test on stimmen data is added.
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yemaozi88
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c185072d5b
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@ -4,7 +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>htk_vs_kaldi.py</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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@ -12,6 +12,10 @@ import defaultfiles as default
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import convert_phoneset
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from phoneset import fame_ipa, fame_asr
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sys.path.append(default.toolbox_dir)
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from htk import pyhtk
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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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@ -371,4 +375,25 @@ def ipa2htk(ipa):
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asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
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asr_splitted = fame_asr.phone_reduction(asr_splitted)
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htk_splitted = convert_phoneset.convert_phoneset(asr_splitted, fame_asr.translation_key_asr2htk)
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return ''.join(htk_splitted)
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return ''.join(htk_splitted)
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def performance_on_stimmen(stimmen_dir, hmmdefs):
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#hmmdefs = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\model_\hmm1\iter20\hmmdefs'
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#stimmen_dir = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\stimmen'
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lattice_file = os.path.join(stimmen_dir, 'word_lattice.ltc')
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hvite_scp = os.path.join(stimmen_dir, 'hvite.scp')
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#fh.make_filelist(os.path.join(stimmen_dir, 'mfc'), hvite_scp, file_type='mfc')
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hresult_scp = os.path.join(stimmen_dir, 'hresult.scp')
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#fh.make_filelist(os.path.join(stimmen_dir, 'mfc'), hresult_scp, file_type='rec')
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lexicon_file = os.path.join(stimmen_dir, 'lexicon_recognition.dic')
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chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_file)
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result = chtk.recognition(
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lattice_file,
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hmmdefs,
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hvite_scp
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)
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per_sentence, per_word = chtk.calc_recognition_performance(hresult_scp)
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return per_sentence['accuracy']
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@ -22,30 +22,27 @@ from htk import pyhtk
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# procedure
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make_lexicon = 0
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make_label = 0 # it takes roughly 4800 sec on Surface pro 2.
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make_htk_files = 0
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make_mlf = 0
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extract_features = 0
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flat_start = 0
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train_model_without_sp = 0
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add_sp = 0
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train_model_with_sp = 0
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train_model_with_sp_align_mlf = 1
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train_model_with_sp_align_mlf = 0
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train_triphone = 0
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# pre-defined values.
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dataset_list = ['devel', 'test', 'train']
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hmmdefs_name = 'hmmdefs'
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proto_name = 'proto39'
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proto_name = 'proto'
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lexicon_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
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lexicon_oov = os.path.join(default.fame_dir, 'lexicon', 'lex.oov')
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config_dir = os.path.join(default.htk_dir, 'config')
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config_hcopy = os.path.join(config_dir, 'config.HCopy')
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config_train = os.path.join(config_dir, 'config.train')
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global_ded = os.path.join(config_dir, 'global.ded')
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mkphones_led = os.path.join(config_dir, 'mkphones.led')
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sil_hed = os.path.join(config_dir, 'sil.hed')
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prototype = os.path.join(config_dir, proto_name)
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@ -53,25 +50,20 @@ model_dir = os.path.join(default.htk_dir, 'model')
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# directories / files to be made.
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lexicon_dir = os.path.join(default.htk_dir, 'lexicon')
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lexicon_htk_asr = os.path.join(lexicon_dir, 'lex.htk_asr')
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lexicon_htk_oov = os.path.join(lexicon_dir, 'lex.htk_oov')
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lexicon_htk = os.path.join(lexicon_dir, 'lex.htk')
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phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
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model0_dir = os.path.join(model_dir, 'hmm0')
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model1_dir = os.path.join(model_dir, 'hmm1')
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#model1_dir = os.path.join(model_dir, 'hmm1')
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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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fh.make_new_directory(feature_dir, existing_dir='leave')
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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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fh.make_new_directory(tmp_dir, existing_dir='leave')
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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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fh.make_new_directory(label_dir, existing_dir='leave')
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## training
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hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
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@ -98,20 +90,21 @@ if make_lexicon:
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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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## fixing the lexicon for HTK.
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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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# http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
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print('>>> fixing the lexicon...')
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fame_functions.fix_lexicon(lexicon_htk)
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print("elapsed time: {}".format(time.time() - timer_start))
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## intialize the instance for HTK.
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chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_htk)
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## ======================= make label files =======================
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if make_label:
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# train_2002_gongfansaken_10347.lab is empty. should be removed.
