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vits-simple-api-gsv / config.py

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	"""
	在首次启动自动生成config.yaml文件后，对配置进行修改时，应该直接在config.yaml文件中进行，而不是在config.py文件中修改。

	初回の起動後にconfig.yamlが自動生成された場合、設定の変更はconfig.pyではなくconfig.yamlで行うべきです。

	After the initial launch that automatically generates the config.yaml file, any modifications to the configuration should be made directly in the config.yaml file, not in the config.py file.
	"""

	import copy
	import logging
	import os
	import secrets
	import shutil
	import string
	import sys
	import traceback
	from dataclasses import dataclass, field, asdict, fields, is_dataclass
	from typing import List, Union, Optional, Dict

	import torch
	import yaml

	JSON_AS_ASCII = False

	MAX_CONTENT_LENGTH = 5242880

	# Absolute path of vits-simple-api
	ABS_PATH = os.path.dirname(os.path.realpath(__file__))

	# WTForms CSRF
	SECRET_KEY = secrets.token_hex(16)

	"""
	模型存放在data/models文件夹下，每个文件夹包含一个模型文件和一个配置文件。请按照以下格式填写路径信息：
	{"model_path": "文件夹名/模型文件.pth", "config_path": "文件夹名/config.json"},
	注意：只有当auto_load（自动加载模型）为False时才有效。当auto_load为True（默认值）时，
	此处填写的模型路径具有最高优先级，将在每次启动时加载。如非必要，请尽量在config.yaml填写模型路径。

	Models are stored in the data/models folder, with each folder containing a model file and a configuration file.
	Please fill in the paths following the format: {"model_path": "folder_name/model_file.pth", "config_path": "folder_name/config.json"},
	Note: This is effective only when auto_load (automatic model loading) is set to False. When auto_load is True (default),
	the model paths specified here have the highest priority and will be loaded each time the program starts. If not necessary,
	it's recommended to specify model paths in config.yaml.
	"""

	model_list = [
	# {"model_path": "model_name/G_9000.pth", "config_path": "model_name/config.json"},
	]


	@dataclass
	class AsDictMixin:
	def asdict(self):
	data = {}
	for attr, value in vars(self).items():
	if isinstance(value, AsDictMixin):
	data[attr] = value.asdict()
	elif isinstance(value, list):
	data[attr] = []
	for item in value:
	data[attr].append(item.asdict())
	elif isinstance(value, dict):
	data[attr] = {}
	for k, v in value.items():
	data[attr].update({k: v.asdict()})
	else:
	data[attr] = value
	return data

	def __iter__(self):
	for key, value in self.asdict().items():
	yield key, value

	def update_config(self, new_config_dict):
	for field in fields(self):
	field_name = field.name
	field_type = field.type

	if field_name in new_config_dict:
	new_value = new_config_dict[field_name]
	if is_dataclass(field_type):
	if isinstance(new_value, list):
	# If the field type is a dataclass and the new value is a list
	# Convert each element of the list to the corresponding class object
	new_value = [field_type(**item) for item in new_value]
	setattr(self, field_name, new_value)
	else:
	# If the field type is a dataclass but not a list, recursively update the dataclass
	nested_config = getattr(self, field_name)
	nested_config.update_config(new_value)
	setattr(self, field_name, nested_config)
	else:
	if field_type == bool:
	new_value = str(new_value).lower() == "true"
	elif field_type == int:
	new_value = int(new_value)
	elif field_type == float:
	new_value = float(new_value)
	elif field_type == str:
	new_value = str(new_value)
	elif field_type == torch.device:
	new_value = torch.device(new_value)

	setattr(self, field_name, new_value)


	@dataclass
	class VitsConfig(AsDictMixin):
	# For VITS: Load models during inference, dynamically release models after inference.
	dynamic_loading: bool = False
	id: int = 0
	format: str = "wav"
	lang: str = "auto"
	length: float = 1
	noise: float = 0.33
	noisew: float = 0.4
	# Batch processing threshold. Text will not be processed in batches if segment_size<=0
	segment_size: int = 50
	use_streaming: bool = False


