diff --git a/.vs/acoustic_model/v15/.suo b/.vs/acoustic_model/v15/.suo
index a3fe250..fea19a8 100644
Binary files a/.vs/acoustic_model/v15/.suo and b/.vs/acoustic_model/v15/.suo differ
diff --git a/acoustic_model.sln b/acoustic_model.sln
index 264d7db..1eca07a 100644
--- a/acoustic_model.sln
+++ b/acoustic_model.sln
@@ -15,6 +15,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
..\forced_alignment\forced_alignment\htk_dict.py = ..\forced_alignment\forced_alignment\htk_dict.py
..\forced_alignment\forced_alignment\lexicon.py = ..\forced_alignment\forced_alignment\lexicon.py
..\forced_alignment\forced_alignment\mlf.py = ..\forced_alignment\forced_alignment\mlf.py
+ ..\accent_classification\accent_classification\output_confusion_matrix.py = ..\accent_classification\accent_classification\output_confusion_matrix.py
..\forced_alignment\forced_alignment\pronunciations.py = ..\forced_alignment\forced_alignment\pronunciations.py
..\forced_alignment\forced_alignment\pyhtk.py = ..\forced_alignment\forced_alignment\pyhtk.py
..\forced_alignment\forced_alignment\scripts.py = ..\forced_alignment\forced_alignment\scripts.py
diff --git a/acoustic_model/acoustic_model.py b/acoustic_model/acoustic_model.py
index 1ef57e7..8cf7789 100644
--- a/acoustic_model/acoustic_model.py
+++ b/acoustic_model/acoustic_model.py
@@ -22,12 +22,11 @@ dataset_list = ['devel', 'test', 'train']
extract_features = 0
make_feature_list = 0
conv_lexicon = 0
-check_lexicon = 1
+check_lexicon = 0
make_mlf = 0
combine_files = 0
flat_start = 0
-train_model = 0
-forced_alignment = 0
+train_model = 1
sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
@@ -288,7 +287,7 @@ if flat_start:
## ======================= estimate monophones =======================
if train_model:
iter_num_max = 3
- for mix_num in [16, 32, 64, 128]:
+ for mix_num in [128, 256, 512, 1024]:
for iter_num in range(1, iter_num_max+1):
print("===== mix{}, iter{} =====".format(mix_num, iter_num))
iter_num_pre = iter_num - 1
@@ -315,5 +314,6 @@ if train_model:
fout.write("MU %d {*.state[2-4].mix}" % (mix_num_next))
subprocessStr = 'HHEd -T 1 -H ' + modelN_dir + '\\' + hmmdefs_name + ' -M ' + modelN_dir_next + ' ' + header_file + ' ' + phonelist
+
subprocess.call(subprocessStr, shell=True)
diff --git a/acoustic_model/acoustic_model.pyproj b/acoustic_model/acoustic_model.pyproj
index fed7965..2230f18 100644
--- a/acoustic_model/acoustic_model.pyproj
+++ b/acoustic_model/acoustic_model.pyproj
@@ -31,6 +31,9 @@
Code
+
+ Code
+
diff --git a/acoustic_model/config.ini b/acoustic_model/config.ini
index 88805f6..9232c5b 100644
--- a/acoustic_model/config.ini
+++ b/acoustic_model/config.ini
@@ -2,4 +2,4 @@
config_hcopy = c:\cygwin64\home\Aki\acoustic_model\config\config.HCopy
config_train = c:\cygwin64\home\Aki\acoustic_model\config\config.train
mkhmmdefs_pl = c:\cygwin64\home\Aki\acoustic_model\src\acoustic_model\mkhmmdefs.pl
-FAME_dir = c:\OneDrive\Research\rug\experiments\friesian\corpus
\ No newline at end of file
+FAME_dir = C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus
\ No newline at end of file
diff --git a/acoustic_model/performance_check.py b/acoustic_model/performance_check.py
index 411d93e..0738ab9 100644
--- a/acoustic_model/performance_check.py
+++ b/acoustic_model/performance_check.py
@@ -4,52 +4,92 @@ import csv
import subprocess
import configparser
from collections import Counter
+import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
+from sklearn.metrics import confusion_matrix
## ======================= functions =======================
def read_fileFA(fileFA):
- """
- read the result file of HTK forced alignment.
