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import argparse |
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import sys |
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import torch |
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import json |
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from multiprocessing import cpu_count |
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global usefp16 |
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usefp16 = False |
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def use_fp32_config(): |
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usefp16 = False |
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device_capability = 0 |
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if torch.cuda.is_available(): |
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device = torch.device("cuda:0") |
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device_capability = torch.cuda.get_device_capability(device)[0] |
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if device_capability >= 7: |
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usefp16 = True |
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for config_file in ["32k.json", "40k.json", "48k.json"]: |
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with open(f"configs/{config_file}", "r") as d: |
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data = json.load(d) |
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if "train" in data and "fp16_run" in data["train"]: |
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data["train"]["fp16_run"] = True |
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with open(f"configs/{config_file}", "w") as d: |
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json.dump(data, d, indent=4) |
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print(f"Set fp16_run to true in {config_file}") |
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with open( |
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"trainset_preprocess_pipeline_print.py", "r", encoding="utf-8" |
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) as f: |
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strr = f.read() |
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strr = strr.replace("3.0", "3.7") |
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with open( |
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"trainset_preprocess_pipeline_print.py", "w", encoding="utf-8" |
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) as f: |
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f.write(strr) |
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else: |
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for config_file in ["32k.json", "40k.json", "48k.json"]: |
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with open(f"configs/{config_file}", "r") as f: |
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data = json.load(f) |
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if "train" in data and "fp16_run" in data["train"]: |
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data["train"]["fp16_run"] = False |
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with open(f"configs/{config_file}", "w") as d: |
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json.dump(data, d, indent=4) |
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print(f"Set fp16_run to false in {config_file}") |
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with open( |
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"trainset_preprocess_pipeline_print.py", "r", encoding="utf-8" |
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) as f: |
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strr = f.read() |
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strr = strr.replace("3.7", "3.0") |
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with open( |
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"trainset_preprocess_pipeline_print.py", "w", encoding="utf-8" |
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) as f: |
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f.write(strr) |
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else: |
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print( |
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"CUDA is not available. Make sure you have an NVIDIA GPU and CUDA installed." |
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) |
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return (usefp16, device_capability) |
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class Config: |
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def __init__(self): |
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self.device = "cuda:0" |
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self.is_half = True |
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self.n_cpu = 0 |
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self.gpu_name = None |
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self.gpu_mem = None |
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( |
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self.python_cmd, |
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self.listen_port, |
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self.iscolab, |
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self.noparallel, |
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self.noautoopen, |
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self.paperspace, |
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self.is_cli, |
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) = self.arg_parse() |
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self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() |
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@staticmethod |
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def arg_parse() -> tuple: |
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exe = sys.executable or "python" |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--port", type=int, default=7865, help="Listen port") |
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parser.add_argument("--pycmd", type=str, default=exe, help="Python command") |
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parser.add_argument("--colab", action="store_true", help="Launch in colab") |
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parser.add_argument( |
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"--noparallel", action="store_true", help="Disable parallel processing" |
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) |
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parser.add_argument( |
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"--noautoopen", |
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action="store_true", |
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help="Do not open in browser automatically", |
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) |
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parser.add_argument( |
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"--paperspace", |
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action="store_true", |
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help="Note that this argument just shares a gradio link for the web UI. Thus can be used on other non-local CLI systems.", |
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) |
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parser.add_argument( |
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"--is_cli", |
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action="store_true", |
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help="Use the CLI instead of setting up a gradio UI. This flag will launch an RVC text interface where you can execute functions from infer-web.py!", |
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) |
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cmd_opts = parser.parse_args() |
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cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865 |
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return ( |
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cmd_opts.pycmd, |
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cmd_opts.port, |
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cmd_opts.colab, |
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cmd_opts.noparallel, |
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cmd_opts.noautoopen, |
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cmd_opts.paperspace, |
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cmd_opts.is_cli, |
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) |
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@staticmethod |
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def has_mps() -> bool: |
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if not torch.backends.mps.is_available(): |
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return False |
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try: |
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torch.zeros(1).to(torch.device("mps")) |
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return True |
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except Exception: |
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return False |
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def device_config(self) -> tuple: |
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if torch.cuda.is_available(): |
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i_device = int(self.device.split(":")[-1]) |
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self.gpu_name = torch.cuda.get_device_name(i_device) |
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if ( |
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("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) |
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or "P40" in self.gpu_name.upper() |
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or "1060" in self.gpu_name |
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or "1070" in self.gpu_name |
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or "1080" in self.gpu_name |
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): |
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print("Found GPU", self.gpu_name, ", force to fp32") |
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self.is_half = False |
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else: |
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print("Found GPU", self.gpu_name) |
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use_fp32_config() |
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self.gpu_mem = int( |
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torch.cuda.get_device_properties(i_device).total_memory |
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/ 1024 |
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/ 1024 |
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/ 1024 |
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+ 0.4 |
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) |
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if self.gpu_mem <= 4: |
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with open("trainset_preprocess_pipeline_print.py", "r") as f: |
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strr = f.read().replace("3.7", "3.0") |
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with open("trainset_preprocess_pipeline_print.py", "w") as f: |
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f.write(strr) |
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elif self.has_mps(): |
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print("No supported Nvidia GPU found, use MPS instead") |
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self.device = "mps" |
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self.is_half = False |
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use_fp32_config() |
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else: |
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print("No supported Nvidia GPU found, use CPU instead") |
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self.device = "cpu" |
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self.is_half = False |
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use_fp32_config() |
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if self.n_cpu == 0: |
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self.n_cpu = cpu_count() |
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if self.is_half: |
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x_pad = 3 |
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x_query = 10 |
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x_center = 60 |
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x_max = 65 |
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else: |
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x_pad = 1 |
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x_query = 6 |
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x_center = 38 |
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x_max = 41 |
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if self.gpu_mem != None and self.gpu_mem <= 4: |
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x_pad = 1 |
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x_query = 5 |
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x_center = 30 |
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x_max = 32 |
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return x_pad, x_query, x_center, x_max |
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