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@ -11,6 +11,7 @@ import glob
@@ -11,6 +11,7 @@ import glob
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import numpy as np |
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import pandas as pd |
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from collections import Counter |
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#import matplotlib.pyplot as plt |
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#from sklearn.metrics import confusion_matrix |
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@ -50,11 +51,14 @@ from htk import pyhtk
@@ -50,11 +51,14 @@ from htk import pyhtk
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#lex_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr') |
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#lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk') |
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## procedure |
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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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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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#eval_forced_alignment_htk = 0 |
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#make_kaldi_data_files = 0 |
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make_kaldi_files = 0 |
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#make_kaldi_lexicon_txt = 0 |
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#load_forced_alignment_kaldi = 1 |
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#eval_forced_alignment_kaldi = 1 |
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@ -66,13 +70,34 @@ from htk import pyhtk
@@ -66,13 +70,34 @@ from htk import pyhtk
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#sys.path.append(os.path.join(default.repo_dir, 'toolbox')) |
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#from evaluation import plot_confusion_matrix |
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config_dir = os.path.join(default.htk_dir, 'config') |
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model_dir = os.path.join(default.htk_dir, 'model') |
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lattice_file = os.path.join(config_dir, 'stimmen.ltc') |
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#pyhtk.create_word_lattice_file( |
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# os.path.join(config_dir, 'stimmen.net'), |
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# lattice_file) |
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hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test.scp') |
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## HTK related files. |
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config_dir = os.path.join(default.htk_dir, 'config') |
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model_dir = os.path.join(default.htk_dir, 'model') |
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feature_dir = os.path.join(default.htk_dir, 'mfc', 'stimmen') |
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config_hcopy = os.path.join(config_dir, 'config.HCopy') |
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# files to be made. |
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lattice_file = os.path.join(config_dir, 'stimmen.ltc') |
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phonelist_txt = os.path.join(config_dir, 'phonelist.txt') |
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stimmen_dic = os.path.join(default.htk_dir, 'lexicon', 'stimmen_recognition.dic') |
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hcopy_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hcopy.scp') |
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hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hvite.scp') |
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hresult_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_result.scp') |
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## Kaldi related files. |
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kaldi_data_dir = os.path.join(default.kaldi_dir, 'data') |
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# files to be made. |
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wav_scp = os.path.join(kaldi_data_dir, 'test', 'wav.scp') |
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text_file = os.path.join(kaldi_data_dir, 'test', 'text') |
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utt2spk = os.path.join(kaldi_data_dir, 'test', 'utt2spk') |
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corpus_txt = os.path.join(kaldi_data_dir, 'local', 'corpus.txt') |
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lexicon_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'lexicon.txt') |
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nonsilence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'nonsilence_phones.txt') |
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silence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'silence_phones.txt') |
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optional_silence_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'optional_silence.txt') |
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## ======================= load test data ====================== |
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@ -85,392 +110,468 @@ df = stimmen_functions.add_row_htk(df)
@@ -85,392 +110,468 @@ df = stimmen_functions.add_row_htk(df)
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word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)] |
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word_list = sorted(word_list) |
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# pronunciation variants |
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## ======================= make dic file to check pronunciation variants ====================== |
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# dic file should be manually modified depends on the task - recognition / forced-alignemnt. |
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if make_dic_file: |
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# for HTK. |
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with open(stimmen_dic, mode='wb') as f: |
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for word in word_list: |
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df_ = df[df['word']==word] |
