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0777735979
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3a98e184fe
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@ -11,7 +11,6 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
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..\forced_alignment\forced_alignment\convert_phone_set.py = ..\forced_alignment\forced_alignment\convert_phone_set.py
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..\toolbox\evaluation.py = ..\toolbox\evaluation.py
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..\forced_alignment\forced_alignment\forced_alignment.pyproj = ..\forced_alignment\forced_alignment\forced_alignment.pyproj
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..\forced_alignment\forced_alignment\htk_dict.py = ..\forced_alignment\forced_alignment\htk_dict.py
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..\forced_alignment\forced_alignment\lexicon.py = ..\forced_alignment\forced_alignment\lexicon.py
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..\forced_alignment\forced_alignment\mlf.py = ..\forced_alignment\forced_alignment\mlf.py
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..\forced_alignment\forced_alignment\pronunciations.py = ..\forced_alignment\forced_alignment\pronunciations.py
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@ -38,7 +38,7 @@ def make_filelist(input_dir, output_txt):
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fout.write(input_dir + '\\' + filename + '\n')
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def make_htk_dict(word, pronvar_, fileDic, output_type):
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def make_dic(word, pronvar_, fileDic, output_type):
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"""
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make dict files which can be used for HTK.
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param word: target word.
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@ -98,8 +98,8 @@ def find_phone(lexicon_file, phone):
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for line in lines:
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line = line.split('\t')
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if len(line) > 1:
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pronunciation = line[1]
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if phone in pronunciation:
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pron = line[1]
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if phone in pron:
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extracted.append(line)
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return extracted
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@ -149,54 +149,3 @@ def read_fileFA(fileFA):
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phones.append(line_split[2])
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return ' '.join(phones)
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def fame_pronunciation_variant(ipa):
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ipa = ipa.replace('æ', 'ɛ')
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ipa = ipa.replace('ɐ', 'a')
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ipa = ipa.replace('ɑ', 'a')
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ipa = ipa.replace('ɾ', 'r')
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ipa = ipa.replace('ɹ', 'r') # ???
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ipa = ipa.replace('ʁ', 'r')
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ipa = ipa.replace('ʀ', 'r') # ???
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ipa = ipa.replace('ʊ', 'u')
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ipa = ipa.replace('χ', 'x')
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pronvar_list = [ipa]
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while 'ø:' in ' '.join(pronvar_list) or 'œ' in ' '.join(pronvar_list) or 'ɒ' in ' '.join(pronvar_list):
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pronvar_list_ = []
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for p in pronvar_list:
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if 'ø:' in p:
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pronvar_list_.append(p.replace('ø:', 'ö'))
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pronvar_list_.append(p.replace('ø:', 'ö:'))
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if 'œ' in p:
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pronvar_list_.append(p.replace('œ', 'ɔ̈'))
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pronvar_list_.append(p.replace('œ', 'ɔ̈:'))
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if 'ɒ' in p:
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pronvar_list_.append(p.replace('ɒ', 'ɔ̈'))
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pronvar_list_.append(p.replace('ɒ', 'ɔ̈:'))
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pronvar_list = np.unique(pronvar_list_)
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return pronvar_list
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def make_fame2ipa_variants(fame):
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fame = 'rɛös'
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ipa = [fame]
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ipa.append(fame.replace('ɛ', 'æ'))
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ipa.append(fame.replace('a', 'ɐ'))
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ipa.append(fame.replace('a', 'ɑ'))
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ipa.append(fame.replace('r', 'ɾ'))
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ipa.append(fame.replace('r', 'ɹ'))
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ipa.append(fame.replace('r', 'ʁ'))
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ipa.append(fame.replace('r', 'ʀ'))
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ipa.append(fame.replace('u', 'ʊ'))
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ipa.append(fame.replace('x', 'χ'))
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ipa.append(fame.replace('ö', 'ø:'))
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ipa.append(fame.replace('ö:', 'ø:'))
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ipa.append(fame.replace('ɔ̈', 'œ'))
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ipa.append(fame.replace('ɔ̈:', 'œ'))
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ipa.append(fame.replace('ɔ̈', 'ɒ'))
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ipa.append(fame.replace('ɔ̈:', 'ɒ'))
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return ipa
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@ -3,7 +3,6 @@ import os
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#default_hvite_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data', 'htk', 'config.HVite')
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cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
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kaldi_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
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#config_hcopy = os.path.join(cygwin_dir, 'config', 'config.HCopy')
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#config_train = os.path.join(cygwin_dir, 'config', 'config.train')
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config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
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@ -10,56 +10,60 @@ import re
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from sklearn.metrics import confusion_matrix
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#from sklearn.metrics import confusion_matrix
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import acoustic_model_functions as am_func
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import convert_xsampa2ipa
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import defaultfiles as default
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from forced_alignment import pyhtk
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## ======================= user define =======================
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#curr_dir = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
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#config_ini = 'config.ini'
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#repo_dir = r'C:\Users\Aki\source\repos'
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#forced_alignment_module = repo_dir + '\\forced_alignment'
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#forced_alignment_module_old = repo_dir + '\\aki_tools'
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#ipa_xsampa_converter_dir = repo_dir + '\\ipa-xsama-converter'
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#accent_classification_dir = repo_dir + '\\accent_classification\accent_classification'
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excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
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#experiments_dir = r'C:\OneDrive\Research\rug\experiments'
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data_dir = os.path.join(default.experiments_dir, 'stimmen', 'data')
