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Fixed up logging
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Pünkösd Marcell 2020-09-30 06:12:36 +02:00
parent 6de17cf962
commit 7bc08fdc8d
1 changed files with 9 additions and 2 deletions
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src/preprocessor/soundpreprocessor.py
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@ -69,13 +69,18 @@ class SoundPreProcessor(AbcPreProcessor):
self._mid_window, self._mid_step, self._short_window, \ self._mid_window, self._mid_step, self._short_window, \
self._short_step, self._compute_beat = load_model(self._temp_model_name) self._short_step, self._compute_beat = load_model(self._temp_model_name)
logging.info("The loaded model contains the following classes: " + ", ".join(self._classes))
if config.SVM_TARGET_CLASS_NAME not in self._classes:
raise Exception(f"The specified target class {config.SVM_TARGET_CLASS_NAME} is not in the possible classes")
def preprocesssignal(self, file_path: str) -> bool: def preprocesssignal(self, file_path: str) -> bool:
""" """
Classify a sound sample. Classify a sound sample.
:param file_path: Access path of the sound sample up for processing. :param file_path: Access path of the sound sample up for processing.
:return: :return:
""" """
logging.info("Running extraction...") logging.debug("Running extraction...")
sampling_rate, signal = audioBasicIO.read_audio_file(file_path) sampling_rate, signal = audioBasicIO.read_audio_file(file_path)
signal = audioBasicIO.stereo_to_mono(signal) signal = audioBasicIO.stereo_to_mono(signal)
@ -101,13 +106,15 @@ class SoundPreProcessor(AbcPreProcessor):
mid_features = numpy.append(mid_features, beat) mid_features = numpy.append(mid_features, beat)
mid_features = numpy.append(mid_features, beat_conf) mid_features = numpy.append(mid_features, beat_conf)
logging.info("Running classification...") logging.debug("Running classification...")
target_id = self._classes.index(config.SVM_TARGET_CLASS_NAME) # Might raise ValueError target_id = self._classes.index(config.SVM_TARGET_CLASS_NAME) # Might raise ValueError
feature_vector = (mid_features - self._mean) / self._std feature_vector = (mid_features - self._mean) / self._std
class_id, probability = classifier_wrapper(self._classifier, self._model_details['type'], feature_vector) class_id, probability = classifier_wrapper(self._classifier, self._model_details['type'], feature_vector)
logging.debug(f"Sample {file_path} identified as {self._classes[class_id]} with the probablility of {probability}")
return bool(class_id == target_id) return bool(class_id == target_id)
def __del__(self): def __del__(self):