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pronunciations = list(np.unique(df_['htk'])) |
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pronunciations_ = [word.upper() + ' sil ' + ' '.join(convert_phoneset.split_word( |
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htk, fame_asr.multi_character_phones_htk)) + ' sil' |
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for htk in pronunciations] |
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f.write(bytes('\n'.join(pronunciations_) + '\n', 'ascii')) |
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f.write(bytes('SILENCE sil\n', 'ascii')) |
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# for Kaldi. |
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fh.make_new_directory(os.path.join(kaldi_data_dir, 'local', 'dict')) |
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with open(lexicon_txt, mode='wb') as f: |
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f.write(bytes('!SIL sil\n', 'utf-8')) |
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f.write(bytes('<UNK> spn\n', 'utf-8')) |
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for word in word_list: |
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df_ = df[df['word']==word] |
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pronunciations = list(np.unique(df_['asr'])) |
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pronunciations_ = [word.lower() + ' ' + ' '.join(convert_phoneset.split_word( |
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asr, fame_asr.multi_character_phones)) |
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for asr in pronunciations] |
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f.write(bytes('\n'.join(pronunciations_) + '\n', 'utf-8')) |
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## ======================= test data for recognition ====================== |
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# only target pronunciation variants. |
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df_rec = pd.DataFrame(index=[], columns=list(df.keys())) |
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for word in word_list: |
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df_ = df[df['word']==word] |
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print('{0} has {1} variants'.format(word, len(np.unique(df_['htk']))) |
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#fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav') |
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#output = pyhtk.recognition( |
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# os.path.join(default.htk_dir, 'config', 'config.rec', |
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# lattice_file, |
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# os.path.join(model_dir, 'hmm1', 'iter13'), |
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# dictionary_file, |
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# os.path.join(config_dir, 'phonelist.txt'), |
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# hvite_scp) |
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#pyhtk.create_label_file( |
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# row['word'], |
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# os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab'))) |
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## ======================= make a HTK dic file ====================== |
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#if make_htk_dic_file: |
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# output_type = 3 |
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dictionary_txt = os.path.join(default.htk_dir, 'lexicon', 'stimmen.dic') |
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#for word in word_list: |
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word = word_list[2] |
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# pronunciation variant of the target word. |
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pronunciations = df_test['asr'][df_test['word'].str.match(word)] |
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# make dic file. |
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#am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type) |
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variants = [htk.replace(' ', '') |
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for htk in stimmen_functions.load_pronunciations(word.upper(), stimmen_dic)] |
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df_ = df[df['word'] == word] |
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for index, row in df_.iterrows(): |
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if row['htk'] in variants: |
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df_rec = df_rec.append(row, ignore_index=True) |
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## ======================= make files required for HTK ====================== |
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if make_HTK_files: |
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# make a word lattice file. |
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pyhtk.create_word_lattice_file( |
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os.path.join(config_dir, 'stimmen.net'), |
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lattice_file) |
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# extract features. |
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with open(hcopy_scp, 'wb') as f: |
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filelist = [os.path.join(stimmen_test_dir, filename) + '\t' |
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+ os.path.join(feature_dir, os.path.basename(filename).replace('.wav', '.mfc')) |
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for filename in df['filename']] |
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f.write(bytes('\n'.join(filelist), 'ascii')) |
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pyhtk.wav2mfc(config_hcopy, hcopy_scp) |
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# make label files. |
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for index, row in df.iterrows(): |
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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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f.write(bytes(label_string, 'ascii')) |
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## ======================= make files required for Kaldi ======================= |
