def peak_picking_subroutine(test_nacta_2017, test_nacta, th, obs_cal, scaler,
len_seq, model_name, full_path_model, architecture,
detection_results_path, jingju_eval_results_path):
"""routine for peak picking decoding"""
from src.utilFunctions import append_or_write
import csv
eval_result_file_name = join(
jingju_eval_results_path, varin['sample_weighting'],
model_name + '_peakPicking_threshold_results.txt')
list_recall_onset_25, list_precision_onset_25, list_F1_onset_25 = [], [], []
list_recall_onset_5, list_precision_onset_5, list_F1_onset_5 = [], [], []
list_recall_25, list_precision_25, list_F1_25 = [], [], []
list_recall_5, list_precision_5, list_F1_5 = [], [], []
for ii in range(5):
stateful = False if varin['overlap'] else True
if obs_cal == 'tocal':
input_shape = (1, len_seq, 1, 80, 15)
# initialize the model
model_keras_cnn_0 = jan_original(filter_density=1,
dropout=0.5,
input_shape=input_shape,
batchNorm=False,
dense_activation='sigmoid',
channel=1,
stateful=stateful,
training=False,
bidi=varin['bidi'])
model_keras_cnn_0.load_weights(full_path_model + str(ii) + '.h5')
else:
model_keras_cnn_0 = None
if varin['dataset'] != 'ismir':
# evaluate nacta 2017 dataset
batch_process_onset_detection(
wav_path=nacta2017_wav_path,
textgrid_path=nacta2017_textgrid_path,
score_path=nacta2017_score_pinyin_path,
test_recordings=test_nacta_2017,
model_keras_cnn_0=model_keras_cnn_0,
cnnModel_name=model_name + str(ii),
detection_results_path=detection_results_path + str(ii),
scaler=scaler,
threshold=th,
obs_cal=obs_cal,
decoding_method='peakPicking',
architecture=architecture,
len_seq=len_seq,
stateful=stateful)
# evaluate nacta dataset
eval_results_decoding_path = \
batch_process_onset_detection(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
score_path=nacta_score_pinyin_path,
test_recordings=test_nacta,
model_keras_cnn_0=model_keras_cnn_0,
cnnModel_name=model_name + str(ii),
detection_results_path=detection_results_path + str(ii),
scaler=scaler,
threshold=th,
obs_cal=obs_cal,
decoding_method='peakPicking',
stateful=stateful,
architecture=architecture,
len_seq=len_seq)
append_write = append_or_write(eval_result_file_name)
with open(eval_result_file_name, append_write) as testfile:
csv_writer = csv.writer(testfile)
csv_writer.writerow([th])
precision_onset, recall_onset, F1_onset, \
precision, recall, F1, \
= eval_write_2_txt(eval_result_file_name,
eval_results_decoding_path,
label=False,
decoding_method='peakPicking')
list_precision_onset_25.append(precision_onset[0])
list_precision_onset_5.append(precision_onset[1])
list_recall_onset_25.append(recall_onset[0])
list_recall_onset_5.append(recall_onset[1])
list_F1_onset_25.append(F1_onset[0])
list_F1_onset_5.append(F1_onset[1])
list_precision_25.append(precision[0])
list_precision_5.append(precision[1])
list_recall_25.append(recall[0])
list_recall_5.append(recall[1])
list_F1_25.append(F1[0])
list_F1_5.append(F1[1])
return list_precision_onset_25, \
list_recall_onset_25, \
list_F1_onset_25, \
list_precision_25, \
list_recall_25, \
list_F1_25, \
list_precision_onset_5, \
list_recall_onset_5, \
list_F1_onset_5, \
list_precision_5, \
list_recall_5, \
list_F1_5
def peak_picking_subroutine(test_nacta_2017, test_nacta, th, obs_cal,
architecture, model_name, full_path_model,
full_path_scaler, detection_results_path,
jingju_eval_results_path):
"""Peak picking routine,
five folds evaluation"""
from src.utilFunctions import append_or_write
import csv
eval_result_file_name = join(
jingju_eval_results_path, varin['sample_weighting'],
model_name + '_peakPicking_threshold_results.txt')
list_recall_onset_25, list_precision_onset_25, list_F1_onset_25 = [], [], []
list_recall_onset_5, list_precision_onset_5, list_F1_onset_5 = [], [], []
list_recall_25, list_precision_25, list_F1_25 = [], [], []
list_recall_5, list_precision_5, list_F1_5 = [], [], []
for ii in range(5):
if obs_cal == 'tocal':
if 'pretrained' in architecture:
scaler = cPickle.load(
gzip.open(full_path_scaler + str(ii) + '.pickle.gz'))
else:
scaler = pickle.load(open(full_path_scaler))
model_keras_cnn_0 = load_model(full_path_model + str(ii) + '.h5')
