Tacit-TTS: From Autoregressive Decoding to Masked Prediction for Efficient Transcript-Free Voice Cloning

Efficient zero-shot voice cloning remains challenging: autoregressive systems are slow, while many non-autoregressive systems still require reference transcripts during inference. We present Tacit-TTS, an efficient transcript-free zero-shot voice cloning system distilled from IndexTTS2. Tacit-TTS replaces autoregressive text-to-semantic decoding with masked non-autoregressive generation, introduces training-free acoustic length estimation, and accelerates the flow-matching renderer through ReFlow distillation. Across two English and two Mandarin datasets, Tacit-TTS achieves competitive zero-shot quality while generating speech over 10 times faster than IndexTTS2 for utterances longer than 5 seconds. Its transcript-free conditioning also supports cross-lingual and non-lexical references. We validate these settings using references from eight other languages, infant babble, and synthetic gibberish, where transcript-dependent systems often degrade or fail because of unreliable ASR transcripts, making reliable voice cloning difficult.