  Recent advancements in text-to-speech (TTS) powered by language models have
showcased remarkable capabilities in achieving naturalness and zero-shot voice
cloning. Notably, the decoder-only transformer is the prominent architecture in
this domain. However, transformers face challenges stemming from their
quadratic complexity in sequence length, impeding training on lengthy sequences
and resource-constrained hardware. Moreover they lack specific inductive bias
with regards to the monotonic nature of TTS alignments. In response, we propose
to replace transformers with emerging recurrent architectures and introduce
specialized cross-attention mechanisms for reducing repeating and skipping
issues. Consequently our architecture can be efficiently trained on long
samples and achieve state-of-the-art zero-shot voice cloning against baselines
of comparable size. Our implementation and demos are available at
https://github.com/theodorblackbird/lina-speech.
