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predict.py
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predict.py
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import argparse
from label2textgrid import create_text_grid
from lib import utils
from post_process import post_process
from lib.utils import *
import front_end.predict_single_file as fe
import os
def predict(input_path, output_path, model, csv_filename):
if not os.path.exists(input_path):
print >> sys.stderr, "%s file does not exits" % input_path
return
t_model = model.upper()
if t_model == 'RNN':
model_path = 'results/1_layer_model.net'
print '==> using single layer RNN'
elif t_model == '2RNN':
model_path = 'results/2_layer_model.net'
print '==> using 2 stacked layers RNN'
elif t_model == 'BIRNN':
model_path = 'results/bi_model.net'
print '==> using bi-directional RNN'
else:
model_path = 'results/1_layer_model.net'
print '==> unknown model, using default model: single RNN'
try:
length = utils.get_wav_file_length(input_path)
except:
print "The input file ", input_path, " is probably not a valid WAV file."
exit(-1)
feature_file = generate_tmp_filename('features')
prob_file = generate_tmp_filename('prob')
predict_file = generate_tmp_filename('prediction')
dur_file = generate_tmp_filename('dur')
print '\n1) Extracting features and classifying ...'
abs_path = os.path.abspath(input_path)
os.chdir("front_end/")
fe.main(abs_path, feature_file)
os.chdir("..")
print '\n2) Model predictions ...'
cmd = 'th classify.lua -x_filename %s -class_path %s -prob_path %s -model_path %s' % (
feature_file, predict_file, prob_file, model_path)
os.chdir("back_end/")
utils.easy_call(cmd)
os.chdir("..")
print '\n3) Extracting duration'
post_process(os.path.abspath(predict_file), dur_file)
print '\n4) Writing TextGrid file to %s ...' % output_path
create_text_grid(dur_file, input_path, output_path, length, float(0.0), csv_filename)
# remove leftovers
os.remove(feature_file)
os.remove(prob_file)
os.remove(predict_file)
os.remove(dur_file)
if __name__ == "__main__":
# -------------MENU-------------- #
# command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("input_path", help="The path to the wav file")
parser.add_argument("output_path", help="The path to save new text-grid file")
parser.add_argument("model", help="The type pf model: rnn | 2rnn | birnn")
parser.add_argument("--csv_output", help="Output results to a CSV file")
args = parser.parse_args()
# main function
predict(args.input_path, args.output_path, args.model, args.csv_output)