fix the bug there are characters in the lexicon which cannot be described in ascii.
This commit is contained in:
yemaozi88
parent
dc6b7b84b6
commit
22cccfb61d
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@ -4,8 +4,7 @@
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<SchemaVersion>2.0</SchemaVersion>
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<ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
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<ProjectHome>.</ProjectHome>
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<StartupFile>
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</StartupFile>
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<StartupFile>fame_hmm.py</StartupFile>
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<SearchPath>
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</SearchPath>
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<WorkingDirectory>.</WorkingDirectory>
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@ -39,11 +39,11 @@ toolbox_dir = os.path.join(repo_dir, 'toolbox')
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#config_hvite = os.path.join(htk_config_dir, 'config.HVite')
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#acoustic_model = os.path.join(htk_config_dir, 'hmmdefs.compo')
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#acoustic_model = r'c:\cygwin64\home\A.Kunikoshi\acoustic_model\model\barbara\hmm128-2\hmmdefs.compo'
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#phonelist_txt = os.path.join(htk_config_dir, 'phonelist.txt')
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phonelist_txt = os.path.join(htk_dir, 'config', 'phonelist.txt')
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WSL_dir = r'C:\OneDrive\WSL'
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#fame_dir = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')
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fame_dir = r'd:\_corpus\fame'
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fame_dir = r'c:\OneDrive\Research\rug\_data\FAME'
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fame_s5_dir = os.path.join(fame_dir, 's5')
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fame_corpus_dir = os.path.join(fame_dir, 'corpus')
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@ -290,15 +290,17 @@ def lexicon_asr2htk(lexicon_file_asr, lexicon_file_htk):
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"""
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lex_asr = load_lexicon(lexicon_file_asr)
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def word2htk_(row):
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return word2htk(row['word'])
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def asr2htk_space_delimited_(row):
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return asr2htk_space_delimited(row['pronunciation'])
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lex_htk = pd.DataFrame({
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'word': lex_asr['word'],
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'word': lex_asr.apply(word2htk_, axis=1).str.upper(),
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'pronunciation': lex_asr.apply(asr2htk_space_delimited_, axis=1)
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})
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lex_htk = lex_htk.ix[:, ['word', 'pronunciation']]
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lex_htk.to_csv(lexicon_file_htk, header=None, index=None, sep='\t')
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lex_htk.to_csv(lexicon_file_htk, header=None, index=None, sep='\t', encoding='utf-8')
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return
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@ -316,20 +318,26 @@ def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
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lex2 = load_lexicon(lexicon_file2)
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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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lex.to_csv(lexicon_out, index=False, header=False, sep='\t', encoding='utf-8')
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def fix_single_quote(lexicon_file):
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""" add '\' before all single quote at the beginning of words.
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convert special characters to ascii compatible characters.
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Args:
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lexicon_file (path): lexicon file, which will be overwitten.
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"""
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lex = load_lexicon(lexicon_file)
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lex = lex.dropna() # remove N/A.
