72 lines
1.4 KiB
Python
72 lines
1.4 KiB
Python
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from typing import *
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from pydantic import BaseModel
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class TrainConfigTrain(BaseModel):
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log_interval: int
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seed: int
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epochs: int
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learning_rate: float
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betas: List[float]
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eps: float
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batch_size: int
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fp16_run: bool
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lr_decay: float
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segment_size: int
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init_lr_ratio: int
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warmup_epochs: int
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c_mel: int
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c_kl: float
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class TrainConfigData(BaseModel):
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max_wav_value: float
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sampling_rate: int
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filter_length: int
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hop_length: int
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win_length: int
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n_mel_channels: int
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mel_fmin: float
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mel_fmax: Any
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class TrainConfigModel(BaseModel):
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inter_channels: int
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hidden_channels: int
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filter_channels: int
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n_heads: int
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n_layers: int
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kernel_size: int
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p_dropout: int
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resblock: str
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resblock_kernel_sizes: List[int]
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resblock_dilation_sizes: List[List[int]]
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upsample_rates: List[int]
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upsample_initial_channel: int
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upsample_kernel_sizes: List[int]
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use_spectral_norm: bool
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gin_channels: int
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emb_channels: int
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spk_embed_dim: int
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class TrainConfig(BaseModel):
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version: Literal["v1", "v2"] = "v2"
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train: TrainConfigTrain
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data: TrainConfigData
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model: TrainConfigModel
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class DatasetMetaItem(BaseModel):
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gt_wav: str
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co256: str
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f0: Optional[str]
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f0nsf: Optional[str]
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speaker_id: int
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class DatasetMetadata(BaseModel):
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files: Dict[str, DatasetMetaItem]
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# mute: DatasetMetaItem
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