diff --git a/README.md b/README.md
index 67218f8..db73427 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,19 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/facebookresearch/audiocraft) · [上游 README](https://github.com/facebookresearch/audiocraft/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 # AudioCraft
 ![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg)
 ![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg)
 ![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg)
 
-AudioCraft is a PyTorch library for deep learning research on audio generation. AudioCraft contains inference and training code
-for two state-of-the-art AI generative models producing high-quality audio: AudioGen and MusicGen.
+AudioCraft 是一个用于音频生成深度学习研究的 PyTorch 库。AudioCraft 包含用于两个最先进（state-of-the-art）AI 生成模型的高质量音频推理和训练代码：AudioGen 和 MusicGen。
 
 
-## Installation
-AudioCraft requires Python 3.9, PyTorch 2.1.0. To install AudioCraft, you can run the following:
+## 安装
+AudioCraft 需要 Python 3.9 和 PyTorch 2.1.0。要安装 AudioCraft，可以运行以下命令：
 
 ```shell
 # Best to make sure you have torch installed first, in particular before installing xformers.
@@ -23,62 +28,61 @@ python -m pip install -e .  # or if you cloned the repo locally (mandatory if yo
 python -m pip install -e '.[wm]'  # if you want to train a watermarking model
 ```
 
-We also recommend having `ffmpeg` installed, either through your system or Anaconda:
+我们还建议安装 `ffmpeg`，可通过系统或 Anaconda 安装：
 ```bash
 sudo apt-get install ffmpeg
 # Or if you are using Anaconda or Miniconda
 conda install "ffmpeg<5" -c conda-forge
 ```
 
-## Models
+## 模型
 
-At the moment, AudioCraft contains the training code and inference code for:
-* [MusicGen](./docs/MUSICGEN.md): A state-of-the-art controllable text-to-music model.
-* [AudioGen](./docs/AUDIOGEN.md): A state-of-the-art text-to-sound model.
-* [EnCodec](./docs/ENCODEC.md): A state-of-the-art high fidelity neural audio codec.
-* [Multi Band Diffusion](./docs/MBD.md): An EnCodec compatible decoder using diffusion.
-* [MAGNeT](./docs/MAGNET.md): A state-of-the-art non-autoregressive model for text-to-music and text-to-sound.
-* [AudioSeal](./docs/WATERMARKING.md): A state-of-the-art audio watermarking.
-* [MusicGen Style](./docs/MUSICGEN_STYLE.md): A state-of-the-art text-and-style-to-music model.
-* [JASCO](./docs/JASCO.md): "High quality text-to-music model conditioned on chords, melodies and drum tracks"
+目前，AudioCraft 包含以下模型的训练代码和推理代码：
+* [MusicGen](./docs/MUSICGEN.md)：最先进的可控文本到音乐（text-to-music）模型。
+* [AudioGen](./docs/AUDIOGEN.md)：最先进的文本到音效（text-to-sound）模型。
+* [EnCodec](./docs/ENCODEC.md)：最先进的高保真神经音频编解码器。
+* [Multi Band Diffusion](./docs/MBD.md)：使用扩散（diffusion）的、与 EnCodec 兼容的解码器。
+* [MAGNeT](./docs/MAGNET.md)：用于文本到音乐和文本到音效的最先进非自回归（non-autoregressive）模型。
+* [AudioSeal](./docs/WATERMARKING.md)：最先进的音频水印（audio watermarking）。
+* [MusicGen Style](./docs/MUSICGEN_STYLE.md)：最先进的文本与风格到音乐（text-and-style-to-music）模型。
+* [JASCO](./docs/JASCO.md)："基于和弦、旋律和鼓轨条件的高质量文本到音乐模型"
 
 
-## Training code
+## 训练代码
 
-AudioCraft contains PyTorch components for deep learning research in audio and training pipelines for the developed models.
-For a general introduction of AudioCraft design principles and instructions to develop your own training pipeline, refer to
-the [AudioCraft training documentation](./docs/TRAINING.md).
+AudioCraft 包含用于音频深度学习研究的 PyTorch 组件，以及已开发模型的训练流水线。
+有关 AudioCraft 设计原则的一般介绍以及开发自定义训练流水线的说明，请参阅
+[AudioCraft 训练文档](./docs/TRAINING.md)。
 
