acoustic_model/acoustic_model/fame_functions.py

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

import sys
from collections import Counter
import pickle

import numpy as np
import pandas as pd

import defaultfiles as default
import convert_phoneset
from phoneset import fame_ipa, fame_asr

#def read_fileFA(fileFA):
#    """
#    read the result file of HTK forced alignment.
#    this function only works when input is one word.
#    """
#    with open(fileFA, 'r') as f:
#        lines = f.read()
#        lines = lines.split('\n')

#    phones = []
#    for line in lines:
#        line_split = line.split()
#        if len(line_split) > 1:
#            phones.append(line_split[2])

#    return ' '.join(phones)


#def fame_pronunciation_variant(ipa):
#    ipa = ipa.replace('æ', 'ɛ')
#    ipa = ipa.replace('ɐ', 'a')
#    ipa = ipa.replace('ɑ', 'a')
#    ipa = ipa.replace('ɾ', 'r')
#    ipa = ipa.replace('ɹ', 'r') # ???
#    ipa = ipa.replace('ʁ', 'r')
#    ipa = ipa.replace('ʀ', 'r') # ???
#    ipa = ipa.replace('ʊ', 'u')
#    ipa = ipa.replace('χ', 'x')

#    pronvar_list = [ipa]
#    while 'ø:' in ' '.join(pronvar_list) or 'œ' in ' '.join(pronvar_list) or 'ɒ' in ' '.join(pronvar_list):
#        pronvar_list_ = []
#        for p in pronvar_list:
#            if 'ø:' in p:
#                pronvar_list_.append(p.replace('ø:', 'ö'))
#                pronvar_list_.append(p.replace('ø:', 'ö:'))
#            if 'œ' in p:
#                pronvar_list_.append(p.replace('œ', 'ɔ̈'))
#                pronvar_list_.append(p.replace('œ', 'ɔ̈:'))
#            if 'ɒ' in p:
#                pronvar_list_.append(p.replace('ɒ', 'ɔ̈'))
#                pronvar_list_.append(p.replace('ɒ', 'ɔ̈:'))
#        pronvar_list = np.unique(pronvar_list_)
#    return pronvar_list


#def make_fame2ipa_variants(fame):
#    fame = 'rɛös'
#    ipa = [fame]
#    ipa.append(fame.replace('ɛ', 'æ'))
#    ipa.append(fame.replace('a', 'ɐ'))
#    ipa.append(fame.replace('a', 'ɑ'))
#    ipa.append(fame.replace('r', 'ɾ'))
#    ipa.append(fame.replace('r', 'ɹ'))
#    ipa.append(fame.replace('r', 'ʁ'))
#    ipa.append(fame.replace('r', 'ʀ'))
#    ipa.append(fame.replace('u', 'ʊ'))
#    ipa.append(fame.replace('x', 'χ'))
		
#    ipa.append(fame.replace('ö', 'ø:'))
#    ipa.append(fame.replace('ö:', 'ø:'))
#    ipa.append(fame.replace('ɔ̈', 'œ'))
#    ipa.append(fame.replace('ɔ̈:', 'œ'))
#    ipa.append(fame.replace('ɔ̈', 'ɒ'))
#    ipa.append(fame.replace('ɔ̈:', 'ɒ'))

#    return ipa


#def make_htk_dict(word, pronvar_, fileDic, output_type):
#    """
#    make dict files which can be used for HTK.
#    param word: target word.
#    param pronvar_: pronunciation variant. nx2 (WORD /t pronunciation) ndarray.
#    param fileDic: output dic file.
#    param output_type: 0:full, 1:statistics, 2:frequency <2% entries are removed. 3:top 3. 
#    """
#    #assert(output_type < 4 and output_type >= 0, 'output_type should be an integer between 0 and 3.')
#    WORD = word.upper()

#    if output_type == 0: # full
#        pronvar  = np.unique(pronvar_)

#        with open(fileDic, 'w') as f:
#            for pvar in pronvar:
#                f.write('{0}\t{1}\n'.format(WORD, pvar))
#    else:
#        c = Counter(pronvar_)
#        total_num = sum(c.values())
#        with open(fileDic, 'w') as f:
#            if output_type == 3:
#                for key, value in c.most_common(3):
#                    f.write('{0}\t{1}\n'.format(WORD, key))
#            else:
#                for key, value in c.items():
#                    percentage = value/total_num*100

#                    if output_type == 1: # all
#                        f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, percentage, WORD, key))
#                    elif output_type == 2: # less than 2 percent
#                        if percentage < 2:
#                            f.write('{0}\t{1}\n'.format(WORD, key))


def make_hcopy_scp_from_filelist_in_fame(fame_dir, dataset, feature_dir, hcopy_scp):
	""" Make a script file for HCopy using the filelist in FAME! corpus. 
	
