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from pydantic import BaseModel, Field
import os
from pathlib import Path
from enum import Enum
from typing import Any
from synthesizer.hparams import hparams
from synthesizer.train import train as synt_train
# Constants
SYN_MODELS_DIRT = f"synthesizer{os.sep}saved_models"
ENC_MODELS_DIRT = f"encoder{os.sep}saved_models"
# 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"
# Pre-Load models
if os.path.isdir(SYN_MODELS_DIRT):
synthesizers = Enum('synthesizers', list((file.name, file) for file in Path(SYN_MODELS_DIRT).glob("**/*.pt")))
print("Loaded synthesizer models: " + str(len(synthesizers)))
else:
raise Exception(f"Model folder {SYN_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):
DEFAULT = "default"
class Input(BaseModel):
model: Model = Field(
Model.DEFAULT, 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\\"
# )
input_root: str = Field(
..., alias="输入目录", description="预处理数据根目录",
format=True,
example=f"..{os.sep}audiodata{os.sep}SV2TTS{os.sep}synthesizer"
)
run_id: str = Field(
"", alias="新模型名/运行ID", description="使用新ID进行重新训练，否则选择下面的模型进行继续训练",
)
synthesizer: synthesizers = Field(
..., alias="已有合成模型",
description="选择语音合成模型文件."
)
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",
)
encoder: encoders = Field(
..., alias="语音编码模型",
description="选择语音编码模型文件."
)
save_every: int = Field(
1000, alias="更新间隔", description="每隔n步则更新一次模型",
)
backup_every: int = Field(
10000, alias="保存间隔", description="每隔n步则保存一次模型",
)
log_every: int = Field(
500, alias="打印间隔", description="每隔n步则打印一次训练统计",
)
class AudioEntity(BaseModel):
content: bytes
mel: Any
class Output(BaseModel):
__root__: int
def render_output_ui(self, streamlit_app) -> None: # type: ignore
"""Custom output UI.
If this method is implmeneted, it will be used instead of the default Output UI renderer.
"""
streamlit_app.subheader(f"Training started with code: {self.__root__}")
def train(input: Input) -> Output:
"""Train(训练)"""
print(">>> Start training ...")
force_restart = len(input.run_id) > 0
if not force_restart:
input.run_id = Path(input.synthesizer.value).name.split('.')[0]
synt_train(
input.run_id,
input.input_root,
f"synthesizer{os.sep}saved_models",
input.save_every,
input.backup_every,
input.log_every,
force_restart,
hparams
)
return Output(__root__=0) |