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arXiv · 2606.18485

MagpieTTS-LF: Inference-Time Long-Form Speech Generation Without Training on Long-Form data

Subhankar Ghosh·Jason Li·Paarth Neekhara·Shehzeen Hussain·Ryan Langman·Xuesong Yang·Roy Fejgin
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Abstract

Neural Text-to-Speech (TTS) systems achieve remarkable quality on short utterances but long-form speech generation shows prosodic drift, speaker inconsistencies and sentence boundary artifacts. Existing approaches either compress sequences, increase context length or naively concatenate independently synthesized chunks. We present an inference-time approach called MagpieTTS-LF that enables MagpieTTS to produce coherent long-form speech without model retraining. Our method introduces three key innovations: (1) soft attention priors to guide monotonic alignment while preserving past and future context; (2) a stateful inference algorithm that maintains context across sentence chunks, ensuring prosodic continuity; (3) history-aware text encoding that uses past text for discourse-level prosodic planning. Experiments on long texts show significant improvements in long-range intelligibility, prosodic coherence, speaker consistency, and boundary naturalness compared to other baselines.

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Subhankar Ghosh, Jason Li, Paarth Neekhara, Shehzeen Hussain, Ryan Langman, Xuesong Yang, Roy Fejgin. 2026-06-16. MagpieTTS-LF: Inference-Time Long-Form Speech Generation Without Training on Long-Form data. https://arxiv.org/abs/2606.18485

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