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if make_kaldi_files: |
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fh.make_new_directory(os.path.join(kaldi_data_dir, 'test')) |
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fh.make_new_directory(os.path.join(kaldi_data_dir, 'test', 'local')) |
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fh.make_new_directory(os.path.join(kaldi_data_dir, 'conf')) |
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# remove previous files. |
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if os.path.exists(wav_scp): |
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os.remove(wav_scp) |
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if os.path.exists(text_file): |
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os.remove(text_file) |
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if os.path.exists(utt2spk): |
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os.remove(utt2spk) |
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f_wav_scp = open(wav_scp, 'a', encoding="utf-8", newline='\n') |
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f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n') |
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f_utt2spk = open(utt2spk, 'a', encoding="utf-8", newline='\n') |
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# make wav.scp, text, and utt2spk files. |
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for i, row in df_rec.iterrows(): |
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filename = row['filename'] |
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print('=== {0}: {1} ==='.format(i, filename)) |
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wav_file = os.path.join(stimmen_test_dir, filename) |
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#if os.path.exists(wav_file): |
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speaker_id = 'speaker_' + str(i).zfill(4) |
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utterance_id = filename.replace('.wav', '') |
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utterance_id = utterance_id.replace(' ', '_') |
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utterance_id = speaker_id + '-' + utterance_id |
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# output |
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f_wav_scp.write('{0} {1}\n'.format( |
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utterance_id, |
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wav_file.replace('c:/', '/mnt/c/').replace('\\', '/'))) # convert path to unix format. |
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f_text_file.write('{0}\t{1}\n'.format(utterance_id, df_rec['word'][i].lower())) |
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f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id)) |
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f_wav_scp.close() |
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f_text_file.close() |
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f_utt2spk.close() |
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with open(corpus_txt, 'wb') as f: |
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f.write(bytes('\n'.join([word.lower() for word in word_list]) + '\n', 'utf-8')) |
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with open(nonsilence_phones_txt, 'wb') as f: |
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f.write(bytes('\n'.join(fame_asr.phoneset_short) + '\n', 'utf-8')) |
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with open(silence_phones_txt, 'wb') as f: |
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f.write(bytes('sil\nspn\n', 'utf-8')) |
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with open(optional_silence_txt, 'wb') as f: |
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f.write(bytes('sil\n', 'utf-8')) |
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with open(os.path.join(kaldi_data_dir, 'conf', 'decode.config'), 'wb') as f: |
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f.write(bytes('first_beam=10.0\n', 'utf-8')) |
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f.write(bytes('beam=13.0\n', 'utf-8')) |
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f.write(bytes('lattice_beam=6.0\n', 'utf-8')) |
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with open(os.path.join(kaldi_data_dir, 'conf', 'mfcc.conf'), 'wb') as f: |
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f.write(bytes('--use-energy=false', 'utf-8')) |
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## ======================= recognition ====================== |
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listdir = glob.glob(os.path.join(feature_dir, '*.mfc')) |
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with open(hvite_scp, 'wb') as f: |
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f.write(bytes('\n'.join(listdir), 'ascii')) |
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with open(hresult_scp, 'wb') as f: |
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f.write(bytes('\n'.join(listdir).replace('.mfc', '.rec'), 'ascii')) |
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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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output = pyhtk.recognition( |
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os.path.join(config_dir, 'config.rec'), |
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lattice_file, |
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os.path.join(default.htk_dir, 'model', 'hmm1', 'iter' + str(niter), 'hmmdefs'), |
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stimmen_dic, phonelist_txt, hvite_scp) |
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output = pyhtk.calc_recognition_performance( |
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stimmen_dic, hresult_scp) |
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per_sentence, per_word = pyhtk.load_recognition_output_all(output) |
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performance_ = np.array([niter, per_sentence['accuracy']]).reshape(1, 2) |
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performance = np.r_[performance, performance_] |
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print('{0}: {1}[%]'.format(niter, per_sentence['accuracy'])) |
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## ======================= forced alignment using HTK ======================= |
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if do_forced_alignment_htk: |
