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from dataclasses import asdict, dataclass, field
from typing import Dict, List
from coqpit import MISSING
from TTS.config.shared_configs import BaseAudioConfig, BaseDatasetConfig, BaseTrainingConfig
@dataclass
class BaseEncoderConfig(BaseTrainingConfig):
"""Defines parameters for a Generic Encoder model."""
model: str = None
audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])
# model params
model_params: Dict = field(
default_factory=lambda: {
"model_name": "lstm",
"input_dim": 80,
"proj_dim": 256,
"lstm_dim": 768,
"num_lstm_layers": 3,
"use_lstm_with_projection": True,
}
)
audio_augmentation: Dict = field(default_factory=lambda: {})
# training params
epochs: int = 10000
loss: str = "angleproto"
grad_clip: float = 3.0
lr: float = 0.0001
optimizer: str = "radam"
optimizer_params: Dict = field(default_factory=lambda: {"betas": [0.9, 0.999], "weight_decay": 0})
lr_decay: bool = False
warmup_steps: int = 4000
# logging params
tb_model_param_stats: bool = False
steps_plot_stats: int = 10
save_step: int = 1000
print_step: int = 20
run_eval: bool = False
# data loader
num_classes_in_batch: int = MISSING
num_utter_per_class: int = MISSING
eval_num_classes_in_batch: int = None
eval_num_utter_per_class: int = None
num_loader_workers: int = MISSING
voice_len: float = 1.6
def check_values(self):
super().check_values()
c = asdict(self)
assert (
c["model_params"]["input_dim"] == self.audio.num_mels
), " [!] model input dimendion must be equal to melspectrogram dimension."
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