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HTK related functions are moved to pyhtk project. fame acoustic models are made using fame_hmm.py. feature extraction is completed. A function is being made to get translation key from ipa to asr.
master
HTK related functions are moved to pyhtk project. fame acoustic models are made using fame_hmm.py. feature extraction is completed. A function is being made to get translation key from ipa to asr.
master
a.kunikoshi
3 years ago
10 changed files with 364 additions and 285 deletions
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BIN.vs/acoustic_model/v15/.suo
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BIN_tmp/phone_to_be_searched.npy
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BIN_tmp/translation_key.npy
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6acoustic_model.sln
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BINacoustic_model/__pycache__/defaultfiles.cpython-36.pyc
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13acoustic_model/acoustic_model.pyproj
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202acoustic_model/acoustic_model_function.py
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20acoustic_model/defaultfiles.py
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252acoustic_model/fame_functions.py
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156acoustic_model/fame_hmm.py
@ -1,202 +0,0 @@ |
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import os |
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import sys |
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from collections import Counter |
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|
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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 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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|
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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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|
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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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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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@ -0,0 +1,252 @@ |
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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|
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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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|
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# if output_type == 0: # full |
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# pronvar = np.unique(pronvar_) |
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|
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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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|
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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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|
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|
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|
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|
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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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|
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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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|
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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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|
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import time |
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|
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timer_start = time.time() |
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|
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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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|
