328 lines
11 KiB
Python
328 lines
11 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 tempfile
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import shutil
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import glob
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#import configparser
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#import subprocess
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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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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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from htk import pyhtk
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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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#config_ini = curr_dir + '\\config.ini'
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#output_dir = 'C:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model'
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#forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
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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_dictionary = 0 # 4800 sec
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make_htk_files = 1
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combine_files = 0
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flat_start = 0
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train_model = 0
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## ======================= load variables =======================
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lexicon_dir = os.path.join(default.fame_dir, 'lexicon')
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lexicon_asr = os.path.join(lexicon_dir, 'lex.asr')
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lexicon_oov = os.path.join(lexicon_dir, 'lex.oov')
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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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#model_dir = output_dir + '\\model'
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#model0_dir = model_dir + '\\hmm0'
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#proto_init = model_dir + '\\proto38'
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#proto_name = 'proto'
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#phonelist = output_dir + '\\config\\phonelist_friesian.txt'
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#hmmdefs_name = 'hmmdefs'
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feature_dir = os.path.join(default.htk_dir, 'mfc')
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if not os.path.exists(feature_dir):
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os.makedirs(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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for dataset in dataset_list:
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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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hcopy_scp = tempfile.NamedTemporaryFile(mode='w', delete=False)
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hcopy_scp.close()
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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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# 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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pyhtk.wav2mfc(default.config_hcopy, hcopy_scp.name)
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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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print('==== make lexicon for HTK ====\n')
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# convert each lexicon from fame_asr phoneset to fame_htk phoneset.
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print('>>> converting each lexicon from fame_asr phoneset to fame_htk phoneset... \n')
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fame_functions.lexicon_asr2htk(lexicon_asr, lexicon_htk_asr)
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fame_functions.lexicon_asr2htk(lexicon_oov, lexicon_htk_oov)
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# combine lexicon
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print('>>> combining lexicon files into one lexicon... \n')
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# pronunciations which is not found in lex.asr are generated using G2P and listed in lex.oov.
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# therefore there is no overlap between lex_asr and lex_oov.
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fame_functions.combine_lexicon(lexicon_htk_asr, lexicon_htk_oov, lexicon_htk)
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## =======================
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## manually make changes to the pronunciation dictionary and save it as lex.htk
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## =======================
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# (1) Replace all tabs with single space;
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# (2) Put a '\' before any dictionary entry beginning with single quote
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#http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
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fame_functions.fix_single_quote(lexicon_htk)
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## ======================= make dic files =======================
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if make_dictionary:
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for dataset in dataset_list:
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timer_start = time.time()
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print("==== generating HTK dictionary files on dataset {}\n".format(dataset))
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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().split('\n')
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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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sentence_htk = fame_functions.word2htk(sentence)
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wav_file = os.path.join(wav_dir, filename + '.wav')
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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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# 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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# 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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# 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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# add the words to the mlf file.
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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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# fscp.close()
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# fmlf.close()
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## ======================= make other required files =======================
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if make_htk_files:
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## 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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## hcomp_v.scp
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print(">>> making a script file for HCompV... \n")
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for dataset in dataset_list:
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#timer_start = time.time()
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wav_dir = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
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listdir = glob.glob(os.path.join(wav_dir, '*.dic'))
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filelist = [filename.replace(wav_dir, feature_dir).replace('.dic', '.fea') for filename in listdir]
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hcompv_scp = os.path.join(tmp_dir, dataset + '.scp')
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with open(hcompv_scp, 'wt', newline='\r\n') as f:
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f.write('\n'.join(filelist))
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## hcomp_scp
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# a script file for HCompV
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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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#if combine_files:
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# print("==== combine scps and mlfs ====\n")
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# fscp = open(hcompv_scp, 'wt')
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# fmlf = open(combined_mlf, 'wt')
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# for dataset in dataset_list:
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# fmlf.write("#!MLF!#\n")
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# for dataset in dataset_list:
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# each_mlf = output_dir + '\\label\\' + dataset + '_phone.mlf'
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# each_scp = output_dir + '\\scp\\' + dataset + '_all_words_in_lexicon.scp'
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# with open(each_mlf, 'r') as fin:
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# lines = fin.read()
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# lines = lines.split('\n')
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# fmlf.write('\n'.join(lines[1:]))
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# with open(each_scp, 'r') as fin:
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# lines = fin.read()
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# fscp.write(lines)
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# fscp.close()
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# fmlf.close()
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## ======================= flat start monophones =======================
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if flat_start:
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subprocessStr = 'HCompV -T 1 -C ' + config_train + ' -m -v 0.01 -S ' + hcompv_scp + ' -M ' + model0_dir + ' ' + proto_init
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subprocess.call(subprocessStr, shell=True)
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# allocate mean & variance to all phones in the phone list
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subprocessStr = 'perl ' + mkhmmdefs_pl + ' ' + model0_dir + '\\proto38' + ' ' + phonelist + ' > ' + model0_dir + '\\' + hmmdefs_name
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subprocess.call(subprocessStr, shell=True)
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## ======================= estimate monophones =======================
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if train_model:
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iter_num_max = 3
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for mix_num in [128, 256, 512, 1024]:
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for iter_num in range(1, iter_num_max+1):
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print("===== mix{}, iter{} =====".format(mix_num, iter_num))
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iter_num_pre = iter_num - 1
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modelN_dir = model_dir + '\\hmm' + str(mix_num) + '-' + str(iter_num)
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if not os.path.exists(modelN_dir):
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os.makedirs(modelN_dir)
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if iter_num == 1 and mix_num == 1:
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modelN_dir_pre = model0_dir
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else:
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modelN_dir_pre = model_dir + '\\hmm' + str(mix_num) + '-' + str(iter_num_pre)
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## re-estimation
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subprocessStr = 'HERest -T 1 -C ' + config_train + ' -v 0.01 -I ' + combined_mlf + ' -H ' + modelN_dir_pre + '\\' + hmmdefs_name + ' -M ' + modelN_dir + ' ' + phonelist + ' -S ' + hcompv_scp
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subprocess.call(subprocessStr, shell=True)
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mix_num_next = mix_num * 2
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modelN_dir_next = model_dir + '\\hmm' + str(mix_num_next) + '-0'
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if not os.path.exists(modelN_dir_next):
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os.makedirs(modelN_dir_next)
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header_file = modelN_dir + '\\mix' + str(mix_num_next) + '.hed'
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with open(header_file, 'w') as fout:
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fout.write("MU %d {*.state[2-4].mix}" % (mix_num_next))
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subprocessStr = 'HHEd -T 1 -H ' + modelN_dir + '\\' + hmmdefs_name + ' -M ' + modelN_dir_next + ' ' + header_file + ' ' + phonelist
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subprocess.call(subprocessStr, shell=True)
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