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wav_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen' # 16k
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#csvfile = data_dir + '\\Frisian Variants Picture Task Stimmen.csv'
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wav_dir = os.path.join(default.experiments_dir, 'stimmen', 'wav')
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acoustic_model_dir = os.path.join(default.experiments_dir, 'friesian', 'acoustic_model', 'model')
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htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
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fa_dir = os.path.join(default.experiments_dir, 'stimmen', 'FA_44k')
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result_dir = os.path.join(default.experiments_dir, 'stimmen', 'result')
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kaldi_data_dir = os.path.join(default.kaldi_dir, 'data', 'alignme')
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kaldi_dict_dir = os.path.join(default.kaldi_dir, 'data', 'local', 'dict')
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lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
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fa_dir = os.path.join(default.experiments_dir, 'stimmen', 'FA')
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#cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
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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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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_dic_files = 0
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do_forced_alignment_htk = 1
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make_kaldi_data_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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load_forced_alignment_kaldi = 0
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eval_forced_alignment = 0
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## ======================= add paths =======================
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sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
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from forced_alignment import convert_phone_set
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from forced_alignment import pyhtk
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sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
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#import pyHTK
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from evaluation import plot_confusion_matrix
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## ======================= convert phones ======================
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
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xls = pd.ExcelFile(excel_file)
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@ -111,11 +115,11 @@ df = pd.DataFrame({'filename': df['Filename'],
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# cleansing.
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df = df[~df['famehtk'].isin(['/', ''])]
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word_list = np.unique(df['word'])
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## ======================= make dict files used for HTK. ======================
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if make_htk_dict_files:
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if make_dic_files:
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word_list = np.unique(df['word'])
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output_type = 3
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for word in word_list:
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@ -125,67 +129,67 @@ if make_htk_dict_files:
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pronvar_ = df['famehtk'][df['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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am_func.make_dic(word, pronvar_, htk_dict_file, output_type)
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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 = 2
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for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 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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predictions = []
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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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if os.path.exists(wav_file) and i in df['filename'].keys():
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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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fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
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pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, default.phonelist, acoustic_model)
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prediction = am_func.read_fileFA(fa_file)
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predictions.append(prediction)
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os.remove(label_file)
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print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
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else:
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prediction = ''
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predictions.append('')
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print('!!!!! file not found.')
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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:
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prediction = ''
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print('!!!!! invalid entry.')
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predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
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predictions = np.array(predictions)
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#match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
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np.save(os.path.join(data_dir, 'predictions_hmm' + hmm_num_str + '.npy'), predictions)
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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_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen'
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kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
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kaldi_data_dir = os.path.join(kaldi_work_dir, 'data', 'alignme')
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kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
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htk_dict_dir = os.path.join(experiments_dir, 'stimmen', 'dic_top3')
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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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lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
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predictions = []
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file_num_max = len(filenames)
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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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@ -199,12 +203,12 @@ if make_kaldi_data_files:
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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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for i in range(0, file_num_max):
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#for i in range(400, 410):
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print('=== {0}/{1} ==='.format(i+1, file_num_max))
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filename = filenames[i]
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wav_file = wav_dir + '\\' + 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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@ -218,7 +222,7 @@ if make_kaldi_data_files:
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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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word = words[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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@ -231,257 +235,203 @@ if make_kaldi_data_files:
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## ======================= make lexicon txt which is used by Kaldi =======================
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if make_kaldi_lexicon_txt:
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option_num = 6
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kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
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kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
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lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
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option_num = 5
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# remove previous file.
