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import os
import sys
from collections import Counter
import numpy as np
import pandas as pd
import defaultfiles as default
from forced_alignment import convert_phone_set
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. """
filelist_txt = FAME_dir + '\\fame\\filelists\\' + dataset + 'list.txt'
with open(filelist_txt) as fin:
filelist =
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 = FAME_dir + '\\fame\\wav\\' + dataset + '\\' + filename + '.wav'
mfc_file = feature_dir + '\\' + filename + '.mfc'
fout.write(wav_file + '\t' + mfc_file + '\n')
def make_filelist(input_dir, output_txt):
""" Make a list of files in the input_dir. """
filenames = os.listdir(input_dir)
with open(output_txt, 'w') as fout:
for filename in filenames:
fout.write(input_dir + '\\' + filename + '\n')
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))
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))
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 get_phonelist(lexicon_file):
""" Make a list of phones which appears in the lexicon. """
with open(lexicon_file, "rt", encoding="utf-8") as fin:
lines =
lines = lines.split('\n')
phonelist = set([])
for line in lines:
line = line.split('\t')
if len(line) > 1:
pronunciation = set(line[1].split())
phonelist = phonelist | pronunciation
return phonelist
def find_phone(lexicon_file, phone):
""" Search where the phone is used in the lexicon. """
with open(lexicon_file, "rt", encoding="utf-8") as fin:
lines =
lines = lines.split('\n')
extracted = []
for line in lines:
line = line.split('\t')
if len(line) > 1:
pronunciation = line[1]
if phone in pronunciation:
return extracted
def ipa2famehtk_lexicon(lexicon_file_in, lexicon_file_out):
""" Convert a lexicon file from IPA to HTK format for FAME! corpus. """
lexicon_in = pd.read_table(lexicon_file_in, names=['word', 'pronunciation'])
with open(lexicon_file_out, "w", encoding="utf-8") as fout:
for word, pronunciation in zip(lexicon_in['word'], lexicon_in['pronunciation']):
pronunciation_no_space = pronunciation.replace(' ', '')
pronunciation_famehtk = convert_phone_set.ipa2famehtk(pronunciation_no_space)
if 'ceh' not in pronunciation_famehtk and 'sh' not in pronunciation_famehtk:
fout.write("{0}\t{1}\n".format(word.upper(), pronunciation_famehtk))
def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
""" Combine two lexicon files and sort by words. """
with open(lexicon_file1, "rt", encoding="utf-8") as fin:
lines1 =
lines1 = lines1.split('\n')
with open(lexicon_file2, "rt", encoding="utf-8") as fin:
lines2 =
lines2 = lines2.split('\n')
lex1 = pd.read_table(lexicon_file1, names=['word', 'pronunciation'])
lex2 = pd.read_table(lexicon_file2, names=['word', 'pronunciation'])
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 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 =
lines = lines.split('\n')
phones = []
for line in lines:
line_split = line.split()
if len(line_split) > 1:
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