We present a unified pipeline for synthesizing high-quality Quechua and Spanish speech for the Peruvian Constitution using three state-of-the-art text-to-speech (TTS) architectures: XTTS v2, F5-TTS, and DiFlow-TTS. Our models are trained on independent Spanish and Quechua speech datasets with heterogeneous sizes and recording conditions, and leverage bilingual and multilingual TTS capabilities to improve synthesis quality in both languages. By exploiting cross-lingual transfer, our framework mitigates data scarcity in Quechua while preserving naturalness in Spanish. We release trained checkpoints, inference code, and synthesized audio for each constitutional article, providing a reusable resource for speech technologies in indigenous and multilingual contexts. This work contributes to the development of inclusive TTS systems for political and legal content in low-resource settings.

翻译：我们提出了一种统一流水线，利用三种最先进的文本到语音（TTS）架构——XTTS v2、F5-TTS 和 DiFlow-TTS——为秘鲁宪法合成高质量的克丘亚语和西班牙语语音。我们的模型在规模各异、录音条件不同的独立西班牙语和克丘亚语语音数据集上训练，并借助双语和多语言 TTS 能力提升在两种语言中的合成质量。通过利用跨语言迁移，我们的框架缓解了克丘亚语的数据稀缺问题，同时保持了西班牙语的自然度。我们发布了每个宪法条款的预训练检查点、推理代码和合成音频，为土著语言和多语言环境中的语音技术提供了可复用的资源。这项工作推动了低资源环境下政治与法律内容的包容性TTS系统发展。