Neural codec language models (or codec LMs) are emerging as a powerful framework for audio generation tasks like text-to-speech (TTS). These models leverage advancements in language modeling and residual vector quantization (RVQ)-based audio codecs, which compress audios into discrete codes for LMs to process. Despite the close interdependence of codecs and LMs in these systems, research on codecs and LMs has largely remained siloed. In this work, we propose three techniques for better codec-LM co-design: (i) a frame-wise codec encoder that improves both LM log-likelihood and end-to-end TTS metrics, (ii) LM codebook level dropout, a method to efficiently navigate a portion of the codec-LM design space by training a single LM, and (iii) increased codec frame duration, which we show can accelerate inference while maintaining end-to-end performance. Our experiments demonstrate that combining all three co-design techniques results in doubled inference speed, and improvements in intelligibility, audio quality, and speaker control in TTS relative to a siloed baseline.

Transformers have been essential to pretraining success in NLP. While other architectures have been used, downstream accuracy is either significantly worse, or requires attention layers to match standard benchmarks such as GLUE. This work explores pretraining without attention by using recent advances in sequence routing based on state-space models (SSMs). Our proposed model, Bidirectional Gated SSM (BiGS), combines SSM layers with a multiplicative gating architecture that has been effective in simplified sequence modeling architectures. The model learns static layers that do not consider pair-wise interactions. Even so, BiGS is able to match BERT pretraining accuracy on GLUE and can be extended to long-form pretraining of 4096 tokens without approximation. Analysis shows that while the models have similar average accuracy, the approach has different inductive biases than BERT and scales more efficiently to longer sequences.