correspondence between lex.asr and lex.ipa is automatically obtained. header is added to the functions in fame_functions.py.
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yemaozi88
parent
813f013d7a
commit
87abbbb95a
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@ -16,14 +16,14 @@ def multi_character_tokenize(line, multi_character_tokens):
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def split_word(word, multi_character_phones):
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"""
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Split a line by given phoneset.
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split a line by given phoneset.
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Args:
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word (str): one word written in given phoneset.
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multi_character_phones:
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word (str): a word written in given phoneset.
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multi_character_phones (list): the list of multicharacter phones which is considered as one phone. this can be obtained with phoneset definition such as fame_phoneset.py.
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Returns:
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word_seperated (str): the word splitted in given phoneset.
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"""
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(word_seperated) (list): the word splitted in given phoneset.
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"""
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return [phone for phone in multi_character_tokenize(word.strip(), multi_character_phones)]
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@ -33,7 +33,6 @@ repo_dir = r'C:\Users\Aki\source\repos'
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ipa_xsampa_converter_dir = os.path.join(repo_dir, 'ipa-xsama-converter')
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forced_alignment_module_dir = os.path.join(repo_dir, 'forced_alignment')
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accent_classification_dir = os.path.join(repo_dir, 'accent_classification', 'accent_classification')
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#pyhtk_dir = os.path.join(repo_dir, 'pyhtk', 'pyhtk')
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toolbox_dir = os.path.join(repo_dir, 'toolbox')
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#htk_config_dir = r'c:\Users\A.Kunikoshi\source\repos\forced_alignment\forced_alignment\data\htk\preset_models\aki_dutch_2017'
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@ -12,24 +12,6 @@ import defaultfiles as default
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import fame_phoneset
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import convert_phone_set
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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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@ -128,25 +110,6 @@ import convert_phone_set
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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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@ -191,48 +154,100 @@ def make_hcopy_scp_from_filelist_in_fame(fame_dir, dataset, feature_dir, hcopy_s
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# f.write('{0}\t{1}\n'.format(WORD, key))
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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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Args:
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fame_dir (path): the directory of FAME corpus.
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dataset (str): 'devel', 'test' or 'train'.
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feature_dir (path): the directory where feature will be stored.
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hcopy_scp (path): a script file for HCopy to be made.
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"""
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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 load_lexicon(lexicon_file):
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""" load lexicon file as Data Frame.
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Args:
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lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
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Returns:
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lex (df): lexicon as Data Frame, which has columns 'word' and 'pronunciation'.
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"""
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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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def get_phoneset_from_lexicon(lexicon_file, phoneset='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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Args:
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lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
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phoneset (str): the phoneset with which lexicon_file is written. 'asr'(default) or 'ipa'.
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Returns:
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(list_of_phones) (set): the set of phones included in the lexicon_file.
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"""
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assert phoneset in ['asr', 'ipa'], 'phoneset should be \'asr\' or \'ipa\''
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lex = load_lexicon(lexicon_file)
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if phoneset == 'asr':
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return set(' '.join(lex['pronunciation']).split(' '))
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elif phoneset == 'ipa':
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join_pronunciations = ''.join(lex['pronunciation'])
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return set(convert_phone_set.split_word(join_pronunciations, fame_phoneset.multi_character_phones_ipa))
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def extract_unknown_phones(word_list, known_phones):
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return [i for i in word_list if not i in known_phones]
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def extract_unknown_phones(ipa, known_phones):
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"""extract unknown phones in the pronunciation written in IPA.
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Args:
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ipa (str): a pronunciation written in IPA.
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known_phones (list): list of phones already know.
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Returns:
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(list_of_phones) (list): unknown phones not included in 'known_phones'.
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"""
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ipa_split = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
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return [i for i in ipa_split if not i in known_phones]
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if __name__ == '__main__':
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import time
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timer_start = time.time()
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def get_translation_key(lexicon_file_ipa, lexicon_file_asr):
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""" get correspondence between lexicon_file_ipa and lexicon_file_asr.
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#def get_translation_key():
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dir_tmp = r'c:\Users\Aki\source\repos\acoustic_model\_tmp'
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lexicon_ipa = r'd:\_corpus\FAME\lexicon\lex.ipa'
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lexicon_asr = r'd:\_corpus\FAME\lexicon\lex.asr'
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Args:
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lexicon_file_ipa (path): lexicon in the format of 'word' /t 'pronunciation (IPA)'.
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lexicon_file_asr (path): lexicon in the format of 'word' /t 'pronunciation (asr)'.
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the each character of 'pronunciation' should be delimited by ' '.
