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# ############################################################################
# Model: ECAPA-TDNN for Accent Identification
# ############################################################################
# Pretrain folder (HuggingFace)
pretrained_path: Jzuluaga/accent-id-commonaccent_ecapa
# Feature parameters
n_mels: 80
# Output parameters
n_languages: 16 # Possible languages in the dataset
emb_dim: 192 # dimensionality of the embeddings
# 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
embedding_model: !new:speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN
input_size: !ref <n_mels>
activation: !name:torch.nn.LeakyReLU
channels: [1024, 1024, 1024, 1024, 3072]
kernel_sizes: [5, 3, 3, 3, 1]
dilations: [1, 2, 3, 4, 1]
attention_channels: 128
lin_neurons: !ref <emb_dim>
# Classifier based on cosine distance
classifier: !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
input_size: !ref <emb_dim>
out_neurons: !ref <n_languages>
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>/accent_encoder.txt
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