253 lines
8.6 KiB
Python
253 lines
8.6 KiB
Python
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import os
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os.chdir(r'C:\Users\A.Kunikoshi\source\repos\acoustic_model\acoustic_model')
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import sys
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from collections import Counter
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import pickle
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import numpy as np
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import pandas as pd
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import defaultfiles as default
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#sys.path.append(default.forced_alignment_module_dir)
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#from forced_alignment import convert_phone_set
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#def find_phone(lexicon_file, phone):
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# """ Search where the phone is used in the lexicon. """
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# with open(lexicon_file, "rt", encoding="utf-8") as fin:
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# lines = fin.read()
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# lines = lines.split('\n')
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# extracted = []
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# for line in lines:
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# line = line.split('\t')
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# if len(line) > 1:
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# pronunciation = line[1]
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# if phone in pronunciation:
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# extracted.append(line)
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# return extracted
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#def ipa2famehtk_lexicon(lexicon_file_in, lexicon_file_out):
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# """ Convert a lexicon file from IPA to HTK format for FAME! corpus. """
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# lexicon_in = pd.read_table(lexicon_file_in, names=['word', 'pronunciation'])
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# with open(lexicon_file_out, "w", encoding="utf-8") as fout:
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# for word, pronunciation in zip(lexicon_in['word'], lexicon_in['pronunciation']):
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# pronunciation_no_space = pronunciation.replace(' ', '')
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# pronunciation_famehtk = convert_phone_set.ipa2famehtk(pronunciation_no_space)
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# if 'ceh' not in pronunciation_famehtk and 'sh' not in pronunciation_famehtk:
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# fout.write("{0}\t{1}\n".format(word.upper(), pronunciation_famehtk))
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#def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
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# """ Combine two lexicon files and sort by words. """
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# with open(lexicon_file1, "rt", encoding="utf-8") as fin:
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# lines1 = fin.read()
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# lines1 = lines1.split('\n')
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# with open(lexicon_file2, "rt", encoding="utf-8") as fin:
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# lines2 = fin.read()
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# lines2 = lines2.split('\n')
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# lex1 = pd.read_table(lexicon_file1, names=['word', 'pronunciation'])
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# lex2 = pd.read_table(lexicon_file2, names=['word', 'pronunciation'])
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# lex = pd.concat([lex1, lex2])
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# lex = lex.sort_values(by='word', ascending=True)
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# lex.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
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#def read_fileFA(fileFA):
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# """
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# read the result file of HTK forced alignment.
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# this function only works when input is one word.
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# """
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# with open(fileFA, 'r') as f:
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# lines = f.read()
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# lines = lines.split('\n')
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# phones = []
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# for line in lines:
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# line_split = line.split()
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# if len(line_split) > 1:
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# phones.append(line_split[2])
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# return ' '.join(phones)
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#def fame_pronunciation_variant(ipa):
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# ipa = ipa.replace('æ', 'ɛ')
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# ipa = ipa.replace('ɐ', 'a')
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# ipa = ipa.replace('ɑ', 'a')
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# ipa = ipa.replace('ɾ', 'r')
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# ipa = ipa.replace('ɹ', 'r') # ???
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# ipa = ipa.replace('ʁ', 'r')
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# ipa = ipa.replace('ʀ', 'r') # ???
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# ipa = ipa.replace('ʊ', 'u')
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# ipa = ipa.replace('χ', 'x')
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# pronvar_list = [ipa]
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# while 'ø:' in ' '.join(pronvar_list) or 'œ' in ' '.join(pronvar_list) or 'ɒ' in ' '.join(pronvar_list):
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# pronvar_list_ = []
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# for p in pronvar_list:
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# if 'ø:' in p:
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# pronvar_list_.append(p.replace('ø:', 'ö'))
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# pronvar_list_.append(p.replace('ø:', 'ö:'))
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# if 'œ' in p:
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# pronvar_list_.append(p.replace('œ', 'ɔ̈'))
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# pronvar_list_.append(p.replace('œ', 'ɔ̈:'))
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# if 'ɒ' in p:
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# pronvar_list_.append(p.replace('ɒ', 'ɔ̈'))
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# pronvar_list_.append(p.replace('ɒ', 'ɔ̈:'))
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# pronvar_list = np.unique(pronvar_list_)
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# return pronvar_list
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#def make_fame2ipa_variants(fame):
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# fame = 'rɛös'
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# ipa = [fame]
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# ipa.append(fame.replace('ɛ', 'æ'))
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# ipa.append(fame.replace('a', 'ɐ'))
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# ipa.append(fame.replace('a', 'ɑ'))
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# ipa.append(fame.replace('r', 'ɾ'))
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# ipa.append(fame.replace('r', 'ɹ'))
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# ipa.append(fame.replace('r', 'ʁ'))
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# ipa.append(fame.replace('r', 'ʀ'))
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# ipa.append(fame.replace('u', 'ʊ'))
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# ipa.append(fame.replace('x', 'χ'))
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# ipa.append(fame.replace('ö', 'ø:'))
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# ipa.append(fame.replace('ö:', 'ø:'))
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# ipa.append(fame.replace('ɔ̈', 'œ'))
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# ipa.append(fame.replace('ɔ̈:', 'œ'))
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# ipa.append(fame.replace('ɔ̈', 'ɒ'))
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# ipa.append(fame.replace('ɔ̈:', 'ɒ'))
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# return ipa
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def make_hcopy_scp_from_filelist_in_fame(fame_dir, dataset, feature_dir, hcopy_scp):
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""" Make a script file for HCopy using the filelist in FAME! corpus. """
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filelist_txt = os.path.join(fame_dir, 'fame', 'filelists', dataset + 'list.txt')
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with open(filelist_txt) as fin:
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filelist = fin.read()
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filelist = filelist.split('\n')
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with open(hcopy_scp, 'w') as fout:
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for filename_ in filelist:
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filename = filename_.replace('.TextGrid', '')
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if len(filename) > 3: # remove '.', '..' and ''
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wav_file = os.path.join(fame_dir, 'fame', 'wav', dataset, filename + '.wav')
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mfc_file = os.path.join(feature_dir, filename + '.mfc')
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fout.write(wav_file + '\t' + mfc_file + '\n')
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#def make_filelist(input_dir, output_txt):
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# """ Make a list of files in the input_dir. """
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# filenames = os.listdir(input_dir)
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# with open(output_txt, 'w') as fout:
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# for filename in filenames:
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# fout.write(input_dir + '\\' + filename + '\n')
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#def make_htk_dict(word, pronvar_, fileDic, output_type):
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# """
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# make dict files which can be used for HTK.
