poa_cm_per_experiment, moa_cm_per_experiment = [[],[],[],[],[]]
for fold in range(1,6):
t = time.time()
test_folder = [folder for folder in os.listdir(os.path.join("./experiments/",experiment,str(fold)))
if partition in folder]
wer_details = os.path.join("./experiments/",experiment,str(fold),test_folder[0],"wer_details","per_utt")
corpus = WERDetails(wer_details)
per_per_experiment.append(corpus.all_pers())
poa_afer_per_experiment.append(corpus.all_poa_afers())
moa_afer_per_experiment.append(corpus.all_moa_afers())
poa_cm_per_experiment.append(corpus.poa_confusion_matrix())
moa_cm_per_experiment.append(corpus.moa_confusion_matrix())
s = time.time() - t
print("Fold took", s, "seconds")
if i == 0:
phoneme_type, phoneme_counts = np.unique(corpus.all_ref_phonemes, return_counts=True)
phoneme_count_per_fold[fold - 1, :] = phoneme_counts
per.append(per_per_experiment)
poa_afer.append(poa_afer_per_experiment)
moa_afer.append(moa_afer_per_experiment)
poa_cm.append(poa_cm_per_experiment)
moa_cm.append(moa_cm_per_experiment)
with open('objs.pkl', 'wb') as file:
pickle.dump([per, poa_afer, moa_afer, poa_cm, moa_cm, phoneme_count_per_fold], file)
import time
from corpus import WERDetails
from utils import HParam
#partition = "test"
#number_of_phonemes = 40
preprocessing = True
import numpy as np
import pandas as pd
if preprocessing:
config = HParam("configs/dutch.yaml")
wer_details = WERDetails(
"experiments/jasmin_example/scoring_kaldi/wer_details/per_utt",
skip_calculation=False,
config=config)
#phoneme, other = wer_details.all_poa_afers()
t = time.time()
moa_mat = wer_details.moa_confusion_matrix()
s = time.time()
print(s - t, "secs")
poa_mat = wer_details.poa_confusion_matrix()
k = time.time()
print(k - s, "secs")
print(poa_mat)
print()
#df = pd.DataFrame(data=other, index=phoneme)