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for dataset in dataset_list:
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timer_start = time.time()
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print("==== making label files on dataset {}".format(dataset))
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@ -120,7 +113,7 @@ if make_label:
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wav_dir_ = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
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label_dir_ = os.path.join(label_dir, dataset)
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dictionary_file = os.path.join(label_dir_, 'temp.dic')
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fh.make_new_directory(label_dir_)
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fh.make_new_directory(label_dir_, existing_dir='leave')
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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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@ -135,56 +128,48 @@ if make_label:
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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) and pyhtk.can_be_ascii(sentence_htk) == 0:
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if pyhtk.create_dictionary_without_log(
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sentence_htk, global_ded, dictionary_file, lexicon_htk) == 0:
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if os.path.exists(wav_file) and chtk.can_be_ascii(sentence_htk) == 0:
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if chtk.get_number_of_missing_words(
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sentence_htk, dictionary_file) == 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(label_dir_, filename + '.dic'))
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label_file = os.path.join(label_dir_, filename + '.lab')
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pyhtk.create_label_file(sentence_htk, label_file)
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chtk.create_label_file(sentence_htk, 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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## ======================= make other required files =======================
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if make_htk_files:
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## ======================= make master label files =======================
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if make_mlf:
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timer_start = time.time()
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print("==== making files required for HTK ====")
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print("==== making master label files ====")
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print(">>> making a phonelist...")
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pyhtk.create_phonelist_file(fame_asr.phoneset_htk, phonelist_txt)
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# train_2002_gongfansaken_10347.lab is empty. should be removed.
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empty_lab_file = os.path.join(label_dir, 'train', 'train_2002_gongfansaken_10347.lab')
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empty_dic_file = empty_lab_file.replace('.lab', '.dic')
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if os.path.exists(empty_lab_file):
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os.remove(empty_lab_file)
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if os.path.exists(empty_dic_file):
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os.remove(empty_dic_file)
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for dataset in dataset_list:
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wav_dir_ = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
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#wav_dir_ = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
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feature_dir_ = os.path.join(feature_dir, dataset)
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label_dir_ = os.path.join(label_dir, dataset)
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mlf_word = os.path.join(label_dir, dataset + '_word.mlf')
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mlf_phone = os.path.join(label_dir, dataset + '_phone.mlf')
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#print(">>> making a script file for {}...".format(dataset))
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#listdir = glob.glob(os.path.join(wav_dir_, '*.dic'))
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#mfc_list = [filename.replace(wav_dir_, feature_dir_).replace('.dic', '.mfc') 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, 'wb') as f:
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# f.write(bytes('\n'.join(mfc_list) + '\n', 'ascii'))
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print(">>> making a mlf file for {}...".format(dataset))
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lab_list = glob.glob(os.path.join(label_dir_, '*.lab'))
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with open(mlf_word, 'wb') as fmlf:
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fmlf.write(bytes('#!MLF!#\n', 'ascii'))
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for label_file in lab_list:
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filename = os.path.basename(label_file)
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fmlf.write(bytes('\"*/{}\"\n'.format(filename), 'ascii'))
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with open(label_file) as flab:
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lines = flab.read()
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fmlf.write(bytes(lines + '.\n', 'ascii'))
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print(">>> generating phone level transcription for {}...".format(dataset))
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pyhtk.mlf_word2phone(lexicon_htk, mlf_phone, mlf_word, mkphones_led)
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print("elapsed time: {}".format(time.time() - timer_start))
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print(">>> generating a word level mlf file for {}...".format(dataset))
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chtk.label2mlf(label_dir_, mlf_word)
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print(">>> generating a phone level mlf file for {}...".format(dataset))
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chtk.mlf_word2phone(mlf_phone, mlf_word)
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print("elapsed time: {}".format(time.time() - timer_start))
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## ======================= extract features =======================
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@ -196,7 +181,7 @@ if extract_features:
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wav_dir_ = os.path.join(default.fame_dir, 'fame', 'wav', dataset)
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label_dir_ = os.path.join(label_dir, dataset)
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feature_dir_ = os.path.join(feature_dir, dataset)
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fh.make_new_directory(feature_dir_)
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fh.make_new_directory(feature_dir_, existing_dir='delete')
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# a script file for HCopy
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print(">>> making a script file for HCopy...")
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@ -212,12 +197,15 @@ if extract_features:
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os.path.join(wav_dir_, os.path.basename(lab_file).replace('.lab', '.wav')) + '\t'
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+ os.path.join(feature_dir_, os.path.basename(lab_file).replace('.lab', '.mfc'))
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for lab_file in lab_list]
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if os.path.exists(empty_mfc_file):
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os.remove(empty_mfc_file)
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with open(hcopy_scp.name, 'wb') as f:
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f.write(bytes('\n'.join(feature_list), 'ascii'))
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# extract features.
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print(">>> extracting features on {}...".format(dataset))
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pyhtk.wav2mfc(config_hcopy, hcopy_scp.name)
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chtk.wav2mfc(hcopy_scp.name)
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os.remove(hcopy_scp.name)
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# make hcompv.scp.
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@ -235,21 +223,18 @@ if extract_features:
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if flat_start:
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timer_start = time.time()
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print('==== flat start ====')
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pyhtk.flat_start(config_train, hcompv_scp_train, model0_dir, prototype)
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feature_size = 39
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model0_dir = os.path.join(model_dir, 'hmm0')
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fh.make_new_directory(model0_dir, existing_dir='leave')
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chtk.flat_start(hcompv_scp_train, model0_dir, feature_size)
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# allocate mean & variance to all phones in the phone list
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print('>>> allocating mean & variance to all phones in the phone list...')
