File size: 9,316 Bytes
78b6bf0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 |
"""
@Desc: 全局配置文件读取
"""
import argparse
import yaml
from typing import Dict, List
import os
import shutil
import sys
class Resample_config:
"""重采样配置"""
def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100):
self.sampling_rate: int = sampling_rate # 目标采样率
self.in_dir: str = in_dir # 待处理音频目录路径
self.out_dir: str = out_dir # 重采样输出路径
@classmethod
def from_dict(cls, dataset_path: str, 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"] = os.path.join(dataset_path, data["in_dir"])
data["out_dir"] = os.path.join(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: str = transcription_path # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。
self.cleaned_path: str = cleaned_path # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成
self.train_path: str = train_path # 训练集路径，可以不填。不填则将在原始文本目录生成
self.val_path: str = val_path # 验证集路径，可以不填。不填则将在原始文本目录生成
self.config_path: str = config_path # 配置文件路径
self.val_per_lang: int = val_per_lang # 每个speaker的验证集条数
self.max_val_total: int = max_val_total # 验证集最大条数，多于的会被截断并放到训练集中
self.clean: bool = clean # 是否进行数据清洗
@classmethod
def from_dict(cls, dataset_path: str, data: Dict[str, any]):
"""从字典中生成实例"""
data["transcription_path"] = os.path.join(
dataset_path, data["transcription_path"]
)
if data["cleaned_path"] == "" or data["cleaned_path"] is None:
data["cleaned_path"] = None
else:
data["cleaned_path"] = os.path.join(dataset_path, data["cleaned_path"])
data["train_path"] = os.path.join(dataset_path, data["train_path"])
data["val_path"] = os.path.join(dataset_path, data["val_path"])
data["config_path"] = os.path.join(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 = 2,
device: str = "cuda",
use_multi_device: bool = False,
):
self.config_path = config_path
self.num_processes = num_processes
self.device = device
self.use_multi_device = use_multi_device
@classmethod
def from_dict(cls, dataset_path: str, data: Dict[str, any]):
data["config_path"] = os.path.join(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 = 2,
device: str = "cuda",
):
self.config_path = config_path
self.num_processes = num_processes
self.device = device
@classmethod
def from_dict(cls, dataset_path: str, data: Dict[str, any]):
data["config_path"] = os.path.join(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: 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 = model # 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 = 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: str, data: Dict[str, any]):
# data["model"] = os.path.join(dataset_path, data["model"])
data["config_path"] = os.path.join(dataset_path, data["config_path"])
return cls(**data)
class Webui_config:
"""webui 配置"""
def __init__(
self,
device: str,
model: str,
config_path: str,
language_identification_library: str,
port: int = 7860,
share: bool = False,
debug: bool = False,
):
self.device: str = device
self.model: str = model # 端口号
self.config_path: str = config_path # 是否公开部署，对外网开放
self.port: int = port # 是否开启debug模式
self.share: bool = share # 模型路径
self.debug: bool = debug # 配置文件路径
self.language_identification_library: str = (
language_identification_library # 语种识别库
)
@classmethod
def from_dict(cls, dataset_path: str, data: Dict[str, any]):
data["config_path"] = os.path.join(dataset_path, data["config_path"])
data["model"] = os.path.join(dataset_path, data["model"])
return cls(**data)
class Server_config:
def __init__(
self, models: List[Dict[str, any]], port: int = 5000, device: str = "cuda"
):
self.models: List[Dict[str, any]] = models # 需要加载的所有模型的配置
self.port: int = port # 端口号
self.device: str = device # 模型默认使用设备
@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):
if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"):
shutil.copy(src="default_config.yml", dst=config_path)
print(
f"A configuration file {config_path} has been generated based on the default configuration file default_config.yml."
)
print(
"If you have no special needs, please do not modify default_config.yml."
)
# sys.exit(0)
with open(file=config_path, mode="r", 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 = yaml_config["dataset_path"]
else:
dataset_path = f"Data/{model_name}"
self.out_dir = yaml_config["out_dir"]
# openi_token: str = yaml_config["openi_token"]
self.dataset_path: str = dataset_path
# self.mirror: str = yaml_config["mirror"]
# self.openi_token: str = openi_token
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"]
# )
parser = argparse.ArgumentParser()
# 为避免与以前的config.json起冲突，将其更名如下
parser.add_argument("-y", "--yml_config", type=str, default="config.yml")
args, _ = parser.parse_known_args()
config = Config(args.yml_config)
|