sbv2-v2/server_fastapi.py

"""
api服务 多版本多模型 fastapi实现
"""
import logging

import utils
from fastapi import FastAPI, Query
from fastapi.responses import Response, FileResponse
from fastapi.staticfiles import StaticFiles
from io import BytesIO
from scipy.io import wavfile
import uvicorn
import torch
import webbrowser
import psutil
import GPUtil
from typing import Dict, Optional, List
import os
from loguru import logger

from infer import infer, get_net_g, latest_version
import tools.translate as trans

from config import config


class Model:
    """模型封装类"""

    def __init__(self, config_path: str, model_path: str, device: str, language: str):
        self.config_path: str = os.path.normpath(config_path)
        self.model_path: str = os.path.normpath(model_path)
        self.device: str = device
        self.language: str = language
        self.hps = utils.get_hparams_from_file(config_path)
        self.spk2id: Dict[str, int] = self.hps.data.spk2id  # spk - id 映射字典
        self.id2spk: Dict[int, str] = dict()  # id - spk 映射字典
        for speaker, speaker_id in self.hps.data.spk2id.items():
            self.id2spk[speaker_id] = speaker
        self.version: str = (
            self.hps.version if hasattr(self.hps, "version") else latest_version
        )
        self.net_g = get_net_g(
            model_path=model_path,
            version=self.version,
            device=device,
            hps=self.hps,
        )

    def to_dict(self) -> Dict[str, any]:
        return {
            "config_path": self.config_path,
            "model_path": self.model_path,
            "device": self.device,
            "language": self.language,
            "spk2id": self.spk2id,
            "id2spk": self.id2spk,
            "version": self.version,
        }


class Models:
    def __init__(self):
        self.models: Dict[int, Model] = dict()
        self.num = 0
        # spkInfo[角色名][模型id] = 角色id
        self.spk_info: Dict[str, Dict[int, int]] = dict()
        self.paths: Dict[str, int] = dict()  # 路径, 引用数

    def add_model(self, model: Model):
        """添加一个模型"""
        self.models[self.num] = model
        # 添加角色信息
        for speaker, speaker_id in model.spk2id.items():
            if speaker not in self.spk_info.keys():
                self.spk_info[speaker] = {self.num: speaker_id}
            else:
                self.spk_info[speaker][self.num] = speaker_id
        # 添加路径信息
        model_path = os.path.realpath(model.model_path)
        if model_path not in self.paths.keys():
            self.paths[model_path] = 1
        else:
            self.paths[model_path] += 1
        # 修改计数
        self.num += 1

    def init_model(
        self, config_path: str, model_path: str, device: str, language: str
    ) -> int:
        """
        初始化并添加一个模型

        :param config_path: 模型config.json路径
        :param model_path: 模型路径
        :param device: 模型推理使用设备
        :param language: 模型推理默认语言
        """
        self.models[self.num] = Model(
            config_path=config_path,
            model_path=model_path,
            device=device,
            language=language,
        )
        # 添加角色信息
        for speaker, speaker_id in self.models[self.num].spk2id.items():
            if speaker not in self.spk_info.keys():
                self.spk_info[speaker] = {self.num: speaker_id}
            else:
                self.spk_info[speaker][self.num] = speaker_id
        # 添加路径信息
        model_path = os.path.realpath(self.models[self.num].model_path)
        if model_path not in self.paths.keys():
            self.paths[model_path] = 1
        else:
            self.paths[model_path] += 1
        # 修改计数
        logger.success(f"添加模型{model_path}，使用配置文件{os.path.realpath(config_path)}")
        self.num += 1
        return self.num - 1

    def del_model(self, index: int) -> Optional[int]:
        """删除对应序号的模型，若不存在则返回None"""
        if index not in self.models.keys():
            return None
        # 删除角色信息
        for speaker, speaker_id in self.models[index].spk2id.items():
            self.spk_info[speaker].pop(index)
            if len(self.spk_info[speaker]) == 0:
                # 若对应角色的所有模型都被删除，则清除该角色信息
                self.spk_info.pop(speaker)
        # 删除路径信息
        model_path = os.path.realpath(self.models[index].model_path)
        self.paths[model_path] -= 1
        assert self.paths[model_path] >= 0
        if self.paths[model_path] == 0:
            # 引用数为零时予以清空
            self.paths.pop(model_path)
        # 删除模型
        logger.success(f"卸载模型{model_path}, id = {index}")
        self.models.pop(index)
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return index

