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/

翻译：我们提出PFluxTTS，一种混合文本转语音系统，旨在解决流匹配TTS中的三个不足：稳定性与自然度之间的权衡、跨语言语音克隆的弱鲁棒性，以及低速率梅尔特征导致的音频质量受限。本文贡献包括：(1) 一种双解码器设计，通过推理时矢量场融合结合了时长引导模型与无对齐模型；(2) 利用FLUX解码器中的语音提示嵌入序列实现稳健克隆，无需提示文本转录即可跨语言保留说话人特征；(3) 改进的PeriodWave声码器，支持到48 kHz的超分辨率。在跨语言野外数据上，PFluxTTS明显优于F5-TTS、FishSpeech和SparkTTS，在自然度（MOS 4.11）上与ChatterBox持平，同时WER降低23%（6.9%对9.0%），并在说话人相似度上超越ElevenLabs（+0.32 SMOS）。该系统在多数开源模型失效的挑战性场景中仍保持稳健，且仅需短参考音频，无需额外训练。音频演示详见https://braskai.github.io/pfluxtts/