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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] |
|||
ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1] |
|||
|
|||
asr_list = asr.split(' ') |
|||
# if there are phones which is not in phone_to_be_searched |
|||
if len([True for i in asr_list if i in phone_to_be_searched]) > 0: |
|||
if(len(ipa) == len(asr_list)): |
|||
print("{0}: {1} --> {2}".format(word, ipa, asr)) |
|||
for ipa_, asr_ in zip(ipa, asr_list): |
|||
if asr_ in phone_to_be_searched: |
|||
#if not translation_key[ipa_] == asr_: |
|||
translation_key[ipa_] = asr_ |
|||
phone_to_be_searched.remove(asr_) |
|||
|
|||
print("elapsed time: {}".format(time.time() - timer_start)) |
|||
|
|||
np.save(os.path.join(dir_tmp, 'translation_key.npy'), translation_key) |
|||
np.save(os.path.join(dir_tmp, 'phone_to_be_searched.npy'), phone_to_be_searched) |
|||
else: |
|||
translation_key = np.load(os.path.join(dir_tmp, 'translation_key.npy')).item() |
|||
phone_to_be_searched = np.load(os.path.join(dir_tmp, 'phone_to_be_searched.npy')).item() |
|||
@ -1,105 +1,127 @@ |
|||
import os |
|||
import sys |
|||
import os |
|||
os.chdir(r'C:\Users\A.Kunikoshi\source\repos\acoustic_model\acoustic_model') |
|||
|
|||
import tempfile |
|||
import configparser |
|||
import subprocess |
|||
from collections import Counter |
|||
#import configparser |
|||
#import subprocess |
|||
#from collections import Counter |
|||
|
|||
#import numpy as np |
|||
#import pandas as pd |
|||
|
|||
import numpy as np |
|||
import pandas as pd |
|||
import fame_functions |
|||
import defaultfiles as default |
|||
sys.path.append(default.pyhtk_dir) |
|||
import pyhtk |
|||
sys.path.append(default.toolbox_dir) |
|||
import file_handling |
|||
|
|||
|
|||
## ======================= user define ======================= |
|||
repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model' |
|||
curr_dir = repo_dir + '\\acoustic_model' |
|||
config_ini = curr_dir + '\\config.ini' |
|||
output_dir = 'C:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model' |
|||
forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment' |
|||
#repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model' |
|||
#curr_dir = repo_dir + '\\acoustic_model' |
|||
#config_ini = curr_dir + '\\config.ini' |
|||
#output_dir = 'C:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model' |
|||
#forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment' |
|||
|
|||
dataset_list = ['devel', 'test', 'train'] |
|||
|
|||
# procedure |
|||
extract_features = 0 |
|||
make_feature_list = 0 |
|||
conv_lexicon = 0 |
|||
check_lexicon = 0 |
|||
make_mlf = 0 |
|||
combine_files = 0 |
|||
flat_start = 0 |
|||
train_model = 1 |
|||
extract_features = 1 |
|||
#conv_lexicon = 0 |
|||
#check_lexicon = 0 |
|||
#make_mlf = 0 |
|||
#combine_files = 0 |
|||
#flat_start = 0 |
|||
#train_model = 1 |
|||
|
|||
|
|||
sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir)) |
|||
sys.path.append(forced_alignment_module) |
|||
from forced_alignment import convert_phone_set |
|||
#sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir)) |
|||
#sys.path.append(forced_alignment_module) |
|||
#from forced_alignment import convert_phone_set |
|||
|
|||
import acoustic_model_functions as am_func |
|||
|
|||
|
|||
## ======================= load variables ======================= |
|||
|
|||
config = configparser.ConfigParser() |
|||
config.sections() |
|||
config.read(config_ini) |
|||
|
|||
config_hcopy = config['Settings']['config_hcopy'] |
|||
config_train = config['Settings']['config_train'] |
|||
mkhmmdefs_pl = config['Settings']['mkhmmdefs_pl'] |
|||
FAME_dir = config['Settings']['FAME_dir'] |
|||
|
|||
lex_asr = FAME_dir + '\\lexicon\\lex.asr' |
|||
lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk' |
|||
lex_oov = FAME_dir + '\\lexicon\\lex.oov' |
|||
lex_oov_htk = FAME_dir + '\\lexicon\\lex.oov_htk' |
|||
#lex_ipa = FAME_dir + '\\lexicon\\lex.ipa' |
|||
#lex_ipa_ = FAME_dir + '\\lexicon\\lex.ipa_' |
|||
#lex_ipa_htk = FAME_dir + '\\lexicon\\lex.ipa_htk' |
|||
lex_htk = FAME_dir + '\\lexicon\\lex_original.htk' |
|||
lex_htk_ = FAME_dir + '\\lexicon\\lex.htk' |
|||
|
|||
hcompv_scp = output_dir + '\\scp\\combined.scp' |
