Core Insights - The article discusses the importance of dialects in preserving cultural diversity and highlights the challenges faced in dialect text-to-speech (TTS) technology, which remains a "gray area" in the industry [2][4] - DiaMoe-TTS is introduced as an open-source solution that aims to provide a comprehensive framework for dialect TTS, enabling researchers and developers to utilize and improve upon it [4][30] - The framework is designed to be low-cost and low-barrier, allowing for the synthesis of various dialects without the need for extensive data [31] Summary by Sections Introduction to DiaMoe-TTS - DiaMoe-TTS is a collaborative project between Giant Network AI Lab and Tsinghua University's SATLab, aimed at creating a dialect TTS model that rivals industrial-grade systems [2][4] - The framework utilizes a unified IPA (International Phonetic Alphabet) representation to address inconsistencies in dialect modeling [13][27] Technical Features - The system incorporates a dialect-aware Mixture-of-Experts (MoE) architecture, which allows different expert networks to focus on specific dialect features, enhancing the preservation of dialect characteristics [15][16] - A parameter-efficient fine-tuning (PEFT) strategy is implemented to adapt the model to low-resource dialects, requiring minimal adjustments to existing parameters [19][22] Training Methodology - The training process is divided into multiple stages, including IPA transfer initialization and joint training across multiple dialects, which improves model performance and adaptability [21][23] - The use of data augmentation techniques, such as pitch and speed perturbation, ensures the model can generate natural-sounding dialect speech even with limited data [20][22] Performance Results - DiaMoe-TTS demonstrates competitive performance metrics, achieving close to industrial-level results in Cantonese with a Word Error Rate (WER) of 76.59% and Mean Opinion Score (MOS) improvements across various dialects [25][26][27] - The framework's ability to support a wide range of dialects, including those with minimal data, presents new opportunities for dialect preservation and cultural transmission [25][30] Future Prospects - The research team plans to expand the dialect and minority language datasets, refine the IPA alignment and data preprocessing processes, and explore more efficient low-resource modeling methods [33] - The goal is to facilitate global participation in dialect and minority language research, ensuring that these languages are not forgotten in the digital age [33]

Core Insights - The rise of no-code SaaS development platforms has enabled even non-technical users to easily create their own mini-program malls, facilitating online business expansion for many merchants [1][3][4] - Different mini-program mall development tools cater to varying levels of technical expertise, from beginner-friendly options to more complex, customizable solutions for technically skilled teams [3][4] Group 1: Development Tools Overview - Vanke Mall stands out with its drag-and-drop page building and intuitive backend management, making it ideal for startups and small projects [1] - WeMall, built on the WeChat framework, offers modular design for merchants with existing technical support, though it may require a learning curve for new users [3] - Major e-commerce platforms like Douyin and Kuaishou provide mini-program generation tools, but these are limited by platform rules, potentially restricting customization [3] Group 2: Considerations for Tool Selection - Merchants should weigh their technical capabilities and development needs when choosing a mini-program mall development tool [4] - No-code or low-code tools are recommended for teams with limited technical skills, as they allow for quick setup of essential features like product display and order processing [4] - For technically proficient teams, self-development or using open-source frameworks can lead to more tailored and unique mall solutions, despite higher initial investments [4] - Stability and performance are critical factors in tool selection, as frequent crashes can lead to customer loss; merchants should review past stability records and seek performance data from providers [4]