import argparse import torch from multiprocessing import cpu_count class Config: def __init__(self): self.device = "cuda:0" self.is_half = True self.n_cpu = 0 self.gpu_name = None self.gpu_mem = None ( self.python_cmd, self.listen_port, self.iscolab, self.noparallel, self.noautoopen, self.api, self.share, self.files ) = self.arg_parse() self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() @staticmethod def arg_parse() -> tuple: parser = argparse.ArgumentParser() parser.add_argument("--port", type=int, default=7865, help="Listen port") parser.add_argument( "--pycmd", type=str, default="python", help="Python command" ) parser.add_argument("--colab", action="store_true", help="Launch in colab") parser.add_argument( "--noparallel", action="store_true", help="Disable parallel processing" ) parser.add_argument( "--noautoopen", action="store_true", help="Do not open in browser automatically", ) parser.add_argument('--api', action="store_true", default=False) parser.add_argument("--share", action="store_true", default=False, help="share gradio app") parser.add_argument("--files", action="store_true", default=False, help="load audio from path") cmd_opts = parser.parse_args() cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865 return ( cmd_opts.pycmd, cmd_opts.port, cmd_opts.colab, cmd_opts.noparallel, cmd_opts.noautoopen, cmd_opts.api, cmd_opts.share, cmd_opts.files, ) def device_config(self) -> tuple: if torch.cuda.is_available(): i_device = int(self.device.split(":")[-1]) self.gpu_name = torch.cuda.get_device_name(i_device) if ( ("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) or "P40" in self.gpu_name.upper() or "1060" in self.gpu_name or "1070" in self.gpu_name or "1080" in self.gpu_name ): print("16系/10系显卡和P40强制单精度") self.is_half = False for config_file in ["32k.json", "40k.json", "48k.json"]: with open(f"configs/{config_file}", "r") as f: strr = f.read().replace("true", "false") with open(f"configs/{config_file}", "w") as f: f.write(strr) with open("trainset_preprocess_pipeline_print.py", "r") as f: strr = f.read().replace("3.7", "3.0") with open("trainset_preprocess_pipeline_print.py", "w") as f: f.write(strr) else: self.gpu_name = None self.gpu_mem = int( torch.cuda.get_device_properties(i_device).total_memory / 1024 / 1024 / 1024 + 0.4 ) if self.gpu_mem <= 4: with open("trainset_preprocess_pipeline_print.py", "r") as f: strr = f.read().replace("3.7", "3.0") with open("trainset_preprocess_pipeline_print.py", "w") as f: f.write(strr) elif torch.backends.mps.is_available(): print("没有发现支持的N卡, 使用MPS进行推理") self.device = "mps" self.is_half = False else: print("没有发现支持的N卡, 使用CPU进行推理") self.device = "cpu" self.is_half = False if self.n_cpu == 0: self.n_cpu = cpu_count() if self.is_half: # 6G显存配置 x_pad = 3 x_query = 10 x_center = 60 x_max = 65 else: # 5G显存配置 x_pad = 1 x_query = 6 x_center = 38 x_max = 41 if self.gpu_mem != None and self.gpu_mem <= 4: x_pad = 1 x_query = 5 x_center = 30 x_max = 32 return x_pad, x_query, x_center, x_max