rename model_ckpt to pretrained_model

bagustris · May 27, 2024 · 7fc73d4 · 7fc73d4
1 parent 92a5605
commit 7fc73d4
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Showing 3 changed files with 7 additions and 7 deletions.
diff --git a/ini_file.md b/ini_file.md
@@ -303,8 +303,8 @@
   * device = cpu
 * **patience**: Number of epochs to wait if the result gets better (for early stopping)
   * patience = 5
-* **model_ckpt**: Base model for finetuning/transfer learning. Variants of wav2vec2, Hubert, and WavLM are tested to work.
-  * model_ckpt = microsoft/wavlm-base
+* **pretrained_model**: Base model for finetuning/transfer learning. Variants of wav2vec2, Hubert, and WavLM are tested to work.
+  * pretrained_model = microsoft/wavlm-base
 
 ### EXPL
 * **model**: Which model to use to estimate feature importance.

diff --git a/nkululeko/models/model_tuned.py b/nkululeko/models/model_tuned.py
@@ -54,14 +54,14 @@ def __init__(self, df_train, df_test, feats_train, feats_test):
 
     def _init_model(self):
         model_path = "facebook/wav2vec2-large-robust-ft-swbd-300h"
-        model_ckpt = self.util.config_val("MODEL", "model_ckpt", model_path)
+        pretrained_model = self.util.config_val("MODEL", "pretrained_model", model_path)
         self.num_layers = None
         self.sampling_rate = 16000
         self.max_duration_sec = 8.0
         self.accumulation_steps = 4
 
         # print finetuning information via debug
-        self.util.debug(f"Finetuning from model: {model_ckpt}")
+        self.util.debug(f"Finetuning from model: {pretrained_model}")
 
         # create dataset
         dataset = {}
@@ -92,7 +92,7 @@ def _init_model(self):
             value in target_mapping.items()}
 
         self.config = transformers.AutoConfig.from_pretrained(
-            model_ckpt,
+            pretrained_model,
             num_labels=len(target_mapping),
             label2id=target_mapping,
             id2label=target_mapping_reverse,
@@ -124,7 +124,7 @@ def _init_model(self):
         assert self.processor.feature_extractor.sampling_rate == self.sampling_rate
 
         self.model = Model.from_pretrained(
-            model_ckpt,
+            pretrained_model,
             config=self.config,
         )
         self.model.freeze_feature_extractor()

diff --git a/tests/exp_emodb_finetune.ini b/tests/exp_emodb_finetune.ini
@@ -18,4 +18,4 @@ type = []
 type = finetune
 device = 1
 batch_size = 8
-model_ckpt = microsoft/wavlm-base
+pretrained_model = microsoft/wavlm-base