#Feature parameters
sample_rate: 16000
n_mels: 40

#Model parameters
emb_dim: 128
n_classes: 5
tdnn_channels: 64
tdnn_channels_out: 128

label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder


#model
compute_features: !new:speechbrain.lobes.features.Fbank
    n_mels: !ref <n_mels>

mean_var_norm: !new:speechbrain.processing.features.InputNormalization
    norm_type: sentence
    std_norm: False

embedding_model: !new:speechbrain.lobes.models.Xvector.Xvector
    in_channels: !ref <n_mels>
    tdnn_blocks: 5
    tdnn_channels:
        - !ref <tdnn_channels>
        - !ref <tdnn_channels>
        - !ref <tdnn_channels>
        - !ref <tdnn_channels>
        - !ref <tdnn_channels_out>
    tdnn_kernel_sizes: [5, 3, 3, 1, 1]
    tdnn_dilations: [1, 2, 3, 1, 1]
    lin_neurons: !ref <emb_dim>

classifier: !new:speechbrain.lobes.models.Xvector.Classifier
    input_shape: [null, null, !ref <emb_dim>]
    activation: !name:torch.nn.LeakyReLU
    lin_blocks: 1
    lin_neurons: !ref <emb_dim>
    out_neurons: !ref <n_classes>

modules:
    compute_features: !ref <compute_features>
    embedding_model: !ref <embedding_model>
    classifier: !ref <classifier>
    mean_var_norm: !ref <mean_var_norm>

pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
    loadables:
        embedding_model: !ref <embedding_model>
        classifier: !ref <classifier>