- this function only works when input is one word.
- """
- with open(fileFA, 'r') as f:
- lines = f.read()
- lines = lines.split('\n')
+ """
+ read the result file of HTK forced alignment.
+ this function only works when input is one word.
+ """
+ with open(fileFA, 'r') as f:
+ lines = f.read()
+ lines = lines.split('\n')
- phones = []
- for line in lines:
- line_split = line.split()
- if len(line_split) > 1:
- phones.append(line_split[2])
+ phones = []
+ for line in lines:
+ line_split = line.split()
+ if len(line_split) > 1:
+ phones.append(line_split[2])
- return ' '.join(phones)
+ return ' '.join(phones)
-#####################
-## USER DEFINE ##
-#####################
+def make_dic(word, pronvar_, fileDic, output_type):
+ """
+ make dict files which can be used for HTK.
+ param word: target word.
+ param pronvar_: pronunciation variant. nx2 (WORD /t pronunciation) ndarray.
+ param fileDic: output dic file.
+ param output_type: 0:full, 1:statistics, 2:frequency <2% entries are removed. 3:top 3.
+ """
+ #assert(output_type < 4 and output_type >= 0, 'output_type should be an integer between 0 and 3.')
+
+ if output_type == 0: # full
+ pronvar = np.unique(pronvar_)
+
+ with open(fileDic, 'w') as f:
+ for pvar in pronvar:
+ f.write('{0}\t{1}\n'.format(WORD, pvar))
+ else:
+ c = Counter(pronvar_)
+ total_num = sum(c.values())
+ with open(fileDic, 'w') as f:
+ if output_type == 3:
+ for key, value in c.most_common(3):
+ f.write('{0}\t{1}\n'.format(WORD, key))
+ else:
+ for key, value in c.items():
+ percentage = value/total_num*100
+
+ if output_type == 1: # all
+ f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, percentage, WORD, key))
+ elif output_type == 2: # less than 2 percent
+ if percentage < 2:
+ f.write('{0}\t{1}\n'.format(WORD, key))
+
+
+## ======================= user define =======================
curr_dir = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
config_ini = curr_dir + '\\config.ini'
forced_alignment_module = r'C:\Users\Aki\source\repos\forced_alignment'
forced_alignment_module_old = r'C:\OneDrive\Research\rug\code\forced_alignment\forced_alignment'
-ipa_xsampa_converter_dir = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
+ipa_xsampa_converter_dir = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
+accent_classification_dir = r'C:\Users\Aki\source\repos\accent_classification\accent_classification'
+
-csvfile = r"C:\OneDrive\Research\rug\stimmen\Frisian Variants Picture Task Stimmen.csv"
experiments_dir = r'C:\OneDrive\Research\rug\experiments'
-data_dir = experiments_dir + '\\stimmen\\data'
-cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
+data_dir = experiments_dir + '\\stimmen\\data'
+csvfile = data_dir + '\\Frisian Variants Picture Task Stimmen.csv'
+
+cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
# procedure
convert_phones = 0
make_dic_files = 0
make_dic_files_short = 0
-do_forced_alignment = 0
-eval_forced_alignment = 1
+do_forced_alignment_htk = 0
+make_kaldi_data_files = 0
+make_kaldi_lexicon_txt = 0
+load_forced_alignment_kaldi = 1
+eval_forced_alignment = 0
@@ -67,6 +107,10 @@ import acoustic_model_functions as am_func
sys.path.append(forced_alignment_module_old)
import pyHTK
+# to output confusion matrix
+sys.path.append(accent_classification_dir)
+from output_confusion_matrix import plot_confusion_matrix
+
## ======================= load variables =======================
config = configparser.ConfigParser()
@@ -81,177 +125,393 @@ lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
## ======================= convert phones ======================
if convert_phones:
- mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
+ mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
- ## check phones included in FAME!
- # the phones used in the lexicon.
- #phonelist = am_func.get_phonelist(lex_htk)
+ ## check phones included in FAME!
+ # the phones used in the lexicon.
+ #phonelist = am_func.get_phonelist(lex_htk)
- # the lines which include a specific phone.
- #lines = am_func.find_phone(lex_asr, 'x')
+ # the lines which include a specific phone.