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if os.path.exists(lexicon_txt):
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os.remove(lexicon_txt)
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lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt')
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if os.path.exists(lexiconp_txt):
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os.remove(lexiconp_txt)
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mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
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with open(csvfile, encoding="utf-8") as fin:
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lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
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next(lines, None) # skip the headers
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filenames = []
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words = []
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pronunciations = []
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p = []
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for line in lines:
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if line[1] is not '' and len(line) > 5:
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filenames.append(line[0])
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words.append(line[1])
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pron_xsampa = line[3]
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pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
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pron_ipa = pron_ipa.replace('ː', ':')
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# adjust to phones used in the acoustic model.
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pronunciations.append(pron_ipa)
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# check if all phones are in the phonelist of the acoustic model.
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#'y', 'b', 'ɾ', 'u', 'ɔ:', 'ø', 't', 'œ', 'n', 'ɒ', 'ɐ', 'f', 'o', 'k', 'x', 'ɡ', 'v', 's', 'ɛ:', 'ɪ:', 'ɑ', 'ɛ', 'a', 'd', 'z', 'ɪ', 'ɔ', 'l', 'i:', 'm', 'p', 'a:', 'i', 'e', 'j', 'o:', 'ʁ', 'h', ':', 'e:', 'ə', 'æ', 'χ', 'w', 'r', 'ə:', 'sp', 'ʊ', 'u:', 'ŋ'
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filenames = np.array(filenames)
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words = np.array(words)
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wordlist = np.unique(words)
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pronunciations = np.array(pronunciations)
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# output lexicon.txt
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f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
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#f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
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pronvar_list_all = []
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for word in word_list:
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# pronunciation variant of the target word.
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pronunciation_variants = df['ipa'][df['word'].str.match(word)]
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pronvar_ = pronunciations[words == word]
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# remove ''
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||||
pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
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c = Counter(pronunciation_variants)
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c = Counter(pronvar_)
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total_num = sum(c.values())
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||||
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||||
#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):
|
||||
#print('{0}\t{1}\t{2}\t{3}'.format(word, key, value, total_num))
|
||||
key = key.replace('æ', 'ɛ')
|
||||
key = key.replace('ɐ', 'a')
|
||||
key = key.replace('ɑ', 'a')
|
||||
key = key.replace('ɾ', 'r')
|
||||
key = key.replace('ʁ', 'r')
|
||||
key = key.replace('ʊ', 'u')
|
||||
key = key.replace('χ', 'x')
|
||||
#print('-->{0}\t{1}\t{2}\t{3}\n'.format(word, key, value, total_num))
|
||||
|
||||
# make possible pronounciation variant list.
|
||||
pronvar_list = am_func.fame_pronunciation_variant(key)
|
||||
pronvar_list = [key]
|
||||
while 'ø:' in ' '.join(pronvar_list) or 'œ' in ' '.join(pronvar_list) or 'ɒ' in ' '.join(pronvar_list):
|
||||
pronvar_list_ = []
|
||||
for p in pronvar_list:
|
||||
if 'ø:' in p:
|
||||
pronvar_list_.append(p.replace('ø:', 'ö'))
|
||||
pronvar_list_.append(p.replace('ø:', 'ö:'))
|
||||
if 'œ' in p:
|
||||
pronvar_list_.append(p.replace('œ', 'ɔ̈'))
|
||||
pronvar_list_.append(p.replace('œ', 'ɔ̈:'))
|
||||
if 'ɒ' in p:
|
||||
pronvar_list_.append(p.replace('ɒ', 'ɔ̈'))
|
||||
pronvar_list_.append(p.replace('ɒ', 'ɔ̈:'))
|
||||
pronvar_list = np.unique(pronvar_list_)
|
||||
|
||||
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])
|
||||
|
||||
# output
|
||||
pronvar_list_all = np.array(pronvar_list_all)
|
||||
pronvar_list_all = np.unique(pronvar_list_all, axis=0)
|
||||
#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')
|
||||
kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
|
||||
phones_txt = kaldi_work_dir + '\\data\\lang\\phones.txt'
|
||||
merged_alignment_txt = kaldi_work_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)
|
||||
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')
|
||||
mappings = f.read().split('\n')
|
||||
|
||||
phones = []
|
||||
phone_ids = [] # ID of phones
|
||||
for m in mapping_phone2id:
|
||||
phone_ids = []
|
||||
for m in mappings:
|
||||
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 = []
|
||||
fa_filenames = []
|
||||
fa_pronunciations = []
|
||||
filename_ = ''
|
||||
pron = []
|
||||
for line in lines:
|
||||
line = line.split(' ')
|
||||
if len(line) == 5:
|
||||
utterance_id = line[0]
|
||||
if utterance_id == utterance_id_:
|
||||
filename = line[0]
|
||||
if filename == filename_:
|
||||
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)
|
||||
phone = phones[phone_ids.index(phone_id)]
|
||||
pron_ = re.sub(r'_[A-Z]', '', phone)
|
||||
if not pron_ == 'SIL':
|
||||
pron.append(pron_)
|
||||
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 = []
|
||||
fa_filenames.append(re.sub(r'speaker_[0-9]{4}-', '', filename))
|
||||
fa_pronunciations.append(' '.join(pron))
|
||||
pron = []