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#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]: |
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for hmm_num in [256, 512, 1024]: |
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hmm_num_str = str(hmm_num) |
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acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs') |
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predictions = pd.DataFrame({'filename': [''], |
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'word': [''], |
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'xsampa': [''], |
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'ipa': [''], |
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'famehtk': [''], |
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'prediction': ['']}) |
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for i, filename in enumerate(df['filename']): |
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print('=== {0}/{1} ==='.format(i, len(df))) |
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if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)): |
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wav_file = os.path.join(wav_dir, filename) |
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if os.path.exists(wav_file): |
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word = df['word'][i] |
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WORD = word.upper() |
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fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str) |
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#if not os.path.exists(fa_file): |
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# make label file. |
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label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab')) |
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with open(label_file, 'w') as f: |
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lines = f.write(WORD) |
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htk_dict_file = os.path.join(htk_dict_dir, word + '.dic') |
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pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, |
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default.phonelist, acoustic_model) |
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|
|
|
os.remove(label_file) |
|
|
|
|
|
|
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|
|
prediction = am_func.read_fileFA(fa_file) |
|
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|
|
|
|
|
|
|
print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction)) |
|
|
|
|
else: |
|
|
|
|
prediction = '' |
|
|
|
|
print('!!!!! file not found.') |
|
|
|
|
|
|
|
|
|
line = pd.Series([df['filename'][i], df['word'][i], df['xsampa'][i], df['ipa'][i], df['famehtk'][i], prediction], index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], name=i) |
|
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|
|
predictions = predictions.append(line) |
|
|
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|
else: |
|
|
|
|
prediction = '' |
|
|
|
|
print('!!!!! invalid entry.') |
|
|
|
|
|
|
|
|
|
predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl')) |
|
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|
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|
|
## ======================= make files which is used for forced alignment by Kaldi ======================= |
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|
|
if make_kaldi_data_files: |
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|
wav_scp = os.path.join(kaldi_data_dir, 'wav.scp') |
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|
text_file = os.path.join(kaldi_data_dir, 'text') |
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|
utt2spk = os.path.join(kaldi_data_dir, 'utt2spk') |
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|
# remove previous files. |
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|
if os.path.exists(wav_scp): |
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|
os.remove(wav_scp) |
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if os.path.exists(text_file): |
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|
os.remove(text_file) |
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|
if os.path.exists(utt2spk): |
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|
os.remove(utt2spk) |
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|
|
f_wav_scp = open(wav_scp, 'a', encoding="utf-8", newline='\n') |
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|
f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n') |
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|
f_utt2spk = open(utt2spk, 'a', encoding="utf-8", newline='\n') |
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|
# make wav.scp, text, and utt2spk files. |
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|
for i in df.index: |
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|
filename = df['filename'][i] |
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|
print('=== {0}: {1} ==='.format(i, filename)) |
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|
#if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)): |
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|
wav_file = os.path.join(wav_dir, filename) |
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|
|
if os.path.exists(wav_file): |
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|
speaker_id = 'speaker_' + str(i).zfill(4) |
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|
|
utterance_id = filename.replace('.wav', '') |
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|
utterance_id = utterance_id.replace(' ', '_') |
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|
utterance_id = speaker_id + '-' + utterance_id |
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|
|
# wav.scp file |
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|
|
wav_file_unix = wav_file.replace('\\', '/') |
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|
|
wav_file_unix = wav_file_unix.replace('c:/', '/mnt/c/') |
|
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|
|
f_wav_scp.write('{0} {1}\n'.format(utterance_id, wav_file_unix)) |
|
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|
|
# text file |
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|
word = df['word'][i].lower() |
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|
|
f_text_file.write('{0}\t{1}\n'.format(utterance_id, word)) |
|
