Update document

.github/workflows/docs.yaml

@@ -0,0 +1,30 @@
+name: ci
+on:
+  push:
+    branches:
+      - main
+
+permissions:
+  contents: write
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Configure Git Credentials
+        run: |
+          git config user.name github-actions[bot]
+          git config user.email 41898282+github-actions[bot]@users.noreply.github.com
+      - uses: actions/setup-python@v4
+        with:
+          python-version: 3.x
+      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
+      - uses: actions/cache@v3
+        with:
+          key: mkdocs-material-${{ env.cache_id }}
+          path: .cache
+          restore-keys: |
+            mkdocs-material-
+      - run: pip install mkdocs-material
+      - run: mkdocs gh-deploy --force

README.md

@@ -1,66 +1,19 @@
 # Fish Speech
 
-**Documentation is under construction, English is not fully supported yet.**
-
-[中文文档](README.zh.md)
-
 This codebase is released under BSD-3-Clause License, and all models are released under CC-BY-NC-SA-4.0 License. Please refer to [LICENSE](LICENSE) for more details. 
 
-## Disclaimer
-We do not hold any responsibility for any illegal usage of the codebase. Please refer to your local laws about DMCA and other related laws.
-
-## Requirements
-- GPU memory: 2GB (for inference), 24GB (for finetuning)
-- System: Linux (full functionality), Windows (inference only, flash-attn is not supported, torch.compile is not supported)
-
-Therefore, we strongly recommend to use WSL2 or docker to run the codebase for Windows users.
-
-## Setup
-```bash
-# Basic environment setup
-conda create -n fish-speech python=3.10
-conda activate fish-speech
-conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
-
-# Install flash-attn (for linux)
-pip3 install ninja && MAX_JOBS=4 pip3 install flash-attn --no-build-isolation
-
-# Install fish-speech
-pip3 install -e .
-```
-
-## Inference (CLI)
-Download required `vqgan` and `text2semantic` model from our huggingface repo.
-
-```bash
-wget https://huggingface.co/fishaudio/speech-lm-v1/raw/main/vqgan-v1.pth -O checkpoints/vqgan-v1.pth
-wget https://huggingface.co/fishaudio/speech-lm-v1/blob/main/text2semantic-400m-v0.2-4k.pth -O checkpoints/text2semantic-400m-v0.2-4k.pth
-```
-
-Generate semantic tokens from text:
-```bash
-python tools/llama/generate.py \
-    --text "Hello" \
-    --num-samples 2 \
-    --compile
-```
-
-You may want to use `--compile` to fuse cuda kernels faster inference (~25 tokens/sec -> ~300 tokens/sec).
+此代码库根据 BSD-3-Clause 许可证发布, 所有模型根据 CC-BY-NC-SA-4.0 许可证发布。请参阅 [LICENSE](LICENSE) 了解更多细节.
 
-Generate vocals from semantic tokens:
-```bash
-python tools/vqgan/inference.py -i codes_0.npy
-```
+## Disclaimer / 免责声明
+We do not hold any responsibility for any illegal usage of the codebase. Please refer to your local laws about DMCA and other related laws.  
+我们不对代码库的任何非法使用承担任何责任. 请参阅您当地关于 DMCA (数字千年法案) 和其他相关法律的法律.
 
-## Rust Data Server
-Since loading and shuffle the dataset is very slow and memory consuming, we use a rust server to load and shuffle the dataset. The server is based on GRPC and can be installed by
+## Documents / 文档
+- [English](https://speech.fish.audio/en/)
+- [中文](https://speech.fish.audio/zh/)
 
-```bash
-cd data_server
-cargo build --release
-```
 
-## Credits
+## Credits / 鸣谢
 - [VITS2 (daniilrobnikov)](https://github.com/daniilrobnikov/vits2)
 - [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2)
 - [GPT VITS](https://github.com/innnky/gpt-vits)

docs/en/index.md

@@ -0,0 +1,3 @@
+# Welcome to Fish Speech
+
+English Document is under construction.

