novoapi_functions.py is adjusted to use convert_phoneset.py.
This commit is contained in:
yemaozi88
@ -16,50 +16,53 @@ 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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#from scripts import run_command
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## ======================= user define =======================
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# procedure
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combine_all = 1
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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_mlf = 0
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extract_features = 0
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flat_start = 0
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train_monophone_without_sp = 0
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add_sp = 0
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train_monophone_with_re_aligned_mlf = 0
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flat_start = 1
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train_monophone_without_sp = 1
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add_sp = 1
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train_monophone_with_re_aligned_mlf = 1
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increase_mixture = 1
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train_triphone = 0
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train_triphone_tied = 1
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train_triphone_tied = 0
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# pre-defined values.
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dataset_list = ['devel', 'test', 'train']
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feature_size = 39
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feature_size = 30
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improvement_threshold = 0.3
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hmmdefs_name = 'hmmdefs'
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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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phonelist_full_txt = os.path.join(config_dir, 'phonelist_full.txt')
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tree_hed = os.path.join(config_dir, 'tree.hed')
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quest_hed = os.path.join(config_dir, 'quests.hed')
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tree_hed = os.path.join(config_dir, 'tree.hed')
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quests_hed = os.path.join(config_dir, 'quests.hed')
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model_dir = os.path.join(default.htk_dir, 'model')
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model_mono0_dir = os.path.join(model_dir, 'mono0')
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model_mono1_dir = os.path.join(model_dir, 'mono1')
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model_mono1sp_dir = os.path.join(model_dir, 'mono1sp')
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model_mono1sp2_dir = os.path.join(model_dir, 'mono1sp2')
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model_tri1_dir = os.path.join(model_dir, 'tri1')
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model_tri1_dir = os.path.join(model_dir, 'tri1')
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model_tri1tied_dir = os.path.join(model_dir, 'tri1tied')
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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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lexicon_htk_with_sp = os.path.join(lexicon_dir, 'lex_with_sp.htk')
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lexicon_htk_triphone = os.path.join(lexicon_dir, 'lex_triphone.htk')
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feature_dir = os.path.join(default.htk_dir, 'mfc')
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@ -71,10 +74,20 @@ 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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mlf_file_train = os.path.join(label_dir, 'train_phone.mlf')
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mlf_file_train_with_sp = os.path.join(label_dir, 'train_phone_with_sp.mlf')
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mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
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if combine_all:
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hcompv_scp_train = os.path.join(tmp_dir, 'all.scp')
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mlf_file_train = os.path.join(label_dir, 'all_phone.mlf')
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mlf_file_train_word = os.path.join(label_dir, 'all_word.mlf')
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mlf_file_train_with_sp = os.path.join(label_dir, 'all_phone_with_sp.mlf')
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mlf_file_train_aligned = os.path.join(label_dir, 'all_phone_aligned.mlf')
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triphone_mlf = os.path.join(label_dir, 'all_triphone.mlf')
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else:
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hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
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mlf_file_train = os.path.join(label_dir, 'train_phone.mlf')
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mlf_file_train_word = os.path.join(label_dir, 'train_word.mlf')
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mlf_file_train_with_sp = os.path.join(label_dir, 'train_phone_with_sp.mlf')
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mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
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triphone_mlf = os.path.join(label_dir, 'train_triphone.mlf')
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hcompv_scp_train_updated = hcompv_scp_train.replace('.scp', '_updated.scp')
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## testing
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@ -104,19 +117,18 @@ if make_lexicon:
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print('>>> fixing the lexicon...')
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fame_functions.fix_lexicon(lexicon_htk)
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## add sp to the end of each line.
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#print('>>> adding sp...')
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#with open(lexicon_htk) as f:
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# lines = f.read().split('\n')
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#lines = [line + ' sp' for line in lines]
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#with open(lexicon_htk_with_sp, 'wb') as f:
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# f.write(bytes('\n'.join(lines), 'ascii'))
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## adding sp to the lexicon for HTK.
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print('>>> adding sp to the lexicon...')
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with open(lexicon_htk) as f:
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lines = f.read().split('\n')
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with open(lexicon_htk_with_sp, 'wb') as f:
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f.write(bytes(' sp\n'.join(lines), 'ascii'))
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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, feature_size)
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chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_htk_with_sp, feature_size)
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## ======================= make label files =======================
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@ -152,7 +164,7 @@ if make_label:
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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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chtk.create_label_file(sentence_htk, label_file)
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chtk.make_label_file(sentence_htk, label_file)
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else:
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os.remove(dictionary_file)
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@ -174,7 +186,6 @@ if make_mlf:
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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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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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@ -183,11 +194,11 @@ if make_mlf:
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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, with_sp=False)
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chtk.mlf_word2phone(mlf_phone_with_sp, mlf_word, with_sp=True)
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print("elapsed time: {}".format(time.time() - timer_start))
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@ -197,7 +208,7 @@ if extract_features:
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timer_start = time.time()
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print('==== extract features on dataset {} ===='.format(dataset))
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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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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_, existing_dir='delete')
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@ -217,8 +228,8 @@ if extract_features:
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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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#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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@ -235,9 +246,64 @@ if extract_features:
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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(">>> extracting features on stimmen...")