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for i in lex[lex['word'].str.startswith('\'')].index.values:
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lex.iat[i, 0] = lex.iat[i, 0].replace('\'', '\\\'')
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# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
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#lex.to_csv(lexicon_file, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
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lex.to_csv(lexicon_file, index=False, header=False, encoding="utf-8", sep='\t')
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lex.to_csv(lexicon_file, index=False, header=False, sep='\t', encoding='utf-8')
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return
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def word2htk(word):
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return ''.join([fame_asr.translation_key_word2htk.get(i, i) for i in word])
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@ -3,6 +3,7 @@ import os
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os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
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import tempfile
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import shutil
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#import configparser
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#import subprocess
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import time
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@ -11,6 +12,7 @@ import numpy as np
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import pandas as pd
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import fame_functions
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from phoneset import fame_ipa, fame_asr
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import defaultfiles as default
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sys.path.append(default.toolbox_dir)
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import file_handling as fh
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@ -28,7 +30,7 @@ dataset_list = ['devel', 'test', 'train']
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# procedure
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extract_features = 0
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make_lexicon = 0
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make_lexicon = 1
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make_mlf = 0
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combine_files = 0
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flat_start = 0
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@ -44,6 +46,9 @@ lexicon_htk_asr = os.path.join(default.htk_dir, 'lexicon', 'lex.htk_asr')
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lexicon_htk_oov = os.path.join(default.htk_dir, 'lexicon', 'lex.htk_oov')
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lexicon_htk = os.path.join(default.htk_dir, 'lexicon', 'lex.htk')
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global_ded = os.path.join(default.htk_dir, 'config', 'global.ded')
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#hcompv_scp = output_dir + '\\scp\\combined.scp'
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#combined_mlf = output_dir + '\\label\\combined.mlf'
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@ -60,14 +65,17 @@ if not os.path.exists(feature_dir):
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tmp_dir = os.path.join(default.htk_dir, 'tmp')
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if not os.path.exists(tmp_dir):
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os.makedirs(tmp_dir)
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label_dir = os.path.join(default.htk_dir, 'label')
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if not os.path.exists(label_dir):
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os.makedirs(label_dir)
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## ======================= extract features =======================
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if extract_features:
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print('==== extract features ====\n')
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for dataset in dataset_list:
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print('==== dataset: {} ===='.format(dataset))
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print('==== extract features on dataset {} ====\n'.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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@ -89,6 +97,8 @@ if extract_features:
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hcompv_scp = os.path.join(tmp_dir, dataset + '.scp')
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fh.make_filelist(feature_dir_, hcompv_scp, '.mfc')
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os.remove(hcopy_scp.name)
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## ======================= make lexicon for HTK =======================
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if make_lexicon:
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@ -114,94 +124,132 @@ if make_lexicon:
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fame_functions.fix_single_quote(lexicon_htk)
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## ======================= make phonelist =======================
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#phonelist_txt = os.path.join(default.htk_dir, 'config', 'phonelist.txt')
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#pyhtk.create_phonelist_file(fame_asr.phoneset_htk, phonelist_txt)
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#sentence = 'ien fan de minsken fan it deiferbliuw sels brúntsje visser'
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#log_txt = os.path.join(default.htk_dir, 'config', 'log.txt')
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#dictionary_file = os.path.join(default.htk_dir, 'config', 'test.dic')
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#pyhtk.create_dictionary(
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# sentence, global_ded, log_txt, dictionary_file, lexicon_htk)
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#pyhtk.create_dictionary_without_log(
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# sentence, global_ded, dictionary_file, lexicon_htk)
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## ======================= make label file =======================
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if make_mlf:
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print("==== make mlf ====\n")
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print("generating word level transcription...\n")
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for dataset in dataset_list:
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hcompv_scp = output_dir + '\\scp\\' + dataset + '.scp'
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hcompv_scp2 = output_dir + '\\scp\\' + dataset + '_all_words_in_lexicon.scp'
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script_list = FAME_dir + '\\data\\' + dataset + '\\text'
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mlf_word = output_dir + '\\label\\' + dataset + '_word.mlf'
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mlf_phone = output_dir + '\\label\\' + dataset + '_phone.mlf'
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timer_start = time.time()
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print("==== generating word level transcription on dataset {}\n".format(dataset))
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# lexicon
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lexicon_htk = pd.read_table(lex_htk, names=['word', 'pronunciation'])
