fireredtts3-mnn
FireRedTTS3（零样本语音克隆 / 多语言 TTS）的 MNN 部署：ONNX → MNN int8 量化， 纯 CPU、无 torch 依赖，相比 fp32 基线约 4 倍加速。
一键运行（uvx）
uvx fireredtts3-mnn
首次运行自动从 Hugging Face 下载模型权重（~12GB）与 MNN 产物（int8，~5GB）， 然后启动 HTTP TTS 服务：
POST /tts
{
"text": "今天天气很好，我们一起去公园散步吧。",
"prompt_text": "参考音频对应的文本",
"prompt_audio_b64": "<参考音频 wav 的 base64>",
"prompt_audio_sr": 16000,
"language": "Chinese" # 可选，默认 Chinese
}
-> {"audio_b64": "<合成 wav base64>", "sample_rate": 24000}
GET /health
单次合成（不启动服务）：
uvx fireredtts3-mnn --text "你好世界" --prompt-wav ref.wav --prompt-text "参考文本" --out gen.wav
模型目录
默认缓存到 ~/.cache/fireredtts3-mnn：
weights/官方权重（来自FireRedTeam/FireRedTTS3）mnn/转换产物（11 个 MNN：campp / redae 编解码 / qwen3 prefill+step(KV cache) / patch_encoder / dit / 线性头，int8 量化）
可用 FIREREDTTS3_MNN_REPO 环境变量覆盖产物仓库，HF_ENDPOINT 走镜像。
转换流程（复现）
# 1) PyTorch -> ONNX（export_onnx.py，见仓库 scripts/）
# 2) ONNX -> MNN（MNNConvert，--weightQuantBits 8 或 mnnquant 全量 int8）
# 3) 校准数据：make_calib.py 用真实管线 dump 中间张量
说明
- 语言标签支持 README 所列 24 种语言与 21 种中文方言（如
ZH_Sichuan） - 未提供参考音频时使用静音 prompt（语音克隆效果依赖参考音频质量）
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