|||
combined_mlf = output_dir + '\\label\\combined.mlf' |
|||
|
|||
model_dir = output_dir + '\\model' |
|||
model0_dir = model_dir + '\\hmm0' |
|||
proto_init = model_dir + '\\proto38' |
|||
proto_name = 'proto' |
|||
phonelist = output_dir + '\\config\\phonelist_friesian.txt' |
|||
hmmdefs_name = 'hmmdefs' |
|||
|
|||
#config = configparser.ConfigParser() |
|||
#config.sections() |
|||
#config.read(config_ini) |
|||
|
|||
#config_hcopy = config['Settings']['config_hcopy'] |
|||
#config_train = config['Settings']['config_train'] |
|||
#mkhmmdefs_pl = config['Settings']['mkhmmdefs_pl'] |
|||
#FAME_dir = config['Settings']['FAME_dir'] |
|||
|
|||
#lex_asr = FAME_dir + '\\lexicon\\lex.asr' |
|||
#lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk' |
|||
#lex_oov = FAME_dir + '\\lexicon\\lex.oov' |
|||
#lex_oov_htk = FAME_dir + '\\lexicon\\lex.oov_htk' |
|||
##lex_ipa = FAME_dir + '\\lexicon\\lex.ipa' |
|||
##lex_ipa_ = FAME_dir + '\\lexicon\\lex.ipa_' |
|||
##lex_ipa_htk = FAME_dir + '\\lexicon\\lex.ipa_htk' |
|||
#lex_htk = FAME_dir + '\\lexicon\\lex_original.htk' |
|||
#lex_htk_ = FAME_dir + '\\lexicon\\lex.htk' |
|||
|
|||
#hcompv_scp = output_dir + '\\scp\\combined.scp' |
|||
#combined_mlf = output_dir + '\\label\\combined.mlf' |
|||
|
|||
#model_dir = output_dir + '\\model' |
|||
#model0_dir = model_dir + '\\hmm0' |
|||
#proto_init = model_dir + '\\proto38' |
|||
#proto_name = 'proto' |
|||
#phonelist = output_dir + '\\config\\phonelist_friesian.txt' |
|||
#hmmdefs_name = 'hmmdefs' |
|||
|
|||
feature_dir = os.path.join(default.htk_dir, 'mfc') |
|||
if not os.path.exists(feature_dir): |
|||
os.makedirs(feature_dir) |
|||
tmp_dir = os.path.join(default.htk_dir, 'tmp') |
|||
if not os.path.exists(tmp_dir): |
|||
os.makedirs(tmp_dir) |
|||
|
|||
|
|||
## ======================= extract features ======================= |
|||
if extract_features: |
|||
print("==== extract features ====\n") |
|||
|
|||
for dataset in dataset_list: |
|||
print(dataset) |
|||
|
|||
#for dataset in ['test']: |
|||
print('==== {} ===='.format(dataset)) |
|||
|
|||
# a script file for HCopy |
|||
print(">>> making a script file for HCopy... \n") |
|||
hcopy_scp = tempfile.NamedTemporaryFile(mode='w', delete=False) |
|||
hcopy_scp.close() |
|||
#hcopy_scp = os.path.join(default.htk_dir, 'tmp', 'HCopy.scp') |
|||
|
|||
# get a list of features (hcopy.scp) from the filelist in FAME! corpus |
|||
feature_dir = output_dir + '\\mfc\\' + dataset |
|||
am_func.make_hcopy_scp_from_filelist_in_fame(FAME_dir, dataset, feature_dir, hcopy_scp.name) |
|||
feature_dir_ = os.path.join(feature_dir, dataset) |
|||
if not os.path.exists(feature_dir_): |
|||
os.makedirs(feature_dir_) |
|||
|
|||
# extract features |
|||
subprocessStr = 'HCopy -C ' + config_hcopy + ' -S ' + hcopy_scp.name |
|||
subprocess.call(subprocessStr, shell=True) |
|||
print(">>> extracting features... \n") |
|||
fame_functions.make_hcopy_scp_from_filelist_in_fame(default.fame_dir, dataset, feature_dir_, hcopy_scp.name) |
|||
|
|||
#subprocessStr = 'HCopy -C ' + config_hcopy + ' -S ' + hcopy_scp.name |
|||
#subprocess.call(subprocessStr, shell=True) |
|||
pyhtk.wav2mfc(default.config_hcopy, hcopy_scp.name) |
|||
|
|||
# a script file for HCompV |
|||
print(">>> making a script file for HCompV... \n") |
|||
|
|||
## ======================= make a list of features ======================= |
|||
if make_feature_list: |
|||
print("==== make a list of features ====\n") |
|||
#if make_feature_list: |
|||
# print("==== make a list of features ====\n") |
|||
|
|||
for dataset in dataset_list: |
|||
print(dataset) |
|||
# for dataset in dataset_list: |
|||
# print(dataset) |
|||
|
|||
feature_dir = output_dir + '\\mfc\\' + dataset |
|||
hcompv_scp = output_dir + '\\scp\\' + dataset + '.scp' |
|||
#feature_dir = output_dir + '\\mfc\\' + dataset |
|||
hcompv_scp = os.path.join(tmp_dir, dataset + '.scp') |
|||
|
|||
am_func.make_filelist(feature_dir, hcompv_scp) |
|||
#am_func.make_filelist(feature_dir, hcompv_scp) |
|||
file_handling.make_filelist(feature_dir_, hcompv_scp, '.mfc') |
|||
|
|||
|
|||
## ======================= convert lexicon from ipa to fame_htk ======================= |
|||
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