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"""
@Desc: 全局配置文件读取
"""
import shutil
from pathlib import Path
from typing import Any
import torch
import yaml
from style_bert_vits2.logging import logger
class PathConfig:
def __init__(self, dataset_root: str, assets_root: str):
self.dataset_root = Path(dataset_root)
self.assets_root = Path(assets_root)
# If not cuda available, set possible devices to cpu
cuda_available = torch.cuda.is_available()
class Resample_config:
"""重采样配置"""
def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100):
self.sampling_rate = sampling_rate # 目标采样率
self.in_dir = Path(in_dir) # 待处理音频目录路径
self.out_dir = Path(out_dir) # 重采样输出路径
@classmethod
def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
"""从字典中生成实例"""
# 不检查路径是否有效,此逻辑在resample.py中处理
data["in_dir"] = dataset_path / data["in_dir"]
data["out_dir"] = dataset_path / data["out_dir"]
return cls(**data)
class Preprocess_text_config:
"""数据预处理配置"""
def __init__(
self,
transcription_path: str,
cleaned_path: str,
train_path: str,
val_path: str,
config_path: str,
val_per_lang: int = 5,
max_val_total: int = 10000,
clean: bool = True,
):
self.transcription_path = Path(transcription_path)
self.train_path = Path(train_path)
if cleaned_path == "" or cleaned_path is None:
self.cleaned_path = self.transcription_path.with_name(
self.transcription_path.name + ".cleaned"
)
else:
self.cleaned_path = Path(cleaned_path)
self.val_path = Path(val_path)
self.config_path = Path(config_path)
self.val_per_lang = val_per_lang
self.max_val_total = max_val_total
self.clean = clean
@classmethod
def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
"""从字典中生成实例"""
data["transcription_path"] = dataset_path / data["transcription_path"]
if data["cleaned_path"] == "" or data["cleaned_path"] is None:
data["cleaned_path"] = ""
else:
data["cleaned_path"] = dataset_path / data["cleaned_path"]
data["train_path"] = dataset_path / data["train_path"]
data["val_path"] = dataset_path / data["val_path"]
data["config_path"] = dataset_path / data["config_path"]
return cls(**data)
class Bert_gen_config:
"""bert_gen 配置"""
def __init__(
self,
config_path: str,
num_processes: int = 1,
device: str = "cuda",
use_multi_device: bool = False,
):
self.config_path = Path(config_path)
self.num_processes = num_processes
if not cuda_available:
device = "cpu"
self.device = device
self.use_multi_device = use_multi_device
@classmethod
def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
data["config_path"] = dataset_path / data["config_path"]
return cls(**data)
class Style_gen_config:
"""style_gen 配置"""
def __init__(
self,
config_path: str,
num_processes: int = 4,
device: str = "cuda",
):
self.config_path = Path(config_path)
self.num_processes = num_processes
if not cuda_available:
device = "cpu"
self.device = device
@classmethod
def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
data["config_path"] = dataset_path / data["config_path"]
return cls(**data)
class Train_ms_config:
"""训练配置"""
def __init__(
self,
config_path: str,
env: dict[str, Any],
# base: Dict[str, any],
model_dir: str,
num_workers: int,
spec_cache: bool,
keep_ckpts: int,
):
self.env = env # 需要加载的环境变量
# self.base = base # 底模配置
self.model_dir = Path(
model_dir
) # 训练模型存储目录,该路径为相对于dataset_path的路径,而非项目根目录
self.config_path = Path(config_path) # 配置文件路径
self.num_workers = num_workers # worker数量
self.spec_cache = spec_cache # 是否启用spec缓存
self.keep_ckpts = keep_ckpts # ckpt数量
@classmethod
def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
# data["model"] = os.path.join(dataset_path, data["model"])
data["config_path"] = dataset_path / data["config_path"]
return cls(**data)
class Webui_config:
"""webui 配置 (for webui.py, not supported now)"""
def __init__(
self,
device: str,
model: str,
config_path: str,
language_identification_library: str,
