Supertonic MNN is a high-performance, lightweight text-to-speech (TTS) library based on MNN. It supports both command-line interface (CLI) and Python API, making it easy to integrate into your projects.
Demo video: https://www.bilibili.com/video/BV1VFqiBSER3
- Fast Inference: RTF ~ 0.07 on CPU.
- Lightweight: Minimal dependencies.
- Supported Precisions: fp32, fp16, int8.
- M1: Male voice 1
- M2: Male voice 2
- F1: Female voice 1
- F2: Female voice 2
Full documentation is available at Supertonic MNN Docs.
pip install supertonic-mnnecho "Hello world" | supertonic-mnn -o hello.wavfrom supertonic_mnn import SupertonicTTS
# 1. Initialize
tts = SupertonicTTS()
# 2. Synthesize
# Models will be downloaded automatically if not present
audio, sample_rate = tts.synthesize("Hello world", voice="M1", output_file="hello.wav")See examples/ for more details.
This project is based on the original Supertonic by Supertone Inc.
Supertonic MNN 是一个基于 MNN 的高性能、轻量级文本转语音 (TTS) 库。它同时支持命令行接口 (CLI) 和 Python API,方便您将其集成到项目中。
Demo video: https://www.bilibili.com/video/BV1VFqiBSER3
- 极速推理: CPU 上 RTF 约为 0.07。
- 轻量级: 依赖极少。
- 多精度支持: fp32, fp16, int8。
- M1: 男声 1
- M2: 男声 2
- F1: 女声 1
- F2: 女声 2
完整文档请访问 Supertonic MNN 文档。
pip install supertonic-mnnecho "你好,世界" | supertonic-mnn -o hello.wavfrom supertonic_mnn import SupertonicTTS
# 1. 初始化
tts = SupertonicTTS()
# 2. 推理
# 如果模型不存在,会自动下载
audio, sample_rate = tts.synthesize("你好,世界", voice="M1", output_file="hello.wav")更多详情请参阅 examples/ 目录。
本项目基于 Supertone Inc. 的原始 Supertonic 工作。