PFluxTTS: Hybrid Flow-Matching TTS with Robust Cross-Lingual Voice Cloning and Inference-Time Model Fusion

Vikentii Pankov; Artem Gribul; Oktai Tatanov; Vladislav Proskurov; Yuliya Korotkova; Darima Mylzenova; Dmitrii Vypirailenko

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arXiv · 2602.04160

PFluxTTS: Hybrid Flow-Matching TTS with Robust Cross-Lingual Voice Cloning and Inference-Time Model Fusion

Vikentii Pankov·Artem Gribul·Oktai Tatanov·Vladislav Proskurov·Yuliya Korotkova·Darima Mylzenova·Dmitrii Vypirailenko

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Abstract

We present PFluxTTS, a hybrid text-to-speech system addressing three gaps in flow-matching TTS: the stability-naturalness trade-off, weak cross-lingual voice cloning, and limited audio quality from low-rate mel features. Our contributions are: (1) a dual-decoder design combining duration-guided and alignment-free models through inference-time vector-field fusion; (2) robust cloning using a sequence of speech-prompt embeddings in a FLUX-based decoder, preserving speaker traits across languages without prompt transcripts; and (3) a modified PeriodWave vocoder with super-resolution to 48 kHz. On cross-lingual in-the-wild data, PFluxTTS clearly outperforms F5-TTS, FishSpeech, and SparkTTS, matches ChatterBox in naturalness (MOS 4.11) while achieving 23% lower WER (6.9% vs. 9.0%), and surpasses ElevenLabs in speaker similarity (+0.32 SMOS). The system remains robust in challenging scenarios where most open-source models fail, while requiring only short reference audio and no extra training. Audio demos are available at https://braskai.github.io/pfluxtts/

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Vikentii Pankov, Artem Gribul, Oktai Tatanov, Vladislav Proskurov, Yuliya Korotkova, Darima Mylzenova, Dmitrii Vypirailenko. 2026-04-01. PFluxTTS: Hybrid Flow-Matching TTS with Robust Cross-Lingual Voice Cloning and Inference-Time Model Fusion. https://arxiv.org/abs/2602.04160

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