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# list of features
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with open(hcompv_scp) as fin:
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features = fin.read()
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features = features.split('\n')
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#hcompv_scp = output_dir + '\\scp\\' + dataset + '.scp'
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#hcompv_scp2 = output_dir + '\\scp\\' + dataset + '_all_words_in_lexicon.scp'
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script_list = os.path.join(default.fame_dir, 'data', dataset, 'text')
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#mlf_word = output_dir + '\\label\\' + dataset + '_word.mlf'
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#mlf_phone = output_dir + '\\label\\' + dataset + '_phone.mlf'
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wav_dir = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
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dictionary_file = os.path.join(wav_dir, 'temp.dic')
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# list of scripts
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with open(script_list, "rt", encoding="utf-8") as fin:
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scripts = fin.read()
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scripts = pd.Series(scripts.split('\n'))
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scripts = fin.read().split('\n')
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i = 0
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missing_words = []
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fscp = open(hcompv_scp2, 'wt')
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fmlf = open(mlf_word, "wt", encoding="utf-8")
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fmlf.write("#!MLF!#\n")
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feature_nr = 1
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for feature in features:
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sys.stdout.write("\r%d/%d" % (feature_nr, len(features)))
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sys.stdout.flush()
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feature_nr += 1
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file_basename = os.path.basename(feature).replace('.mfc', '')
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for line in scripts:
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#for line in ['sp0035m_train_1975_fragmentenvraaggesprekkenruilverkaveling_15413 en dat kan men nog meer']:
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# sample line:
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# sp0457m_test_1968_plakkenfryslanterhorne_2168 en dan begjinne je natuerlik
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filename_ = line.split(' ')[0]
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filename = '_'.join(filename_.split('_')[1:])
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sentence = ' '.join(line.split(' ')[1:])
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# get words from scripts.
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wav_file = os.path.join(wav_dir, filename + '.wav')
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if len(re.findall(r'[\w]+[âêûô\'ú]+[\w]+', sentence))==0:
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try:
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script = scripts[scripts.str.contains(file_basename)]
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except IndexError:
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script = []
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sentence_ascii = bytes(sentence, 'ascii')
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except UnicodeEncodeError:
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print(sentence)
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#if os.path.exists(wav_file):
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# #dictionary_file = os.path.join(wav_dir, filename + '.dic')
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# if pyhtk.create_dictionary_without_log(
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# sentence, global_ded, dictionary_file, lexicon_htk) == 0:
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# # when the file name is too long, HDMan command does not work.
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# # therefore first temporary dictionary_file is made, then renamed.
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# shutil.move(dictionary_file, os.path.join(wav_dir, filename + '.dic'))
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# label_file = os.path.join(wav_dir, filename + '.lab')
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# pyhtk.create_label_file(sentence, label_file)
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# else:
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# os.remove(dictionary_file)
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print("elapsed time: {}".format(time.time() - timer_start))
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# lexicon
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#lexicon_htk = pd.read_table(lex_htk, names=['word', 'pronunciation'])
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if len(script) != 0:
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script_id = script.index[0]
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script_txt = script.get(script_id)
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script_words = script_txt.split(' ')
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del script_words[0]
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# list of features
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#with open(hcompv_scp) as fin:
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# features = fin.read()
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# features = features.split('\n')
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#i = 0
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#missing_words = []
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#fscp = open(hcompv_scp2, 'wt')
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#fmlf = open(mlf_word, "wt", encoding="utf-8")
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#fmlf.write("#!MLF!#\n")
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#feature_nr = 1
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#for feature in features:
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# sys.stdout.write("\r%d/%d" % (feature_nr, len(features)))
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# sys.stdout.flush()
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# feature_nr += 1
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# file_basename = os.path.basename(feature).replace('.mfc', '')
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# # get words from scripts.
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# try:
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# script = scripts[scripts.str.contains(file_basename)]
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# except IndexError:
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# script = []
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# if len(script) != 0:
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# script_id = script.index[0]
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# script_txt = script.get(script_id)
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# script_words = script_txt.split(' ')
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# del script_words[0]
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# check if all words can be found in the lexicon.