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|
|
# utt2spk |
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|
|
f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id)) |
|
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|
|
f_wav_scp.close() |
|
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|
|
f_text_file.close() |
|
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|
|
f_utt2spk.close() |
|
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|
|
#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]: |
|
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|
|
for hmm_num in [256, 512, 1024]: |
|
|
|
|
hmm_num_str = str(hmm_num) |
|
|
|
|
acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs') |
|
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|
|
predictions = pd.DataFrame({'filename': [''], |
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|
|
|
'word': [''], |
|
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|
|
'xsampa': [''], |
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|
|
|
'ipa': [''], |
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|
|
'famehtk': [''], |
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|
|
|
'prediction': ['']}) |
|
|
|
|
for i, filename in enumerate(df['filename']): |
|
|
|
|
print('=== {0}/{1} ==='.format(i, len(df))) |
|
|
|
|
if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)): |
|
|
|
|
wav_file = os.path.join(wav_dir, filename) |
|
|
|
|
if os.path.exists(wav_file): |
|
|
|
|
word = df['word'][i] |
|
|
|
|
WORD = word.upper() |
|
|
|
|
fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str) |
|
|
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|
|
|
|
|
|
#if not os.path.exists(fa_file): |
|
|
|
|
# make label file. |
|
|
|
|
label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab')) |
|
|
|
|
with open(label_file, 'w') as f: |
|
|
|
|
lines = f.write(WORD) |
|
|
|
|
|
|
|
|
|
htk_dict_file = os.path.join(htk_dict_dir, word + '.dic') |
|
|
|
|
|
|
|
|
|
pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, |
|
|
|
|
default.phonelist, acoustic_model) |
|
|
|
|
os.remove(label_file) |
|
|
|
|
|
|
|
|
|
prediction = am_func.read_fileFA(fa_file) |
|
|
|
|
|
|
|
|
|
print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction)) |
|
|
|
|
else: |
|
|
|
|
prediction = '' |
|
|
|
|
print('!!!!! file not found.') |
|
|
|
|
|
|
|
|
|
line = pd.Series([df['filename'][i], df['word'][i], df['xsampa'][i], df['ipa'][i], df['famehtk'][i], prediction], index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], name=i) |
|
|
|
|
predictions = predictions.append(line) |
|
|
|
|
else: |
|
|
|
|
prediction = '' |
|
|
|
|
print('!!!!! invalid entry.') |
|
|
|
|
|
|
|
|
|
predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl')) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
## ======================= make lexicon txt which is used by Kaldi ======================= |
|
|
|
|
if make_kaldi_lexicon_txt: |
|
|
|
|
option_num = 6 |
|
|
|
|
option_num = 6 |
|
|
|
|
|
|
|
|
|
# remove previous file. |
|
|
|
|
if os.path.exists(lexicon_txt): |
|
|
|
|
os.remove(lexicon_txt) |
|
|
|
|
lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt') |
|
|
|
|
if os.path.exists(lexiconp_txt): |
|
|
|
|
os.remove(lexiconp_txt) |
|
|
|
|
|
|
|
|
|
# output lexicon.txt |
|
|
|
|
f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n') |
|
|
|
|
pronvar_list_all = [] |
|
|
|
|
for word in word_list: |
|
|
|
|
# remove previous file. |
|
|
|
|
if os.path.exists(lexicon_txt): |
|
|
|
|
os.remove(lexicon_txt) |
|
|
|
|
lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt') |
|
|
|
|
if os.path.exists(lexiconp_txt): |
|
|
|
|
os.remove(lexiconp_txt) |
|
|
|
|
|
|
|
|
|
# output lexicon.txt |
|
|
|
|
f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n') |
|
|
|
|
pronvar_list_all = [] |
|
|
|
|
for word in word_list: |
|
|
|
|
|
|
|
|
|
# pronunciation variant of the target word. |
|
|
|
|
pronunciation_variants = df['ipa'][df['word'].str.match(word)] |
|
|
|
|
# pronunciation variant of the target word. |
|
|
|
|
pronunciation_variants = df['ipa'][df['word'].str.match(word)] |
|
|
|
|
|
|
|
|
|
c = Counter(pronunciation_variants) |
|
|
|
|
total_num = sum(c.values()) |
|
|
|
|
c = Counter(pronunciation_variants) |
|
|
|
|
total_num = sum(c.values()) |
|
|
|
|
|
|
|
|
|
#with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f: |
|
|
|
|
# for key in c.keys(): |
|
|
|
|
# f.write("{0},{1}\n".format(key,c[key])) |
|
|
|
|
#with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f: |
|
|
|
|
# for key in c.keys(): |
|
|
|
|
# f.write("{0},{1}\n".format(key,c[key])) |
|
|
|
|
|
|
|
|
|
for key, value in c.most_common(option_num): |
|
|
|
|
# make possible pronunciation variant list. |
|
|
|
|
pronvar_list = am_func.fame_pronunciation_variant(key) |
|
|
|
|
for key, value in c.most_common(option_num): |
|
|
|
|
# make possible pronunciation variant list. |
|
|
|
|
pronvar_list = am_func.fame_pronunciation_variant(key) |
|
|
|
|
|
|
|
|
|
for pronvar_ in pronvar_list: |
|
|
|
|
split_ipa = convert_phone_set.split_fame_ipa(pronvar_) |
|
|
|
|
pronvar_out = ' '.join(split_ipa) |
|
|
|
|
pronvar_list_all.append([word, pronvar_out]) |
|
|
|
|
for pronvar_ in pronvar_list: |
|
|
|
|
split_ipa = convert_phone_set.split_fame_ipa(pronvar_) |
|
|
|
|
pronvar_out = ' '.join(split_ipa) |
|
|
|
|
pronvar_list_all.append([word, pronvar_out]) |
|
|
|
|
|
|
|
|
|
pronvar_list_all = np.array(pronvar_list_all) |
|
|
|
|
pronvar_list_all = np.unique(pronvar_list_all, axis=0) |
|
|
|
|
pronvar_list_all = np.array(pronvar_list_all) |
|
|
|
|
pronvar_list_all = np.unique(pronvar_list_all, axis=0) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# output |
|
|
|
|
f_lexicon_txt.write('<UNK>\tSPN\n') |
|
|
|
|
for line in pronvar_list_all: |
|
|
|
|
f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1])) |
|
|
|
|
|
|
|
|
|
# output |
|
|
|
|
f_lexicon_txt.write('<UNK>\tSPN\n') |
|
|
|
|
for line in pronvar_list_all: |
|
|
|
|
f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1])) |
|
|
|
|
|
|
|
|
|
f_lexicon_txt.close() |
|
|
|
|
f_lexicon_txt.close() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
## ======================= load kaldi forced alignment result ======================= |
|
|
|
|
if load_forced_alignment_kaldi: |
|
|
|
|
phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt') |
|
|
|
|
merged_alignment_txt = os.path.join(default.kaldi_dir, 'exp', 'tri1_alignme', 'merged_alignment.txt') |
|
|
|
|
|
|
|
|
|
#filenames = np.load(data_dir + '\\filenames.npy') |
|
|
|
|
#words = np.load(data_dir + '\\words.npy') |
|
|
|
|
#pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy') |
|
|
|
|
#pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy') |
|
|
|
|
#word_list = np.unique(words) |
|
|
|
|
|
|
|
|
|
# load the mapping between phones and ids. |
|
|
|
|
with open(phones_txt, 'r', encoding="utf-8") as f: |
|
|
|
|
mapping_phone2id = f.read().split('\n') |
|
|
|
|
|
|
|
|
|
phones = [] |
|
|
|
|
phone_ids = [] # ID of phones |
|
|
|
|
for m in mapping_phone2id: |
|
|
|
|
m = m.split(' ') |
|
|
|
|
if len(m) > 1: |
|
|
|
|
phones.append(m[0]) |
|
|
|
|
phone_ids.append(int(m[1])) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# load the result of FA. |
|
|
|
|
with open(merged_alignment_txt, 'r') as f: |
|
|
|
|
lines = f.read() |
|
|
|
|
lines = lines.split('\n') |
|
|
|
|
|
|
|
|
|
predictions = pd.DataFrame({'filename': [''], |
|
|
|
|
'word': [''], |
|
|
|
|
'xsampa': [''], |
|
|
|
|
'ipa': [''], |
|
|
|
|
'famehtk': [''], |
|
|
|
|
'prediction': ['']}) |
|
|
|
|
#fa_filenames = [] |
|
|
|
|
#fa_pronunciations = [] |
|
|
|
|
utterance_id_ = '' |
|
|
|
|
pronunciation = [] |
|
|
|
|
for line in lines: |
|
|
|
|
line = line.split(' ') |
|
|
|
|
if len(line) == 5: |
|
|
|
|
utterance_id = line[0] |
|
|
|
|
if utterance_id == utterance_id_: |
|
|
|
|
phone_id = int(line[4]) |
|
|
|
|
#if not phone_id == 1: |
|
|
|
|
phone_ = phones[phone_ids.index(phone_id)] |
|
|
|
|
phone = re.sub(r'_[A-Z]', '', phone_) |
|
|
|
|
if not phone == 'SIL': |
|
|
|
|
pronunciation.append(phone) |
|
|
|
|
else: |
|
|
|
|
filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_) |
|
|
|
|
prediction = ''.join(pronunciation) |
|
|
|
|
df_ = df[df['filename'].str.match(filename)] |
|
|
|
|
df_idx = df_.index[0] |
|
|
|
|
prediction_ = pd.Series([#filename, |
|
|
|
|
#df_['word'][df_idx], |
|
|
|
|
#df_['xsampa'][df_idx], |
|
|
|
|
#df_['ipa'][df_idx], |
|
|
|
|
#df_['famehtk'][df_idx], |
|
|
|
|
df_.iloc[0,1], |
|
|
|
|
df_.iloc[0,3], |
|
|
|
|
df_.iloc[0,4], |
|
|
|
|
df_.iloc[0,2], |
|
|
|
|
df_.iloc[0,0], |
|
|
|
|
prediction], |
|
|
|
|
index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], |
|
|
|
|
name=df_idx) |
|
|
|
|
predictions = predictions.append(prediction_) |
|
|
|
|
#fa_filenames.append() |
|
|
|
|
#fa_pronunciations.append(' '.join(pronunciation)) |
|
|
|
|
pronunciation = [] |
|
|
|
|
|
|
|
|
|
utterance_id_ = utterance_id |
|
|
|
|
predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl')) |
|
|
|
|
phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt') |
|
|
|
|
merged_alignment_txt = os.path.join(default.kaldi_dir, 'exp', 'tri1_alignme', 'merged_alignment.txt') |
|
|
|
|
|
|
|
|
|
#filenames = np.load(data_dir + '\\filenames.npy') |
|
|
|
|
#words = np.load(data_dir + '\\words.npy') |
|
|
|
|
#pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy') |
|
|
|
|
#pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy') |
|
|
|
|
#word_list = np.unique(words) |
|
|
|
|
|
|
|
|
|
# load the mapping between phones and ids. |
|
|
|
|
with open(phones_txt, 'r', encoding="utf-8") as f: |
|
|
|
|
mapping_phone2id = f.read().split('\n') |
|
|
|
|
|
|
|
|
|
phones = [] |
|
|
|
|
phone_ids = [] # ID of phones |
|
|
|
|
for m in mapping_phone2id: |
|
|
|
|
m = m.split(' ') |
|
|
|
|
if len(m) > 1: |
|
|
|
|
phones.append(m[0]) |
|
|
|
|
phone_ids.append(int(m[1])) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# load the result of FA. |
|
|
|
|
with open(merged_alignment_txt, 'r') as f: |
|
|
|
|
lines = f.read() |
|
|
|
|
lines = lines.split('\n') |
|
|
|
|
|
|
|
|
|
predictions = pd.DataFrame({'filename': [''], |
|
|
|
|
'word': [''], |
|
|
|
|
'xsampa': [''], |
|
|
|
|
'ipa': [''], |
|
|
|
|
'famehtk': [''], |
|
|
|
|
'prediction': ['']}) |
|
|
|
|
#fa_filenames = [] |
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#fa_pronunciations = [] |
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utterance_id_ = '' |
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pronunciation = [] |
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for line in lines: |
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line = line.split(' ') |
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if len(line) == 5: |
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utterance_id = line[0] |
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if utterance_id == utterance_id_: |
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phone_id = int(line[4]) |
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#if not phone_id == 1: |
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phone_ = phones[phone_ids.index(phone_id)] |
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phone = re.sub(r'_[A-Z]', '', phone_) |
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if not phone == 'SIL': |
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pronunciation.append(phone) |
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else: |
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filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_) |
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prediction = ''.join(pronunciation) |
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df_ = df[df['filename'].str.match(filename)] |
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df_idx = df_.index[0] |
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prediction_ = pd.Series([#filename, |
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#df_['word'][df_idx], |
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#df_['xsampa'][df_idx], |
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#df_['ipa'][df_idx], |
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#df_['famehtk'][df_idx], |
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df_.iloc[0,1], |
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df_.iloc[0,3], |
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df_.iloc[0,4], |
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df_.iloc[0,2], |
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df_.iloc[0,0], |
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prediction], |
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index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], |
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name=df_idx) |
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predictions = predictions.append(prediction_) |
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#fa_filenames.append() |
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#fa_pronunciations.append(' '.join(pronunciation)) |
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pronunciation = [] |
