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fix: custom verifier type hints

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fix: be sure to close pickle file after opening
This commit is contained in:
Mike Gray 2024-06-08 20:46:32 -05:00
parent c40fe924ff
commit e5113c2022
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1 changed files with 14 additions and 11 deletions
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25
openwakeword/custom_verifier_model.py
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@ -13,17 +13,19 @@
# limitations under the License.
# Imports
import os
from tqdm import tqdm
import collections
import openwakeword
import os
import pickle
from typing import List, Union
import numpy as np
import scipy
import pickle
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)