""" @Desc: 全局配置文件读取 """ import os import shutil from typing import Dict, List import torch import yaml from common.log import logger # 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: 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 if not cuda_available: device = "cpu" 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 = 4, device: str = "cuda", ): self.config_path = config_path self.num_processes = num_processes if not cuda_available: device = "cpu" 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_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 = 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 = 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 (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: 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, port: int = 5000, device: str = "cuda", limit: int = 100, language: str = "JP", origins: List[str] = None, ): 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: dict[str, str]): if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"): 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( "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 = os.path.join(path_config["dataset_root"], model_name) self.dataset_path: str = dataset_path self.assets_root: str = path_config["assets_root"] self.out_dir = os.path.join(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"] # ) with open(os.path.join("configs", "paths.yml"), "r", encoding="utf-8") as f: path_config: dict[str, str] = yaml.safe_load(f.read()) # Should contain the following keys: # - dataset_root: the root directory of the dataset, default to "Data" # - assets_root: the root directory of the assets, default to "model_assets" 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)