    def get_models(self):
        """获取所有模型"""
        return self.models


if __name__ == "__main__":
    app = FastAPI()
    app.logger = logger
    # 挂载静态文件
    StaticDir: str = "./Web"
    dirs = [fir.name for fir in os.scandir(StaticDir) if fir.is_dir()]
    files = [fir.name for fir in os.scandir(StaticDir) if fir.is_dir()]
    for dirName in dirs:
        app.mount(
            f"/{dirName}",
            StaticFiles(directory=f"./{StaticDir}/{dirName}"),
            name=dirName,
        )
    loaded_models = Models()
    # 加载模型
    models_info = config.server_config.models
    for model_info in models_info:
        loaded_models.init_model(
            config_path=model_info["config"],
            model_path=model_info["model"],
            device=model_info["device"],
            language=model_info["language"],
        )

    @app.get("/")
    async def index():
        return FileResponse("./Web/index.html")

    @app.get("/voice")
    def voice(
        text: str = Query(..., description="输入文字"),
        model_id: int = Query(..., description="模型ID"),  # 模型序号
        speaker_name: str = Query(
            None, description="说话人名"
        ),  # speaker_name与 speaker_id二者选其一
        speaker_id: int = Query(None, description="说话人id，与speaker_name二选一"),
        sdp_ratio: float = Query(0.2, description="SDP/DP混合比"),
        noise: float = Query(0.2, description="感情"),
        noisew: float = Query(0.9, description="音素长度"),
        length: float = Query(1, description="语速"),
        language: str = Query(None, description="语言"),  # 若不指定使用语言则使用默认值
    ):
        """语音接口"""

        # 检查模型是否存在
        if model_id not in loaded_models.models.keys():
            return {"status": 10, "detail": f"模型model_id={model_id}未加载"}
        # 检查是否提供speaker
        if speaker_name is None and speaker_id is None:
            return {"status": 11, "detail": "请提供speaker_name或speaker_id"}
        elif speaker_name is None:
            # 检查speaker_id是否存在
            if speaker_id not in loaded_models.models[model_id].id2spk.keys():
                return {"status": 12, "detail": f"角色speaker_id={speaker_id}不存在"}
            speaker_name = loaded_models.models[model_id].id2spk[speaker_id]
        # 检查speaker_name是否存在
        if speaker_name not in loaded_models.models[model_id].spk2id.keys():
            return {"status": 13, "detail": f"角色speaker_name={speaker_name}不存在"}
        if language is None:
            language = loaded_models.models[model_id].language
        with torch.no_grad():
            audio = infer(
                text=text,
                sdp_ratio=sdp_ratio,
                noise_scale=noise,
                noise_scale_w=noisew,
                length_scale=length,
                sid=speaker_name,
                language=language,
                hps=loaded_models.models[model_id].hps,
                net_g=loaded_models.models[model_id].net_g,
                device=loaded_models.models[model_id].device,
            )
        wavContent = BytesIO()
        wavfile.write(
            wavContent, loaded_models.models[model_id].hps.data.sampling_rate, audio
        )
        response = Response(content=wavContent.getvalue(), media_type="audio/wav")
        return response

    @app.get("/models/info")
    def get_loaded_models_info():
        """获取已加载模型信息"""

        result: Dict[str, Dict] = dict()
        for key, model in loaded_models.models.items():
            result[str(key)] = model.to_dict()
        return result

    @app.get("/models/delete")
    def delete_model(model_id: int = Query(..., description="删除模型id")):
        """删除指定模型"""

        result = loaded_models.del_model(model_id)
        if result is None:
            return {"status": 14, "detail": f"模型{model_id}不存在，删除失败"}
        return {"status": 0, "detail": "删除成功"}

