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MARS6: A Small and Robust Hierarchical-Codec Text-to-Speech Model
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Codec-based text-to-speech (TTS) models have shown impressive quality with zero-shot voice cloning abilities. However, they often struggle with more expressive references or complex text inputs. We present MARS6, a robust encoder-decoder transformer for rapid, expressive TTS. MARS6 is built on recent improvements in spoken language modelling. Utilizing a hierarchical setup for its decoder, new speech tokens are processed at a rate of only 12 Hz, enabling efficient modelling of long-form text while retaining reconstruction quality. We combine several recent training and inference techniques to reduce repetitive generation and improve output stability and quality. This enables the 70M-parameter MARS6 to achieve similar performance to models many times larger. We show this in objective and subjective evaluations, comparing TTS output quality and reference speaker cloning ability. Project page: https://camb-ai.github.io/mars6-turbo/
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Cited by 1 Pith paper
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Speech Token Prediction via Compressed-to-fine Language Modeling for Speech Generation
Compressed-to-fine language modeling improves speech token prediction by retaining prompt and local tokens while compressing long-range token spans into compact summaries.
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