docs/index.md

@@ -0,0 +1,4 @@
+---
+template: redirect.html
+location: /zh/
+---

docs/requirements.txt

@@ -0,0 +1 @@
+mkdocs-material

docs/zh/index.md

@@ -0,0 +1,85 @@
+# 介绍
+
+此代码库根据 BSD-3-Clause 许可证发布, 所有模型根据 CC-BY-NC-SA-4.0 许可证发布。请参阅 [LICENSE](LICENSE) 了解更多细节.
+
+<p align="center">
+<img src="/assets/figs/diagram.png" width="75%">
+</p>
+
+## 免责声明
+我们不对代码库的任何非法使用承担任何责任。请参阅您当地关于 DMCA (数字千年法案) 和其他相关法律的法律。
+
+## 要求
+- GPU内存: 2GB (用于推理), 16GB (用于微调)
+- 系统: Linux (全部功能), Windows (仅推理, 不支持 `flash-attn`, 不支持 `torch.compile`)
+
+因此, 我们强烈建议 Windows 用户使用 WSL2 或 docker 来运行代码库.
+
+## 设置
+```bash
+# 基本环境设置
+conda create -n fish-speech python=3.10
+conda activate fish-speech
+conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
+
+# 安装 flash-attn (适用于linux)
+pip3 install ninja && MAX_JOBS=4 pip3 install flash-attn --no-build-isolation
+
+# 安装 fish-speech
+pip3 install -e .
+```
+
+## 推理 (命令行)
+
+从我们的 huggingface 仓库下载所需的 `vqgan` 和 `text2semantic` 模型。
+
+```bash
+wget https://huggingface.co/fishaudio/speech-lm-v1/raw/main/vqgan-v1.pth -O checkpoints/vqgan-v1.pth
+wget https://huggingface.co/fishaudio/speech-lm-v1/blob/main/text2semantic-400m-v0.2-4k.pth -O checkpoints/text2semantic-400m-v0.2-4k.pth
+```
+
+### 1. [可选] 从语音生成 prompt: 
+```bash
+python tools/vqgan/inference.py -i paimon.wav --checkpoint-path checkpoints/vqgan-v1.pth
+```
+
+你应该能得到一个 `fake.npy` 文件.
+
+### 2. 从文本生成语义 token: 
+```bash
+python tools/llama/generate.py \
+    --text "要转换的文本" \
+    --prompt-text "你的参考文本" \
+    --prompt-tokens "fake.npy" \
+    --checkpoint-path "checkpoints/text2semantic-400m-v0.1-4k.pth" \
+    --num-samples 2 \
+    --compile
+```
+
+该命令会在工作目录下创建 `codes_N` 文件, 其中 N 是从 0 开始的整数.
+您可能希望使用 `--compile` 来融合 cuda 内核以实现更快的推理 (~30 个 token/秒 -> ~500 个 token/秒).
+
+### 3. 从语义 token 生成人声: 
+```bash
+python tools/vqgan/inference.py -i codes_0.npy --checkpoint-path checkpoints/vqgan-v1.pth
+```
+
+## Rust 数据服务器
+由于加载和打乱数据集非常缓慢且占用内存, 因此我们使用 rust 服务器来加载和打乱数据. 该服务器基于 GRPC, 可以通过以下方式安装:
+
+```bash
+cd data_server
+cargo build --release
+```
+
+## 更新日志
+
+- 2023/12/17: 更新了 `text2semantic` 模型, 支持无音素模式.
+- 2023/12/13: 测试版发布, 包含 VQGAN 模型和一个基于 LLAMA 的语言模型 (只支持音素).
+
+## 致谢
+- [VITS2 (daniilrobnikov)](https://github.com/daniilrobnikov/vits2)
+- [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2)
+- [GPT VITS](https://github.com/innnky/gpt-vits)
+- [MQTTS](https://github.com/b04901014/MQTTS)
+- [GPT Fast](https://github.com/pytorch-labs/gpt-fast)

mkdocs.yml

@@ -0,0 +1,53 @@
+site_name: Fish Speech
+repo_url: https://github.com/fishaudio/fish-speech
+
+theme:
+  name: material
+  language: en
+  features:
+    - navigation.instant
+    - navigation.instant.prefetch
+    - navigation.tracking
+    - search
+    - search.suggest
+    - search.highlight
+    - search.share
+
+  palette:
+    # Palette toggle for automatic mode
+    - media: "(prefers-color-scheme)"
+      toggle:
+        icon: material/brightness-auto
+        name: Switch to light mode
+
+    # Palette toggle for light mode
+    - media: "(prefers-color-scheme: light)"
+      scheme: default
+      toggle:
+        icon: material/brightness-7 
+        name: Switch to dark mode
+      primary: black
+      font:
+        code: Roboto Mono
+
+    # Palette toggle for dark mode
+    - media: "(prefers-color-scheme: dark)"
+      scheme: slate
+      toggle:
+        icon: material/brightness-4
+        name: Switch to light mode
+      primary: black
+      font:
+        code: Roboto Mono
+
+extra:
+  homepage: https://speech.fish.audio
+  version:
+    provider: mike
+  alternate:
+    - name: English
+      link: /en/ 
+      lang: en
+    - name: 中文
+      link: /zh/
+      lang: zh