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estimation in pytorch

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This commit is contained in:
Jason
2022-03-31 22:00:37 -07:00
parent 5c5110ee83
commit dffdcf1f92
2 changed files with 17 additions and 15 deletions
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formants.py
+10 -8
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@@ -1,26 +1,26 @@
from json import load
import extract_features as features
import argparse
from helpers.textgrid import *
from helpers.utilities import *
import shutil
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"
print(tmp_features_filename)
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)
easy_call("luajit load_estimation_model.lua " + tmp_features_filename + ' ' + preds_filename)
load_estimation_model(tmp_features_filename, preds_filename)
#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)
def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textgrid_tier):
print(wav_filename)
if os.path.exists(preds_filename):
@@ -42,7 +42,8 @@ def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textg
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())
easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
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]:
@@ -50,7 +51,8 @@ def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textg
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())
easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
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)
if __name__ == "__main__":