+ #lines = am_func.find_phone(lex_asr, 'x')
- with open(csvfile, encoding="utf-8") as fin:
- lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
- next(lines, None) # skip the headers
+ with open(csvfile, encoding="utf-8") as fin:
+ lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
+ next(lines, None) # skip the headers
- filenames = []
- words = []
- pronunciations = []
- for line in lines:
- if line[1] is not '' and len(line) > 5:
- filenames.append(line[0])
- words.append(line[1])
- pron_xsampa = line[3]
- pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
- pron_ipa = pron_ipa.replace('ː', ':')
- pron_famehtk = convert_phone_set.ipa2famehtk(pron_ipa)
-
- # adjust to phones used in the acoustic model.
- pron_famehtk = pron_famehtk.replace('sp', 'sil')
- pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
- pron_famehtk = pron_famehtk.replace('w :', 'wh')
- pron_famehtk = pron_famehtk.replace('e :', 'eh')
- pron_famehtk = pron_famehtk.replace('eh :', 'eh')
- pron_famehtk = pron_famehtk.replace('ih :', 'ih')
+ filenames = []
+ words = []
+ pronunciations = []
+ for line in lines:
+ if line[1] is not '' and len(line) > 5:
+ filenames.append(line[0])
+ words.append(line[1])
+ pron_xsampa = line[3]
+ pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
+ pron_ipa = pron_ipa.replace('ː', ':')
+ pron_famehtk = convert_phone_set.ipa2famehtk(pron_ipa)
+
+ # adjust to phones used in the acoustic model.
+ pron_famehtk = pron_famehtk.replace('sp', 'sil')
+ pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
+ pron_famehtk = pron_famehtk.replace('w :', 'wh')
+ pron_famehtk = pron_famehtk.replace('e :', 'eh')
+ pron_famehtk = pron_famehtk.replace('eh :', 'eh')
+ pron_famehtk = pron_famehtk.replace('ih :', 'ih')
- #translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
- #pron = []
- #for phoneme in pron_famehtk.split(' '):
- # pron.append(translation_key.get(phoneme, phoneme))
- #pronunciations.append(' '.join(pron_famehtk))
- pronunciations.append(pron_famehtk)
+ #translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
+ #pron = []
+ #for phoneme in pron_famehtk.split(' '):
+ # pron.append(translation_key.get(phoneme, phoneme))
+ #pronunciations.append(' '.join(pron_famehtk))
+ pronunciations.append(pron_famehtk)
- # check if all phones are in the phonelist of the acoustic model.
- #phonelist = ' '.join(pronunciations)
- #np.unique(phonelist.split(' '))
- #phonelist.find(':')
+ # check if all phones are in the phonelist of the acoustic model.
+ #phonelist = ' '.join(pronunciations)
+ #np.unique(phonelist.split(' '))
+ #phonelist.find(':')
- filenames = np.array(filenames)
- words = np.array(words)
- pronunciations = np.array(pronunciations)
+ filenames = np.array(filenames)
+ words = np.array(words)
+ pronunciations = np.array(pronunciations)
- del line, lines
- del pron_xsampa, pron_ipa, pron_famehtk
+ del line, lines
+ del pron_xsampa, pron_ipa, pron_famehtk
- np.save(data_dir + '\\filenames.npy', filenames)
- np.save(data_dir + '\\words.npy', words)
- np.save(data_dir + '\\pronunciations.npy', pronunciations)
+ np.save(data_dir + '\\filenames.npy', filenames)
+ np.save(data_dir + '\\words.npy', words)
+ np.save(data_dir + '\\pronunciations.npy', pronunciations)
else:
- filenames = np.load(data_dir + '\\filenames.npy')
- words = np.load(data_dir + '\\words.npy')
-
- pronunciations = np.load(data_dir + '\\pronunciations.npy')
+ filenames = np.load(data_dir + '\\filenames.npy')
+ words = np.load(data_dir + '\\words.npy')
+
+ pronunciations = np.load(data_dir + '\\pronunciations.npy')
word_list = np.unique(words)
## ======================= make dict files used for HTK. ======================
if make_dic_files:
- output_dir = experiments_dir + r'\stimmen\dic'
+ output_type = 2
+ output_dir = experiments_dir + r'\stimmen\dic_short'
+
+ for word in word_list:
+ WORD = word.upper()
+ fileDic = output_dir + '\\' + word + '.dic'
- for word in word_list:
- WORD = word.upper()
- fileDic = output_dir + '\\' + word + '.dic'
+ # pronunciation variant of the target word.
