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# ############################################################################
# Model: ECAPA pre-trained with SimCLR using VGGSound
# ############################################################################
# Feature parameters
n_mels: 80
# Pretrain folder (HuggingFace)
pretrained_path: gorinars/sb-ecapa-vggsound-simclr
# Output parameters
out_n_neurons: 308
# Model params
compute_features: !new:speechbrain.lobes.features.Fbank
n_mels: 80
left_frames: 0
right_frames: 0
deltas: false
sample_rate: 16000
n_fft: 400
win_length: 25
hop_length: 10
f_min: 0
mean_var_norm: !new:speechbrain.processing.features.InputNormalization
norm_type: sentence
std_norm: False
embedding_model: !new:speechbrain.nnet.containers.LengthsCapableSequential
input_shape: [null, 1, null]
embedding: !new:speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN
input_size: !ref <n_mels>
channels: [1024, 1024, 1024, 1024, 3072]
kernel_sizes: [5, 3, 3, 3, 1]
dilations: [1, 2, 3, 4, 1]
groups: [1, 1, 1, 1, 1]
attention_channels: 128
lin_neurons: 256
projector: !new:crytorch.models.components.pann.SimSiamProjector
input_size: 256
hidden_size: 256
output_size: 256
norm_type: bn
classifier: !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
input_size: 256
out_neurons: !ref <out_n_neurons>
modules:
compute_features: !ref <compute_features>
mean_var_norm: !ref <mean_var_norm>
embedding_model: !ref <embedding_model>
classifier: !ref <classifier>
label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder
pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
loadables:
embedding: !ref <embedding_model.embedding>
projector: !ref <embedding_model.projector>
paths:
embedding: !ref <pretrained_path>/embedding_model.ckpt
projector: !ref <pretrained_path>/projector.ckpt
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