|
||||
|
||||
filename_ = filename
|
||||
|
||||
# correct or not.
|
||||
#for filename, fa_pronunciation in zip(fa_filenames, fa_pronunciations):
|
||||
|
||||
utterance_id_ = utterance_id
|
||||
predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
|
||||
|
||||
|
||||
## ======================= evaluate the result of forced alignment =======================
|
||||
if eval_forced_alignment_htk:
|
||||
htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
|
||||
|
||||
compare_hmm_num = 1
|
||||
|
||||
if compare_hmm_num:
|
||||
f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
|
||||
f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n")
|
||||
|
||||
for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
|
||||
#for hmm_num in [256]:
|
||||
if eval_forced_alignment:
|
||||
match_num = []
|
||||
for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256]:
|
||||
#hmm_num = 256
|
||||
hmm_num_str = str(hmm_num)
|
||||
if compare_hmm_num:
|
||||
f_result.write("{},".format(hmm_num_str))
|
||||
match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
|
||||
|
||||
#match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
|
||||
#prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'))
|
||||
#prediction = pd.Series(prediction, index=df.index, name='prediction')
|
||||
#result = pd.concat([df, prediction], axis=1)
|
||||
result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
|
||||
|
||||
|
||||
# load pronunciation variants
|
||||
# use dic_short?
|
||||
if 1:
|
||||
pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
|
||||
for word in word_list:
|
||||
htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
|
||||
with open(htk_dict_file, 'r') as f:
|
||||
lines = f.read().split('\n')[:-1]
|
||||
pronunciation_variants = [line.split('\t')[1] for line in lines]
|
||||
fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
|
||||
pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
|
||||
|
||||
# see only words which appears in top 3.
|
||||
result_ = result[result['word'].str.match(word)]
|
||||
result_ = result_[result_['famehtk'].isin(pronunciation_variants)]
|
||||
match_short = []
|
||||
for line in match:
|
||||
word = line[0]
|
||||
WORD = word.upper()
|
||||
pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
|
||||
|
||||
match_num = sum(result_['famehtk'] == result_['prediction'])
|
||||
total_num = len(result_)
|
||||
if line[1] in pronvar:
|
||||
match_short.append(line)
|
||||
|
||||
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'])
|
||||
match_short = np.array(match_short)
|
||||
match = np.copy(match_short)
|
||||
|
||||
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.close()
|
||||
# number of match
|
||||
total_match = sum(match[:, 1] == match[:, 2])
|
||||
print("{}: {}/{}".format(hmm_num_str, total_match, match.shape[0]))
|
||||
match_num.append([hmm_num, total_match, match.shape[0]])
|
||||
|
||||
|
||||
## ======================= 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'))
|
||||
# number of mixtures vs accuracy
|
||||
match_num = np.array(match_num)
|
||||
plt.xscale("log")
|
||||
plt.plot(match_num[:, 0], match_num[:, 1]/match_num[0, 2], 'o-')
|
||||
plt.xlabel('number of mixtures', fontsize=14, fontweight='bold')
|
||||
plt.ylabel('accuracy', fontsize=14, fontweight='bold')
|
||||
plt.show()
|
||||
|
||||
f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
|
||||
f_result.write("word,total,valid,match,[%]\n")
|
||||
# confusion matrix
|
||||
#dir_out = r'C:\OneDrive\Research\rug\experiments\stimmen\result'
|
||||
#word_list = np.unique(match[:, 0])
|
||||
|
||||
# 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]
|
||||
#for word in word_list:
|
||||
# match_ = match[match[:, 0] == word, :]
|
||||
# cm = confusion_matrix(match_[:, 1], match_[:, 2])
|
||||
# pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
|
||||
|
||||
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')
|
||||
# plt.figure()
|
||||
# plot_confusion_matrix(cm, classes=pronvar, normalize=True)
|
||||
# plt.savefig(dir_out + '\\cm_' + word + '.png')
|
||||
Loading…
Reference in New Issue
Block a user