-For reproducing existing work and using the developed training pipelines, refer to the instructions for each specific model
-that provides pointers to configuration, example grids and model/task-specific information and FAQ.
+若要复现现有工作并使用已开发的训练流水线，请参阅各具体模型的说明，其中提供了配置、示例网格以及模型/任务特定信息和常见问题解答的指引。
 
 
-## API documentation
+## API 文档
 
-We provide some [API documentation](https://facebookresearch.github.io/audiocraft/api_docs/audiocraft/index.html) for AudioCraft.
+我们为 AudioCraft 提供了部分 [API 文档](https://facebookresearch.github.io/audiocraft/api_docs/audiocraft/index.html)。
 
 
-## FAQ
+## 常见问题
 
-#### Is the training code available?
+#### 训练代码是否可用？
 
-Yes! We provide the training code for [EnCodec](./docs/ENCODEC.md), [MusicGen](./docs/MUSICGEN.md),[Multi Band Diffusion](./docs/MBD.md) and [JASCO](./docs/JASCO.md).
+是的！我们提供了 [EnCodec](./docs/ENCODEC.md)、[MusicGen](./docs/MUSICGEN.md)、[Multi Band Diffusion](./docs/MBD.md) 和 [JASCO](./docs/JASCO.md) 的训练代码。
 
-#### Where are the models stored?
+#### 模型存储在哪里？
 
-Hugging Face stored the model in a specific location, which can be overridden by setting the `AUDIOCRAFT_CACHE_DIR` environment variable for the AudioCraft models.
-In order to change the cache location of the other Hugging Face models, please check out the [Hugging Face Transformers documentation for the cache setup](https://huggingface.co/docs/transformers/installation#cache-setup).
-Finally, if you use a model that relies on Demucs (e.g. `musicgen-melody`) and want to change the download location for Demucs, refer to the [Torch Hub documentation](https://pytorch.org/docs/stable/hub.html#where-are-my-downloaded-models-saved).
+Hugging Face 将模型存储在特定位置，可通过为 AudioCraft 模型设置 `AUDIOCRAFT_CACHE_DIR` 环境变量来覆盖该位置。
+若要更改其他 Hugging Face 模型的缓存位置，请参阅 [Hugging Face Transformers 缓存设置文档](https://huggingface.co/docs/transformers/installation#cache-setup).
+最后，如果你使用的模型依赖 Demucs（例如 `musicgen-melody`），并希望更改 Demucs 的下载位置，请参阅 [Torch Hub 文档](https://pytorch.org/docs/stable/hub.html#where-are-my-downloaded-models-saved).
 
 
-## License
-* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
-* The models weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights).
+## 许可证
+* 本仓库中的代码根据 [LICENSE 文件](LICENSE) 中所载的 MIT 许可证发布。
+* 本仓库中的模型权重根据 [LICENSE_weights 文件](LICENSE_weights) 中所载的 CC-BY-NC 4.0 许可证发布。
 
 
-## Citation
+## 引用
 
-For the general framework of AudioCraft, please cite the following.
+若引用 AudioCraft 的一般框架，请引用以下内容。
 ```
 @inproceedings{copet2023simple,
     title={Simple and Controllable Music Generation},
@@ -88,5 +92,5 @@ For the general framework of AudioCraft, please cite the following.
 }
 ```
 
-When referring to a specific model, please cite as mentioned in the model specific README, e.g
-[./docs/MUSICGEN.md](./docs/MUSICGEN.md), [./docs/AUDIOGEN.md](./docs/AUDIOGEN.md), etc.
+引用特定模型时，请按各模型专属 README 中的说明进行引用，例如
+[./docs/MUSICGEN.md](./docs/MUSICGEN.md)、[./docs/AUDIOGEN.md](./docs/AUDIOGEN.md) 等。