lang-id-commonlanguage_ecapa / hyperparams.yaml
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update model + fix shape
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# ############################################################################
# Model: ECAPA-TDNN for Language Identification
# ############################################################################
# Pretrain folder (HuggingFace)
pretrained_path: speechbrain/lang-id-commonlanguage_ecapa
# Feature parameters
n_mels: 80
# Output parameters
out_n_neurons: 45 # Possible languages in the dataset
# Model params
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.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]
attention_channels: 128
lin_neurons: 192
classifier: !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
input_size: 192
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_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