## 1 Introduction  
The Zhuang people are the largest ethnic minority in China, with a population of approximately 15 M. They predominantly inhabit the southern region of China. Furthermore, there is a significant Zhuang community in the regions bordering China in northern Vietnam, constituting one of the largest ethnic minority groups in Vietnam [1]. The Zhuang language is primarily used in areas of Guangxi inhabited by the Zhuang people [2]. Its underlying vocabulary and pronunciation system are closely related to those of the Dong-Tai language group, which is part of the Zhuang-Dong language family [3]. Internationally, Zhuang is considered an independent language group. However, in China it is classified as part of the \"Sino-Tibetan languages\" [4]. Zhuang is a minority language with limited coverage and audience as it is highly localized and regionalized. There are no traditional characters, and the language is only transmitted orally, resulting in a lack of standardized and unified official characters to inherit. Additionally, it is impossible to form a sufficient amount of paper or electronic corpus resources. The lack of an extensive Chinese-Zhuang parallel corpus results in limited research on ASR and MT of Zhuang. Consequently, the current research is still relatively scarce, unable to meet the application requirements of industrial production and community life. An established resolution would be to explore the edge of the cloud platform system with remote speech recognition functions and multilingual translation. Additionally, Guangxi is the window of \"Association of Southeast Asian Nations (ASEAN)\" cooperation and exchanges in the \"Silk Road Economic Belt and the 21 st-Century Maritime Silk Road\". The development of a machine translation (MT) system for the Zhuang language can not only enrich minority languages and cultures but also enhance communication between Zhuang and other languages in Guangxi and ASEAN nations. For ASR tasks, the focus is to transcribe the information of the input speech sequence into the corresponding linguistic text. The traditional approach to implementing speech recognition systems involves combining an acoustic model, a pronunciation dictionary, and a language model. However, in recent years, Convolutional Neural Network (CNN) has achieved considerable success in computer vision (CV), as demonstrated by models such as VGG [5], Res-Net [6], and Google-LeNet [7]. Meanwhile, the Transformer [8] has also had breakthroughs in Natural Language Processing (NLP) through the utilization of an attention mechanism. While both CNN and Transformer architectures possess their own advantages and limitations, the newly introduced hybrid CNN-Transformer [9; 10; 11] architecture overcomes the lack of CNN's ability to capture global context. This enables end-to-end neural network model to achieve remarkable progress in ASR tasks while also resolving the challenges of forced alignment and multi-module training faced by conventional speech recognition systems. Hybrid architecture becomes a trend. The rapid development of neural networks also provides novel research directions for a subfield of computational linguistics: Machine Translation. End-to-end Neural Machine Translation (NMT) has emerged as the prominent approach for MT research. Nevertheless, it cannot yet be trusted to work independently in real-world applications. On the one hand, it is challenging for NMT to sufficiently take into account the contextual factors present in the source text, where social, cultural, economic, religious, and political contexts constrain the production of the original text [12]. On the other hand, researchers require an assessment of the Machine Translation Output (MTO) and a considerable quantity of high-quality corpora to improve translation models [13]; for instance, BLEU [14; 15]. However, these metrics focus solely on exact match identification, which leads to minimal correlation with assessments made by humans that account for rich morphology [16].