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chtk.wav2mfc(os.path.join(htk_stimmen_dir, 'hcopy.scp'))
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print("elapsed time: {}".format(time.time() - timer_start))
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## ======================= flat start monophones =======================
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if combine_all:
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# script files.
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fh.concatenate(
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os.path.join(tmp_dir, 'devel.scp'),
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os.path.join(tmp_dir, 'test.scp'),
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hcompv_scp_train
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)
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fh.concatenate(
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hcompv_scp_train,
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os.path.join(tmp_dir, 'train.scp'),
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hcompv_scp_train
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)
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# phone level mlfs.
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fh.concatenate(
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os.path.join(label_dir, 'devel_phone.mlf'),
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os.path.join(label_dir, 'test_phone.mlf'),
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mlf_file_train
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)
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fh.concatenate(
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mlf_file_train,
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os.path.join(label_dir, 'train_phone.mlf'),
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mlf_file_train
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)
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# phone level mlfs with sp.
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fh.concatenate(
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os.path.join(label_dir, 'devel_phone_with_sp.mlf'),
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os.path.join(label_dir, 'test_phone_with_sp.mlf'),
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mlf_file_train_with_sp
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)
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fh.concatenate(
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mlf_file_train_with_sp,
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os.path.join(label_dir, 'train_phone_with_sp.mlf'),
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mlf_file_train_with_sp
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)
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# word level mlfs.
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fh.concatenate(
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os.path.join(label_dir, 'devel_word.mlf'),
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os.path.join(label_dir, 'test_word.mlf'),
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mlf_file_train_word
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)
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fh.concatenate(
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mlf_file_train_word,
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os.path.join(label_dir, 'train_word.mlf'),
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mlf_file_train_word
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)
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## ======================= flat start monophones =======================
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if flat_start:
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timer_start = time.time()
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@ -246,17 +312,14 @@ if flat_start:
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chtk.flat_start(hcompv_scp_train, model_mono0_dir)
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# create macros.
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# make macros.
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vFloors = os.path.join(model_mono0_dir, 'vFloors')
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if os.path.exists(vFloors):
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chtk.create_macros(vFloors)
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chtk.make_macros(vFloors)
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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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chtk.create_hmmdefs(
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os.path.join(model_mono0_dir, proto_name),
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os.path.join(model_mono0_dir, 'hmmdefs')
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)
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chtk.make_hmmdefs(model_mono0_dir)
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print("elapsed time: {}".format(time.time() - timer_start))
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@ -320,8 +383,9 @@ if train_monophone_with_re_aligned_mlf:
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os.path.join(modeln_dir, 'macros'),
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os.path.join(modeln_dir, 'hmmdefs'),
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mlf_file_train_aligned,
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os.path.join(label_dir, 'train_word.mlf'),
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mlf_file_train_word,
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hcompv_scp_train)
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chtk.fix_mlf(mlf_file_train_aligned)
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print('>>> updating the script file... ')
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chtk.update_script_file(
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@ -349,24 +413,55 @@ if train_monophone_with_re_aligned_mlf:
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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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print('==== traina triphone model ====')
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## ======================= increase mixture =======================
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if increase_mixture:
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print('==== increase mixture ====')
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timer_start = time.time()
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for nmix in [2, 4, 8, 16]:
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if nmix == 2:
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modeln_dir_ = model_mono1sp2_dir
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else:
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modeln_dir_ = os.path.join(model_dir, 'mono'+str(nmix_))
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modeln_dir = os.path.join(model_dir, 'mono'+str(nmix))
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triphonelist_txt = os.path.join(config_dir, 'triphonelist.txt')
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triphone_mlf = os.path.join(default.htk_dir, 'label', 'train_triphone.mlf')
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print('mixture: {}'.format(nmix))
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fh.make_new_directory(modeln_dir, existing_dir='delete')
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niter = chtk.get_niter_max(modeln_dir_)
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chtk.increase_mixture(
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os.path.join(modeln_dir_, 'iter'+str(niter), 'hmmdefs'),
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nmix,
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os.path.join(modeln_dir, 'iter0'),
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model_type='monophone_with_sp')
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shutil.copy2(os.path.join(modeln_dir_, 'iter'+str(niter), 'macros'),
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os.path.join(modeln_dir, 'iter0', 'macros'))
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print('>>> making triphone list... ')
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chtk.make_triphonelist(
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triphonelist_txt,
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triphone_mlf,
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mlf_file_train_aligned)
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#improvement_threshold = -10
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niter = chtk.re_estimation_until_saturated(
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modeln_dir,
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os.path.join(modeln_dir_, 'iter0'),
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improvement_threshold,
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hcompv_scp_train_updated,
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os.path.join(htk_stimmen_dir, 'mfc'),
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'mfc',
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os.path.join(htk_stimmen_dir, 'word_lattice.ltc'),
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mlf_file=mlf_file_train_aligned,
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lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'),
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model_type='monophone_with_sp'
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)
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nmix_ = nmix
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print('>>> making triphone header... ')
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chtk.make_tri_hed(
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os.path.join(config_dir, 'mktri.hed')
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)
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print("elapsed time: {}".format(time.time() - timer_start))
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## ======================= train triphone =======================
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print('>>> making triphone list... ')
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chtk.make_triphonelist(
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mlf_file_train_aligned,
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triphone_mlf)
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if train_triphone:
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print('==== train triphone model ====')
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timer_start = time.time()
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print('>>> init triphone model... ')
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niter = chtk.get_niter_max(model_mono1sp2_dir)
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@ -377,8 +472,8 @@ if train_triphone:
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)
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print('>>> re-estimation... ')
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# I wanted to train until satulated:
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# #niter = chtk.re_estimation_until_saturated(
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## I wanted to train until satulated:
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#niter = chtk.re_estimation_until_saturated(
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# model_tri1_dir,
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# os.path.join(model_tri1_dir, 'iter0'),
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# improvement_threshold,
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@ -395,7 +490,6 @@ if train_triphone:
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# ERROR [+8231] GetHCIModel: Cannot find hmm [i:-]r[+???]
