import os
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
import sys

import shutil
import glob

#import numpy as np
import pandas as pd

import defaultfiles as default
import convert_xsampa2ipa
import stimmen_functions
import fame_functions
import convert_phoneset
from phoneset import fame_ipa, fame_asr
sys.path.append(default.toolbox_dir)
import file_handling as fh
from htk import pyhtk


## ======================= user define =======================



## ======================= make test data ======================
# copy wav files which is in the stimmen data.
stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'
fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')

df = stimmen_functions.load_transcriptions()
#word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
#word_list = sorted(word_list)

#for index, row in df.iterrows():
#	filename = row['filename']
#	if isinstance(filename, str):
#		wav_file = os.path.join(default.stimmen_wav_dir, filename)
#		if os.path.exists(wav_file):
#			shutil.copy(wav_file, os.path.join(stimmen_test_dir, filename))
#			pyhtk.create_label_file(
#				row['word'], 
#				os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))


## check phones included in stimmen but not in FAME!
splitted_ipas = [' '.join(
		convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)) 
		for ipa in df['ipa']]
stimmen_phones = set(' '.join(splitted_ipas))
stimmen_phones = list(stimmen_phones)
fame_phones = fame_ipa.phoneset
stimmen_phones.sort()
fame_phones.sort()
print('phones which are used in stimmen transcription but not in FAME corpus are:\n{}'.format(
	set(stimmen_phones) - set(fame_phones)
	))
for ipa in df['ipa']:
	ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
	if ':' in ipa_splitted:
		print(ipa_splitted)