# ############################################################################
# Model: WAV2VEC base for Emotion Recognition
 ############################################################################
# Hparams NEEDED
HPARAMS_NEEDED: [ "out_n_neurons", "label_encoder", ]
# Modules Needed
MODULES_NEEDED: ["transf", "avg_pool", "enc", 'classifier']

# Feature parameters
wavlm_hub: "microsoft/wavlm-large"

# Pretrain folder (HuggingFace)
pretrained_path: "mtauro/wavlm_vrs_ck_iva_k492"

# parameters
#encoder_dim: 768
out_n_neurons: 2

transf: !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2
    source: !ref <wavlm_hub>
    save_path: wavlm_checkpoint

avg_pool: !new:speechbrain.nnet.pooling.StatisticsPooling
    return_std: False

enc: !new:speechbrain.lobes.models.VanillaNN.VanillaNN
    input_shape: [null, null, 1024]
    dnn_blocks: 1
    dnn_neurons: 1024 # this will be output size of 3rd dimension


classifier: !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
    input_size: 1024 #192 for ecapa, double number of channels
    out_neurons: !ref <out_n_neurons>

softmax: !new:speechbrain.nnet.activations.Softmax

model: !new:torch.nn.ModuleList
    - [!ref <avg_pool>, !ref <enc>,  !ref <classifier>]

modules:
    transf: !ref <transf> 
    avg_pool: !ref <avg_pool>
    enc: !ref <enc>
    classifier: !ref <classifier>

        
label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder


pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
   loadables:
      transf: !ref <transf>
      avg_pool: !ref <avg_pool>
      enc: !ref <enc>
      classifier: !ref <classifier>
      label_encoder: !ref <label_encoder>
   paths:
      transf: !ref <pretrained_path>/transf.ckpt
      avg_pool: !ref <pretrained_path>/avg_pool.ckpt
      enc: !ref <pretrained_path>/enc.ckpt
      classifier: !ref <pretrained_path>/classifier.ckpt
      label_encoder: !ref <pretrained_path>/label_encoder.txt