setup SLMs scale more efficiently with compute. Additionally, our results
indicate that the scaling-dynamics are significantly different than
textless-SLMs, suggesting one should allocate notably more of the compute
budget for increasing model size over training tokens. We also study the role
of synthetic data and TextLM model families in unlocking this potential.
Results suggest, that our scaled up model achieves comparable performance with
leading models on speech semantic metrics while using less compute and data
than other approaches. We open source models, samples, and data –
https://pages.cs.huji.ac.il/adiyoss-lab/sims.
