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#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> |