+ pronvar_ = pronunciations[words == word]
+ # remove ''
+ pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
- # make dic file.
- pronvar_ = pronunciations[words == word]
- pronvar = np.unique(pronvar_)
+ # make dic file.
+ make_dic(word, pronvar_, fileDic, output_type)
+
- with open(fileDic, 'w') as f:
- for pvar in pronvar:
- f.write('{0}\t{1}\n'.format(WORD, pvar))
+## ======================= forced alignment using HTK =======================
+if do_forced_alignment_htk:
+ configHVite = cygwin_dir + r'\config\config.HVite'
+ filePhoneList = experiments_dir + r'\friesian\acoustic_model\config\phonelist_friesian.txt'
+ wav_dir = experiments_dir + r'\stimmen\wav'
+
+ #hmm_num = 128
+ for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256]:
+ hmm_num_str = str(hmm_num)
+ AcousticModel = experiments_dir + r'\friesian\acoustic_model\model\hmm' + hmm_num_str + r'-2\hmmdefs'
+
+ predictions = []
+ file_num_max = len(filenames)
+ for i in range(0, file_num_max):
+ #for i in range(500, 502):
+ print('=== {0}/{1} ==='.format(i, file_num_max))
+ filename = filenames[i]
+ fileWav = wav_dir + '\\' + filename
+
+ if os.path.exists(fileWav):
+ word = words[i]
+ WORD = word.upper()
+
+ # make label file.
+ fileLab = wav_dir + '\\' + filename.replace('.wav', '.lab')
+ with open(fileLab, 'w') as f:
+ lines = f.write(WORD)
+
+ fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
+ fileFA = experiments_dir + r'\stimmen\FA' + '\\' + filename.replace('.wav', '.txt') + hmm_num_str
+
+ pyHTK.doHVite(fileWav, fileLab, fileDic, fileFA, configHVite, filePhoneList, AcousticModel)
+ prediction = read_fileFA(fileFA)
+ predictions.append(prediction)
+
+ os.remove(fileLab)
+ print('{0}: {1} -> {2}'.format(WORD, pronunciations[i], prediction))
+ else:
+ predictions.append('')
+ print('!!!!! file not found.')
+
+ predictions = np.array(predictions)
+ match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
+ np.save(data_dir + '\\match_hmm' + hmm_num_str + '.npy', match)
-## ======================= make dict files for most popular words. ======================
-if make_dic_files_short:
- output_dir = experiments_dir + r'\stimmen\dic'
+## ======================= make files which is used for forced alignment by Kaldi =======================
+if make_kaldi_data_files:
+ wav_dir = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen'
+ kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
+ kaldi_data_dir = os.path.join(kaldi_work_dir, 'data', 'alignme')
+ kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
+ htk_dict_dir = os.path.join(experiments_dir, 'stimmen', 'dic_top3')
- #word = word_list[3]
- for word in word_list:
- WORD = word.upper()
- fileStat = output_dir + '\\' + word + '_stat.csv'
-
- pronvar = pronunciations[words == word]
- c = Counter(pronvar)
- total_num = sum(c.values())
+ wav_scp = os.path.join(kaldi_data_dir, 'wav.scp')
+ text_file = os.path.join(kaldi_data_dir, 'text')
+ utt2spk = os.path.join(kaldi_data_dir, 'utt2spk')
- with open(fileStat, 'w') as f:
- for key, value in c.items():
- f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, value/total_num*100, WORD, key))
+ lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
+
+ predictions = []
+ file_num_max = len(filenames)
+
+ # remove previous files.
+ if os.path.exists(wav_scp):
+ os.remove(wav_scp)
+ if os.path.exists(text_file):
+ os.remove(text_file)
+ if os.path.exists(utt2spk):
+ os.remove(utt2spk)
+
+ f_wav_scp = open(wav_scp, 'a', encoding="utf-8", newline='\n')
+ f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
+ f_utt2spk = open(utt2spk, 'a', encoding="utf-8", newline='\n')
+
+ # make wav.scp, text, and utt2spk files.
