# ############################################################################
# Model: ECAPA big for Speaker verification
# ############################################################################

# Feature parameters
n_mels: 80

# Pretrain folder (HuggingFace)
pretrained_path: assets/models/speaker_diarization/models/speechbrain

# Output parameters
out_n_neurons: 7205

# Model params
compute_features: !new:main.library.speaker_diarization.features.Fbank
    n_mels: !ref <n_mels>

mean_var_norm: !new:main.library.speaker_diarization.features.InputNormalization
    norm_type: sentence
    std_norm: False

embedding_model: !new:main.library.speaker_diarization.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:main.library.speaker_diarization.ECAPA_TDNN.Classifier
    input_size: 192
    out_neurons: !ref <out_n_neurons>

mean_var_norm_emb: !new:main.library.speaker_diarization.features.InputNormalization
    norm_type: global
    std_norm: False

modules:
    compute_features: !ref <compute_features>
    mean_var_norm: !ref <mean_var_norm>
    embedding_model: !ref <embedding_model>
    mean_var_norm_emb: !ref <mean_var_norm_emb>
    classifier: !ref <classifier>
        
label_encoder: !new:main.library.speaker_diarization.encoder.CategoricalEncoder

        
pretrainer: !new:main.library.speaker_diarization.parameter_transfer.Pretrainer
    loadables:
        embedding_model: !ref <embedding_model>
        mean_var_norm_emb: !ref <mean_var_norm_emb>
        classifier: !ref <classifier>
        label_encoder: !ref <label_encoder>
    paths:
        embedding_model: !ref <pretrained_path>/embedding_model.ckpt
        mean_var_norm_emb: !ref <pretrained_path>/mean_var_norm_emb.ckpt
        classifier: !ref <pretrained_path>/classifier.ckpt
        label_encoder: !ref <pretrained_path>/label_encoder.txt