93 lines
3.8 KiB
Python
93 lines
3.8 KiB
Python
import sys
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import os
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os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
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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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import fame_functions
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import defaultfiles as default
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sys.path.append(default.toolbox_dir)
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from phoneset import fame_ipa, fame_asr
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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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## check if all the phones in lexicon.ipa are in fame_ipa.py.
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#timer_start = time.time()
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#phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_ipa, phoneset='ipa')
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#phoneset_py = fame_ipa.phoneset
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#print("phones which is in lexicon.ipa but not in fame_ipa.py:\n{}".format(
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# set(phoneset_lex) - set(phoneset_py)))
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#print("elapsed time: {}".format(time.time() - timer_start))
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# check which word has the phone.
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#timer_start = time.time()
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#extracted = find_phone(lexicon_ipa, 'ⁿ')
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#print("elapsed time: {}".format(time.time() - timer_start))
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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 0:
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timer_start = time.time()
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translation_key_ipa2asr, 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(os.path.join('phoneset', 'output_get_translation_key_translation_key.npy'), translation_key_ipa2asr)
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np.save(os.path.join('phoneset', 'output_get_translation_key_phone_unknown.npy'), phone_unknown)
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else:
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translation_key_ipa2asr = np.load(os.path.join('phoneset', 'output_get_translation_key_translation_key.npy')).item()
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phone_unknown = np.load(os.path.join('phoneset', 'output_get_translation_key_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_ipa.multi_character_phones)
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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_ipa2asr[ipa_] = asr_
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# phone_unknown.remove(ipa_)
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translation_key_ipa2asr['ə:'] = 'ə'
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translation_key_ipa2asr['r.'] = 'r'
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translation_key_ipa2asr['r:'] = 'r'
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np.save(os.path.join('phoneset', 'fame_ipa2asr.npy'), translation_key_ipa2asr)
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## check if all the phones in lexicon.asr are in translation_key_ipa2asr.
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timer_start = time.time()
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phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_asr, phoneset='asr')
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phoneset_lex.remove("")
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phoneset_asr = list(set(translation_key_ipa2asr.values()))
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print("phones which is in lexicon.asr but not in the translation_key_ipa2asr:\n{}".format(
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set(phoneset_lex) - set(phoneset_asr)))
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print("elapsed time: {}".format(time.time() - timer_start))
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## make the translation key between asr to htk.
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#multi_character_phones = [i for i in phoneset_asr if len(i) > 1]
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#multi_character_phones.sort(key=len, reverse=True)
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#lexicon_ipa = pd.read_table(lex_ipa, names=['word', 'pronunciation'])
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#with open(lex_ipa_, "w", encoding="utf-8") as fout:
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# for word, pronunciation in zip(lexicon_ipa['word'], lexicon_ipa['pronunciation']):
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# # ignore nasalization and '.'
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# pronunciation_ = pronunciation.replace(u'ⁿ', '')
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# pronunciation_ = pronunciation_.replace('.', '')
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# pronunciation_split = convert_phone_set.split_ipa_fame(pronunciation_)
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# fout.write("{0}\t{1}\n".format(word, ' '.join(pronunciation_split))) |