# delete detection results path if it exists
detection_results_path_model = join(detection_results_path +
str(ii))
if os.path.exists(detection_results_path_model) and os.path.isdir(
detection_results_path + str(ii)):
shutil.rmtree(detection_results_path + str(ii))
else:
model_keras_cnn_0 = None
scaler = None
if varin['dataset'] != 'ismir':
# nacta2017
batch_process_onset_detection(
wav_path=nacta2017_wav_path,
textgrid_path=nacta2017_textgrid_path,
score_path=nacta2017_score_pinyin_path,
test_recordings=test_nacta_2017,
model_keras_cnn_0=model_keras_cnn_0,
cnnModel_name=model_name + str(ii),
detection_results_path=detection_results_path + str(ii),
scaler=scaler,
architecture=architecture,
threshold=th,
obs_cal=obs_cal,
decoding_method='peakPicking')
eval_results_decoding_path = \
batch_process_onset_detection(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
score_path=nacta_score_pinyin_path,
test_recordings=test_nacta,
model_keras_cnn_0=model_keras_cnn_0,
cnnModel_name=model_name + str(ii),
detection_results_path=detection_results_path + str(ii),
scaler=scaler,
architecture=architecture,
threshold=th,
obs_cal=obs_cal,
decoding_method='peakPicking')
append_write = append_or_write(eval_result_file_name)
with open(eval_result_file_name, append_write) as testfile:
csv_writer = csv.writer(testfile)
csv_writer.writerow([th])
precision_onset, recall_onset, F1_onset, \
precision, recall, F1, \
= eval_write_2_txt(eval_result_file_name,
eval_results_decoding_path,
label=False,
decoding_method='peakPicking')
list_precision_onset_25.append(precision_onset[0])
list_precision_onset_5.append(precision_onset[1])
list_recall_onset_25.append(recall_onset[0])
list_recall_onset_5.append(recall_onset[1])
list_F1_onset_25.append(F1_onset[0])
list_F1_onset_5.append(F1_onset[1])
list_precision_25.append(precision[0])
list_precision_5.append(precision[1])
list_recall_25.append(recall[0])
list_recall_5.append(recall[1])
list_F1_25.append(F1[0])
list_F1_5.append(F1[1])
return list_precision_onset_25, \
list_recall_onset_25, \
list_F1_onset_25, \
list_precision_25, \
list_recall_25, \
list_F1_25, \
list_precision_onset_5, \
list_recall_onset_5, \
list_F1_onset_5, \
list_precision_5, \
list_recall_5, \
list_F1_5
def viterbi_subroutine(test_nacta_2017, test_nacta, eval_label, obs_cal,
len_seq, model_name, architecture, scaler,
full_path_model, detection_results_path):
"""routine for viterbi decoding"""
list_recall_onset_25, list_precision_onset_25, list_F1_onset_25 = [], [], []
list_recall_onset_5, list_precision_onset_5, list_F1_onset_5 = [], [], []
list_recall_25, list_precision_25, list_F1_25 = [], [], []
list_recall_5, list_precision_5, list_F1_5 = [], [], []
for ii in range(5):
if obs_cal == 'tocal':
stateful = False if varin['overlap'] else True
input_shape = (1, len_seq, 1, 80, 15)
# initialize the model
model_keras_cnn_0 = jan_original(filter_density=1,
dropout=0.5,
input_shape=input_shape,
batchNorm=False,
dense_activation='sigmoid',
channel=1,
stateful=stateful,
training=False,
bidi=varin['bidi'])
# load the model weights
model_keras_cnn_0.load_weights(full_path_model + str(ii) + '.h5')
# delete detection results path if it exists
detection_results_path_model = join(detection_results_path +
str(ii))
if os.path.exists(detection_results_path_model) and os.path.isdir(
detection_results_path + str(ii)):
shutil.rmtree(detection_results_path + str(ii))
if varin['dataset'] != 'ismir':
# evaluate nacta 2017 data set
batch_process_onset_detection(
wav_path=nacta2017_wav_path,
textgrid_path=nacta2017_textgrid_path,
score_path=nacta2017_score_unified_path,
test_recordings=test_nacta_2017,
model_keras_cnn_0=model_keras_cnn_0,
len_seq=len_seq,
cnnModel_name=model_name + str(ii),
detection_results_path=detection_results_path + str(ii),
scaler=scaler,
obs_cal=obs_cal,
decoding_method='viterbi',
architecture=architecture,
stateful=stateful)
# evaluate nacta dataset
eval_results_decoding_path = \
batch_process_onset_detection(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
score_path=nacta_score_unified_path,
test_recordings=test_nacta,
model_keras_cnn_0=model_keras_cnn_0,
cnnModel_name=model_name + str(ii),
