File size: 1,891 Bytes
09fb42e 34ed81c 09fb42e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
pretrained_path: ChainYo/speaker-recognition-meetup
# Model parameters
n_mels: 23
sample_rate: 16000
n_classes: 2
emb_dim: 512
# Feature extraction
compute_features: !new:speechbrain.lobes.features.Fbank
n_mels: !ref <n_mels>
# Mean and std normalization of the input features
mean_var_norm: !new:speechbrain.processing.features.InputNormalization
norm_type: sentence
std_norm: False
# To design a custom model, either just edit the simple CustomModel
# class that's listed here, or replace this `!new` call with a line
# pointing to a different file you've defined.
embedding_model: !new:custom_model.Xvector
in_channels: !ref <n_mels>
activation: !name:torch.nn.LeakyReLU
tdnn_blocks: 5
tdnn_channels: [512, 512, 512, 512, 1500]
tdnn_kernel_sizes: [5, 3, 3, 1, 1]
tdnn_dilations: [1, 2, 3, 1, 1]
lin_neurons: !ref <emb_dim>
classifier: !new:custom_model.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>
label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder
# Objects in "modules" dict will have their parameters moved to the correct
# device, as well as having train()/eval() called on them by the Brain class.
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>
label_encoder: !ref <label_encoder>
paths:
embedding_model: !ref <pretrained_path>/embedding_model.ckpt
classifier: !ref <pretrained_path>/classifier.ckpt
label_encoder: !ref <pretrained_path>/label_encoder.txt |