+ for i in range(0, file_num_max):
+ #for i in range(400, 410):
+ print('=== {0}/{1} ==='.format(i+1, file_num_max))
+ filename = filenames[i]
+ wav_file = wav_dir + '\\' + filename
+
+ if os.path.exists(wav_file):
+ speaker_id = 'speaker_' + str(i).zfill(4)
+ utterance_id = filename.replace('.wav', '')
+ utterance_id = utterance_id.replace(' ', '_')
+ utterance_id = speaker_id + '-' + utterance_id
+
+ # wav.scp file
+ wav_file_unix = wav_file.replace('\\', '/')
+ wav_file_unix = wav_file_unix.replace('c:/', '/mnt/c/')
+
+ f_wav_scp.write('{0} {1}\n'.format(utterance_id, wav_file_unix))
+
+ # text file
+ word = words[i].lower()
+ f_text_file.write('{0}\t{1}\n'.format(utterance_id, word))
+
+ # utt2spk
+ f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
+
+ f_wav_scp.close()
+ f_text_file.close()
+ f_utt2spk.close()
-## ======================= forced alignment =======================
-if do_forced_alignment:
- configHVite = cygwin_dir + r'\config\config.HVite'
- filePhoneList = experiments_dir + r'\friesian\acoustic_model\config\phonelist_friesian.txt'
- wav_dir = experiments_dir + r'\stimmen\wav'
+## ======================= make lexicon txt which is used by Kaldi =======================
+if make_kaldi_lexicon_txt:
+ kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
+ kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
+ lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
+ option_num = 5
- #for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128]:
- for hmm_num in [64]:
- hmm_num_str = str(hmm_num)
- AcousticModel = experiments_dir + r'\friesian\acoustic_model\model\hmm' + hmm_num_str + r'-3\hmmdefs'
+ # remove previous file.
+ if os.path.exists(lexicon_txt):
+ os.remove(lexicon_txt)
- predictions = []
- file_num_max = len(filenames)
- for i in range(0, file_num_max):
- print('=== {0}/{1} ==='.format(i, file_num_max))
- filename = filenames[i]
- fileWav = wav_dir + '\\' + filename
-
- if os.path.exists(fileWav):
- word = words[i]
- WORD = word.upper()
+ mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
+ with open(csvfile, encoding="utf-8") as fin:
+ lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
+ next(lines, None) # skip the headers
- # make label file.
- fileLab = wav_dir + '\\' + filename.replace('.wav', '.lab')
- with open(fileLab, 'w') as f:
- lines = f.write(WORD)
+ filenames = []
+ words = []
+ pronunciations = []
+ p = []
+ for line in lines:
+ if line[1] is not '' and len(line) > 5:
+ filenames.append(line[0])
+ words.append(line[1])
+ pron_xsampa = line[3]
+ pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
+ pron_ipa = pron_ipa.replace('ː', ':')
+
+ # adjust to phones used in the acoustic model.
+ pronunciations.append(pron_ipa)
- fileDic = experiments_dir + r'\stimmen\dic_short' + '\\' + word + '.dic'
- fileFA = experiments_dir + r'\stimmen\FA_short' + '\\' + filename.replace('.wav', '.txt') + hmm_num_str
+ # check if all phones are in the phonelist of the acoustic model.
+ #'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'
- pyHTK.doHVite(fileWav, fileLab, fileDic, fileFA, configHVite, filePhoneList, AcousticModel)
- prediction = read_fileFA(fileFA)
- predictions.append(prediction)
+ filenames = np.array(filenames)
+ words = np.array(words)
+ wordlist = np.unique(words)
+ pronunciations = np.array(pronunciations)
+
+ # output lexicon.txt
+ #f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
+ pronvar_list_all = []
+ for word in word_list:
- os.remove(fileLab)
- print('{0}: {1} -> {2}'.format(WORD, pronunciations[i], prediction))
- else:
- predictions.append('')
- print('!!!!! file not found.')
+ # pronunciation variant of the target word.
+ pronvar_ = pronunciations[words == word]
+ # remove ''
+ pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
- predictions = np.array(predictions)
- match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
- np.save(data_dir + '\\match_hmm' + hmm_num_str + '.npy', match)
+ c = Counter(pronvar_)
+ total_num = sum(c.values())
+
+ 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 = [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_ipa_fame(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('\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()
+
+## ======================= load kaldi forced alignment result =======================
+if load_forced_alignment_kaldi:
+ 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)
+
+ # load the mapping between phones and ids.