detection_results_path=detection_results_path + str(ii),
scaler=scaler,
obs_cal=obs_cal,
decoding_method='viterbi',
architecture=architecture,
stateful=stateful,
len_seq=len_seq)
else:
eval_results_decoding_path = detection_results_path + str(ii)
precision_onset, recall_onset, F1_onset, \
precision, recall, F1, \
= eval_write_2_txt(eval_result_file_name=join(eval_results_decoding_path, 'results.csv'),
segSyllable_path=eval_results_decoding_path,
label=eval_label,
decoding_method='viterbi')
list_precision_onset_25.append(precision_onset[0])
list_precision_onset_5.append(precision_onset[1])
list_recall_onset_25.append(recall_onset[0])
list_recall_onset_5.append(recall_onset[1])
list_F1_onset_25.append(F1_onset[0])
list_F1_onset_5.append(F1_onset[1])
list_precision_25.append(precision[0])
list_precision_5.append(precision[1])
list_recall_25.append(recall[0])
list_recall_5.append(recall[1])
list_F1_25.append(F1[0])
list_F1_5.append(F1[1])
return list_precision_onset_25, \
list_recall_onset_25, \
list_F1_onset_25, \
list_precision_25, \
list_recall_25, \
list_F1_25, \
list_precision_onset_5, \
list_recall_onset_5, \
list_F1_onset_5, \
list_precision_5, \
list_recall_5, \
list_F1_5
def viterbi_subroutine(test_nacta_2017, test_nacta, eval_label, obs_cal,
architecture, model_name, full_path_model,
full_path_scaler, detection_results_path):
"""5 run times routine for the viterbi decoding onset detection"""
list_recall_onset_25, list_precision_onset_25, list_F1_onset_25 = [], [], []
list_recall_onset_5, list_precision_onset_5, list_F1_onset_5 = [], [], []
list_recall_25, list_precision_25, list_F1_25 = [], [], []
list_recall_5, list_precision_5, list_F1_5 = [], [], []
for ii in range(5):
if obs_cal == 'tocal':
if 'pretrained' in architecture:
scaler = cPickle.load(
gzip.open(full_path_scaler + str(ii) + '.pickle.gz'))
else:
scaler = pickle.load(open(full_path_scaler))
model_keras_cnn_0 = load_model(full_path_model + str(ii) + '.h5')
# print(model_keras_cnn_0.summary())
print('Model name:', full_path_model)
# delete detection results path if it exists
detection_results_path_model = join(detection_results_path +
str(ii))
if os.path.exists(detection_results_path_model) and os.path.isdir(
detection_results_path + str(ii)):
shutil.rmtree(detection_results_path + str(ii))
if varin['dataset'] != 'ismir':
# nacta2017
batch_process_onset_detection(
wav_path=nacta2017_wav_path,
textgrid_path=nacta2017_textgrid_path,
score_path=nacta2017_score_unified_path,
test_recordings=test_nacta_2017,
model_keras_cnn_0=model_keras_cnn_0,
cnnModel_name=model_name + str(ii),
detection_results_path=detection_results_path + str(ii),
scaler=scaler,
architecture=architecture,
obs_cal=obs_cal,
decoding_method='viterbi')
# nacta
eval_results_decoding_path = \
batch_process_onset_detection(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
score_path=nacta_score_unified_path,
test_recordings=test_nacta,
model_keras_cnn_0=model_keras_cnn_0,
cnnModel_name=model_name + str(ii),
detection_results_path=detection_results_path + str(ii),
scaler=scaler,
architecture=architecture,
obs_cal=obs_cal,
decoding_method='viterbi')
else:
eval_results_decoding_path = detection_results_path + str(ii)
precision_onset, recall_onset, F1_onset, \
precision, recall, F1, \
= eval_write_2_txt(eval_result_file_name=join(eval_results_decoding_path, 'results.csv'),
segSyllable_path=eval_results_decoding_path,
label=eval_label,
decoding_method='viterbi')
list_precision_onset_25.append(precision_onset[0])
list_precision_onset_5.append(precision_onset[1])
list_recall_onset_25.append(recall_onset[0])
list_recall_onset_5.append(recall_onset[1])
list_F1_onset_25.append(F1_onset[0])
list_F1_onset_5.append(F1_onset[1])
list_precision_25.append(precision[0])
list_precision_5.append(precision[1])
list_recall_25.append(recall[0])
list_recall_5.append(recall[1])
list_F1_25.append(F1[0])
list_F1_5.append(F1[1])
return list_precision_onset_25, \
list_recall_onset_25, \
list_F1_onset_25, \
list_precision_25, \
list_recall_25, \
list_F1_25, \
list_precision_onset_5, \
list_recall_onset_5, \
list_F1_onset_5, \
list_precision_5, \
list_recall_5, \
list_F1_5