	@dataclass
	class W2V2VitsConfig(AsDictMixin):
	id: int = 0
	format: str = "wav"
	lang: str = "auto"
	length: float = 1
	noise: float = 0.33
	noisew: float = 0.4
	# Batch processing threshold. Text will not be processed in batches if segment_size<=0
	segment_size: int = 50
	emotion: int = 0


	@dataclass
	class HuBertVitsConfig(AsDictMixin):
	id: int = 0
	format: str = "wav"
	length: float = 1
	noise: float = 0.33
	noisew: float = 0.4


	@dataclass
	class BertVits2Config(AsDictMixin):
	id: int = 0
	format: str = "wav"
	lang: str = "auto"
	length: float = 1
	noise: float = 0.33
	noisew: float = 0.4
	# Batch processing threshold. Text will not be processed in batches if segment_size<=0
	segment_size: int = 50
	sdp_ratio: float = 0.2
	emotion: int = 0
	text_prompt: str = "Happy"
	style_text: str = None
	style_weight: float = 0.7
	use_streaming: bool = False
	# Can be set to "float16"/"fp16".
	torch_data_type: str = ""


	@dataclass
	class GPTSoVitsPreset(AsDictMixin):
	refer_wav_path: str = None
	prompt_text: str = None
	prompt_lang: str = "auto"


	@dataclass
	class GPTSoVitsConfig(AsDictMixin):
	hz: int = 50
	is_half: bool = False
	id: int = 0
	lang: str = "auto"
	format: str = "wav"
	segment_size: int = 30
	top_k: int = 5
	top_p: float = 1.0
	temperature: float = 1.0
	use_streaming: bool = False
	batch_size: int = 5
	speed: float = 1.0
	presets: Dict[str, GPTSoVitsPreset] = field(default_factory=lambda: {"default": GPTSoVitsPreset(),
	"default2": GPTSoVitsPreset()})

	def update_config(self, new_config_dict):
	for field in fields(self):
	field_name = field.name
	field_type = field.type
	if field_name in new_config_dict:
	new_value = new_config_dict[field_name]
	if is_dataclass(field_type):
	if isinstance(new_value, list):
	# If the field type is a dataclass and the new value is a list
	# Convert each element of the list to the corresponding class object
	new_value = [field_type(**item) for item in new_value]
	setattr(self, field_name, new_value)
	else:
	# If the field type is a dataclass but not a list, recursively update the dataclass
	nested_config = getattr(self, field_name)
	nested_config.update_config(new_value)
	else:
	if field_type == Dict[str, GPTSoVitsPreset]:
	new_dict = {}

	for k, v in new_value.items():
	refer_wav_path = v.get("refer_wav_path")
	prompt_text = v.get("prompt_text")
	prompt_lang = v.get("prompt_lang")
	new_dict.update({k: GPTSoVitsPreset(refer_wav_path, prompt_text, prompt_lang)})
	new_value = new_dict

	elif field_type == bool:
	new_value = str(new_value).lower() == "true"
	elif field_type == int:
	new_value = int(new_value)
	elif field_type == float:
	new_value = float(new_value)
	elif field_type == str:
	new_value = str(new_value)
	elif field_type == torch.device:
	new_value = torch.device(new_value)

	setattr(self, field_name, new_value)


	@dataclass
	class Reader(AsDictMixin):
	model_type: str = "VITS"
	id: int = 0
	preset: str = "default"


	@dataclass
	class ReadingConfig(AsDictMixin):
	interlocutor: Reader = Reader()
	narrator: Reader = Reader()


