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| import argparse | |
| import sys | |
| import torch | |
| from multiprocessing import cpu_count | |
| class Config: | |
| """ | |
| The code focuses on adapting the configuration based on available | |
| hardware resources and specified command-line arguments, | |
| aiming to optimize the performance and capabilities of the voice conversion process. | |
| """ | |
| def __init__(self): | |
| """ | |
| Calls the arg_parse() and device_config() methods to set up configuration based on command-line arguments | |
| and available hardware. | |
| Returns: None | |
| """ | |
| self.device = "cuda:0" | |
| self.is_half = True | |
| self.n_cpu = 0 | |
| self.gpu_name = None | |
| self.gpu_mem = None | |
| ( | |
| self.colab, | |
| self.api, | |
| self.unsupported | |
| ) = self.arg_parse() | |
| self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() | |
| def arg_parse() -> tuple: | |
| """ | |
| Uses the argparse library to parse command-line arguments. | |
| Three boolean arguments are defined: --colab, --api, and --unsupported. | |
| Returns: a tuple indicating whether each argument is specified or not. | |
| """ | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--colab", action="store_true", help="Launch in colab") | |
| parser.add_argument("--api", action="store_true", help="Launch with api") | |
| parser.add_argument("--unsupported", action="store_true", help="Enable unsupported feature") | |
| cmd_opts = parser.parse_args() | |
| return ( | |
| cmd_opts.colab, | |
| cmd_opts.api, | |
| cmd_opts.unsupported | |
| ) | |
| # has_mps is only available in nightly pytorch (for now) and MasOS 12.3+. | |
| # check `getattr` and try it for compatibility | |
| def has_mps() -> bool: | |
| """ | |
| Determines if Multi-Process Service (MPS) is available in the current PyTorch backend. | |
| If MPS is available, it checks whether it can be used by trying to move a tensor to the "mps" device. | |
| Returns a boolean indicating MPS support. | |
| """ | |
| if not torch.backends.mps.is_available(): | |
| return False | |
| try: | |
| torch.zeros(1).to(torch.device("mps")) | |
| return True | |
| except Exception: | |
| return False | |
| def device_config(self) -> tuple: | |
| """ | |
| Checks if a CUDA-compatible GPU is available. | |
| If a compatible GPU is found: | |
| Determines the GPU's name and memory capacity. | |
| Adjusts the is_half parameter based on the GPU's characteristics. | |
| If no compatible GPU is found and MPS is available, configures the device to use MPS. | |
| If no compatible GPU and MPS support, configures the device to use CPU. | |
| Determines the number of available CPU cores (n_cpu). | |
| Based on the is_half value and GPU memory capacity, configures several variables related to voice conversion, | |
| such as x_pad, x_query, x_center, and x_max. | |
| """ | |
| 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("INFO: Found GPU", self.gpu_name, ", force to fp32") | |
| self.is_half = False | |
| else: | |
| print("INFO: Found GPU", self.gpu_name) | |
| self.gpu_mem = int( | |
| torch.cuda.get_device_properties(i_device).total_memory | |
| / 1024 | |
| / 1024 | |
| / 1024 | |
| + 0.4 | |
| ) | |
| elif self.has_mps(): | |
| print("INFO: No supported Nvidia GPU found, use MPS instead") | |
| self.device = "mps" | |
| self.is_half = False | |
| else: | |
| print("INFO: No supported Nvidia GPU found, use CPU instead") | |
| 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 | |