Update hyperparams.yalm

hyperparams.yalm

@@ -7,7 +7,7 @@ __set_seed: !apply:torch.manual_seed [1234]
 
 skip_training: True
 
-output_folder: output_folder_wavlm_base
+output_folder: output_folder_wavlm_base_full_data
 label_encoder_file: !ref <output_folder>/label_encoder.txt
 
 train_log: !ref <output_folder>/train_log.txt
@@ -17,6 +17,8 @@ save_folder: !ref <output_folder>/save
 
 wav2vec2_hub: microsoft/wavlm-base-plus-sv
 
+pretrained_path: Porjaz/wavlm-base-emo-fi
+
 wav2vec2_folder: !ref <save_folder>/wav2vec2_checkpoint
 
 # Feature parameters
@@ -52,7 +54,7 @@ encoder_dims: 768
 n_classes: 5
 
 # Wav2vec2 encoder
-embedding_model: !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2
+wav2vec2: !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2
     source: !ref <wav2vec2_hub>
     output_norm: True
     freeze: !ref <freeze_wav2vec2>
@@ -60,25 +62,20 @@ embedding_model: !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWa
     save_path: !ref <wav2vec2_folder>
     output_all_hiddens: True
 
-
 avg_pool: !new:speechbrain.nnet.pooling.StatisticsPooling
     return_std: False
 
-
-classifier: !new:speechbrain.nnet.linear.Linear
+label_lin: !new:speechbrain.nnet.linear.Linear
     input_size: !ref <encoder_dims>
     n_neurons: !ref <n_classes>
     bias: False
 
-
 log_softmax: !new:speechbrain.nnet.activations.Softmax
     apply_log: True
 
-
 opt_class: !name:torch.optim.Adam
     lr: !ref <lr>
 
-
 wav2vec2_opt_class: !name:torch.optim.Adam
     lr: !ref <lr_wav2vec2>
 
@@ -88,41 +85,45 @@ epoch_counter: !new:speechbrain.utils.epoch_loop.EpochCounter
 # Functions that compute the statistics to track during the validation step.
 accuracy_computer: !name:speechbrain.utils.Accuracy.AccuracyStats
 
-
 compute_cost: !name:speechbrain.nnet.losses.nll_loss
 
-
 error_stats: !name:speechbrain.utils.metric_stats.MetricStats
     metric: !name:speechbrain.nnet.losses.classification_error
         reduction: batch
-
 modules:
     wav2vec2: !ref <wav2vec2>
     label_lin: !ref <label_lin>
 
-
 model: !new:torch.nn.ModuleList
       - [!ref <label_lin>]
 
-
 lr_annealing: !new:speechbrain.nnet.schedulers.NewBobScheduler
     initial_value: !ref <lr>
     improvement_threshold: 0.0025
     annealing_factor: 0.9
     patient: 0
 
-
 lr_annealing_wav2vec2: !new:speechbrain.nnet.schedulers.NewBobScheduler
     initial_value: !ref <lr_wav2vec2>
     improvement_threshold: 0.0025
     annealing_factor: 0.9
 
-
 checkpointer: !new:speechbrain.utils.checkpoints.Checkpointer
     checkpoints_dir: !ref <save_folder>
     recoverables:
-        model: !ref <classifier>
-        wav2vec2: !ref <embedding_model>
+        model: !ref <model>
+        wav2vec2: !ref <wav2vec2>
         lr_annealing_output: !ref <lr_annealing>
         lr_annealing_wav2vec2: !ref <lr_annealing_wav2vec2>
         counter: !ref <epoch_counter>
+
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+    loadables:
+        wav2vec2: !ref <wav2vec2>
+        model: !ref <model>
+        label_encoder: !ref <label_encoder>
+    paths:
+        wav2vec2: !ref <pretrained_path>/wav2vec2.ckpt
+        model: !ref <pretrained_path>/model.ckpt
+        label_encoder: !ref <pretrained_path>/label_encoder.txt