phoneme_count_per_fold = np.zeros((len(experiment_folders), number_of_phonemes))
for i, experiment in enumerate(experiment_folders):
per_per_experiment, poa_afer_per_experiment, moa_afer_per_experiment,\
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)
preprocessing = False
separation = True
if separation:
files = glob("experiments/voicefilter_experiment/*_ss_result.txt")
else:
files = glob("experiments/voicefilter_experiment/*_se_result.txt")
config = HParam("../configs/eng_espnet.yaml")
if preprocessing:
dfs = list()
for file in files:
wer_details = WERDetails(file, skip_calculation=False, config=config)
phoneme, other = wer_details.all_pers()
dfs.append(pd.DataFrame(data=[other[1:]], columns=phoneme[1:], index=[file]))
result = pd.concat(dfs, axis=0, join="outer")
if separation:
result.to_csv("csvs/separation_results_with_clean.csv")
else:
result.to_csv("csvs/enhancement_results_with_clean.csv")
else:
if separation:
df = pd.read_csv("../csvs/separation_results_with_clean.csv")
else: