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

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 ======================
stimmen_test_dir = r'c:\OneDrive\Research\rug\_data\stimmen_test'

## copy wav files which is in the stimmen data.
df = stimmen_functions.load_transcriptions()
#for index, row in df.iterrows():
#	filename = row['filename']
#	wav_file = os.path.join(default.stimmen_wav_dir, filename)
#	shutil.copy(wav_file, os.path.join(stimmen_test_dir, filename))

# after manually removed files which has too much noise and multiple words...
# update the info.
df_clean = stimmen_functions.load_transcriptions_clean(stimmen_test_dir)

# count how many files are removed due to the quality.
word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
word_list = sorted(word_list)
for word in word_list:
	df_ = df[df['word']==word]
	df_clean_ = df_clean[df_clean['word']==word]
	print('word {0} has {1} clean files among {2} files ({3:.2f} [%]).'.format(
		word, len(df_clean_), len(df_), len(df_clean_)/len(df_)*100))


## 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)


## check pronunciation variants
df_clean = stimmen_functions.load_transcriptions_clean(stimmen_test_dir)
df_clean = stimmen_functions.add_row_asr(df_clean)
df_clean = stimmen_functions.add_row_htk(df_clean)

for word in word_list:
#word = word_list[1]
	df_ = df_clean[df_clean['word']==word]
	c = Counter(df_['htk'])
	pronunciations = dict()
	for key, value in zip(c.keys(), c.values()):
		if value > 3:
			pronunciations[key] = value
	print(pronunciations)


monophone_mlf = os.path.join(default.htk_dir, 'label', 'train_phone_aligned.mlf')
triphone_mlf = os.path.join(default.htk_dir, 'label', 'train_triphone.mlf')
def filenames_in_mlf(file_mlf):
	with open(file_mlf) as f:
		lines_ = f.read().split('\n')
	lines = [line for line in lines_ if len(line.split(' ')) == 1 and line != '.']
	filenames = [line.replace('"', '').replace('*/', '') for line in lines[1:-1]]
	return filenames
filenames_mono = filenames_in_mlf(monophone_mlf)
filenames_tri  = filenames_in_mlf(triphone_mlf)