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sample_rate: 8000 |
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snrmin: 0 |
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snrmax: 10 |
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use_snr_compression: true |
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separation_norm_type: stnorm |
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latent_dim: 128 |
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n_inp: 256 |
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encoder: &id006 !new:speechbrain.nnet.containers.Sequential |
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input_shape: [!!null '', 2, !!null ''] |
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cnn1: !new:speechbrain.nnet.CNN.Conv1d |
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in_channels: 2 |
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kernel_size: 4 |
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out_channels: 128 |
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stride: 1 |
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skip_transpose: true |
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padding: valid |
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relu1: !new:torch.nn.ReLU |
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cnn2: !new:speechbrain.nnet.CNN.Conv1d |
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in_channels: 128 |
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kernel_size: 4 |
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out_channels: 128 |
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stride: 2 |
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skip_transpose: true |
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padding: valid |
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relu2: !new:torch.nn.ReLU |
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cnn3: !new:speechbrain.nnet.CNN.Conv1d |
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in_channels: 128 |
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kernel_size: 4 |
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out_channels: 128 |
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stride: 2 |
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skip_transpose: true |
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padding: valid |
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relu3: !new:torch.nn.ReLU |
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cnn4: !new:speechbrain.nnet.CNN.Conv1d |
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in_channels: 128 |
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kernel_size: 4 |
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out_channels: 128 |
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stride: 2 |
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skip_transpose: true |
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padding: valid |
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relu4: !new:torch.nn.ReLU |
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cnn5: !new:speechbrain.nnet.CNN.Conv1d |
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in_channels: 128 |
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kernel_size: 4 |
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out_channels: 128 |
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stride: 2 |
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skip_transpose: true |
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padding: valid |
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stat_pooling: !new:speechbrain.nnet.pooling.StatisticsPooling |
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encoder_out: &id007 !new:speechbrain.nnet.containers.Sequential |
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input_shape: [!!null '', 256] |
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layer1: !new:speechbrain.nnet.linear.Linear |
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input_size: 256 |
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n_neurons: 256 |
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relu: !new:torch.nn.ReLU |
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layer2: !new:speechbrain.nnet.linear.Linear |
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input_size: 256 |
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n_neurons: 1 |
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sigm: !new:torch.nn.Sigmoid |
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modules: |
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encoder: *id006 |
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encoder_out: *id007 |
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pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer |
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loadables: |
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encoder: !ref <encoder> |
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encoder_out: !ref <encoder_out> |
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