mirror of
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106 lines
3.5 KiB
Python
106 lines
3.5 KiB
Python
from pydantic import BaseModel, Field
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import os
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from pathlib import Path
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from enum import Enum
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from typing import Any
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from synthesizer.hparams import hparams
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from synthesizer.train import train as synt_train
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# Constants
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SYN_MODELS_DIRT = f"synthesizer{os.sep}saved_models"
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ENC_MODELS_DIRT = f"encoder{os.sep}saved_models"
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# EXT_MODELS_DIRT = f"ppg_extractor{os.sep}saved_models"
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# CONV_MODELS_DIRT = f"ppg2mel{os.sep}saved_models"
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# ENC_MODELS_DIRT = f"encoder{os.sep}saved_models"
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# Pre-Load models
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if os.path.isdir(SYN_MODELS_DIRT):
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synthesizers = Enum('synthesizers', list((file.name, file) for file in Path(SYN_MODELS_DIRT).glob("**/*.pt")))
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print("Loaded synthesizer models: " + str(len(synthesizers)))
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else:
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raise Exception(f"Model folder {SYN_MODELS_DIRT} doesn't exist.")
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if os.path.isdir(ENC_MODELS_DIRT):
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encoders = Enum('encoders', list((file.name, file) for file in Path(ENC_MODELS_DIRT).glob("**/*.pt")))
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print("Loaded encoders models: " + str(len(encoders)))
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else:
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raise Exception(f"Model folder {ENC_MODELS_DIRT} doesn't exist.")
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class Model(str, Enum):
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DEFAULT = "default"
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class Input(BaseModel):
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model: Model = Field(
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Model.DEFAULT, title="模型类型",
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)
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# datasets_root: str = Field(
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# ..., 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",
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# format=True,
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# example="..\\trainning_data\\"
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# )
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input_root: str = Field(
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..., alias="输入目录", description="预处理数据根目录",
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format=True,
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example=f"..{os.sep}audiodata{os.sep}SV2TTS{os.sep}synthesizer"
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)
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run_id: str = Field(
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"", 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",
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)
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synthesizer: synthesizers = Field(
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..., alias="已有合成模型",
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description="选择语音合成模型文件."
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)
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gpu: bool = Field(
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True, alias="GPU训练", description="选择“是”，则使用GPU训练",
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)
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verbose: bool = Field(
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True, alias="打印详情", description="选择“是”，输出更多详情",
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)
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encoder: encoders = Field(
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..., alias="语音编码模型",
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description="选择语音编码模型文件."
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)
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save_every: int = Field(
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1000, alias="更新间隔", description="每隔n步则更新一次模型",
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)
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backup_every: int = Field(
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10000, alias="保存间隔", description="每隔n步则保存一次模型",
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)
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log_every: int = Field(
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500, alias="打印间隔", description="每隔n步则打印一次训练统计",
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)
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class AudioEntity(BaseModel):
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content: bytes
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mel: Any
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class Output(BaseModel):
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__root__: int
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def render_output_ui(self, streamlit_app) -> None: # type: ignore
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"""Custom output UI.
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If this method is implmeneted, it will be used instead of the default Output UI renderer.
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"""
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streamlit_app.subheader(f"Training started with code: {self.__root__}")
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def train(input: Input) -> Output:
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"""Train(训练)"""
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print(">>> Start training ...")
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force_restart = len(input.run_id) > 0
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if not force_restart:
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input.run_id = Path(input.synthesizer.value).name.split('.')[0]
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synt_train(
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input.run_id,
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input.input_root,
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f"synthesizer{os.sep}saved_models",
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input.save_every,
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input.backup_every,
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input.log_every,
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force_restart,
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hparams
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)
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return Output(__root__=0) |