Jason
74 lines
3.6 KiB
Python
74 lines
3.6 KiB
Python
from json import load
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import extract_features as features
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import argparse
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from helpers.textgrid import *
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from helpers.utilities import *
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from load_estimation_model import load_estimation_model
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def predict_from_times(wav_filename, preds_filename, begin, end):
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tmp_features_filename = tempfile._get_default_tempdir() + "/" + next(tempfile._get_candidate_names()) + ".txt"
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print("Input Array Path: " + tmp_features_filename)
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if begin > 0.0 or end > 0.0:
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print(wav_filename + " interval " + str(begin) + "-" + str(end) + ":")
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features.create_features(wav_filename, tmp_features_filename, begin, end)
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load_estimation_model(tmp_features_filename, preds_filename)
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#easy_call("luajit load_estimation_model.lua " + tmp_features_filename + ' ' + preds_filename)
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else:
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features.create_features(wav_filename, tmp_features_filename)
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easy_call("luajit load_tracking_model.lua " + tmp_features_filename + ' ' + preds_filename)
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def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textgrid_tier):
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print(wav_filename)
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if os.path.exists(preds_filename):
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os.remove(preds_filename)
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textgrid = TextGrid()
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# read TextGrid
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textgrid.read(textgrid_filename)
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# extract tier names
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tier_names = textgrid.tierNames()
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if textgrid_tier in tier_names:
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tier_index = tier_names.index(textgrid_tier)
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# run over all intervals in the tier
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for interval in textgrid[tier_index]:
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if re.search(r'\S', interval.mark()):
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tmp_features_filename = generate_tmp_filename("features")
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tmp_preds = generate_tmp_filename("preds")
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features.create_features(wav_filename, tmp_features_filename, interval.xmin(), interval.xmax())
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load_estimation_model(tmp_features_filename, tmp_preds)
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#easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
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csv_append_row(tmp_preds, preds_filename)
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else: # process first tier
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for interval in textgrid[0]:
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if re.search(r'\S', interval.mark()):
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tmp_features_filename = generate_tmp_filename("features")
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tmp_preds = generate_tmp_filename("preds")
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features.create_features(wav_filename, tmp_features_filename, interval.xmin(), interval.xmax())
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load_estimation_model(tmp_features_filename, tmp_preds)
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#easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
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csv_append_row(tmp_preds, preds_filename)
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if __name__ == "__main__":
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# parse arguments
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parser = argparse.ArgumentParser(description='Estimation and tracking of formants.')
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parser.add_argument('wav_file', default='', help="WAV audio filename (single vowel or an whole utternace)")
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parser.add_argument('formants_file', default='', help="output formant CSV file")
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parser.add_argument('--textgrid_filename', default='', help="get beginning and end times from a TextGrid file")
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parser.add_argument('--textgrid_tier', default='', help="a tier name with portion to process (default first tier)")
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parser.add_argument('--begin', help="beginning time in the WAV file", default=0.0, type=float)
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parser.add_argument('--end', help="end time in the WAV file", default=-1.0, type=float)
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args = parser.parse_args()
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if args.textgrid_filename:
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predict_from_textgrid(args.wav_file, args.formants_file, args.textgrid_filename, args.textgrid_tier)
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else:
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predict_from_times(args.wav_file, args.formants_file, args.begin, args.end)
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