评测4种开源语音合成工具，发现部署难、效率低，影响小语种应用。
How Open is Open TTS? A Practical Evaluation of Open Source TTS Tools
- 对比FastPitch、VITS等4个开源模型的安装与训练流程
- 罗马尼亚语合成在易用性与计算开销上均表现不佳
- 适合关注低资源语言语音合成的开发者参考
开源文本到语音（TTS）框架已成为多语言语音合成系统开发的灵活平台。然而，其适用性并不均衡，尤其在目标语言资源匮乏或计算资源受限时。本研究系统评估了四种广泛使用的开源架构——FastPitch、VITS、Grad-TTS和Matcha-TTS——构建新TTS模型的可行性。评估涵盖安装难易度、数据准备复杂度、硬件需求等定性维度，以及罗马尼亚语合成质量的定量分析。通过客观指标与主观听感测试，评估语音的可懂度、说话人相似度与自然度。结果揭示工具链配置、数据预处理及计算效率存在显著挑战，可能阻碍低资源环境下的应用。本研究基于可复现的协议与开放评估标准，旨在推动更包容、语言多样化的TTS开发实践。所有代码与数据已公开于GitLab仓库：https://gitlab.com/opentts_ragman/OpenTTS。
原文摘要 · Abstract (English)
Open-source text-to-speech (TTS) frameworks have emerged as highly adaptable platforms for developing speech synthesis systems across a wide range of languages. However, their applicability is not uniform -- particularly when the target language is under-resourced or when computational resources are constrained. In this study, we systematically assess the feasibility of building novel TTS models using four widely adopted open-source architectures: FastPitch, VITS, Grad-TTS, and Matcha-TTS. Our evaluation spans multiple dimensions, including qualitative aspects such as ease of installation, dataset preparation, and hardware requirements, as well as quantitative assessments of synthesis quality for Romanian. We employ both objective metrics and subjective listening tests to evaluate intelligibility, speaker similarity, and naturalness of the generated speech. The results reveal significant challenges in tool chain setup, data preprocessing, and computational efficiency, which can hinder adoption in low-resource contexts. By grounding the analysis in reproducible protocols and accessible evaluation criteria, this work aims to inform best practices and promote more inclusive, language-diverse TTS development. All information needed to reproduce this study (i.e. code and data) are available in our git repository: https://gitlab.com/opentts_ragman/OpenTTS
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