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from pydantic import BaseModel, Field
import os
from pathlib import Path
from enum import Enum
from typing import Any, Tuple
import numpy as np
from utils.load_yaml import HpsYaml
from utils.util import AttrDict
import torch
# Constants
EXT_MODELS_DIRT = f"ppg_extractor{os.sep}saved_models"
CONV_MODELS_DIRT = f"ppg2mel{os.sep}saved_models"
ENC_MODELS_DIRT = f"encoder{os.sep}saved_models"
if os.path.isdir(EXT_MODELS_DIRT):
extractors = Enum('extractors', list((file.name, file) for file in Path(EXT_MODELS_DIRT).glob("**/*.pt")))
print("Loaded extractor models: " + str(len(extractors)))
else:
raise Exception(f"Model folder {EXT_MODELS_DIRT} doesn't exist.")
if os.path.isdir(CONV_MODELS_DIRT):
convertors = Enum('convertors', list((file.name, file) for file in Path(CONV_MODELS_DIRT).glob("**/*.pth")))
print("Loaded convertor models: " + str(len(convertors)))
else:
raise Exception(f"Model folder {CONV_MODELS_DIRT} doesn't exist.")
if os.path.isdir(ENC_MODELS_DIRT):
encoders = Enum('encoders', list((file.name, file) for file in Path(ENC_MODELS_DIRT).glob("**/*.pt")))
print("Loaded encoders models: " + str(len(encoders)))
else:
raise Exception(f"Model folder {ENC_MODELS_DIRT} doesn't exist.")
class Model(str, Enum):
VC_PPG2MEL = "ppg2mel"
class Dataset(str, Enum):
AIDATATANG_200ZH = "aidatatang_200zh"
AIDATATANG_200ZH_S = "aidatatang_200zh_s"
class Input(BaseModel):
# def render_input_ui(st, input) -> Dict:
# input["selected_dataset"] = st.selectbox(
# '选择数据集',
# ("aidatatang_200zh", "aidatatang_200zh_s")
# )
# return input
model: Model = Field(
Model.VC_PPG2MEL, title="模型类型",
)
# datasets_root: str = Field(
# ..., alias="预处理数据根目录", description="输入目录（相对/绝对）,不适用于ppg2mel模型",
# format=True,
# example="..\\trainning_data\\"
# )
output_root: str = Field(
..., alias="输出目录(可选)", description="建议不填，保持默认",
format=True,
example=""
)
continue_mode: bool = Field(
True, alias="继续训练模式", description="选择“是”，则从下面选择的模型中继续训练",
)
gpu: bool = Field(
True, alias="GPU训练", description="选择“是”，则使用GPU训练",
)
verbose: bool = Field(
True, alias="打印详情", description="选择“是”，输出更多详情",
)
# TODO: Move to hiden fields by default
convertor: convertors = Field(
..., alias="转换模型",
description="选择语音转换模型文件."
)
extractor: extractors = Field(
..., alias="特征提取模型",
description="选择PPG特征提取模型文件."
)
encoder: encoders = Field(
..., alias="语音编码模型",
description="选择语音编码模型文件."
)
njobs: int = Field(
8, alias="进程数", description="适用于ppg2mel",
)
seed: int = Field(
default=0, alias="初始随机数", description="适用于ppg2mel",
)
model_name: str = Field(
..., alias="新模型名", description="仅在重新训练时生效,选中继续训练时无效",
example="test"
)
model_config: str = Field(
..., alias="新模型配置", description="仅在重新训练时生效,选中继续训练时无效",
example=".\\ppg2mel\\saved_models\\seq2seq_mol_ppg2mel_vctk_libri_oneshotvc_r4_normMel_v2"
)
class AudioEntity(BaseModel):
content: bytes
mel: Any
class Output(BaseModel):
__root__: Tuple[str, int]
def render_output_ui(self, streamlit_app, input) -> None: # type: ignore
"""Custom output UI.
If this method is implmeneted, it will be used instead of the default Output UI renderer.
"""
sr, count = self.__root__
streamlit_app.subheader(f"Dataset {sr} done processed total of {count}")
def train_vc(input: Input) -> Output:
"""Train VC(训练 VC)"""
print(">>> OneShot VC training ...")
params = AttrDict()
params.update({
"gpu": input.gpu,
"cpu": not input.gpu,
"njobs": input.njobs,
"seed": input.seed,
"verbose": input.verbose,
"load": input.convertor.value,
"warm_start": False,
})
if input.continue_mode:
# trace old model and config
p = Path(input.convertor.value)
params.name = p.parent.name
# search a config file
model_config_fpaths = list(p.parent.rglob("*.yaml"))
if len(model_config_fpaths) == 0:
raise "No model yaml config found for convertor"
config = HpsYaml(model_config_fpaths[0])
params.ckpdir = p.parent.parent
params.config = model_config_fpaths[0]
params.logdir = os.path.join(p.parent, "log")
else:
# Make the config dict dot visitable
config = HpsYaml(input.config)
np.random.seed(input.seed)
torch.manual_seed(input.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(input.seed)
mode = "train"
from ppg2mel.train.train_linglf02mel_seq2seq_oneshotvc import Solver
solver = Solver(config, params, mode)
solver.load_data()
solver.set_model()
solver.exec()
print(">>> Oneshot VC train finished!")
# TODO: pass useful return code
return Output(__root__=(input.dataset, 0)) |