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：本文提出PFluxTTS，一种混合式文本转语音系统，旨在解决流匹配语音合成中的三个关键问题：稳定性与自然度的权衡、跨语言语音克隆能力弱以及低速率梅尔特征导致的音频质量受限。我们的主要贡献包括：（1）通过推理时向量场融合，结合时长引导模型与无对齐模型的双解码器架构；（2）在基于FLUX的解码器中采用语音提示嵌入序列实现鲁棒克隆，无需提示文本转录即可跨语言保持说话人特征；（3）配备超分辨率至48 kHz的改进型PeriodWave声码器。在跨语言真实场景数据测试中，PFluxTTS显著优于F5-TTS、FishSpeech和SparkTTS，其自然度与ChatterBox相当（平均意见分4.11）同时词错误率降低23%（6.9%对比9.0%），说话人相似度超越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鲁棒性，且仅需短参考音频且无需额外训练。音频示例请访问：https://braskai.github.io/pfluxtts/