port: int = 7860,
share: bool = False,
debug: bool = False,
):
if not cuda_available:
device = "cpu"
self.device = device
self.model = Path(model)
self.config_path = Path(config_path)
self.port: int = port
self.share: bool = share
self.debug: bool = debug
self.language_identification_library: str = language_identification_library
@classmethod
def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
data["config_path"] = dataset_path / data["config_path"]
data["model"] = dataset_path / data["model"]
return cls(**data)
class Server_config:
def __init__(
self,
port: int = 5000,
device: str = "cuda",
limit: int = 100,
language: str = "JP",
origins: list[str] = ["*"],
):
self.port: int = port
if not cuda_available:
device = "cpu"
self.device: str = device
self.language: str = language
self.limit: int = limit
self.origins: list[str] = origins
@classmethod
def from_dict(cls, data: dict[str, Any]):
return cls(**data)
class Translate_config:
"""翻译api配置"""
def __init__(self, app_key: str, secret_key: str):
self.app_key = app_key
self.secret_key = secret_key
@classmethod
def from_dict(cls, data: dict[str, Any]):
return cls(**data)
class Config:
def __init__(self, config_path: str, path_config: PathConfig):
if not Path(config_path).exists():
shutil.copy(src="default_config.yml", dst=config_path)
logger.info(
f"A configuration file {config_path} has been generated based on the default configuration file default_config.yml."
)
logger.info(
"Please do not modify default_config.yml. Instead, modify config.yml."
)
# sys.exit(0)
with open(config_path, encoding="utf-8") as file:
yaml_config: dict[str, Any] = yaml.safe_load(file.read())
model_name: str = yaml_config["model_name"]
self.model_name: str = model_name
if "dataset_path" in yaml_config:
dataset_path = Path(yaml_config["dataset_path"])
else:
dataset_path = path_config.dataset_root / model_name
self.dataset_path = dataset_path
self.dataset_root = path_config.dataset_root
self.assets_root = path_config.assets_root
self.out_dir = self.assets_root / model_name
self.resample_config: Resample_config = Resample_config.from_dict(
dataset_path, yaml_config["resample"]
)
self.preprocess_text_config: Preprocess_text_config = (
Preprocess_text_config.from_dict(
dataset_path, yaml_config["preprocess_text"]
)
)
self.bert_gen_config: Bert_gen_config = Bert_gen_config.from_dict(
dataset_path, yaml_config["bert_gen"]
)
self.style_gen_config: Style_gen_config = Style_gen_config.from_dict(
dataset_path, yaml_config["style_gen"]
)
self.train_ms_config: Train_ms_config = Train_ms_config.from_dict(
dataset_path, yaml_config["train_ms"]
)
self.webui_config: Webui_config = Webui_config.from_dict(
dataset_path, yaml_config["webui"]
)
self.server_config: Server_config = Server_config.from_dict(
yaml_config["server"]
)
# self.translate_config: Translate_config = Translate_config.from_dict(
# yaml_config["translate"]
# )
# Load and initialize the configuration
def get_path_config() -> PathConfig:
path_config_path = Path("configs/paths.yml")
if not path_config_path.exists():
shutil.copy(src="configs/default_paths.yml", dst=path_config_path)
logger.info(
f"A configuration file {path_config_path} has been generated based on the default configuration file default_paths.yml."
)
logger.info(
"Please do not modify configs/default_paths.yml. Instead, modify configs/paths.yml."
)
with open(path_config_path, encoding="utf-8") as file:
path_config_dict: dict[str, str] = yaml.safe_load(file.read())
return PathConfig(**path_config_dict)
def get_config() -> Config:
path_config = get_path_config()
try:
config = Config("config.yml", path_config)
except (TypeError, KeyError):
logger.warning("Old config.yml found. Replace it with default_config.yml.")
shutil.copy(src="default_config.yml", dst="config.yml")
config = Config("config.yml", path_config)
return config