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| #!/usr/bin/python3 | |
| # -*- coding: utf-8 -*- | |
| from typing import Tuple | |
| from toolbox.torchaudio.configuration_utils import PretrainedConfig | |
| class DfNetConfig(PretrainedConfig): | |
| def __init__(self, | |
| sample_rate: int = 8000, | |
| nfft: int = 512, | |
| win_size: int = 200, | |
| hop_size: int = 80, | |
| win_type: str = "hann", | |
| spec_bins: int = 256, | |
| erb_bins: int = 32, | |
| min_freq_bins_for_erb: int = 2, | |
| conv_channels: int = 64, | |
| conv_kernel_size_input: Tuple[int, int] = (3, 3), | |
| conv_kernel_size_inner: Tuple[int, int] = (1, 3), | |
| conv_lookahead: int = 0, | |
| convt_kernel_size_inner: Tuple[int, int] = (1, 3), | |
| embedding_hidden_size: int = 256, | |
| encoder_combine_op: str = "concat", | |
| encoder_emb_skip_op: str = "none", | |
| encoder_emb_linear_groups: int = 16, | |
| encoder_emb_hidden_size: int = 256, | |
| encoder_linear_groups: int = 32, | |
| decoder_emb_num_layers: int = 3, | |
| decoder_emb_skip_op: str = "none", | |
| decoder_emb_linear_groups: int = 16, | |
| decoder_emb_hidden_size: int = 256, | |
| df_decoder_hidden_size: int = 256, | |
| df_num_layers: int = 2, | |
| df_order: int = 5, | |
| df_bins: int = 96, | |
| df_gru_skip: str = "grouped_linear", | |
| df_decoder_linear_groups: int = 16, | |
| df_pathway_kernel_size_t: int = 5, | |
| df_lookahead: int = 2, | |
| n_frame: int = 3, | |
| max_local_snr: int = 30, | |
| min_local_snr: int = -15, | |
| norm_tau: float = 1., | |
| min_snr_db: float = -10, | |
| max_snr_db: float = 20, | |
| lr: float = 0.001, | |
| lr_scheduler: str = "CosineAnnealingLR", | |
| lr_scheduler_kwargs: dict = None, | |
| max_epochs: int = 100, | |
| clip_grad_norm: float = 10., | |
| seed: int = 1234, | |
| num_workers: int = 4, | |
| batch_size: int = 4, | |
| eval_steps: int = 25000, | |
| use_post_filter: bool = False, | |
| **kwargs | |
| ): | |
| super(DfNetConfig, self).__init__(**kwargs) | |
| # transform | |
| self.sample_rate = sample_rate | |
| self.nfft = nfft | |
| self.win_size = win_size | |
| self.hop_size = hop_size | |
| self.win_type = win_type | |
| # spectrum | |
| self.spec_bins = spec_bins | |
| self.erb_bins = erb_bins | |
| self.min_freq_bins_for_erb = min_freq_bins_for_erb | |
| # conv | |
| self.conv_channels = conv_channels | |
| self.conv_kernel_size_input = conv_kernel_size_input | |
| self.conv_kernel_size_inner = conv_kernel_size_inner | |
| self.conv_lookahead = conv_lookahead | |
| self.convt_kernel_size_inner = convt_kernel_size_inner | |
| self.embedding_hidden_size = embedding_hidden_size | |
| # encoder | |
| self.encoder_emb_skip_op = encoder_emb_skip_op | |
| self.encoder_emb_linear_groups = encoder_emb_linear_groups | |
| self.encoder_emb_hidden_size = encoder_emb_hidden_size | |
| self.encoder_linear_groups = encoder_linear_groups | |
| self.encoder_combine_op = encoder_combine_op | |
| # decoder | |
| self.decoder_emb_num_layers = decoder_emb_num_layers | |
| self.decoder_emb_skip_op = decoder_emb_skip_op | |
| self.decoder_emb_linear_groups = decoder_emb_linear_groups | |
| self.decoder_emb_hidden_size = decoder_emb_hidden_size | |
| # df decoder | |
| self.df_decoder_hidden_size = df_decoder_hidden_size | |
| self.df_num_layers = df_num_layers | |
| self.df_order = df_order | |
| self.df_bins = df_bins | |
| self.df_gru_skip = df_gru_skip | |
| self.df_decoder_linear_groups = df_decoder_linear_groups | |
| self.df_pathway_kernel_size_t = df_pathway_kernel_size_t | |
| self.df_lookahead = df_lookahead | |
| # lsnr | |
| self.n_frame = n_frame | |
| self.max_local_snr = max_local_snr | |
| self.min_local_snr = min_local_snr | |
| self.norm_tau = norm_tau | |
| # data snr | |
| self.min_snr_db = min_snr_db | |
| self.max_snr_db = max_snr_db | |
| # train | |
| self.lr = lr | |
| self.lr_scheduler = lr_scheduler | |
| self.lr_scheduler_kwargs = lr_scheduler_kwargs or dict() | |
| self.max_epochs = max_epochs | |
| self.clip_grad_norm = clip_grad_norm | |
| self.seed = seed | |
| self.num_workers = num_workers | |
| self.batch_size = batch_size | |
| self.eval_steps = eval_steps | |
| # runtime | |
| self.use_post_filter = use_post_filter | |
| if __name__ == "__main__": | |
| pass | |