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import torch
import json
import os
version_config_paths = [
os.path.join("48000.json"),
os.path.join("40000.json"),
os.path.join("44100.json"),
os.path.join("32000.json"),
]
def singleton(cls):
instances = {}
def get_instance(*args, **kwargs):
if cls not in instances:
instances[cls] = cls(*args, **kwargs)
return instances[cls]
return get_instance
@singleton
class Config:
def __init__(self):
self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
self.gpu_name = (
torch.cuda.get_device_name(int(self.device.split(":")[-1]))
if self.device.startswith("cuda")
else None
)
self.json_config = self.load_config_json()
self.gpu_mem = None
self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
def load_config_json(self):
configs = {}
for config_file in version_config_paths:
config_path = os.path.join("rvc", "configs", config_file)
with open(config_path, "r") as f:
configs[config_file] = json.load(f)
return configs
def device_config(self):
if self.device.startswith("cuda"):
self.set_cuda_config()
else:
self.device = "cpu"
# Configuration for 6GB GPU memory
x_pad, x_query, x_center, x_max = (1, 6, 38, 41)
if self.gpu_mem is not None and self.gpu_mem <= 4:
# Configuration for 5GB GPU memory
x_pad, x_query, x_center, x_max = (1, 5, 30, 32)
return x_pad, x_query, x_center, x_max
def set_cuda_config(self):
i_device = int(self.device.split(":")[-1])
self.gpu_name = torch.cuda.get_device_name(i_device)
self.gpu_mem = torch.cuda.get_device_properties(i_device).total_memory // (
1024**3
)
def max_vram_gpu(gpu):
if torch.cuda.is_available():
gpu_properties = torch.cuda.get_device_properties(gpu)
total_memory_gb = round(gpu_properties.total_memory / 1024 / 1024 / 1024)
return total_memory_gb
else:
return "8"
def get_gpu_info():
ngpu = torch.cuda.device_count()
gpu_infos = []
if torch.cuda.is_available() or ngpu != 0:
for i in range(ngpu):
gpu_name = torch.cuda.get_device_name(i)
mem = int(
torch.cuda.get_device_properties(i).total_memory / 1024 / 1024 / 1024
+ 0.4
)
gpu_infos.append(f"{i}: {gpu_name} ({mem} GB)")
if len(gpu_infos) > 0:
gpu_info = "\n".join(gpu_infos)
else:
gpu_info = "Unfortunately, there is no compatible GPU available to support your training."
return gpu_info
def get_number_of_gpus():
if torch.cuda.is_available():
num_gpus = torch.cuda.device_count()
return "-".join(map(str, range(num_gpus)))
else:
return "-"
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