    @app.get("/models/add")
    def add_model(
        model_path: str = Query(..., description="添加模型路径"),
        config_path: str = Query(
            None, description="添加模型配置文件路径，不填则使用./config.json或../config.json"
        ),
        device: str = Query("cuda", description="推理使用设备"),
        language: str = Query("ZH", description="模型默认语言"),
    ):
        """添加指定模型：允许重复添加相同路径模型，注意，当前实现中模型会重复加载，加载两次占用两份内存"""
        if config_path is None:
            model_dir = os.path.dirname(model_path)
            if os.path.isfile(os.path.join(model_dir, "config.json")):
                config_path = os.path.join(model_dir, "config.json")
            elif os.path.isfile(os.path.join(model_dir, "../config.json")):
                config_path = os.path.join(model_dir, "../config.json")
            else:
                return {
                    "status": 15,
                    "detail": "查询未传入配置文件路径，同时默认路径./与../中不存在配置文件config.json。",
                }
        try:
            model_id = loaded_models.init_model(
                config_path=config_path,
                model_path=model_path,
                device=device,
                language=language,
            )
        except Exception:
            logging.exception("模型加载出错")
            return {
                "status": 16,
                "detail": "模型加载出错，详细查看日志",
            }
        return {
            "status": 0,
            "detail": "模型添加成功",
            "Data": {
                "model_id": model_id,
                "model_info": loaded_models.models[model_id].to_dict(),
            },
        }

    def _get_all_models(root_dir: str = "Data", only_unloaded: bool = False):
        result: Dict[str, List[str]] = dict()
        files = os.listdir(root_dir)
        for file in files:
            if os.path.isdir(os.path.join(root_dir, file)):
                sub_dir = os.path.join(root_dir, file)
                # 搜索 "sub_dir" 、 "sub_dir/models" 两个路径
                result[file] = list()
                sub_files = os.listdir(sub_dir)
                for sub_file in sub_files:
                    relpath = os.path.realpath(os.path.join(sub_dir, sub_file))
                    if only_unloaded and relpath in loaded_models.paths.keys():
                        continue
                    if sub_file.endswith(".pth") and sub_file.startswith("G_"):
                        if os.path.isfile(relpath):
                            result[file].append(sub_file)
                models_dir = os.path.join(sub_dir, "models")
                if os.path.isdir(models_dir):
                    sub_files = os.listdir(models_dir)
                    for sub_file in sub_files:
                        relpath = os.path.realpath(os.path.join(models_dir, sub_file))
                        if only_unloaded and relpath in loaded_models.paths.keys():
                            continue
                        if sub_file.endswith(".pth") and sub_file.startswith("G_"):
                            if os.path.isfile(os.path.join(models_dir, sub_file)):
                                result[file].append(f"models/{sub_file}")
                if len(result[file]) == 0:
                    result.pop(file)
        return result

    @app.get("/models/get_unloaded")
    def get_unloaded_models_info(root_dir: str = "Data"):
        """获取未加载模型"""
        return _get_all_models(root_dir, only_unloaded=True)

    @app.get("/models/get_local")
    def get_local_models_info(root_dir: str = "Data"):
        """获取全部本地模型"""
        return _get_all_models(root_dir, only_unloaded=False)

    @app.get("/status")
    def get_status():
        """获取电脑运行状态"""
        cpu_percent = psutil.cpu_percent(interval=1)
        memory_info = psutil.virtual_memory()
        memory_total = memory_info.total
        memory_available = memory_info.available
        memory_used = memory_info.used
        memory_percent = memory_info.percent
        gpuInfo = []
        devices = ["cpu"]
        for i in range(torch.cuda.device_count()):
            devices.append(f"cuda:{i}")
        gpus = GPUtil.getGPUs()
        for gpu in gpus:
            gpuInfo.append(
                {
                    "gpu_id": gpu.id,
                    "gpu_load": gpu.load,
                    "gpu_memory": {
                        "total": gpu.memoryTotal,
                        "used": gpu.memoryUsed,
                        "free": gpu.memoryFree,
                    },
                }
            )
        return {
            "devices": devices,
            "cpu_percent": cpu_percent,
            "memory_total": memory_total,
            "memory_available": memory_available,
            "memory_used": memory_used,
            "memory_percent": memory_percent,
            "gpu": gpuInfo,
        }

    @app.get("/tools/translate")
    def translate(texts: str, to_language: str):
        """翻译"""
        return {"texts": trans.translate(Sentence=texts, to_Language=to_language)}

    logger.warning("本地服务，请勿将服务端口暴露于外网")
    print(f"api文档地址 http://127.0.0.1:{config.server_config.port}/docs")
    webbrowser.open(f"http://127.0.0.1:{config.server_config.port}")
    uvicorn.run(app, port=config.server_config.port)