fix: custom verifier type hints

fix: be sure to close pickle file after opening

openwakeword/custom_verifier_model.py

@@ -13,17 +13,19 @@
 # limitations under the License.
 
 # Imports
-import os
-from tqdm import tqdm
 import collections
-import openwakeword
-import numpy as np
-import scipy
+import os
 import pickle
+from typing import List, Union
 
+import numpy as np
+import scipy
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import FunctionTransformer, StandardScaler
+from tqdm import tqdm
+
+import openwakeword
 
 
 # Define functions to prepare data for speaker dependent verifier model
@@ -112,8 +114,8 @@ def train_verifier_model(features: np.ndarray, labels: np.ndarray):
 
 
 def train_custom_verifier(
-        positive_reference_clips: str,
-        negative_reference_clips: str,
+        positive_reference_clips: List[Union[str, os.PathLike]],
+        negative_reference_clips: List[Union[str, os.PathLike]],
         output_path: str,
         model_name: str,
         **kwargs
@@ -123,11 +125,11 @@ def train_custom_verifier(
     from a single user.
 
     Args:
-        positive_reference_clips (str): The path to a directory containing single-channel 16khz, 16-bit WAV files
+        positive_reference_clips (List[Union[str, os.PathLike]]): The path(s) to single-channel 16khz, 16-bit WAV files
                                         of the target wake word/phrase.
-        negative_reference_clips (str): The path to a directory containing single-channel 16khz, 16-bit WAV files
+        negative_reference_clips (List[Union[str, os.PathLike]]): The path(s) to single-channel 16khz, 16-bit WAV files
                                         of miscellaneous speech not containing the target wake word/phrase.
-        output_path (str): The location to save the trained verifier model (as a scikit-learn .joblib file)
+        output_path (str): The location to save the trained verifier model (as a Python pickle file (.pkl))
         model_name (str): The name or path of the trained openWakeWord model that the verifier model will be
                           based on. If only a name, it must be one of the pre-trained models included in the
                           openWakeWord release.
@@ -171,4 +173,5 @@ def train_custom_verifier(
 
     # Save logistic regression model to specified output location
     print("Done!")
-    pickle.dump(lr_model, open(output_path, "wb"))
+    with open(output_path, "wb") as f:
+        pickle.dump(lr_model, f)