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"""
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Style-Bert-VITS2 モデルのハイパーパラメータを表す Pydantic モデル。
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デフォルト値は configs/config_jp_extra.json 内の定義と概ね同一で、
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万が一ロードした config.json に存在しないキーがあった際のフェイルセーフとして適用される。
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"""
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from pathlib import Path
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from typing import Optional, Union
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from pydantic import BaseModel, ConfigDict
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class HyperParametersTrain(BaseModel):
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log_interval: int = 200
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eval_interval: int = 1000
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seed: int = 42
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epochs: int = 1000
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learning_rate: float = 0.0001
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betas: tuple[float, float] = (0.8, 0.99)
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eps: float = 1e-9
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batch_size: int = 2
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bf16_run: bool = False
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fp16_run: bool = False
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lr_decay: float = 0.99996
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segment_size: int = 16384
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init_lr_ratio: int = 1
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warmup_epochs: int = 0
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c_mel: int = 45
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c_kl: float = 1.0
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c_commit: int = 100
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skip_optimizer: bool = False
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freeze_ZH_bert: bool = False
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freeze_JP_bert: bool = False
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freeze_EN_bert: bool = False
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freeze_emo: bool = False
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freeze_style: bool = False
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freeze_decoder: bool = False
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class HyperParametersData(BaseModel):
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use_jp_extra: bool = True
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training_files: str = "Data/Dummy/train.list"
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validation_files: str = "Data/Dummy/val.list"
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max_wav_value: float = 32768.0
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sampling_rate: int = 44100
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filter_length: int = 2048
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hop_length: int = 512
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win_length: int = 2048
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n_mel_channels: int = 128
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mel_fmin: float = 0.0
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mel_fmax: Optional[float] = None
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add_blank: bool = True
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n_speakers: int = 1
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cleaned_text: bool = True
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spk2id: dict[str, int] = {
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"Dummy": 0,
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}
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num_styles: int = 1
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style2id: dict[str, int] = {
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"Neutral": 0,
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}
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class HyperParametersModelSLM(BaseModel):
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model: str = "./slm/wavlm-base-plus"
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sr: int = 16000
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hidden: int = 768
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nlayers: int = 13
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initial_channel: int = 64
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class HyperParametersModel(BaseModel):
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use_spk_conditioned_encoder: bool = True
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use_noise_scaled_mas: bool = True
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use_mel_posterior_encoder: bool = False
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use_duration_discriminator: bool = False
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use_wavlm_discriminator: bool = True
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inter_channels: int = 192
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hidden_channels: int = 192
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filter_channels: int = 768
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n_heads: int = 2
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n_layers: int = 6
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kernel_size: int = 3
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p_dropout: float = 0.1
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resblock: str = "1"
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resblock_kernel_sizes: list[int] = [3, 7, 11]
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resblock_dilation_sizes: list[list[int]] = [
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[1, 3, 5],
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[1, 3, 5],
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[1, 3, 5],
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]
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upsample_rates: list[int] = [8, 8, 2, 2, 2]
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upsample_initial_channel: int = 512
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upsample_kernel_sizes: list[int] = [16, 16, 8, 2, 2]
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n_layers_q: int = 3
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use_spectral_norm: bool = False
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gin_channels: int = 512
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slm: HyperParametersModelSLM = HyperParametersModelSLM()
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class HyperParameters(BaseModel):
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model_name: str = "Dummy"
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version: str = "2.0-JP-Extra"
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train: HyperParametersTrain = HyperParametersTrain()
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data: HyperParametersData = HyperParametersData()
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model: HyperParametersModel = HyperParametersModel()
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model_dir: Optional[str] = None
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speedup: bool = False
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repo_id: Optional[str] = None
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model_config = ConfigDict(protected_namespaces=())
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@staticmethod
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def load_from_json(json_path: Union[str, Path]) -> "HyperParameters":
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"""
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与えられた JSON ファイルからハイパーパラメータを読み込む。
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Args:
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json_path (Union[str, Path]): JSON ファイルのパス
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Returns:
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HyperParameters: ハイパーパラメータ
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"""
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with open(json_path, encoding="utf-8") as f:
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return HyperParameters.model_validate_json(f.read())
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