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# therefore only two times re-estimation is performed.
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output_dir = model_tri1_dir
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for niter in range(1, 4):
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hmm_n = 'iter' + str(niter)
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hmm_n_pre = 'iter' + str(niter-1)
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@ -414,18 +508,59 @@ if train_triphone:
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print("elapsed time: {}".format(time.time() - timer_start))
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## ======================= train triphone =======================
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## ======================= train tied-state triphones =======================
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if train_triphone_tied:
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print('==== traina tied-state triphone ====')
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print('==== train tied-state triphones ====')
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timer_start = time.time()
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print('>>> making lexicon for triphone... ')
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chtk.make_triphone_full(phonelist_full_txt, lexicon_htk_triphone)
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chtk.make_lexicon_triphone(phonelist_full_txt, lexicon_htk_triphone)
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chtk.combine_phonelists(phonelist_full_txt)
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print('>>> making headers... ')
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chtk.make_tree_header(tree_hed)
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fame_phonetics.make_quests_hed(quest_hed)
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print('>>> making a tree header... ')
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fame_phonetics.make_quests_hed(quests_hed)
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stats = os.path.join(r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\model\tri1\iter3', 'stats')
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chtk.make_tree_header(tree_hed, quests_hed, stats, config_dir)
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print("elapsed time: {}".format(time.time() - timer_start))
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print('>>> init triphone model... ')
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niter = chtk.get_niter_max(model_tri1_dir)
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fh.make_new_directory(os.path.join(model_tri1tied_dir, 'iter0'), existing_dir='leave')
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chtk.init_triphone(
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os.path.join(model_tri1_dir, 'iter'+str(niter)),
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os.path.join(model_tri1tied_dir, 'iter0'),
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tied=True)
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# I wanted to train until satulated:
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#niter = chtk.re_estimation_until_saturated(
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# model_tri1tied_dir,
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# os.path.join(model_tri1tied_dir, 'iter0'),
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# improvement_threshold,
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# hcompv_scp_train_updated,
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# os.path.join(htk_stimmen_dir, 'mfc'),
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# 'mfc',
|
||||
# os.path.join(htk_stimmen_dir, 'word_lattice.ltc'),
|
||||
# mlf_file=triphone_mlf,
|
||||
# lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'),
|
||||
# model_type='triphone'
|
||||
# )
|
||||
#
|
||||
# but because the data size is limited, some triphone cannot be trained and received the error:
|
||||
# ERROR [+8231] GetHCIModel: Cannot find hmm [i:-]r[+???]
|
||||
# therefore only 3 times re-estimation is performed.
|
||||
output_dir = model_tri1tied_dir
|
||||
for niter in range(1, 4):
|
||||
hmm_n = 'iter' + str(niter)
|
||||
hmm_n_pre = 'iter' + str(niter-1)
|
||||
_modeln_dir = os.path.join(output_dir, hmm_n)
|
||||
_modeln_dir_pre = os.path.join(output_dir, hmm_n_pre)
|
||||
|
||||
fh.make_new_directory(_modeln_dir, 'leave')
|
||||
chtk.re_estimation(
|
||||
os.path.join(_modeln_dir_pre, 'hmmdefs'),
|
||||
_modeln_dir,
|
||||
hcompv_scp_train_updated,
|
||||
mlf_file=triphone_mlf,
|
||||
macros=os.path.join(_modeln_dir_pre, 'macros'),
|
||||
model_type='triphone')
|
||||
|
||||
print("elapsed time: {}".format(time.time() - timer_start))
|
||||
Reference in New Issue
Block a user