	Args:
		fame_dir (path): the directory of FAME corpus.
		dataset (str): 'devel', 'test' or 'train'.
		feature_dir (path): the directory where feature will be stored.
		hcopy_scp (path): a script file for HCopy to be made.

	"""
	filelist_txt = os.path.join(fame_dir, 'fame', 'filelists', dataset + 'list.txt')
	with open(filelist_txt) as fin:
		filelist = fin.read()
		filelist = filelist.split('\n')
	
	with open(hcopy_scp, 'w') as fout:
		for filename_ in filelist:
			filename = filename_.replace('.TextGrid', '')

			if len(filename) > 3: # remove '.', '..' and ''
				wav_file = os.path.join(fame_dir, 'fame', 'wav', dataset, filename + '.wav')
				mfc_file = os.path.join(feature_dir, filename + '.mfc')

				fout.write(wav_file + '\t' + mfc_file + '\n')

	return


def load_lexicon(lexicon_file):
	""" load lexicon file as data frame.

	Args:
		lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
	
	Returns:
		lex (df): lexicon as Data Frame, which has columns 'word' and 'pronunciation'.

	"""
	lex = pd.read_csv(lexicon_file, delimiter='\t', header=None, encoding="utf-8")
	lex.rename(columns={0: 'word', 1: 'pronunciation'}, inplace=True)
	return lex


def get_phoneset_from_lexicon(lexicon_file, phoneset_name='asr'):
	""" Make a list of phones which appears in the lexicon. 
	
	Args:
		lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
		phoneset_name (str): the name of phoneset with which lexicon_file is written. 'asr'(default) or 'ipa'.

	Returns:
		(list_of_phones) (set): the set of phones included in the lexicon_file.

	"""
	assert phoneset_name in ['asr', 'ipa'], 'phoneset_name should be \'asr\' or \'ipa\''

	lex = load_lexicon(lexicon_file)
	if phoneset_name == 'asr':
		return set(' '.join(lex['pronunciation']).split(' '))
	elif phoneset_name == 'ipa':
		join_pronunciations = ''.join(lex['pronunciation'])
		return set(convert_phone_set.split_word(join_pronunciations, fame_ipa.multi_character_phones))

	return


def extract_unknown_phones(ipa, known_phones):
	"""extract unknown phones in the pronunciation written in IPA.

	Args:
		ipa (str): a pronunciation written in IPA. 
		known_phones (list): list of phones already know.

	Returns:
		(list_of_phones) (list): unknown phones not included in 'known_phones'.

	"""
	ipa_split = convert_phone_set.split_word(ipa, fame_ipa.multi_character_phones)
	return [i for i in ipa_split if not i in known_phones]


def get_translation_key(lexicon_file_ipa, lexicon_file_asr):
	""" get correspondence between lexicon_file_ipa and lexicon_file_asr.

	Args:
		lexicon_file_ipa (path): lexicon in the format of 'word' /t 'pronunciation (IPA)'.
		lexicon_file_asr (path): lexicon in the format of 'word' /t 'pronunciation (asr)'.
			the each character of 'pronunciation' should be delimited by ' '.

	Returns:
		translation_key (dict): translation key from ipa to asr. 
		(phone_unknown) (list): the list of IPA phones, which does not appear in lexicon_file_asr. 