	@dataclass
	class ModelConfig(AsDictMixin):
	chinese_roberta_wwm_ext_large: str = "bert/chinese-roberta-wwm-ext-large"
	bert_base_japanese_v3: str = "bert/bert-base-japanese-v3"
	bert_large_japanese_v2: str = "bert/bert-large-japanese-v2"
	deberta_v2_large_japanese: str = "bert/deberta-v2-large-japanese"
	deberta_v3_large: str = "bert/deberta-v3-large"
	deberta_v2_large_japanese_char_wwm: str = "bert/deberta-v2-large-japanese-char-wwm"
	wav2vec2_large_robust_12_ft_emotion_msp_dim: str = "emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
	clap_htsat_fused: str = "emotional/clap-htsat-fused"
	erlangshen_MegatronBert_1_3B_Chinese: str = "bert/Erlangshen-MegatronBert-1.3B-Chinese"
	vits_chinese_bert: str = "bert/vits_chinese_bert"
	# hubert-vits
	hubert_soft_0d54a1f4: str = "hubert/hubert_soft/hubert-soft-0d54a1f4.pt"
	# w2v2-vits: .npy file or folder are alvailable
	dimensional_emotion_npy: Union[str, List[str]] = "emotional/dimensional_emotion_npy"
	# w2v2-vits: Need to have both `models.onnx` and `models.yaml` files in the same path.
	dimensional_emotion_model: str = "emotional/dimensional_emotion_model/models.yaml"
	g2pw_model: str = "G2PWModel"
	chinese_hubert_base: str = "hubert/chinese_hubert_base"


	@dataclass
	class TTSModelConfig(AsDictMixin):
	model_path: Optional[str] = None
	config_path: Optional[str] = None
	sovits_path: Optional[str] = None
	gpt_path: Optional[str] = None

	def asdict(self):
	data = {}
	for attr, value in vars(self).items():
	if value is not None:
	data[attr] = value
	return data


	@dataclass
	class TTSConfig(AsDictMixin):
	# Directory name for models under the data folder
	models_path: str = "models"
	# If set to True (default), models under the specified models_path will be automatically loaded.
	# When set to False, you can manually specify the models to load.
	auto_load: bool = True
	# List to store configurations of Text-to-Speech models
	models: List[TTSModelConfig] = field(default_factory=list)

	def asdict(self):
	data = {}
	for attr, value in vars(self).items():
	if isinstance(value, list):
	data[attr] = []
	for item in value:
	data[attr].append(item.asdict())
	else:
	data[attr] = value
	return data

	def update_config(self, new_config_dict):
	for field in fields(self):
	field_name = field.name
	field_type = field.type

	if field_name in new_config_dict:
	new_value = new_config_dict[field_name]

	if is_dataclass(field_type):
	nested_config = getattr(self, field_name)
	nested_config.update_config(new_value)
	else:
	if field_type == bool:
	new_value = str(new_value).lower() == "true"
	elif field_type == int:
	new_value = int(new_value)
	elif field_type == float:
	new_value = float(new_value)
	elif field_type == str:
	new_value = str(new_value)
	elif field_type == torch.device:
	new_value = torch.device(new_value)
	elif field_type == List[TTSModelConfig]:
	new_value = [TTSModelConfig(model.get("model_path"),
	model.get("config_path"),
	model.get("sovits_path"),
	model.get("gpt_path")) for model in
	new_value]

	setattr(self, field_name, new_value)


	@dataclass
	class HttpService(AsDictMixin):
	host: str = "0.0.0.0"
	port: int = 23456
	debug: bool = False


	@dataclass
	class LogConfig(AsDictMixin):
	# Logs path
	logs_path: str = "logs"
	# Set the number of backup log files to keep.
	logs_backupcount: int = 30
	# logging_level:DEBUG/INFO/WARNING/ERROR/CRITICAL
	logging_level: str = "DEBUG"


	@dataclass
	class System(AsDictMixin):
	device: torch.device = torch.device(
	"cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
	# Upload path
	upload_folder: str = "upload"
	# Cahce path
	cache_path: str = "cache"
	# If CLEAN_INTERVAL_SECONDS <= 0, the cleaning task will not be executed.
	clean_interval_seconds: int = 3600
	# save audio to CACHE_PATH
	cache_audio: bool = False
	# Set to True to enable API Key authentication
	api_key_enabled: bool = False
	# API_KEY is required for authentication
	api_key: str = ''.join(secrets.choice(string.ascii_letters + string.digits) for _ in range(24))
	# Control whether to enable the admin backend functionality. Set to False to disable the admin backend.
	is_admin_enabled: bool = True
	# Define the route for the admin backend. You can change this to your desired route
	admin_route: str = '/admin'
	# Path to the 'data' folder, where various models are stored
	data_path: str = "data"

	def asdict(self):
	data = {}
	for attr, value in vars(self).items():
	if attr == "device":
	data[attr] = str(value)
	else:
	data[attr] = value
	return data


