mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
96 lines
3.2 KiB
Python
96 lines
3.2 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, Tuple
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# Constants
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EXT_MODELS_DIRT = f"ppg_extractor{os.sep}saved_models"
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ENC_MODELS_DIRT = f"encoder{os.sep}saved_models"
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if os.path.isdir(EXT_MODELS_DIRT):
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extractors = Enum('extractors', list((file.name, file) for file in Path(EXT_MODELS_DIRT).glob("**/*.pt")))
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print("Loaded extractor models: " + str(len(extractors)))
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else:
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raise Exception(f"Model folder {EXT_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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VC_PPG2MEL = "ppg2mel"
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class Dataset(str, Enum):
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AIDATATANG_200ZH = "aidatatang_200zh"
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AIDATATANG_200ZH_S = "aidatatang_200zh_s"
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class Input(BaseModel):
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# def render_input_ui(st, input) -> Dict:
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# input["selected_dataset"] = st.selectbox(
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# '选择数据集',
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# ("aidatatang_200zh", "aidatatang_200zh_s")
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# )
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# return input
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model: Model = Field(
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Model.VC_PPG2MEL, title="目标模型",
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)
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dataset: Dataset = Field(
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Dataset.AIDATATANG_200ZH, title="数据集选择",
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)
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datasets_root: str = Field(
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..., alias="数据集根目录", description="输入数据集根目录（相对/绝对）",
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format=True,
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example="..\\trainning_data\\"
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)
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output_root: str = Field(
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..., alias="输出根目录", description="输出结果根目录（相对/绝对）",
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format=True,
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example="..\\trainning_data\\"
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)
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n_processes: int = Field(
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2, alias="处理线程数", description="根据CPU线程数来设置",
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le=32, ge=1
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)
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extractor: extractors = Field(
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..., alias="特征提取模型",
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description="选择PPG特征提取模型文件."
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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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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__: Tuple[str, int]
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def render_output_ui(self, streamlit_app, input) -> 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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sr, count = self.__root__
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streamlit_app.subheader(f"Dataset {sr} done processed total of {count}")
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def preprocess(input: Input) -> Output:
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"""Preprocess(预处理)"""
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finished = 0
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if input.model == Model.VC_PPG2MEL:
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from ppg2mel.preprocess import preprocess_dataset
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finished = preprocess_dataset(
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datasets_root=Path(input.datasets_root),
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dataset=input.dataset,
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out_dir=Path(input.output_root),
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n_processes=input.n_processes,
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ppg_encoder_model_fpath=Path(input.extractor.value),
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speaker_encoder_model=Path(input.encoder.value)
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)
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# TODO: pass useful return code
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return Output(__root__=(input.dataset, finished)) |