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| #!/usr/bin/python3 | |
| # -*- coding: utf-8 -*- | |
| from toolbox.torchaudio.configuration_utils import PretrainedConfig | |
| class LstmConfig(PretrainedConfig): | |
| def __init__(self, | |
| sample_rate: int = 8000, | |
| segment_size: int = 32000, | |
| nfft: int = 512, | |
| win_size: int = 512, | |
| hop_size: int = 256, | |
| win_type: str = "hann", | |
| hidden_size: int = 1024, | |
| num_layers: int = 2, | |
| dropout: float = 0.2, | |
| min_snr_db: float = -10, | |
| max_snr_db: float = 20, | |
| max_epochs: int = 100, | |
| batch_size: int = 4, | |
| num_workers: int = 4, | |
| seed: int = 1234, | |
| lr: float = 0.001, | |
| lr_scheduler: str = "CosineAnnealingLR", | |
| lr_scheduler_kwargs: dict = None, | |
| weight_decay: float = 0.00001, | |
| clip_grad_norm: float = 10., | |
| eval_steps: int = 25000, | |
| **kwargs | |
| ): | |
| super(LstmConfig, self).__init__(**kwargs) | |
| self.sample_rate = sample_rate | |
| self.segment_size = segment_size | |
| self.nfft = nfft | |
| self.win_size = win_size | |
| self.hop_size = hop_size | |
| self.win_type = win_type | |
| self.hidden_size = hidden_size | |
| self.num_layers = num_layers | |
| self.dropout = dropout | |
| self.min_snr_db = min_snr_db | |
| self.max_snr_db = max_snr_db | |
| self.max_epochs = max_epochs | |
| self.batch_size = batch_size | |
| self.num_workers = num_workers | |
| self.seed = seed | |
| self.lr = lr | |
| self.lr_scheduler = lr_scheduler | |
| self.lr_scheduler_kwargs = lr_scheduler_kwargs or dict() | |
| self.weight_decay = weight_decay | |
| self.clip_grad_norm = clip_grad_norm | |
| self.eval_steps = eval_steps | |
| def main(): | |
| config = LstmConfig() | |
| config.to_yaml_file("config.yaml") | |
| return | |
| if __name__ == "__main__": | |
| main() | |