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pyhtk.create_hmmdefs(
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chtk.create_hmmdefs(
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os.path.join(model0_dir, proto_name),
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os.path.join(model0_dir, 'hmmdefs'),
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phonelist_txt)
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# make macros
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print('>>> making macros...')
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with open(os.path.join(model0_dir, 'vFloors')) as f:
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lines = f.read()
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with open(os.path.join(model0_dir, 'macros'), 'wb') as f:
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f.write(bytes('~o <MFCC_0_D_A> <VecSize> 39\n' + lines, 'ascii'))
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os.path.join(model0_dir, 'hmmdefs')
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)
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print("elapsed time: {}".format(time.time() - timer_start))
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@ -362,4 +347,24 @@ if train_model_with_sp_align_mlf:
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hcompv_scp_train, phonelist_txt,
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mlf_file=mlf_file_train_aligned,
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macros=os.path.join(modeln_dir_pre, 'macros'))
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print("elapsed time: {}".format(time.time() - timer_start))
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print("elapsed time: {}".format(time.time() - timer_start))
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# train triphone.
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if train_triphone:
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triphone_mlf = os.path.join(default.htk_dir, 'label', 'train_triphone.mlf')
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macros = os.path.join(model_dir, 'hmm1_tri', 'iter0', 'macros')
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hmmdefs = os.path.join(model_dir, 'hmm1_tri', 'iter0', 'hmmdefs')
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model_out_dir = os.path.join(model_dir, 'hmm1_tri', 'iter1')
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run_command([
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'HERest', '-B',
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'-C', config_train,
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'-I', triphone_mlf,
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'-t', '250.0', '150.0', '1000.0',
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'-s', 'stats'
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'-S', hcompv_scp_train,
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'-H', macros,
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'-H', hmmdefs,
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'-M', model_out_dir,
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os.path.join(config_dir, 'triphonelist.txt')
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])
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@ -53,7 +53,7 @@ from htk import pyhtk
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# procedure
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make_dic_file = 0
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make_HTK_files = 1
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make_HTK_files = 0
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extract_features = 0
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#make_htk_dict_files = 0
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#do_forced_alignment_htk = 0
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@ -171,7 +171,7 @@ if make_HTK_files:
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filename = row['filename'].replace('.wav', '.lab')
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label_file = os.path.join(feature_dir, filename)
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with open(label_file, 'wb') as f:
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label_string = 'START\n' + row['word'].upper() + '\nEND\n'
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label_string = 'SILENCE\n' + row['word'].upper() + '\nSILENCE\n'
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f.write(bytes(label_string, 'ascii'))
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@ -249,7 +249,7 @@ with open(hresult_scp, 'wb') as f:
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# calculate result
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performance = np.zeros((1, 2))
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for niter in range(1, 50):
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for niter in range(50, 60):
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output = pyhtk.recognition(
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os.path.join(config_dir, 'config.rec'),
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lattice_file,
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@ -265,6 +265,16 @@ for niter in range(1, 50):
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#output = run_command_with_output([
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# 'HVite', '-T', '1',
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# '-C', config_rec,
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# '-w', lattice_file,
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# '-H', hmm,
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# dictionary_file, phonelist_txt,
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# '-S', HVite_scp
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#])
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## ======================= forced alignment using HTK =======================
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if do_forced_alignment_htk:
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@ -128,7 +128,11 @@ translation_key_word2htk = {
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'ä': 'ao', 'ë': 'ee', 'ï': 'ie', 'ö': 'oe', 'ü': 'ue',
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}
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#[translation_key_word2htk.get(i, i) for i in not_in_ascii]
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#Stop: p, b, t, d, k, g
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#Nasal: m, n, ng(ŋ)
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#Fricative: s, z, f, v, h, x
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#Liquid: l, r
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#Vowel: a, a:, e:, i, i:, i_(i̯), o, o:, u, u:, u_(ṷ), oe(ö), oe:(ö:), ue(ü), ue:(ü:), O(ɔ), O:(ɔ:), Oe(ɔ̈), A(ə), E(ɛ), E:(ɛ:), I(ɪ), I:(ɪ:)
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## the list of multi character phones.
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@ -77,4 +77,17 @@ for word in word_list:
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for key, value in zip(c.keys(), c.values()):
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if value > 3:
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pronunciations[key] = value
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print(pronunciations)
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print(pronunciations)
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monophone_mlf = os.path.join(default.htk_dir, 'label', 'train_phone_aligned.mlf')
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triphone_mlf = os.path.join(default.htk_dir, 'label', 'train_triphone.mlf')
|
||||
def filenames_in_mlf(file_mlf):
|
||||
with open(file_mlf) as f:
|
||||
lines_ = f.read().split('\n')
|
||||
lines = [line for line in lines_ if len(line.split(' ')) == 1 and line != '.']
|
||||
filenames = [line.replace('"', '').replace('*/', '') for line in lines[1:-1]]
|
||||
return filenames
|
||||
filenames_mono = filenames_in_mlf(monophone_mlf)
|
||||
filenames_tri = filenames_in_mlf(triphone_mlf)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user