	"""
	lex_ipa = load_lexicon(lexicon_file_ipa)
	lex_asr = load_lexicon(lexicon_file_asr)
	phone_unknown = fame_ipa.phoneset[:]
	translation_key = dict()
	for word in lex_ipa['word']:
		if np.sum(lex_ipa['word'] == word) == 1 and np.sum(lex_asr['word'] == word) == 1:
			ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
			asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
	
			ipa_list = convert_phone_set.split_word(ipa, fame_ipa.multi_character_phones)
			asr_list = asr.split(' ')

			# if there are phones which is not in phone_unknown
			#if len([True for i in asr_list if i in phone_unknown]) > 0:
			if(len(ipa_list) == len(asr_list)):
				print("{0}: {1} --> {2}".format(word, ipa_list, asr_list))
				for ipa_, asr_ in zip(ipa_list, asr_list):
					if ipa_ in phone_unknown:
						translation_key[ipa_] = asr_
						phone_unknown.remove(ipa_)
	return translation_key, list(phone_unknown)


def find_phone(lexicon_file, phone, phoneset_name='ipa'):
	""" extract rows where the phone is used in the lexicon_file. 

	Args:
		lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
		phone (str): the phone to be searched.
		phoneset_name (str): the name of phoneset_name with which lexicon_file is written. 'asr' or 'ipa'(default).

	Returns:
		extracted (df): rows where the phone is used.

	ToDo:
		* develop when the phonset == 'asr'.

	"""
	assert phoneset_name in ['asr', 'ipa'], 'phoneset_name should be \'asr\' or \'ipa\''
	
	lex = load_lexicon(lexicon_file)
	
	# to reduce the calculation time, only target rows which include 'phone' at least once. 
	lex_ = lex[lex['pronunciation'].str.count(phone)>0]

	extracted = pd.DataFrame(index=[], columns=['word', 'pronunciation'])
	for index, row in lex_.iterrows():
		if phoneset_name == 'ipa':
			pronunciation = convert_phone_set.split_word(row['pronunciation'], fame_ipa.multi_character_phones)
		if phone in pronunciation:
			extracted_ = pd.Series([row['word'], pronunciation], index=extracted.columns)
			extracted  = extracted.append(extracted_, ignore_index=True)
	return extracted


def asr2htk_space_delimited(pronunciation):
	"""convert phoneset from asr to htk.
	
	Args:
		pronunciation (str): space delimited asr phones. 

	Returns:
		(pronunciation) (str): space delimited asr phones in htk format (ascii).

	"""
	pronunciation_short = [fame_asr.reduction_key.get(i, i) for i in pronunciation.split(' ') 
			   if not i in fame_asr.phones_to_be_removed]
	return ' '.join(convert_phoneset.convert_phoneset(
		pronunciation_short, fame_asr.translation_key_asr2htk))


def lexicon_asr2htk(lexicon_file_asr, lexicon_file_htk):
	""" Convert a lexicon file from asr to htk format (ascii). 

	Args:
		lexicon_file_asr (path): a lexicon file written in asr format e.g. fame/lex.asr.
		lexicon_file_htk (path): a lexicon file written in htk format (ascii).

	"""
	lex_asr = load_lexicon(lexicon_file_asr)
	def asr2htk_space_delimited_(row):
		return asr2htk_space_delimited(row['pronunciation'])

	lex_htk = pd.DataFrame({
		'word': lex_asr['word'], 
        'pronunciation': lex_asr.apply(asr2htk_space_delimited_, axis=1)
		})
	lex_htk = lex_htk.ix[:, ['word', 'pronunciation']]
	lex_htk.to_csv(lexicon_file_htk, header=None, index=None, sep='\t')
	return


def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
	""" Combine two lexicon files and sort by words. 

	Args:
		lexicon_file1, lexicon_file2 (path): input lexicon files.
		
	Returns:
		lexicon_file_out (path): lexicon_file which lexcion_file1 and 2 are combined and sorted.

	"""
	lex1 = load_lexicon(lexicon_file1)
	lex2 = load_lexicon(lexicon_file2)
	lex  = pd.concat([lex1, lex2])
	lex  = lex.sort_values(by='word', ascending=True)
	lex.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')


def fix_single_quote(lexicon_file):
	""" add '\' before all single quote at the beginning of words.

	Args:
		lexicon_file (path): lexicon file, which will be overwitten.

	"""
	lex = load_lexicon(lexicon_file)
	for i in lex[lex['word'].str.startswith('\'')].index.values:
		lex.iat[i, 0] = lex.iat[i, 0].replace('\'', '\\\'')
		# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
		#lex.to_csv(lexicon_file, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
		lex.to_csv(lexicon_file, index=False, header=False, encoding="utf-8", sep='\t')
	return