	@dataclass
	class LanguageIdentification(AsDictMixin):
	# Language identification library. Optional fastlid, langid
	language_identification_library: str = "langid"
	# To use the english_cleaner, you need to install espeak and provide the path of libespeak-ng.dll as input here.
	# If ESPEAK_LIBRARY is set to empty, it will be read from the environment variable.
	# For windows : "C:/Program Files/eSpeak NG/libespeak-ng.dll"
	espeak_library: str = r"C:/Program Files/eSpeak NG/libespeak-ng.dll" if "win" in sys.platform else ""
	# zh ja ko en... If it is empty, it will be read based on the text_cleaners specified in the config.json.
	language_automatic_detect: list = field(default_factory=list)


	@dataclass
	class User(AsDictMixin):
	id: int = 0
	username: str = ''.join(secrets.choice(string.ascii_letters + string.digits) for _ in range(8))
	password: str = ''.join(secrets.choice(string.ascii_letters + string.digits) for _ in range(16))

	def is_authenticated(self):
	return True

	def is_active(self):
	return True

	def is_anonymous(self):
	return False

	def get_id(self):
	return str(self.id)


	@dataclass
	class Config(AsDictMixin):
	abs_path: str = ABS_PATH
	http_service: HttpService = HttpService()
	model_config: ModelConfig = ModelConfig()
	tts_config: TTSConfig = TTSConfig()
	admin: User = User()
	system: System = System()
	log_config: LogConfig = LogConfig()
	language_identification: LanguageIdentification = LanguageIdentification()
	reading_config: ReadingConfig = ReadingConfig()
	vits_config: VitsConfig = VitsConfig()
	w2v2_vits_config: W2V2VitsConfig = W2V2VitsConfig()
	hubert_vits_config: HuBertVitsConfig = HuBertVitsConfig()
	bert_vits2_config: BertVits2Config = BertVits2Config()
	gpt_sovits_config: GPTSoVitsConfig = GPTSoVitsConfig()

	def asdict(self):
	data = {}
	for attr, value in vars(self).items():
	if isinstance(value, AsDictMixin):
	data[attr] = value.asdict()
	else:
	data[attr] = value
	return data

	@staticmethod
	def load_config():
	logging.getLogger().setLevel(logging.INFO)
	config_path = os.path.join(Config.abs_path, "config.yaml")
	if not os.path.exists(config_path) or not os.path.isfile(config_path):
	logging.info("config.yaml not found. Generating a new config.yaml based on config.py.")
	config = Config()

	# 初始化管理员账号密码
	logging.info(
	f"New admin user created:\n"
	f"{'-' * 40}\n"
	f"\| Username: {config.admin.username:<26} \|\n"
	f"\| Password: {config.admin.password:<26} \|\n"
	f"{'-' * 40}\n"
	f"Please do not share this information.")
	Config.save_config(config)

	return config
	else:
	try:
	logging.info("Loading config...")
	with open(config_path, 'r', encoding='utf-8') as f:
	loaded_config = yaml.safe_load(f)
	config = Config()

	if loaded_config is not None:
	config.update_config(loaded_config)
	logging.info("Loading config success!")
	else:
	logging.info("config.yaml is empty, initializing config.yaml...")

	# Load default models from config.py.
	# config.update_config(model_list)

	# If parameters are incomplete, they will be automatically filled in upon saving.
	Config.save_config(config)

	return config
	except Exception as e:
	logging.error(traceback.print_exc())
	ValueError(e)

	@staticmethod
	def save_config(config):
	temp_filename = os.path.join(Config.abs_path, "config.yaml.tmp")
	with open(temp_filename, 'w', encoding='utf-8') as f:
	yaml.dump(config.asdict(), f, allow_unicode=True, default_style='', sort_keys=False)
	shutil.move(temp_filename, os.path.join(Config.abs_path, "config.yaml"))
	logging.info(f"Config is saved.")