def eval_all_metrics_lab(refs_URI, detected_URI):
"""
run all eval metrics on one file
"""
ref_intervals, ref_labels = load_labeled_intervals(refs_URI)
detected_intervals, detected_labels = load_labeled_intervals(detected_URI)
# metric 1: alignment error
alignmentErrors = _eval_alignment_error(ref_intervals, detected_intervals,
tierAliases.phrases, ref_labels)
mean, stDev, median = getMeanAndStDevError(alignmentErrors)
# metric 2: percentage correct
initialTimeOffset_refs = ref_intervals[0][0]
finalts_refs = ref_intervals[-1][1]
durationCorrect, totalLength = _eval_percentage_correct(
reference_token_list=ref_intervals,
detected_token_List=detected_intervals,
final_ts_anno=finalts_refs,
initial_time_offset_refs=initialTimeOffset_refs,
reference_labels=ref_labels)
percentage_correct = durationCorrect / totalLength
# metric 3: percentage tolerance
percentage_tolerance = _eval_percentage_tolerance(
ref_intervals=ref_intervals,
detected_intervals=detected_intervals,
reference_labels=ref_labels,
tolerance=0.3)
return mean, percentage_correct, percentage_tolerance
def eval_error_textgrid_test():
audio_name = '05_Semahat_Ozdenses_-_Bir_Ihtimal_Daha_Var_0_zemin_from_69_5205_to_84_2'
annotation_url = os.path.join(PATH_TEST_DATASET,
audio_name + ANNOTATION_EXT)
start_index = 0
end_index = -1
detected_token_list = [[0.61, 0.94, u'Bir'], [1.02, 3.41, u'ihtimal'],
[3.42, 4.11, u'daha'], [4.12, 5.4, u'var'],
[8.03, 8.42, u'o'], [8.46, 8.83, u'da'],
[8.86, 10.65, u'\xf6lmek'], [10.66, 11.04, u'mi'],
[11.05, 14.39, u'dersin']]
annotation_token_list, detected_token_list, dummy, dummy = \
strip_non_lyrics_tokens(annotation_url,
detected_token_list,
tierAliases.phrases,
start_index,
end_index)
alignment_errors = _eval_alignment_error(annotation_token_list,
detected_token_list,
tierAliases.phrases)
mean, std_dev, median = getMeanAndStDevError(alignment_errors)
print("mean : ", mean, "st dev: ", std_dev)
def eval_all_metrics_lab(refs_URI, detected_URI, tolerance=0.3):
"""
run all eval metrics on one file
"""
ref_intervals, ref_labels = load_labeled_intervals(refs_URI)
detected_intervals, use_end_ts = load_detected_intervals(detected_URI)
# metric 1: alignment error
alignmentErrors = _eval_alignment_error(ref_intervals, detected_intervals, ref_labels, use_end_ts)
mean, stDev, median = getMeanAndStDevError(alignmentErrors)
# metric 2: percentage correct
initialTimeOffset_refs = ref_intervals[0][0]
finalts_refs = ref_intervals[-1][1]
durationCorrect, totalLength = _eval_percentage_correct(reference_token_list=ref_intervals,
detected_token_List=detected_intervals,
final_ts_anno=finalts_refs,
initial_time_offset_refs=initialTimeOffset_refs,
reference_labels=ref_labels)
percentage_correct = durationCorrect / totalLength
# metric 3: percentage tolerance
percentage_tolerance = _eval_percentage_tolerance(ref_intervals=ref_intervals,
detected_intervals=detected_intervals,
reference_labels=ref_labels,
tolerance=tolerance)
return mean, percentage_correct, percentage_tolerance
def eval_error_textgrid_test():
audio_name = '05_Semahat_Ozdenses_-_Bir_Ihtimal_Daha_Var_0_zemin_from_69_5205_to_84_2'
annotation_url = os.path.join(PATH_TEST_DATASET, audio_name + ANNOTATION_EXT)
start_index = 0
end_index = -1
detected_token_list = [[0.61, 0.94, u'Bir'],
[1.02, 3.41, u'ihtimal'],
[3.42, 4.11, u'daha'],
[4.12, 5.4, u'var'],
[8.03, 8.42, u'o'],
[8.46, 8.83, u'da'],
[8.86, 10.65, u'\xf6lmek'],
[10.66, 11.04, u'mi'],
[11.05, 14.39, u'dersin']]
annotation_token_list, detected_token_list, dummy, dummy = \
strip_non_lyrics_tokens(annotation_url,
detected_token_list,
tierAliases.phrases,
start_index,
end_index)
alignment_errors = _eval_alignment_error(annotation_token_list,
detected_token_list,
tierAliases.phrases)
mean, std_dev, median = getMeanAndStDevError(alignment_errors)
print("mean : ", mean, "st dev: ", std_dev)
def test_eval_error_lab_mauch():
"""
test mean average error/displacement (in seconds) of alignment with loading the .lab files
"""
ref_intervals, detected_intervals, ref_labels = load_ref_and_detections(dataset='mauch')
alignment_errors = _eval_alignment_error(ref_intervals, detected_intervals, ref_labels)
mean_mauch, std_dev_mauch, median_mauch = getMeanAndStDevError(alignment_errors)
assert mean_mauch == 0.0 and std_dev_mauch == 0.0
def test_eval_error_lab_mauch():
"""
test mean average error/displacement (in seconds) of alignment with loading the .lab files
"""
ref_intervals, detected_intervals, ref_labels = load_ref_and_detections(
dataset='mauch')
alignment_errors = _eval_alignment_error(ref_intervals, detected_intervals,
ref_labels)
mean_mauch, std_dev_mauch, median_mauch = getMeanAndStDevError(
alignment_errors)
assert mean_mauch == 0.0 and std_dev_mauch == 0.0
def test_eval_error_lab_hansen():
"""
test mean average error/displacement (in seconds) of alignment with loading the .lab files
"""
ref_intervals, detected_intervals, ref_labels = load_ref_and_detections(
dataset='hansen')
alignment_errors = _eval_alignment_error(ref_intervals, detected_intervals,
tierAliases.phrases, ref_labels)
mean_hansen, std_dev_hansen, median_hansen = getMeanAndStDevError(
alignment_errors)
assert mean_hansen == 0.0 and std_dev_hansen == 0.0