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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 1:
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phone_to_be_searched = fame_phoneset.phoneset_ipa[:]
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Returns:
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translation_key (dict): translation key from ipa to asr.
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(phone_unknown) (list): the list of IPA phones, which does not appear in lexicon_file_asr.
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"""
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lex_ipa = load_lexicon(lexicon_file_ipa)
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lex_asr = load_lexicon(lexicon_file_asr)
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phone_unknown = fame_phoneset.phoneset_ipa[:]
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translation_key = dict()
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for word in lex_ipa['word']:
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if np.sum(lex_ipa['word'] == word) == 1 and np.sum(lex_asr['word'] == word) == 1:
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@ -242,47 +257,44 @@ if __name__ == '__main__':
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ipa_list = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
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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 there are phones which is not in phone_unknown
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#if len([True for i in asr_list if i in phone_unknown]) > 0:
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if(len(ipa_list) == len(asr_list)):
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print("{0}: {1} --> {2}".format(word, ipa_list, asr_list))
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for ipa_, asr_ in zip(ipa_list, asr_list):
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if ipa_ in phone_to_be_searched:
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if ipa_ in phone_unknown:
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translation_key[ipa_] = asr_
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phone_to_be_searched.remove(ipa_)
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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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phone_unknown.remove(ipa_)
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return translation_key, list(phone_unknown)
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#phone_unknown = list(phone_to_be_searched)
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##phone_unknown.remove('')
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#phone_known = list(translation_key.keys())
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def find_phone(lexicon_file, phone, phoneset='ipa'):
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""" extract rows where the phone is used in the lexicon_file.
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#p = phone_unknown[0]
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Args:
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lexicon_file (path): lexicon in the format of 'word' /t 'pronunciation'.
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phone (str): the phone to be searched.
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phoneset (str): the phoneset with which lexicon_file is written. 'asr' or 'ipa'(default).
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### extract lines which contains 'unknown' phone.
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#lex_ipa_ = lex_ipa[lex_ipa['pronunciation'].str.count(p)>0]
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##phone_unknown_ = phone_unknown[:]
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##phone_unknown_.remove(p)
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#phone_known_ = phone_known[:]
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#phone_known_.append(p)
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#for index, row in lex_ipa_.iterrows():
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# ipa = row['pronunciation']
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# phone_extract_unknown_phones(asr_list, phone_known_):
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Returns:
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extracted (df): rows where the phone is used.
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# # check the number of phones in phone_unknown_
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# if len([True for i in asr_list if i in phone_unknown_]) == 0:
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# word = row['word']
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# ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
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# print("{0}: {1} --> {2}".format(word, ipa, asr))
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# #print("{0}:{1}".format(index, row['pronunciation']))
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ToDo:
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* develop when the phonset == 'asr'.
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"""
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assert phoneset in ['asr', 'ipa'], 'phoneset should be \'asr\' or \'ipa\''
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lex = load_lexicon(lexicon_file)
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# to reduce the calculation time, only target rows which include 'phone' at least once.
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lex_ = lex[lex['pronunciation'].str.count(phone)>0]
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extracted = pd.DataFrame(index=[], columns=['word', 'pronunciation'])
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for index, row in lex_.iterrows():
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if phoneset == 'ipa':
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pronunciation = convert_phone_set.split_word(row['pronunciation'], fame_phoneset.multi_character_phones_ipa)
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if phone in pronunciation:
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extracted_ = pd.Series([row['word'], pronunciation], index=extracted.columns)
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extracted = extracted.append(extracted_, ignore_index=True)
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return extracted
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@ -6,6 +6,7 @@ import tempfile
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#import configparser
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#import subprocess
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#from collections import Counter
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import time
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#import numpy as np
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#import pandas as pd
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@ -27,8 +28,8 @@ from htk import pyhtk
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dataset_list = ['devel', 'test', 'train']
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# procedure
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extract_features = 1
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#conv_lexicon = 0
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extract_features = 0
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conv_lexicon = 1
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#check_lexicon = 0