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SCRIPT_WORDS = []
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script_prons = []
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is_in_lexicon = 1
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for word in script_words:
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WORD = word.upper()
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SCRIPT_WORDS.append(WORD)
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extracted = lexicon_htk[lexicon_htk['word']==WORD]
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if len(extracted) == 0:
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missing_words.append(word)
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script_prons.append(extracted)
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is_in_lexicon *= len(extracted)
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# SCRIPT_WORDS = []
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# script_prons = []
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# is_in_lexicon = 1
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# for word in script_words:
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# WORD = word.upper()
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# SCRIPT_WORDS.append(WORD)
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# extracted = lexicon_htk[lexicon_htk['word']==WORD]
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# if len(extracted) == 0:
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# missing_words.append(word)
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# script_prons.append(extracted)
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# is_in_lexicon *= len(extracted)
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# if all pronunciations are found in the lexicon, update scp and mlf files.
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if is_in_lexicon:
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# if is_in_lexicon:
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# add the feature filename into the .scp file.
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fscp.write("{}\n".format(feature))
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i += 1
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# fscp.write("{}\n".format(feature))
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# i += 1
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# add the words to the mlf file.
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fmlf.write('\"*/{}.lab\"\n'.format(file_basename))
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# fmlf.write('\"*/{}.lab\"\n'.format(file_basename))
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#fmlf.write('{}'.format('\n'.join(SCRIPT_WORDS)))
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for word_ in SCRIPT_WORDS:
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if word_[0] == '\'':
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word_ = '\\' + word_
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fmlf.write('{}\n'.format(word_))
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fmlf.write('.\n')
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print("\n{0} has {1} samples.\n".format(dataset, i))
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np.save(output_dir + '\\missing_words' + '_' + dataset + '.npy', missing_words)
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# for word_ in SCRIPT_WORDS:
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# if word_[0] == '\'':
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# word_ = '\\' + word_
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# fmlf.write('{}\n'.format(word_))
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# fmlf.write('.\n')
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# print("\n{0} has {1} samples.\n".format(dataset, i))
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# np.save(output_dir + '\\missing_words' + '_' + dataset + '.npy', missing_words)
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fscp.close()
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fmlf.close()
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# fscp.close()
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# fmlf.close()
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## generate phone level transcription
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print("generating phone level transcription...\n")
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mkphones = output_dir + '\\label\\mkphones0.txt'
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subprocessStr = r"HLEd -l * -d " + lex_htk_ + ' -i ' + mlf_phone + ' ' + mkphones + ' ' + mlf_word
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subprocess.call(subprocessStr, shell=True)
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# print("generating phone level transcription...\n")
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# mkphones = output_dir + '\\label\\mkphones0.txt'
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# subprocessStr = r"HLEd -l * -d " + lex_htk_ + ' -i ' + mlf_phone + ' ' + mkphones + ' ' + mlf_word
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# subprocess.call(subprocessStr, shell=True)
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## ======================= combined scps and mlfs =======================
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@ -3,6 +3,7 @@ import os
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os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
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from collections import Counter
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import time
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import re
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import numpy as np
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import pandas as pd
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@ -82,22 +83,52 @@ np.save(os.path.join('phoneset', 'fame_ipa2asr.npy'), translation_key_ipa2asr)
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## check if all the phones in lexicon.htk are in fame_asr.py.
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#timer_start = time.time()
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#phoneset_htk = fame_asr.phoneset_htk
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#phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_htk)
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#phoneset_lex.remove('')
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#print("phones which is in lexicon.htk but not in the fame_asr.py are:\n{}".format(
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# set(phoneset_htk) - set(phoneset_lex)))
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#print("elapsed time: {}".format(time.time() - timer_start))
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## statistics over the lexicon
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#lex_htk = fame_functions.load_lexicon(lexicon_htk)
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#phones_all = (' '.join(lex_htk['pronunciation'])).split(' ')
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#c = Counter(phones_all)
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#lexicon_out = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\lexicon\lex.htk2'
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#for i in lex_htk[lex_htk['word'].str.startswith('\'')].index.values:
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# lex_htk.iat[i, 0] = lex_htk.iat[i, 0].replace('\'', '\\\'')
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## to_csv does not work with space seperator. therefore all tabs should manually be replaced.
