fix: add preprocess paths to running config

Author: nglehuy

examples/conformer/config.yml

@@ -31,7 +31,7 @@ decoder_config:
   beam_width: 0
   norm_score: True
   corpus_files:
-    - /mnt/Data/MLDL/Datasets/ASR/Raw/LibriSpeech/train-clean-100/transcripts.tsv
+    - /h/MLDL/Datasets/ASR/Raw/LibriSpeech/train-clean-100/transcripts.tsv
 
 model_config:
   name: conformer
@@ -75,8 +75,8 @@ learning_config:
           num_masks: 1
           mask_factor: 27
     data_paths:
-      - /mnt/Data/MLDL/Datasets/ASR/Raw/LibriSpeech/train-clean-100/transcripts.tsv
-    tfrecords_dir: /mnt/Data/MLDL/Datasets/ASR/Raw/LibriSpeech/tfrecords_1030
+      - /h/MLDL/Datasets/ASR/Raw/LibriSpeech/train-clean-100/transcripts.tsv
+    tfrecords_dir: null
     shuffle: True
     cache: True
     buffer_size: 100
@@ -86,7 +86,7 @@ learning_config:
   eval_dataset_config:
     use_tf: True
     data_paths: null
-    tfrecords_dir: /mnt/Data/MLDL/Datasets/ASR/Raw/LibriSpeech/tfrecords_1030
+    tfrecords_dir: null
     shuffle: False
     cache: True
     buffer_size: 100
@@ -113,13 +113,13 @@ learning_config:
     batch_size: 2
     num_epochs: 50
     checkpoint:
-      filepath: /mnt/Miscellanea/Models/local/conformer/checkpoints/{epoch:02d}.h5
+      filepath: /d/Models/local/conformer/checkpoints/{epoch:02d}.h5
       save_best_only: False
       save_weights_only: True
       save_freq: epoch
-    states_dir: /mnt/Miscellanea/Models/local/conformer/states
+    states_dir: /d/Models/local/conformer/states
     tensorboard:
-      log_dir: /mnt/Miscellanea/Models/local/conformer/tensorboard
+      log_dir: /d/Models/local/conformer/tensorboard
       histogram_freq: 1
       write_graph: True
       write_images: True

tensorflow_asr/configs/config.py

@@ -67,6 +67,14 @@ def __init__(self, config: dict = None):
         self.num_epochs = config.pop("num_epochs", 20)
         for k, v in config.items():
             setattr(self, k, v)
+            if k == "checkpoint":
+                if v and v.get("filepath"):
+                    file_util.preprocess_paths(v.get("filepath"))
+            elif k == "states_dir" and v:
+                file_util.preprocess_paths(v)
+            elif k == "tensorboard":
+                if v and v.get("log_dir"):
+                    file_util.preprocess_paths(v.get("log_dir"))
 
 
 class LearningConfig: