用大模型打造一站式语音理解生成工具，开箱即用且可复现。
WEST: LLM based Speech Toolkit for Speech Understanding, Generation, and Interaction
- 全链路基于大语言模型，复用成熟架构与开源生态。
- 支持语音识别、合成、对话等多任务，提供高性能与可复现双版本。
- 设计极简，适合研究者快速实验和开发者直接部署。
本文提出WEST（WE Speech Toolkit），一个基于大语言模型的语音理解、生成与交互工具包。其三大特点：1）完全基于大模型，复用成熟架构、生态系统（如Hugging Face）和方法（如序列打包）；2）全栈支持语音识别、合成、理解、对话及多模态任务，可扩展集成开源模型；3）设计简洁，人人可触。WEST提供两类方案：一是完全基于开源模型与数据，支持实验复现，作为验证或最小基准系统；二是基于海量数据训练，性能优越，可直接使用。项目已公开于https://github.com/wenet-e2e/west/
原文摘要 · Abstract (English)
In this paper, we present WEST(WE Speech Toolkit), a speech toolkit based on a large language model (LLM) for speech understanding, generation, and interaction. There are three key features of WEST: 1) Fully LLM-based: Standing on the shoulders of giants by reusing mature architectures, ecosystems (e.g., Hugging Face), and methods (e.g., sequence packing) from large models. 2) Full-stack: Supports tasks such as recognition, synthesis, understanding, dialogue, and multimodal capabilities, with extensibility to incorporate open-source models. 3) Simple and Stupid: A simple and stupid speech toolkit that everyone can Touch. In addition, WEST provides two types of recipes, models, and experimental results. The first is entirely based on open-source models and open-source data, allowing users to fully reproduce the experiments in this paper and serving as a verification system or minimal system baseline. The second is trained on massive data, offering superior performance so the user can directly apply it out of the box. WEST is publicly avilable at https://github.com/wenet-e2e/west/
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