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utterance_id_ = utterance_id |
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predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl')) |
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## ======================= evaluate the result of forced alignment ======================= |
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if eval_forced_alignment_htk: |
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htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short') |
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htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short') |
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compare_hmm_num = 1 |
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compare_hmm_num = 1 |
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if compare_hmm_num: |
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f_result = open(os.path.join(result_dir, 'result.csv'), 'w') |
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f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n") |
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if compare_hmm_num: |
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|
f_result = open(os.path.join(result_dir, 'result.csv'), 'w') |
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f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n") |
|
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for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]: |
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#for hmm_num in [256]: |
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hmm_num_str = str(hmm_num) |
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if compare_hmm_num: |
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|
f_result.write("{},".format(hmm_num_str)) |
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for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]: |
|
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#for hmm_num in [256]: |
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hmm_num_str = str(hmm_num) |
|
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if compare_hmm_num: |
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|
f_result.write("{},".format(hmm_num_str)) |
|
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#match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy') |
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#prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy')) |
|
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|
#prediction = pd.Series(prediction, index=df.index, name='prediction') |
|
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#result = pd.concat([df, prediction], axis=1) |
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|
result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl')) |
|
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|
#match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy') |
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#prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy')) |
|
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#prediction = pd.Series(prediction, index=df.index, name='prediction') |
|
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|
#result = pd.concat([df, prediction], axis=1) |
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|
result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl')) |
|
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|
# load pronunciation variants |
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|
for word in word_list: |
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|
htk_dict_file = os.path.join(htk_dict_dir, word + '.dic') |
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|
with open(htk_dict_file, 'r') as f: |
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|
lines = f.read().split('\n')[:-1] |
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|
pronunciation_variants = [line.split('\t')[1] for line in lines] |
|
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|
|
# load pronunciation variants |
|
|
|
|
for word in word_list: |
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|
htk_dict_file = os.path.join(htk_dict_dir, word + '.dic') |
|
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|
|
with open(htk_dict_file, 'r') as f: |
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|
lines = f.read().split('\n')[:-1] |
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|
|
pronunciation_variants = [line.split('\t')[1] for line in lines] |
|
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|
# see only words which appears in top 3. |
|
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|
|
result_ = result[result['word'].str.match(word)] |
|
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|
|
result_ = result_[result_['famehtk'].isin(pronunciation_variants)] |
|
|
|
|
# see only words which appears in top 3. |
|
|
|
|
result_ = result[result['word'].str.match(word)] |
|
|
|
|
result_ = result_[result_['famehtk'].isin(pronunciation_variants)] |
|
|
|
|
|
|
|
|
|
match_num = sum(result_['famehtk'] == result_['prediction']) |
|
|
|
|
total_num = len(result_) |
|
|
|
|
match_num = sum(result_['famehtk'] == result_['prediction']) |
|
|
|
|
total_num = len(result_) |
|
|
|
|
|
|
|
|
|
print("word '{0}': {1}/{2} ({3:.2f} %)".format(word, match_num, total_num, match_num/total_num*100)) |
|
|
|
|
if compare_hmm_num: |
|
|
|
|
f_result.write("{0},{1},".format(match_num, total_num)) |
|
|
|
|
else: |
|
|
|
|
# output confusion matrix |
|
|
|
|
cm = confusion_matrix(result_['famehtk'], result_['prediction']) |
|
|
|
|
print("word '{0}': {1}/{2} ({3:.2f} %)".format(word, match_num, total_num, match_num/total_num*100)) |
|
|
|
|
if compare_hmm_num: |
|
|
|
|
f_result.write("{0},{1},".format(match_num, total_num)) |
|
|
|
|
else: |
|
|
|
|
# output confusion matrix |
|
|
|
|
cm = confusion_matrix(result_['famehtk'], result_['prediction']) |
|
|
|
|
|
|
|
|
|
plt.figure() |
|
|
|
|
plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False) |
|
|
|
|
plt.savefig(result_dir + '\\cm_' + word + '.png') |
|
|
|
|
plt.figure() |
|
|
|
|
plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False) |
|
|
|
|
plt.savefig(result_dir + '\\cm_' + word + '.png') |
|
|
|
|
|
|
|
|
|
if compare_hmm_num: |
|
|
|
|
f_result.write('\n') |
|
|
|
|
if compare_hmm_num: |
|
|
|
|
f_result.write('\n') |
|
|
|
|
|
|
|
|
|
if compare_hmm_num: |
|
|
|
|
f_result.close() |
|
|
|
|
if compare_hmm_num: |
|
|
|
|