+ with open(phones_txt, 'r', encoding="utf-8") as f:
+ mappings = f.read().split('\n')
+
+ phones = []
+ phone_ids = []
+ for m in mappings:
+ m = m.split(' ')
+ if len(m) > 1:
+ phones.append(m[0])
+ phone_ids.append(int(m[1]))
+
+ with open(merged_alignment_txt, 'r') as f:
+ lines = f.read()
+ lines = lines.split('\n')
+
+ fa_filenames = []
+ fa_pronunciations = []
+ filename_ = ''
+ pron = []
+ for line in lines:
+ line = line.split(' ')
+ if len(line) == 5:
+ filename = line[0]
+ if filename == filename_:
+ phone_id = int(line[4])
+ #if not phone_id == 1:
+ phone = phones[phone_ids.index(phone_id)]
+ pron_ = re.sub(r'_[A-Z]', '', phone)
+ if not pron_ == 'SIL':
+ pron.append(pron_)
+ else:
+ 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):
+
## ======================= evaluate the result of forced alignment =======================
if eval_forced_alignment:
- #for hmm_num in [1, 2, 4, 8, 16, 32, 64]:
- hmm_num = 64
- hmm_num_str = str(hmm_num)
- match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
-
- # use dic_short?
- if 1:
- pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
- for word in word_list:
- fileDic = experiments_dir + r'\stimmen\dic_short' + '\\' + word + '.dic'
- pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
+ match_num = []
+ for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256]:
+ #hmm_num = 256
+ hmm_num_str = str(hmm_num)
+ match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
+
+ # use dic_short?
+ if 1:
+ pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
+ for word in word_list:
+ fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
+ pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
- match_short = []
- for line in match:
- word = line[0]
- WORD = word.upper()
- pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
-
- if line[1] in pronvar:
- match_short.append(line)
+ # see only words which appears in top 3.
+ match_short = []
+ for line in match:
+ word = line[0]
+ WORD = word.upper()
+ pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
+
+ if line[1] in pronvar:
+ match_short.append(line)
- match_short = np.array(match_short)
- match = np.copy(match_short)
+ match_short = np.array(match_short)
+ match = np.copy(match_short)
- # number of match
- total_match = sum(match[:, 1] == match[:, 2])
- print("{}: {}/{}".format(hmm_num_str, total_match, match.shape[0]))
+ # 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]])
+
+ # 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()
+
+ # confusion matrix
+ #dir_out = r'C:\OneDrive\Research\rug\experiments\stimmen\result'
+ #word_list = np.unique(match[:, 0])
+
+ #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]
+
+ # plt.figure()
+ # plot_confusion_matrix(cm, classes=pronvar, normalize=True)
+ # plt.savefig(dir_out + '\\cm_' + word + '.png')
\ No newline at end of file
diff --git a/acoustic_model/pyKaldi.py b/acoustic_model/pyKaldi.py
new file mode 100644
index 0000000..c65a99b
--- /dev/null
+++ b/acoustic_model/pyKaldi.py
@@ -0,0 +1,26 @@
+import os
+import sys
+
+forced_alignment_module = r'C:\Users\Aki\source\repos\forced_alignment'
+
+## ======================= add paths =======================
+
+sys.path.append(forced_alignment_module)
+from forced_alignment import convert_phone_set
+
+
+htk_dict_file = r'C:\OneDrive\Research\rug\experiments\stimmen\dic_top3\Reus.dic'
+#kaldi_lexicon = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\data\lang\phones\'
+alignment_txt = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\exp\tri1_alignme\merged_alignment.txt'
+phones_txt = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\exp\tri1_alignme\phones.txt'
+phone_map_txt = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\data\local\lang\phone_map.txt'
+
+with open(phone_map_txt, 'r', encoding="utf-8") as f:
+ lines = f.read()
+ lines = lines.split('\n')
+
+with open(alignment_txt, 'r', encoding="utf-8") as f:
+ lines =
+#phone_in = [line for line in lines if 'SIL' in line]
+#if len(phone_in) == 1:
+
\ No newline at end of file