# ################################ # Model: SepFormer for source separation # Dataset : WHAMR! # ################################ sample_rate: 8000 num_spks: 1 # Encoder parameters N_encoder_out: 256 out_channels: 256 kernel_size: 16 kernel_stride: 8 # Specifying the network Encoder: !new:speechbrain.lobes.models.dual_path.Encoder kernel_size: 16 out_channels: 256 SBtfintra: !new:speechbrain.lobes.models.dual_path.SBTransformerBlock num_layers: 8 d_model: 256 nhead: 8 d_ffn: 1024 dropout: 0 use_positional_encoding: true norm_before: true SBtfinter: !new:speechbrain.lobes.models.dual_path.SBTransformerBlock num_layers: 8 d_model: 256 nhead: 8 d_ffn: 1024 dropout: 0 use_positional_encoding: true norm_before: true MaskNet: !new:speechbrain.lobes.models.dual_path.Dual_Path_Model num_spks: 1 in_channels: 256 out_channels: 256 num_layers: 2 K: 250 intra_model: !ref inter_model: !ref norm: ln linear_layer_after_inter_intra: false skip_around_intra: true Decoder: !new:speechbrain.lobes.models.dual_path.Decoder in_channels: 256 out_channels: 1 kernel_size: 16 stride: 8 bias: false modules: encoder: !ref decoder: !ref masknet: !ref pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer loadables: encoder: !ref masknet: !ref decoder: !ref