102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
import os
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import sys
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import pandas as pd
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## ======================= user define =======================
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repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
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curr_dir = repo_dir + '\\acoustic_model'
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forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
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sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
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sys.path.append(forced_alignment_module)
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from forced_alignment import convert_phone_set
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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 = 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 = FAME_dir + '\\fame\\wav\\' + dataset + '\\' + filename + '.wav'
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mfc_file = 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 get_phonelist(lexicon_file):
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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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return phonelist
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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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pron = line[1]
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if phone in pron:
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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') |