f_result.close() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
## ======================= evaluate the result of forced alignment of kaldi ======================= |
|
|
|
|
if eval_forced_alignment_kaldi: |
|
|
|
|
result = pd.read_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl')) |
|
|
|
|
|
|
|
|
|
f_result = open(os.path.join(result_dir, 'result.csv'), 'w') |
|
|
|
|
f_result.write("word,total,valid,match,[%]\n") |
|
|
|
|
|
|
|
|
|
# load pronunciation variants |
|
|
|
|
with open(lexicon_txt, 'r', encoding="utf-8", newline='\n') as f: |
|
|
|
|
lines = f.read().split('\n')[:-1] |
|
|
|
|
pronunciation_variants_all = [line.split('\t') for line in lines] |
|
|
|
|
|
|
|
|
|
word_list = np.delete(word_list, [0], 0) # remove 'Oog' |
|
|
|
|
for word in word_list: |
|
|
|
|
|
|
|
|
|
# load pronunciation variant of the word. |
|
|
|
|
pronunciation_variants = [] |
|
|
|
|
for line in pronunciation_variants_all: |
|
|
|
|
if line[0] == word.lower(): |
|
|
|
|
pronunciation_variants.append(line[1].replace(' ', '')) |
|
|
|
|
|
|
|
|
|
# see only words which appears in top 3. |
|
|
|
|
result_ = result[result['word'].str.match(word)] |
|
|
|
|
result_tolerant = pd.DataFrame({ |
|
|
|
|
'filename': [''], |
|
|
|
|
'word': [''], |
|
|
|
|
'xsampa': [''], |
|
|
|
|
'ipa': [''], |
|
|
|
|
'prediction': [''], |
|
|
|
|
'match': ['']}) |
|
|
|
|
|
|
|
|
|
for i in range(0, len(result_)): |
|
|
|
|
line = result_.iloc[i] |
|
|
|
|
|
|
|
|
|
# make a list of all possible pronunciation variants of ipa description. |
|
|
|
|
# i.e. possible answers from forced alignment. |
|
|
|
|
ipa = line['ipa'] |
|
|
|
|
pronvar_list = [ipa] |
|
|
|
|
pronvar_list_ = am_func.fame_pronunciation_variant(ipa) |
|
|
|
|
if not pronvar_list_ is None: |
|
|
|
|
pronvar_list += list(pronvar_list_) |
|
|
|
|
|
|
|
|
|
# only focus on pronunciations which can be estimated from ipa. |
|
|
|
|
if len(set(pronvar_list) & set(pronunciation_variants)) > 0: |
|
|
|
|
if line['prediction'] in pronvar_list: |
|
|
|
|
ismatch = True |
|
|
|
|
else: |
|
|
|
|
ismatch = False |
|
|
|
|
|
|
|
|
|
line_df = pd.DataFrame(result_.iloc[i]).T |
|
|
|
|
df_idx = line_df.index[0] |
|
|
|
|
result_tolerant_ = pd.Series([line_df.loc[df_idx, 'filename'], |
|
|
|
|
line_df.loc[df_idx, 'word'], |
|
|
|
|
line_df.loc[df_idx, 'xsampa'], |
|
|
|
|
line_df.loc[df_idx, 'ipa'], |
|
|
|
|
line_df.loc[df_idx, 'prediction'], |
|
|
|
|
ismatch], |
|
|
|
|
index=['filename', 'word', 'xsampa', 'ipa', 'prediction', 'match'], |
|
|
|
|
name=df_idx) |
|
|
|
|
result_tolerant = result_tolerant.append(result_tolerant_) |
|
|
|
|
# remove the first entry (dummy) |
|
|
|
|
result_tolerant = result_tolerant.drop(0, axis=0) |
|
|
|
|
|
|
|
|
|
total_num = len(result_) |
|
|
|
|
valid_num = len(result_tolerant) |
|
|
|
|
match_num = np.sum(result_tolerant['match']) |
|
|
|
|
|
|
|
|
|
print("word '{0}': {1}/{2} ({3:.2f} %) originally {4}".format(word, match_num, valid_num, match_num/valid_num*100, total_num)) |
|
|
|
|
f_result.write("{0},{1},{2},{3},{4}\n".format(word, total_num, valid_num, match_num, match_num/valid_num*100)) |
|
|
|
|
|
|
|
|
|
f_result.close() |
|
|
|
|
## output confusion matrix |
|
|
|
|
#cm = confusion_matrix(result_['ipa'], result_['prediction']) |
|
|
|
|
|
|
|
|
|
#plt.figure() |
|
|
|
|
#plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False) |
|
|
|
|
#plt.savefig(result_dir + '\\cm_' + word + '.png') |
|
|
|
|
result = pd.read_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl')) |
|
|
|
|
|
|
|
|
|
f_result = open(os.path.join(result_dir, 'result.csv'), 'w') |
|
|
|
|
f_result.write("word,total,valid,match,[%]\n") |
|
|
|
|
|
|
|
|
|
# load pronunciation variants |
|
|
|
|
with open(lexicon_txt, 'r', encoding="utf-8", newline='\n') as f: |
|
|
|
|
lines = f.read().split('\n')[:-1] |
|
|
|
|
pronunciation_variants_all = [line.split('\t') for line in lines] |
|
|
|
|
|
|
|
|
|
word_list = np.delete(word_list, [0], 0) # remove 'Oog' |
|
|
|
|
for word in word_list: |
|
|
|
|
|
|
|
|
|
# load pronunciation variant of the word. |
|
|
|
|
pronunciation_variants = [] |
|
|
|
|
for line in pronunciation_variants_all: |
|
|
|
|
if line[0] == word.lower(): |
|
|
|
|
pronunciation_variants.append(line[1].replace(' ', '')) |
|
|
|
|
|
|
|
|
|
# see only words which appears in top 3. |
|
|
|
|
result_ = result[result['word'].str.match(word)] |
|
|
|
|
result_tolerant = pd.DataFrame({ |
|
|
|
|
'filename': [''], |
|
|
|
|
'word': [''], |
|
|
|
|
'xsampa': [''], |
|
|
|
|
'ipa': [''], |
|
|
|
|
'prediction': [''], |
|
|
|
|
'match': ['']}) |
|
|
|
|
|
|
|
|
|
for i in range(0, len(result_)): |
|
|
|
|
line = result_.iloc[i] |
|
|
|
|
|
|
|
|
|
# make a list of all possible pronunciation variants of ipa description. |
|
|
|
|
# i.e. possible answers from forced alignment. |
|
|
|
|
ipa = line['ipa'] |
|
|
|
|
pronvar_list = [ipa] |
|
|
|
|
pronvar_list_ = am_func.fame_pronunciation_variant(ipa) |
|
|
|
|
if not pronvar_list_ is None: |
|
|
|
|
pronvar_list += list(pronvar_list_) |
|
|
|
|
|
|
|
|
|
# only focus on pronunciations which can be estimated from ipa. |
|
|
|
|
if len(set(pronvar_list) & set(pronunciation_variants)) > 0: |
|
|
|
|
if line['prediction'] in pronvar_list: |
|
|
|
|
ismatch = True |
|
|
|
|
else: |
|
|
|
|
ismatch = False |
|
|
|
|
|
|
|
|
|
line_df = pd.DataFrame(result_.iloc[i]).T |
|
|
|
|
df_idx = line_df.index[0] |
|
|
|
|
result_tolerant_ = pd.Series([line_df.loc[df_idx, 'filename'], |
|
|
|
|
line_df.loc[df_idx, 'word'], |
|
|
|
|
line_df.loc[df_idx, 'xsampa'], |
|
|
|
|
line_df.loc[df_idx, 'ipa'], |
|
|
|
|
line_df.loc[df_idx, 'prediction'], |
|
|
|
|
ismatch], |
|
|
|
|
index=['filename', 'word', 'xsampa', 'ipa', 'prediction', 'match'], |
|
|
|
|
name=df_idx) |
|
|
|
|
result_tolerant = result_tolerant.append(result_tolerant_) |
|
|
|
|
# remove the first entry (dummy) |
|
|
|
|
result_tolerant = result_tolerant.drop(0, axis=0) |
|
|
|
|
|
|
|
|
|
total_num = len(result_) |
|
|
|
|
valid_num = len(result_tolerant) |
|
|
|
|
match_num = np.sum(result_tolerant['match']) |
|
|
|
|
|
|
|
|
|
print("word '{0}': {1}/{2} ({3:.2f} %) originally {4}".format(word, match_num, valid_num, match_num/valid_num*100, total_num)) |
|
|
|
|
f_result.write("{0},{1},{2},{3},{4}\n".format(word, total_num, valid_num, match_num, match_num/valid_num*100)) |
|
|
|
|
|
|
|
|
|
f_result.close() |
|
|
|
|
## output confusion matrix |
|
|
|
|
#cm = confusion_matrix(result_['ipa'], result_['prediction']) |
|
|
|
|
|
|
|
|
|
#plt.figure() |
|
|
|
|
#plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False) |
|
|
|
|
#plt.savefig(result_dir + '\\cm_' + word + '.png') |
|
|
|
|
|
yemaozi88
3 years ago