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#make_mlf = 0
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#combine_files = 0
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@ -84,16 +85,14 @@ if not os.path.exists(tmp_dir):
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## ======================= extract features =======================
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if extract_features:
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for dataset in dataset_list:
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#for dataset in ['test']:
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print('==== {} ===='.format(dataset))
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# a script file for HCopy
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print(">>> making a script file for HCopy... \n")
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hcopy_scp = tempfile.NamedTemporaryFile(mode='w', delete=False)
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hcopy_scp.close()
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#hcopy_scp = os.path.join(default.htk_dir, 'tmp', 'HCopy.scp')
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## get a list of features (hcopy.scp) from the filelist in FAME! corpus
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# get a list of features (hcopy.scp) from the filelist in FAME! corpus
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feature_dir_ = os.path.join(feature_dir, dataset)
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if not os.path.exists(feature_dir_):
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os.makedirs(feature_dir_)
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@ -101,26 +100,11 @@ if extract_features:
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# extract features
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print(">>> extracting features... \n")
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fame_functions.make_hcopy_scp_from_filelist_in_fame(default.fame_dir, dataset, feature_dir_, hcopy_scp.name)
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#subprocessStr = 'HCopy -C ' + config_hcopy + ' -S ' + hcopy_scp.name
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#subprocess.call(subprocessStr, shell=True)
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pyhtk.wav2mfc(default.config_hcopy, hcopy_scp.name)
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# a script file for HCompV
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print(">>> making a script file for HCompV... \n")
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## ======================= make a list of features =======================
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#if make_feature_list:
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# print("==== make a list of features ====\n")
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# for dataset in dataset_list:
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# print(dataset)
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#feature_dir = output_dir + '\\mfc\\' + dataset
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hcompv_scp = os.path.join(tmp_dir, dataset + '.scp')
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#am_func.make_filelist(feature_dir, hcompv_scp)
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fh.make_filelist(feature_dir_, hcompv_scp, '.mfc')
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@ -128,6 +112,59 @@ if extract_features:
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if conv_lexicon:
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print('==== convert lexicon from ipa 2 fame ====\n')
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#dir_out = r'c:\Users\Aki\source\repos\acoustic_model\_tmp'
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lexicon_dir = os.path.join(default.fame_dir, 'lexicon')
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lexicon_ipa = os.path.join(lexicon_dir, 'lex.ipa')
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lexicon_asr = os.path.join(lexicon_dir, 'lex.asr')
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# get the correspondence between lex_ipa and lex_asr.
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lex_asr = fame_functions.load_lexicon(lexicon_asr)
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lex_ipa = fame_functions.load_lexicon(lexicon_ipa)
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if 1:
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timer_start = time.time()
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translation_key, phone_unknown = fame_functions.get_translation_key(lexicon_ipa, lexicon_asr)
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print("elapsed time: {}".format(time.time() - timer_start))
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np.save('translation_key_ipa2asr.npy', translation_key)
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np.save('phone_unknown.npy', phone_unknown)
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else:
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translation_key = np.load('translation_key_ipa2asr.npy').item()
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phone_unknown = np.load('phone_unknown.npy')
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phone_unknown = list(phone_unknown)
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## manually check the correspondence for the phone in phone_unknown.
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#p = phone_unknown[0]
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#lex_ipa_ = find_phone(lexicon_ipa, p, phoneset='ipa')
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#for word in lex_ipa_['word']:
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# ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
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# if np.sum(lex_asr['word'] == word) > 0:
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# asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
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# ipa_list = convert_phone_set.split_word(ipa, fame_phoneset.multi_character_phones_ipa)
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# asr_list = asr.split(' ')
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# if p in ipa_list and (len(ipa_list) == len(asr_list)):
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# print("{0}: {1} --> {2}".format(word, ipa_list, asr_list))
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# for ipa_, asr_ in zip(ipa_list, asr_list):
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# if ipa_ in phone_unknown:
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# translation_key[ipa_] = asr_
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# phone_unknown.remove(ipa_)
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## check if all the phones in lexicon_ipa are in fame_phoneset.py.
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#timer_start = time.time()
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#phoneset_lex = get_phoneset_from_lexicon(lexicon_ipa, phoneset='ipa')
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#print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
#phoneset_py = fame_phoneset.phoneset_ipa
|
||||
#set(phoneset_lex) - set(phoneset_py)
|
||||
|
||||
##timer_start = time.time()
|
||||
##extracted = find_phone(lexicon_ipa, 'ⁿ')
|
||||
##print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
|
||||
# lex.asr is Kaldi compatible version of lex.ipa.
|
||||
# to check...
|
||||
#lexicon_ipa = pd.read_table(lex_ipa, names=['word', 'pronunciation'])
|
||||
@ -140,13 +177,13 @@ if conv_lexicon:
|
||||
# fout.write("{0}\t{1}\n".format(word, ' '.join(pronunciation_split)))
|
||||
|
||||
# convert each lexicon from ipa description to fame_htk phoneset.
|
||||
am_func.ipa2famehtk_lexicon(lex_oov, lex_oov_htk)
|
||||
am_func.ipa2famehtk_lexicon(lex_asr, lex_asr_htk)
|
||||
#am_func.ipa2famehtk_lexicon(lex_oov, lex_oov_htk)
|
||||
#am_func.ipa2famehtk_lexicon(lex_asr, lex_asr_htk)
|
||||
|
||||
# combine lexicon
|
||||
# pronunciations which is not found in lex.asr are generated using G2P and listed in lex.oov.
|
||||
# therefore there is no overlap between lex_asr and lex_oov.
|
||||
am_func.combine_lexicon(lex_asr_htk, lex_oov_htk, lex_htk)
|
||||
#am_func.combine_lexicon(lex_asr_htk, lex_oov_htk, lex_htk)
|
||||
|
||||
|
||||
## ======================= check if all the phones are successfully converted =======================
|
||||
|
||||
@ -1,41 +1,79 @@
|
||||
""" definition of the phones to be used. """
|
||||
|
||||
## phones in IPA.
|
||||
phoneset_ipa = [
|
||||
# vowels
|
||||
'i̯',
|
||||
'i̯ⁿ',
|
||||
'y',
|
||||
'i',
|
||||
'i.',
|
||||
'iⁿ',
|
||||
'i:',
|
||||
'i:ⁿ',
|
||||
'ɪ',
|
||||
'ɪ:',
|
||||
'ɪⁿ',
|
||||
'ɪ.',
|
||||
#'ɪ:', # not included in lex.ipa
|
||||
'ɪ:ⁿ',
|
||||
'e',
|
||||
'e:',
|
||||
'e:ⁿ',
|
||||
'ə',
|
||||
'əⁿ',
|
||||
'ə:',
|
||||
'ɛ',
|
||||
'ɛ.',
|
||||
'ɛⁿ',
|
||||
'ɛ:',
|
||||
'ɛ:ⁿ',
|
||||
'a',
|
||||
'aⁿ',
|
||||
'a.',
|
||||
'a:',
|
||||
'a:ⁿ',
|
||||
'ṷ',
|
||||
'ú',
|
||||
'ṷ.',
|
||||
'ṷⁿ',
|
||||
#'ú', # only appears in word 'feeste'(út) and 'gaste'(út) which are 'f e: s t ə' and 'yn' in lex_asr.
|
||||
'u',
|
||||
'uⁿ',
|
||||
'u.',
|
||||
'u:',
|
||||
'u:ⁿ',
|
||||
'ü',
|
||||
'ü.',
|
||||
'üⁿ',
|
||||
'ü:',
|
||||
'ü:ⁿ',
|
||||
'o',
|
||||
'oⁿ',
|
||||
'o.',
|
||||
'o:',
|
||||
'o:ⁿ',
|
||||
'ö',
|
||||
'ö.',
|
||||
'öⁿ',
|
||||
'ö:',
|
||||
'ö:ⁿ',
|
||||
'ɔ',
|
||||
'ɔ.',
|
||||
'ɔⁿ',
|
||||
'ɔ:',
|
||||
'ɔ̈',
|
||||
'ɔ:ⁿ',
|
||||
#'ɔ̈', # not included in lex.ipa
|
||||
'ɔ̈.',
|
||||
'ɔ̈:',
|
||||
|
||||
# plosives
|
||||
'p',
|
||||
'b',
|
||||
't',
|
||||
'tⁿ',
|
||||
'd',
|
||||
'k',
|
||||
'g',
|
||||
'ɡ', # = 'g'
|
||||
|
||||
# nasals
|
||||
'm',
|
||||
@ -48,8 +86,22 @@ phoneset_ipa = [
|
||||
's',
|
||||
's:',
|
||||
'z',
|
||||
'zⁿ',
|
||||
'x',
|
||||
'h',
|
||||
|
||||
# tap and flip
|
||||
'r',
|
||||
'r.', # only appears in word 'mearpartijestelsel'(does not exist in lex_asr) and 'tenoarpartij'.
|
||||
'r:', # only appears in word 'mûsearflearmûs' and 'sjochdêr'.
|
||||
|
||||
# approximant
|
||||
'j',
|
||||
'j.',
|
||||
'l'
|
||||
]
|
||||
|
||||
## the list of multi character phones.
|
||||
# for example, the length of 'i̯ⁿ' is 3, but in the codes it is treated as one letter.
|
||||
multi_character_phones_ipa = [i for i in phoneset_ipa if len(i) > 1]
|
||||
multi_character_phones_ipa.sort(key=len, reverse=True)
|
||||
Loading…
Reference in New Issue
Block a user