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README.md

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----
-license: agpl-3.0
----
+---
+license: agpl-3.0
+tags:
+- rknn
+---
+
+
+# MeloTTS-RKNN2
+
+## (English README see below)
+
+在RK3588上运行MeloTTS文字转语音模型!
+
+- 推理速度(RK3588): 约5倍速
+- 内存占用(RK3588): 约0.2GB
+
+## 使用方法
+
+1. 克隆或者下载此仓库到瑞芯微SoC的系统上.
+  
+2. 安装依赖
+
+```bash
+pip install -r requirements.txt
+pip install rknn-toolkit-lite2
+```
+
+4. 运行
+   
+```bash
+python melotts_rknn.py -s "你想要生成的文本"
+```
+
+## 模型转换
+
+1. 安装依赖
+
+```bash
+pip install -r requirements.txt
+pip install rknn-toolkit2==2.3.0
+```
+
+2. 转换模型
+
+```bash
+python convert_rknn.py
+```
+
+## 已知问题
+
+- 和原项目一样，Encoder部分并没有使用NPU加速，但是耗时不大，应该不会对推理速度有太大影响。
+
+## 参考
+
+- [melotts.axera](https://github.com/ml-inory/melotts.axera)
+- [MeloTTS](https://github.com/myshell-ai/MeloTTS)
+
+
+## English README
+
+# MeloTTS-RKNN2
+
+Run the MeloTTS text-to-speech model on RK3588!
+
+- Inference speed (RK3588): about 5x real-time
+- Memory usage (RK3588): about 0.2GB
+
+## Usage
+
+1. Clone or download this repository to your Rockchip SoC system.
+  
+2. Install dependencies
+
+```bash
+pip install -r requirements.txt
+pip install rknn-toolkit-lite2
+```
+
+3. Run
+   
+```bash
+python melotts_rknn.py -s "The text you want to generate."
+```
+
+## Model Conversion
+
+1. Install dependencies
+
+```bash
+pip install -r requirements.txt
+pip install rknn-toolkit2==2.3.0
+```
+
+2. Convert the model
+
+```bash
+python convert_rknn.py
+```
+
+## Known Issues
+
+- Same as the original project, the Encoder part is not accelerated by the NPU. However, its processing time is short and should not significantly affect the inference speed.
+
+## References
+
+- [melotts.axera](https://github.com/ml-inory/melotts.axera)
+- [MeloTTS](https://github.com/myshell-ai/MeloTTS)

requirements.txt

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+numpy==1.24.4
+onnx==1.16.0
+onnxruntime==1.16.0
+soundfile
+cn2an
+inflect==7.3.1
+pykakasi==2.2.1
+pypinyin==0.50.0
+cn2an==0.5.22
+g2p_en==2.1.0
+g2pkk==0.1.2
+jamo==0.4.1
+jieba==0.42.1
+librosa==0.9.1
+MeCab==0.996.5
+mecab_python3==1.0.9
+num2words==0.5.12
+unidic-lite==1.0.8
+fugashi==1.4.0