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# param word: target word.
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# param pronvar_: pronunciation variant. nx2 (WORD /t pronunciation) ndarray.
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# param fileDic: output dic file.
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# param output_type: 0:full, 1:statistics, 2:frequency <2% entries are removed. 3:top 3.
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# """
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# #assert(output_type < 4 and output_type >= 0, 'output_type should be an integer between 0 and 3.')
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# WORD = word.upper()
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# if output_type == 0: # full
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# pronvar = np.unique(pronvar_)
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# with open(fileDic, 'w') as f:
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# for pvar in pronvar:
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# f.write('{0}\t{1}\n'.format(WORD, pvar))
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# else:
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# c = Counter(pronvar_)
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# total_num = sum(c.values())
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# with open(fileDic, 'w') as f:
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# if output_type == 3:
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# for key, value in c.most_common(3):
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# f.write('{0}\t{1}\n'.format(WORD, key))
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# else:
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# for key, value in c.items():
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# percentage = value/total_num*100
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# if output_type == 1: # all
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# f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, percentage, WORD, key))
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# elif output_type == 2: # less than 2 percent
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# if percentage < 2:
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# f.write('{0}\t{1}\n'.format(WORD, key))
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def load_lexicon(lexicon_file):
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lex = pd.read_csv(lexicon_file, delimiter='\t', header=None, encoding="utf-8")
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lex.rename(columns={0: 'word', 1: 'pronunciation'}, inplace=True)
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return lex
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def get_phonelist(lexicon_asr):
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""" Make a list of phones which appears in the lexicon. """
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#with open(lexicon_file, "rt", encoding="utf-8") as fin:
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# lines = fin.read()
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# lines = lines.split('\n')
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# phonelist = set([])
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# for line in lines:
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# line = line.split('\t')
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# if len(line) > 1:
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# pronunciation = set(line[1].split())
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# phonelist = phonelist | pronunciation
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lex = load_lexicon(lexicon_asr)
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return set(' '.join(lex['pronunciation']).split(' '))
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import time
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timer_start = time.time()
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#def get_translation_key():
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dir_tmp = r'c:\Users\A.Kunikoshi\source\repos\acoustic_model\_tmp'
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lexicon_ipa = r'f:\_corpus\FAME\lexicon\lex.ipa'
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lexicon_asr = r'f:\_corpus\FAME\lexicon\lex.asr'
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lex_ipa = load_lexicon(lexicon_ipa)
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lex_asr = load_lexicon(lexicon_asr)
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if 0:
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phone_to_be_searched = get_phonelist(lexicon_asr)
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translation_key = dict()
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for word in lex_asr['word']:
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if np.sum(lex_asr['word'] == word) == 1 and np.sum(lex_ipa['word'] == word) == 1:
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asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
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ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
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asr_list = asr.split(' ')
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# if there are phones which is not in phone_to_be_searched
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if len([True for i in asr_list if i in phone_to_be_searched]) > 0:
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if(len(ipa) == len(asr_list)):
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print("{0}: {1} --> {2}".format(word, ipa, asr))
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for ipa_, asr_ in zip(ipa, asr_list):
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if asr_ in phone_to_be_searched:
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#if not translation_key[ipa_] == asr_:
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translation_key[ipa_] = asr_
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phone_to_be_searched.remove(asr_)
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print("elapsed time: {}".format(time.time() - timer_start))
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np.save(os.path.join(dir_tmp, 'translation_key.npy'), translation_key)
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np.save(os.path.join(dir_tmp, 'phone_to_be_searched.npy'), phone_to_be_searched)
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else:
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translation_key = np.load(os.path.join(dir_tmp, 'translation_key.npy')).item()
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phone_to_be_searched = np.load(os.path.join(dir_tmp, 'phone_to_be_searched.npy')).item()
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