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##lex_htk.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
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#lex_htk.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
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## check which letters are not coded in ascii.
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print('asr phones which cannot be coded in ascii:\n')
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for i in fame_asr.phoneset_short:
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try:
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i_encoded = i.encode("ascii")
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#print("{0} --> {1}".format(i, i.encode("ascii")))
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except UnicodeEncodeError:
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print(">>> {}".format(i))
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print("letters in the scripts which is not coded in ascii:\n")
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for dataset in ['train', 'devel', 'test']:
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timer_start = time.time()
|
||||
phoneset_htk = fame_asr.phoneset_htk
|
||||
phoneset_lex = fame_functions.get_phoneset_from_lexicon(lexicon_htk)
|
||||
phoneset_lex.remove('')
|
||||
print("phones which is in lexicon.htk but not in the fame_asr.py are:\n{}".format(
|
||||
set(phoneset_htk) - set(phoneset_lex)))
|
||||
print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
# statistics over the lexicon
|
||||
lex_htk = fame_functions.load_lexicon(lexicon_htk)
|
||||
phones_all = (' '.join(lex_htk['pronunciation'])).split(' ')
|
||||
c = Counter(phones_all)
|
||||
script_list = os.path.join(default.fame_dir, 'data', dataset, 'text')
|
||||
with open(script_list, "rt", encoding="utf-8") as fin:
|
||||
scripts = fin.read().split('\n')
|
||||
|
||||
for line in scripts:
|
||||
sentence = ' '.join(line.split(' ')[1:])
|
||||
sentence_htk = fame_functions.word2htk(sentence)
|
||||
|
||||
#if len(re.findall(r'[âêôûč\'àéèúćäëïöü]', sentence))==0:
|
||||
try:
|
||||
sentence_htk = bytes(sentence_htk, 'ascii')
|
||||
except UnicodeEncodeError:
|
||||
print(sentence)
|
||||
print(sentence_htk)
|
||||
|
||||
lexicon_out = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\lexicon\lex.htk2'
|
||||
for i in lex_htk[lex_htk['word'].str.startswith('\'')].index.values:
|
||||
lex_htk.iat[i, 0] = lex_htk.iat[i, 0].replace('\'', '\\\'')
|
||||
# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
|
||||
#lex_htk.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
|
||||
lex_htk.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
|
||||
|
||||
@ -103,12 +103,22 @@ translation_key_asr2htk = {
|
||||
}
|
||||
phoneset_htk = [translation_key_asr2htk.get(i, i) for i in phoneset_short]
|
||||
|
||||
## check
|
||||
#for i in phoneset_short:
|
||||
# try:
|
||||
# print("{0} --> {1}".format(i, i.encode("ascii")))
|
||||
# except UnicodeEncodeError:
|
||||
# print(">>> {}".format(i))
|
||||
#not_in_ascii = [
|
||||
# '\'',
|
||||
# 'â', 'ê', 'ô', 'û', 'č',
|
||||
# 'à', 'í', 'é', 'è', 'ú', 'ć',
|
||||
# 'ä', 'ë', 'ï', 'ö', 'ü'
|
||||
#]
|
||||
translation_key_word2htk = {
|
||||
'\'': '\\\'',
|
||||
'í':'i1', 'é':'e1', 'ú':'u1', 'ć':'c1',
|
||||
'à':'a2', 'è':'e2',
|
||||
'â':'a3', 'ê':'e3', 'ô':'o3', 'û':'u3',
|
||||
'č':'c4',
|
||||
'ä': 'ao', 'ë': 'ee', 'ï': 'ie', 'ö': 'oe', 'ü': 'ue',
|
||||
}
|
||||
#[translation_key_word2htk.get(i, i) for i in not_in_ascii]
|
||||
|
||||
|
||||
|
||||
## the list of multi character phones.
|
||||
|
||||
0
acoustic_model/test.txt
Normal file
0
acoustic_model/test.txt
Normal file
Loading…
Reference in New Issue
Block a user