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text grid integration; temp file management

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This commit is contained in:
Jason
2022-04-03 11:25:18 -07:00
parent 906d649a39
commit ca26e3f058
5 changed files with 42 additions and 29 deletions
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formants.py
+24 -22
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@@ -1,4 +1,3 @@
from json import load
import extract_features as features
import argparse
from helpers.textgrid import *
@@ -7,17 +6,19 @@ from load_estimation_model import load_estimation_model
def predict_from_times(wav_filename, preds_filename, begin, end):
tmp_features_filename = tempfile._get_default_tempdir() + "/" + next(tempfile._get_candidate_names()) + ".txt"
tmp_features_filename = "temp/" + next(tempfile._get_candidate_names()) + ".txt"
print("Input Array Path: " + tmp_features_filename)
if begin > 0.0 or end > 0.0:
print(wav_filename + " interval " + str(begin) + "-" + str(end) + ":")
features.create_features(wav_filename, tmp_features_filename, begin, end)
load_estimation_model(tmp_features_filename, preds_filename)
load_estimation_model(tmp_features_filename, preds_filename, begin, end)
#easy_call("luajit load_estimation_model.lua " + tmp_features_filename + ' ' + preds_filename)
else:
features.create_features(wav_filename, tmp_features_filename)
easy_call("luajit load_tracking_model.lua " + tmp_features_filename + ' ' + preds_filename)
delete_temp_files()
def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textgrid_tier):
@@ -34,26 +35,27 @@ def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textg
# extract tier names
tier_names = textgrid.tierNames()
if textgrid_tier in tier_names:
if textgrid_tier in tier_names: # run over all intervals in the tier
tier_index = tier_names.index(textgrid_tier)
# run over all intervals in the tier
for interval in textgrid[tier_index]:
if re.search(r'\S', interval.mark()):
tmp_features_filename = generate_tmp_filename("features")
tmp_preds = generate_tmp_filename("preds")
features.create_features(wav_filename, tmp_features_filename, interval.xmin(), interval.xmax())
load_estimation_model(tmp_features_filename, tmp_preds)
#easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
csv_append_row(tmp_preds, preds_filename)
else: # process first tier
for interval in textgrid[0]:
if re.search(r'\S', interval.mark()):
tmp_features_filename = generate_tmp_filename("features")
tmp_preds = generate_tmp_filename("preds")
features.create_features(wav_filename, tmp_features_filename, interval.xmin(), interval.xmax())
load_estimation_model(tmp_features_filename, tmp_preds)
#easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
csv_append_row(tmp_preds, preds_filename)
textgrid_tier = textgrid[tier_index]
else: # process first tier
textgrid_tier = textgrid[0]
for interval in textgrid_tier:
if re.search(r'\S', interval.mark()):
tmp_features_filename = generate_tmp_filename("features")
tmp_preds = generate_tmp_filename("preds")
begin = interval.xmin()
end = interval.xmax()
features.create_features(wav_filename, tmp_features_filename, begin, end)
load_estimation_model(tmp_features_filename, tmp_preds, begin, end)
#easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
csv_append_row(tmp_preds, preds_filename)
delete_temp_files()
delete_temp_files()
if __name__ == "__main__":
# parse arguments